How-To Tutorials - ChatGPT

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn an era dominated by instantaneous digital communication, businesses are constantly on the hunt for ways to refine and amplify their sales operations. Chatbots and platforms like ChatGPT stand at the forefront of this revolution, offering a blend of speed, accuracy, and personalized service. As companies look to the horizon, it becomes evident that AI-driven tools aren't just an option; they're a necessity. In this guide, we'll navigate through the transformative impacts of chatbots and ChatGPT on sales and offer a blueprint for leveraging their capabilities to their fullest potential. Let’s have a look at the advantages of implementing a chatbot as part of your sales channel1. Lead Generation Made EasyHow it works: Chatbots can be positioned on your website, social media pages, or even within apps. They initiate conversations, engage potential customers, and capture essential contact details.Benefits:Higher Engagement: Visitors are more likely to interact with an interactive bot than fill out a static form.Immediate Interaction: Chatbots can engage visitors instantly, increasing the likelihood of capturing leads.2. Efficient Lead QualificationHow it works: Instead of manual surveys or questionnaires, chatbots can quickly determine the potential of leads by asking relevant questions.Benefits:Time-saving: Automated lead qualification reduces manual efforts.Accuracy: Chatbots can consistently gather data and categorize leads based on predefined criteria.3. Instant Customer Queries Resolution with ChatGPTHow it works: ChatGPT uses advanced AI algorithms to comprehend user queries and provide precise answers.Benefits:Fast Responses: Instant answers mean no waiting time for customers.Consistency: AI ensures uniformity in responses, maintaining brand messaging.4. Personalized Product RecommendationsHow it works: ChatGPT can analyze past customer interactions, purchase histories, and preferences to suggest products or services.Benefits:Increased Sales: Personalized recommendations often resonate better with customers, leading to increased purchases.Enhanced Customer Experience: Customers appreciate tailored suggestions, enhancing their overall interaction with your brand.5. Elevating Customer ServiceHow it works: ChatGPT can address a wide range of customer concerns, from basic FAQs to complex product inquiries.Benefits:24/7 Support: AI doesn't sleep, ensuring round-the-clock customer assistance.Improved Satisfaction: Quick and accurate responses lead to happier, more loyal customers.6. Automation and Workload ReductionHow it works: Beyond answering questions, ChatGPT can manage routine tasks like booking appointments, sending reminders, or following up on leads.Benefits:Focus on Strategy: Sales teams can redirect their attention to more significant, high-value tasks.Consistent Follow-ups: Automated reminders and follow-ups ensure no potential lead is overlooked.7. Time-saving and Sales AugmentationHow it works: By automating repetitive tasks and offering intelligent recommendations, ChatGPT streamlines the sales process.Benefits:Faster Deal Closures: Efficient processes lead to quicker sales.Increased Revenue: More sales in less time amplify revenue generation.The fusion of chatbots and AI-driven platforms like ChatGPT offers an unprecedented advantage in the sales domain. By integrating these technologies into your sales strategy, you not only optimize operations but also provide an enhanced customer experience, setting the stage for sustainable business growth. Embrace the future of sales, and watch your business thrive!Let’s look at how to create a sales channel framework for chatbot using ChatGPTStep 1: Setting Up for Lead GenerationObjective: Engage visitors and gather essential contact details.Actions:Integrate the chatbot on prominent areas of your website, social media pages, and apps.Design conversation starters to engage users, such as: "Hello! How can I assist you today?"Prompt for ChatGPT: "Hello [User's Name], let's start by defining your sales objectives. Think about your main goals in sales. Is it to generate more leads? Maybe you want to qualify them more efficiently? Or perhaps you're looking to elevate your customer service game?Please list down all your sales objectives. Once you have them listed, we'll work together to prioritize them based on your immediate business requirements.Remember, the clearer your objectives, the more effective your tech solution deployment will be!"This prompt provides a clear directive and offers a little guidance, making it easier for the user to start listing their objectives.Step 2: Implement Efficient Lead QualificationObjective: Determine the potential of leads without manual surveys.Actions:Predefine criteria for categorizing leads (e.g., high potential, low interest).Create a set of relevant questions to gauge the user's interest and intent.Prompt: "Welcome, user! For a tailored experience with our SaaS collaboration tools, please answer a few questions. This automated process saves time, ensures accuracy, and helps us better understand your needs. From team size to current tools, your responses will guide our recommendations. Let's begin!"Step 3: Incorporate Instant Customer Query Resolution with AIObjective: Provide immediate and consistent answers.Actions:Train your chatbot using AI platforms like ChatGPT on frequently asked questions and typical user concerns.Continuously update the bot based on new questions and feedback to improve precision.Prompt: "Hello [User's Name], I'm here to assist you with your questions or concerns. Please provide a detailed description of your query or issue, and I'll do my best to give you a precise and accurate answer. Remember, the more specific you are, the better I can assist. Let's get started!"Step 4: Personalized Product RecommendationsObjective: Enhance user experience by suggesting tailored products/services.Actions:Integrate the chatbot with your CRM to analyze past customer interactions and purchase histories.Design the bot to recommend products based on user preferences and past behavior.Prompt: “Hello [User's Name], based on your history with us, I'd like to suggest fitting products or services. Can you mention the last item you engaged with? Also, are you seeking an upgrade, complementary items, or exploring new areas? Any specific preferences for the recommendation?"Step 5: Elevate Customer ServiceObjective: Provide round-the-clock support and address a wide range of concerns.Actions:Implement a 24/7 availability feature.Train the bot on both basic FAQs and in-depth product inquiries.Prompt: “Hello [User's Name], I'm here to assist you with any questions or concerns you may have about our products or services. From basic FAQs to in-depth product inquiries, I've got you covered.Please detail your query, and I'll provide a quick and accurate response. Remember, I'm available 24/7, so don't hesitate to reach out at any time. Your satisfaction is our priority. Let's get your questions answered!"ConclusionThe evolution of sales operations hinges on the symbiotic relationship between human expertise and AI efficiency. As evidenced in this guide, chatbots, especially powerhouse models like ChatGPT, aren't just transforming sales—they're redefining the entire customer journey. By integrating these technologies, businesses don't just streamline operations; they usher in a new era of customer interaction, marked by personalized experiences, swift resolutions, and enhanced satisfaction. The future of sales is here, and it beckons businesses to adapt, innovate, and thrive. Embracing these tools is no longer a luxury; it's an imperative for sustainable growth in a digital-first world.Author BioDr. Anshul Saxena is an author, corporate consultant, inventor, and educator who assists clients in finding financial solutions using quantum computing and generative AI. He has filed over three Indian patents and has been granted an Australian Innovation Patent. Anshul is the author of two best-selling books in the realm of HR Analytics and Quantum Computing (Packt Publications). He has been instrumental in setting up new-age specializations like decision sciences and business analytics in multiple business schools across India. Currently, he is working as Assistant Professor and Coordinator – Center for Emerging Business Technologies at CHRIST (Deemed to be University), Pune Lavasa Campus. Dr. Anshul has also worked with reputed companies like IBM as a curriculum designer and trainer and has been instrumental in training 1000+ academicians and working professionals from universities and corporate houses like UPES, CRMIT, and NITTE Mangalore, Vishwakarma University, Pune & Kaziranga University, and KPMG, IBM, Altran, TCS, Metro CASH & Carry, HPCL & IOC. With a work experience of 5 years in the domain of financial risk analytics with TCS and Northern Trust, Dr. Anshul has guided master's students in creating projects on emerging business technologies, which have resulted in 8+ Scopus-indexed papers. Dr. Anshul holds a PhD in Applied AI (Management), an MBA in Finance, and a BSc in Chemistry. He possesses multiple certificates in the field of Generative AI and Quantum Computing from organizations like SAS, IBM, IISC, Harvard, and BIMTECH.Author of the book: Financial Modeling Using Quantum Computing

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Cybersecurity Cookbook, by Clint Bodungen. Master ChatGPT and the OpenAI API, and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.IntroductionWith the rise of cyber threats, organizations of all sizes are increasingly aware of the importance of training their staff on email phishing, a common and potentially dangerous tactic employed by cybercriminals. In this recipe, we'll be using ChatGPT to create a tool for interactive email phishing training. This recipe guides you through the process of crafting a specialized prompt to turn ChatGPT into a simulation tool for phishing attack awareness. With this approach, you can use ChatGPT to train users to identify potential phishing emails, thereby increasing their awareness and helping to protect your organization from potential security threats. What makes this truly powerful is its interactive nature. ChatGPT will present the user with a series of email scenarios. The user will then decide whether the email is a phishing attempt or a legitimate email, and can even ask for more details such as the URL to a link in the email or header information, for example. ChatGPT will provide feedback, ensuring a continuous, engaging, and efficient learning experience. Additionally, we will also cover how to use Python in conjunction with these prompts to create exportable email simulation scenarios. This feature can be beneficial in situations where you might want to use the generated scenarios outside of ChatGPT, such as in a live course or in a Learning Management System (LMS). Getting ready Before diving into this recipe, ensure you have your OpenAI account set up and your API key on hand. If not, you should refer back to Chapter 1 for the necessary setup details. You will also need Python version 3.10.x or later.  Additionally, confirm you have the following Python libraries installed: 1. openai: This library enables you to interact with the OpenAI API. Install it using the command pip install openai. 2. os: This is a built-in Python library, which allows you to interact with the operating system, especially for accessing environment variables. 3. tqdm: This library is utilized for showing progress bars during the policy generation process. Install it with pip install tqdm. How to do it… In this section, we will walk you through the process of creating an interactive email phishing training simulation using ChatGPT. The instructions are step-by-step, starting from logging into your OpenAI account and ending with generating phishing training simulations. 1. Access the ChatGPT interface. Log into your OpenAI account and go to the ChatGPT interface at https://chat.openai.com. 2.Initialize the simulation by entering the specialized prompt. The following prompt is carefully designed to instruct ChatGPT to act as a phishing training simulator. Enter the prompt into the text box and press Enter. "You are a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience. Create an interactive email phishing training simulation (for employees). Provide no other response other than to ask the question, "Is the following email real or a phishing attempt? (You may ask clarification questions such as URL information, header information, etc.)" followed by simulated email, using markdown language formatting. The email you present can represent a legitimate email or a phishing attempt, which can use one or more various techniques. Provide no further generation or response until I answer the question. If I answer correctly, just respond with "Correct" and a short description to further explain the answer, and then restart the process from the beginning. If I answer incorrectly, respond with "Incorrect", then the correct answer, then a short description to further explain the answer. Then repeat the process from the beginning.    Present me with only 3 simulations in total throughout the process and remember my answer to them all. At least one of the simulations should simulate a real email. After the last question has been answered, and after your response, end the assessment and give me my total score, the areas I did well in and where I need to improve." Tip Be sure to change the number of simulations ChatGPT provides, to suit your needs. Now, ChatGPT will generate interactive email phishing scenarios based on your instructions. Respond to each scenario as if you were the employee undergoing the training. After the third scenario and your final response, ChatGPT will calculate and provide your total score, areas of strength, and areas for improvement. How it works… At the heart of this recipe lies the specialized prompt. This prompt is constructed to instruct ChatGPT to act as an interactive phishing training tool, delivering a series of email phishing scenarios. The prompt follows certain design principles which are essential to its effectiveness and interaction with the OpenAI models. Here, we'll dissect those principles: 1. Defining the role: The prompt starts by setting up the role of the AI model – a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience. By defining the AI's persona, we direct the model to generate responses using the knowledge and expertise expected from such a role. 2. Detailed instructions and simulation: The instructions given in the prompt are meticulously detailed, and it is this precision that enables ChatGPT to create effective and realistic phishing simulations. The prompt asks the AI model to generate a phishing email scenario, followed by the question, "Is the following email real or a phishing attempt?". Notably, the AI model is given the liberty to provide additional clarifying questions, such as asking about URL information, header information, etc., giving it the freedom to generate more nuanced and complex scenarios. By asking the model to generate these emails using markdown language formatting, we ensure that the simulated emails have the structure and appearance of genuine emails, enhancing the realism of the simulation. The AI is also instructed to present emails that can represent either legitimate correspondence or a phishing attempt, ensuring a diverse range of scenarios for the user to evaluate. How can ChatGPT convincingly simulate phishing emails? Well, ChatGPT's strength comes from the wide variety of text it has been trained on, including (but not limited to) countless examples of email correspondences and probably some instances of phishing attempts or discussions around them. From this extensive training, the model has developed a robust understanding of the format, tone, and common phrases used in both legitimate and phishing emails. So, when prompted to simulate a phishing email, it can draw on this knowledge to generate a believable email that mirrors the features of a real-world phishing attempt. As the model doesn't generate responses until it receives an answer to its question, it guarantees an interactive user experience. Based on the user's response, the model provides relevant feedback ("Correct" or "Incorrect"), the correct answer if the user was wrong, and a brief explanation. This detailed, immediate feedback aids the learning process and helps to embed the knowledge gained from each simulated scenario. It's worth noting that, although the model has been trained to generate human-like text, it doesn't understand the content in the same way humans do. It doesn't have beliefs, opinions, or access to real-time, world-specific information or personal data unless explicitly provided in the conversation. Its responses are merely predictions based on its training data. The carefully designed prompt and structure are what guide the model to generate useful, contextually appropriate content for this particular task. 3. Feedback mechanism: The prompt instructs the AI to provide feedback based on the user's answer, further explaining the answer. This creates an iterative feedback loop that enhances the learning experience. 4. Keeping track of progress: The prompt instructs the AI to present three simulations in total and to remember the user's answer to all of them. This ensures continuity in the training and enables tracking of the user's progress. 5. Scoring and areas of improvement: After the final simulation and response, the prompt instructs the AI to end the assessment and provide a total score along with areas of strength and areas for improvement. This helps the user understand their proficiency and the areas they need to focus on for improvement. ChatGPT’s models are trained on a broad range of internet text. However, it's essential to note that it does not know specifics about which documents were part of its training set or have access to any private, confidential, or proprietary information. It generates responses to prompts by recognizing patterns and producing text that statistically aligns with the patterns it observed in its training data. By structuring our prompt in a way that clearly defines the interactive assessment context and expected behavior, we're able to leverage this pattern recognition to create a highly specialized interactive tool. The ability of the OpenAI models to handle such a complex and interactive use case demonstrates their powerful capability and flexibility. There’s more… If you're using a Learning Management System (LMS) or conducting a live class, you might prefer to have a list of scenarios and details rather than an interactive method like ChatGPT. In these settings, it's often more practical to provide learners with specific scenarios to ponder and discuss in a group setting. The list can also be used for assessments or training materials, offering a static reference point that learners can revisit as needed, or as content for a phishing simulation system. By modifying the script from the previous recipe, you can instruct the ChatGPT model to produce a set of phishing email simulations complete with all necessary details. The resulting text can be saved into a file for easy distribution and usage in your training environment. Since this script is so similar to the one from the previous recipe, we’ll just cover the modifications instead of steppping through the entire script again. Let's walk through the necessary modifications: 1.      Rename and modify the function: The function generate_question is renamed to generate_email_simulations, and its argument list and body are updated to reflect its new purpose. It will now generate the phishing email simulations instead of cybersecurity awareness questions. This is done by updating the messages that are passed to the OpenAI API within this function. def generate_email_simulations() -> str:    # Define the conversation messages    messages = [        {"role": "system", "content": 'You are a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience.'},        {"role": "user", "content": 'Create a list of fictitious emails for an interactive email phishing training. The emails can represent a legitimate email or a phishing attempt, using one or more various techniques. After each email, provide the answer, contextual descriptions, and details for any other relevant information such as the URL for any links in the email, header information. Generate all necessary information in the email and supporting details. Present 3 simulations in total. At least one of the simulations should simulate a real email.'},    ]    ... Note You can adjust the number of scenarios here to suit your needs. In this example, we've requested 3 scenarios. 2. Remove unnecessary code: The script no longer reads content categories from an input file, as it's not required in your use case. 3. Update variable and function names: All variable and function names referring to "questions" or "assessment" have been renamed to refer to "email simulations" instead, to make the script more understandable in the context of its new purpose. 4. Call the appropriate function: The generate_email_simulations function is called instead of the generate_question function. This function initiates the process of generating the email simulations. # Generate the email simulations email_simulations = generate_email_simulations() Tip Like the previous method, more scenarios will require a model that supports a larger context window. However, the gpt-4 model seems to provide better results in terms of accuracy, depth, and consistency with its generations for this recipe. Here’s how the complete script should look: import openai import os import threading import time from datetime import datetime # Set up the OpenAI API openai.api_key = os.getenv("OPENAI_API_KEY") current_datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') assessment_name = f"Email_Simulations_{current_datetime}.txt" def generate_email_simulations() -> str:    # Define the conversation messages    messages = [        {"role": "system", "content": 'You are a cybersecurity professional and expert in adversarial social engineering tactics, techniques, and procedures, with 25 years of experience.'},        {"role": "user", "content": 'Create a list of fictitious emails for an interactive email phishing training. The emails can represent a legitimate email or a phishing attempt, using one or more various techniques. After each email, provide the answer, contextual descriptions, and details for any other relevant information such as the URL for any links in the email, header information. Generate all necessary information in the email and supporting details. Present 3 simulations in total. At least one of the simulations should simulate a real email.'},    ]    # Call the OpenAI API    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=messages,        max_tokens=2048,        n=1,        stop=None,        temperature=0.7,    )    # Return the generated text    return response['choices'][0]['message']['content'].strip() # Function to display elapsed time while waiting for the API call def display_elapsed_time():    start_time = time.time()    while not api_call_completed:        elapsed_time = time.time() - start_time        print(f"\rElapsed time: {elapsed_time:.2f} seconds", end="")        time.sleep(1) api_call_completed = False elapsed_time_thread = threading.Thread(target=display_elapsed_time) elapsed_time_thread.start() # Generate the report using the OpenAI API try:    # Generate the email simulations    email_simulations = generate_email_simulations() except Exception as e:    print(f"\nAn error occurred during the API call: {e}")    exit() api_call_completed = True elapsed_time_thread.join() # Save the email simulations into a text file try:    with open(assessment_name, 'w') as file:        file.write(email_simulations)    print("\nEmail simulations generated successfully!") except Exception as e:    print(f"\nAn error occurred during the email simulations generation: {e}") By running this modified script, the ChatGPT model is directed to generate a series of interactive email phishing training scenarios. The script then collects the generated scenarios, checks them for errors, and writes them to a text file. This gives you a ready-made training resource that you can distribute to your learners or incorporate into your LMS or live training sessions. ConclusionIn conclusion, leveraging ChatGPT for interactive email phishing training empowers users with immersive, realistic simulations, bolstering cybersecurity awareness and defense. This innovative approach fosters a proactive stance against threats, ensuring organizations stay ahead in the ever-evolving landscape of cyber risks. With adaptable learning and dynamic feedback, ChatGPT transforms education, creating a robust line of defense against potential security breaches.Author BioClint Bodungen is a cybersecurity professional with 25+ years of experience and the author of Hacking Exposed: Industrial Control Systems. He began his career in the United States Air Force and has since many of the world's largest energy companies and organizations, working for notable cybersecurity companies such as Symantec, Kaspersky Lab, and Booz Allen Hamilton. He has published multiple articles, technical papers, and training courses on cybersecurity and aims to revolutionize cybersecurity education using computer gaming (“gamification”) and AI technology. His flagship product, ThreatGEN® Red vs. Blue, is the world’s first online multiplayer cybersecurity simulation game, designed to teach real-world cybersecurity.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Cybersecurity Cookbook, by Clint Bodungen. Master ChatGPT and the OpenAI API, and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.IntroductionIn this recipe, you'll learn how to harness the power of ChatGPT and the OpenAI API to create comprehensive vulnerability assessment plans using network, system, and business details as input. This recipe is invaluable for both cybersecurity students and beginners looking to familiarize themselves with proper methods and tools for vulnerability assessments, as well as experienced cybersecurity professionals aiming to save time on planning and documentation. Building upon the skills acquired in Chapter 1, you will delve deeper into establishing the system role of a cybersecurity professional specializing in vulnerability assessments. You'll learn how to craft effective prompts that generate well-formatted output using markdown language. This recipe will also expand on the techniques explored in Enhancing Output with Templates (Application: Threat Report) and Formatting Output as a Table (Application: Security Controls Table) in Chapter 1, enabling you to design prompts that produce the desired output format. Finally, you'll discover how to use the OpenAI API and Python to generate the vulnerability assessment plan, and then export it as a Microsoft Word file. This recipe will serve as a practical guide for creating detailed and efficient vulnerability assessment plans using ChatGPT and the OpenAI API. Getting Ready Before diving into the recipe, you should already have your OpenAI account setup and have obtained your API key. If not, revisit Chapter 1 for details. You will also need to be sure you have the following Python libraries installed: 1. python-docx: This library will be used to generate Microsoft Word files. You can install it using the command pip install python-docx.  2. tqdm: This library will be used to display progress bars. You can install it using the command:pip install tqdmHow to do it… In this section, we will walk you through the process of using ChatGPT to create a comprehensive vulnerability assessment plan tailored to a specific network and organization's needs. By providing the necessary details and using the given system role and prompt, you will be able to generate a well-structured assessment plan. 1. Begin by logging in to your ChatGPT account and navigating to the ChatGPT web UI. 2. Start a new conversation with ChatGPT by clicking the "New chat" button. 3.  Enter the following prompt to establish a system role: You are a cybersecurity professional specializing in vulnerability assessment. 4. Enter the following message text, but replace the placeholders in the “{ }” brackets with the appropriate data of your choice. You can either combine this prompt with the system role or enter it separately as follows: Using cybersecurity industry standards and best practices, create a complete and detailed assessment plan (not a penetration test) that includes: Introduction, outline of the process/methodology, tools needed, and a very detailed multi-layered outline of the steps. Provide a thorough and descriptive introduction and as much detail and description as possible throughout the plan. The plan should not only assessment of technical vulnerabilities on systems but also policies, procedures, and compliance. It should include the use of scanning tools as well as configuration review, staff interviews, and site walk-around. All recommendations should following industry standard best practices and methods. The plan should be a minimum of 1500 words. Create the plan so that it is specific for the following details: Network Size: {Large} Number of Nodes: {1000} Type of Devices: {Desktops, Laptops, Printers, Routers} Specific systems or devices that need to be excluded from the assessment: {None} Operating Systems: {Windows 10, MacOS, Linux} Network Topology: {Star} Access Controls: {Role-based access control} Previous Security Incidents: {3 incidents in the last year} Compliance Requirements: {HIPAA} Business Critical Assets: {Financial data, Personal health information} Data Classification: {Highly confidential} Goals and objectives of the vulnerability assessment: {To identify and prioritize potential vulnerabilities in the network and provide recommendations for remediation and risk mitigation.} Timeline for the vulnerability assessment: {4 weeks{ Team: {3 cybersecurity professionals, including a vulnerability assessment lead and two security analysts} Expected deliverables of the assessment: {A detailed report outlining the results of the vulnerability assessment, including identified vulnerabilities, their criticality, potential impact on the network, and recommendations for remediation and risk mitigation.} Audience: {The organization's IT department, senior management, and any external auditors or regulators.} Provide the plan using the following format and markdown language: #Vulnerability Assessment Plan ##Introduction Thorough Introduction to the plan including the scope, reasons for doing it, goals and objectives, and summary of the plan ##Process/Methodology Description and Outline of the process/Methodology ##Tools Required List of required tools and applications, with their descriptions and reasons needed ##Assessment Steps Detailed, multi-layered outline of the assessment steps Hint If you are performing this in the OpenAI Playground, it is advisable to use Chat mode and enter the role in the System window, and the prompt in the User message window. Figure 2.1 shows the system role and user prompt entered into the OpenAI Playground.  Figure 2.1 – OpenAI Playground Method 5. Review the generated output from ChatGPT. If the output is satisfactory and meets the requirements, you can proceed to the next step. If not, you can either refine your prompt or re-run the conversation to generate a new output. 6. Once you have obtained the desired output, you can use the generated markdown to create a well-structured vulnerability assessment plan in your preferred text editor or markdown viewer.  Figure 2.2 shows an example of ChatGPT generation of a vulnerability assessment plan using markdown language formatting.  Figure 2.2 – Example ChatGPT Assessment Plan Output How it works… This GPT-assisted vulnerability assessment plan recipe leverages the sophistication of natural language processing (NLP) and machine learning algorithms to generate a comprehensive and detailed vulnerability assessment plan. By adopting a specific system role and an elaborate user request as a prompt, ChatGPT is able to customize its response to meet the requirements of a seasoned cybersecurity professional who is tasked with assessing an extensive network system. Here's a closer look at how this process works: 1. System Role and Detailed Prompt: The system role designates ChatGPT as a seasoned cybersecurity professional specializing in vulnerability assessment. The prompt, which serves as the user request, is detailed and outlines the specifics of the assessment plan, from the size of the network and types of devices to the required compliance and the expected deliverables. These inputs provide context and guide ChatGPT's response, ensuring it is tailored to the complexities and requirements of the vulnerability assessment task. 2. Natural Language Processing and Machine Learning: NLP and machine learning form the bedrock of ChatGPT's capabilities. It applies these technologies to understand the intricacies of the user request, learn from the patterns, and generate a well-structured vulnerability assessment plan that is detailed, specific, and actionable. 3. Knowledge and Language Understanding Capabilities: ChatGPT uses its extensive knowledge base and language understanding capabilities to conform to industry-standard methodologies and best practices. This is particularly important in the rapidly evolving field of cybersecurity, ensuring that the resulting vulnerability assessment plan is up-to-date and adheres to recognized standards. 4. Markdown Language Output: The use of markdown language output ensures that the plan is formatted in a consistent and easy-to-read manner. This format can be easily integrated into reports, presentations, and other formal documents, which is crucial when communicating the plan to IT departments, senior management, and external auditors or regulators. 5. Streamlining the Assessment Planning Process: The overall advantage of using this GPT-assisted vulnerability assessment plan recipe is that it streamlines the process of creating a comprehensive vulnerability assessment plan. You save time on planning and documentation and can generate a professional-grade assessment plan that aligns with industry standards and is tailored to the specific needs of your organization. By applying these detailed inputs, you transform ChatGPT into a potential tool that can assist in creating a comprehensive, tailored vulnerability assessment plan. This not only bolsters your cybersecurity efforts but also ensures your resources are utilized effectively in protecting your network systems. ConclusionIn harnessing ChatGPT and the OpenAI API, this guide unlocks a streamlined approach to crafting detailed vulnerability assessment plans. Whether a novice or seasoned cybersecurity professional, leveraging these tools optimizes planning and documentation. By tailoring assessments to specific network intricacies, it fosters precision in identifying potential threats and fortifying defenses. Embrace this method to not only save time but also ensure comprehensive security measures aligned with industry standards, safeguarding networks effectively.Author BioClint Bodungen is a cybersecurity professional with 25+ years of experience and the author of Hacking Exposed: Industrial Control Systems. He began his career in the United States Air Force and has since many of the world's largest energy companies and organizations, working for notable cybersecurity companies such as Symantec, Kaspersky Lab, and Booz Allen Hamilton. He has published multiple articles, technical papers, and training courses on cybersecurity and aims to revolutionize cybersecurity education using computer gaming (“gamification”) and AI technology. His flagship product, ThreatGEN® Red vs. Blue, is the world’s first online multiplayer cybersecurity simulation game, designed to teach real-world cybersecurity.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, The Future of Finance with ChatGPT and Power BI, by James Bryant, Alok Mukherjee. Enhance decision-making, transform your market approach, and find investment opportunities by exploring AI, finance, and data visualization with ChatGPT's analytics and Power BI's visualsIntroductionIn this section, we will explore an interesting example of how ChatGPT can be used to analyze and summarize earnings reports, enabling you to identify key insights and trends quickly. With the vast amount of information available in earnings reports, it can be challenging to sift through data and identify the most critical elements. Let’s see how ChatGPT can help.Here’s the scenario – Palo Alto Networks has just released its quarterly earnings report. You want to understand the company’s financial performance and identify any trends or potential issues that may impact the stock price or investment potential:Step 1 – Extract key data points:To get started, provide ChatGPT with the relevant earnings report data, such as revenue, net income, EPS, and any other important metrics. Be sure to include both current and historical data for comparison purposes. You can either input this data manually or automate the process using an API or web scraper. Let’s explore the automated process to add Palo Alto Networks’ financial information from September 2021 to March 2023 to ChatGPT.Step 1.1 – Automating data collection with Python and API/web scraping:1.  Choose a financial API or web scraping library in Python:If using an API, explore options such as Alpha Vantage (alphavantage.co):Obtain an API key from the Alpha Vantage website (free and paid versions).Choose a method – Python requests.  Make a request.If web scraping, use libraries such as Requests and Beautiful SoupFor web scraping, identify the URLs of the company’s financial statements or earnings reports from websites such as Yahoo Finance (finance.yahoo.com), Nasdaq (nasdaq. com), or the company’s investor relations page.2. Set up your Python script for data collection:For APIs: a. Import the necessary libraries (e.g., requests or pandas) – for example, import requests import pandas as pd. b. Define the API key, endpoint URL, and required parameters. c. Make a request to the API to fetch data using the requests library. d. Parse the response data and convert it into a pandas DataFrame.For web scraping: a. Import the necessary libraries (e.g., requests, BeautifulSoup, or pandas) – for example, import requests from bs4 import BeautifulSoup import pandas as pd. b. Define the URL(s) containing the financial data. c. Use the requests library to fetch the HTML content of the web page. d. Parse the HTML content using BeautifulSoup to extract the required financial data. e. Convert the extracted data into a pandas DataFrame.3. Collect historical data from September 2021 to March 2023 for the relevant financial metrics:Adjust the parameters in your API request or web scraping script to target the specified date range.4. Save the collected data in a structured format, such as a CSV file or a pandas DataFrame, for further processing and analysis:Use pandas’ DataFrame.to_csv() method to save the collected data as a CSV fileAlternatively, keep the data in a pandas DataFrame for further analysis within the Python script.With these additions, you should have a better understanding of where to obtain financial data and the necessary Python libraries to import for their data collection scripts.We will now provide a step-by-step guide using Python code for Palo Alto Networks’ financial data.Extract Palo Alto Networks’ quarterly financial data (revenue, net income, and EPS) for the time period September 2021–March 2023, and save it in a CSV file as text input, using the Alpha Vantage API key (finance website):1. Install the necessary Python package and pandas library in Command Prompt:pip install requests pip install pandas2. Create a new Python script file in Notepad, Notepad++, PyCharm, or Visual Studio code. It is important that you add your Alpha Vantage API key in the following api_key line. Copy and paste the following code into your Python script file, and name it PANW.py:import requests import pandas as pd api_key = "YOUR_API_KEY" symbol = "PANW" url = f"https://www.alphavantage.co/ query?function=EARNINGS&symbol={symbol}&apikey={api_key}" try: response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses data = response.json() if 'quarterlyEarnings' in data: quarterly_data = data['quarterlyEarnings'] df = pd.DataFrame(quarterly_data) df_filtered = df[(df['reportedDate'] >= '2021-09-01') & (df['reportedDate'] <= '2023-03-31')] df_filtered.to_csv("palo_alto_financial_data.csv", index=False) input_text = "Analyze the earnings data of Palo Alto Networks from September 2021 to March 2023.\n\n" for idx, row in df_filtered.iterrows(): quarter = idx + 1 revenue = row.get('revenue', 'N/A') net_income = row.get('netIncome', 'N/A') eps = row.get('earningsPerShare', 'N/A') input_text += f"Quarter {quarter}:\n" input_text += f"Revenue: ${revenue}\n" input_text += f"Net Income: ${net_income}\n" input_text += f"Earnings Per Share: ${eps}\n\n" with open("palo_alto_financial_summary.txt", "w") as f: f.write(input_text) else: print("Data not available.") except requests.RequestException as e: print(f"An error occurred: {e}")3.  Run the Python script file:Python PANW.py4.  A separate text file, palo_alto_financial_summary.txt, and a CSV file, palo_ alto_financial_data.csv, will be created once the Python script has been executed:When the Python script, PANW.py, is executed, it performs several tasks to fetch and analyze the earnings data of Palo Alto Networks (the symbol PANW). First, it imports two essential libraries – requests to make API calls and pandas for data manipulation.The script starts by defining a few key variables – the API key to access financial data, the stock symbol of the company, and the URL to the Alpha Vantage API where the data can be retrieved. Then, a try block is initiated to safely execute the following operations.The script uses the requests.get() method to query the Alpha Vantage API. If the request is successful, the response is parsed as JSON and stored in a variable named data. It then checks whether data contains a key called quarterly earnings.If this key exists, the script proceeds to convert the quarterly earnings data into a pandas DataFrame. It filters this DataFrame to include only the entries between September 2021 and March 2023. The filtered data is then saved as a CSV file named palo_alto_financial_ data.csv:The CSV file contains raw financial data in tabular formThe CSV file can be imported into Excel, Google Sheets, or other specialized data analysis toolsThe script also constructs a text-based summary of the filtered earnings data, including revenue, net income, and EPS for each quarter within the specified date range. This summary is saved as a text file named palo_alto_financial_summary.txt:The TXT file provides a human-readable summary of the financial data for Palo Alto Networks for the specified data rangeTXT files can be used for quick overviews and presentationsIf any errors occur during this process, such as a failed API request, the script will catch these exceptions and print an error message, thanks to the except block. This ensures that the script fails gracefully, providing useful feedback instead of crashing.You can upload the CSV file (palo_alto_financial_data.csv) to ChatGPT directly if you are a ChatGPT Plus user by following these steps:Uploading a CSV file directly into ChatGPT is supported through the Advanced Data Analysis option for ChatGPT Plus users. You can access the OpenAI website at https://openai.com/, and then log in using your login credentials. Once logged in, access your Settings and Beta options by clicking on the three dots near your email address in the bottom-left corner of the screen. Go to Beta features and activate the Advanced data analysis function by moving the slider to the right to activate (the option will turn green). Once this feature is activated, go to GPT-4 at the top center of the screen and then select Advanced Data Analysis from the drop-down list. You can click on the plus sign in the dialog box to upload the CSV file to ChatGPT:•  CSV file size limitations: 500 MB•  CSV file retention: Files are retained while a conversation is active and for three hours after the conversation is pausedIf you are not a ChatGPT Plus user, follow the following instructions using the OpenAI API to upload the CSV file (palo_alto_financial_data.csv) into ChatGPT, and analyze the data using the GPT 3.5 turbo model:1. Create a new Python script file in Notepad, Notepad++, PyCharm, or Visual Studio Code. It is important that you add your OpenAI API key to the following api_key line. Copy and paste the following code into your Python script file and name it OPENAIAPI.py:import openai import pandas as pd df = pd.read_csv("palo_alto_financial_data.csv") csv_string = df.to_string(index=False) api_key = "your_openai_api_key_here" openai.api_key = api_key input_text = f"Here is the financial data for Palo Alto Networks:\n\n{csv_string}\n\nPlease analyze the data and provide insights." response = openai.Completion.create( engine="gpt-3.5-turbo", # Specifying GPT-3.5-turbo engine prompt=input_text, max_tokens=200 # Limiting the length of the generated text ) generated_text = response.choices[0].text.strip() print("GPT-3.5-turbo PANW Analysis:", generated_text)2.  Run the Python script file:Python OPENAIAPI.pyThis Python code snippet is responsible for interacting with the OpenAI API to send the formatted text input (the financial data prompt) to ChatGPT and receive the generated response. Here’s a breakdown of each part:The Python code snippet starts by importing two essential Python libraries – openai for interacting with the OpenAI API, and pandas for data manipulation.The script reads financial data from a CSV file named palo_alto_financial_data. csv using pandas, converting this data into a formatted string. It then sets up the OpenAI API by initializing it with a user-provided API key.Following this, the script prepares a prompt for GPT-3.5-turbo, consisting of the loaded financial data and a request for analysis. This prompt is sent to the GPT-3.5-turbo engine via the OpenAI API, which returns a text-based analysis, limited to 200 tokens.The generated analysis is then extracted from the API’s response and printed to the console with the label “GPT-3.5-turbo PANW Analysis.” The script essentially automates the process of sending financial data to the GPT-3.5-turbo engine for insightful analysis, making it easy to obtain quick, AI-generated insights on Palo Alto Networks’ financial performance.ConclusionIn conclusion, harnessing ChatGPT's capabilities, we've navigated Palo Alto Networks' earnings landscape. From automated data extraction to insightful analysis, this journey unveiled crucial financial trends. Whether utilizing APIs or scraping web data, the process demystified complexities, offering a streamlined approach. By generating comprehensive summaries and interacting with ChatGPT for deeper insights, the pathway to understanding financial data has been simplified. Embracing AI-powered analysis enables swift comprehension of earnings reports, empowering informed decisions in the realm of financial scrutiny and investment strategies.Author BioJames Bryant, a finance and technology expert, excels at identifying untapped opportunities and leveraging cutting-edge tools to optimize financial processes. With expertise in finance automation, risk management, investments, trading, and banking, he's known for staying ahead of trends and driving innovation in the financial industry. James has built corporate treasuries like Salesforce and transformed companies like Stanford Health Care through digital innovation. He is passionate about sharing his knowledge and empowering others to excel in finance. Outside of work, James enjoys skiing with his family in Lake Tahoe, running half marathons, and exploring new destinations and culinary experiences with his wife and daughter.Aloke Mukherjee is a seasoned technologist with over a decade of experience in business architecture, digital transformation, and solutions architecture. He excels at applying data-driven solutions to real-world problems and has proficiency in data analytics and planning. Aloke worked at EMC Corp and Genentech and currently spearheads the digital transformation of Finance Business Intelligence at Stanford Health Care. In addition to his work, Aloke is a Certified Personal Trainer and is passionate about helping his clients stay fit. Aloke also has a passion for wine and exploring new vineyards.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,“We will have for the first time something smarter than the smartest human. It's hard to say exactly what that moment is, but there will come a point where no job is needed.” -Elon Musk, CEO, Tesla.  Musk has released an important FSD v12 update to Tesla employees, a move touted as a breakthrough in the realization of true self-driving capabilities powered by neural nets. In the words of Musk, can self-driving cars be smarter than the smartest drivers? Only time will tell, but the future is exciting. Welcome to another AI_Distilled, you one-stop hub for all things Gen AI. Let's kick off today's edition with some of the latest news and analysis across the AI domain: 👉 Amazon and Salesforce Fortify Alliance to Boost AI Integration 👉 Tesla Initiates Rollout of FSD v12 to Employees 👉 Anthropic Unveils Claude 2.1 with Enhanced AI Capabilities 👉 Stability AI Unveils 'Stable Video Diffusion' AI Tool for Animated Images 👉 Amazon Unveils Amazon Q: A Generative AI-Powered Business Assistant 👉 Amazon Introduces New AI Chips for Model Training and Inference 👉 Pika Labs Raises $55M, Launches AI Video Platform 👉 Amazon AWS Unveils Ambitious Generative AI Vision at Re:Invent Next, we'll swiftly explore the secret knowledge column that features some key LLM resources: 💎 How to Enhance LLM Reasoning with System 2 Attention 💎 Unlocking AWS Wisdom with Amazon Q 💎 How to Optimize LLMs on Modest Hardware 💎 How to Assess AI System Risks 💎 Prompting Strategies for Domain-Specific Expertise in GPT-4 Hold on, there's additional news! Discover the hands-on tips and proven methods straight from the AI community:   📍 Customizing Models in Amazon Bedrock 📍 Building an Infinite Chat Memory GPT Voice Assistant in Python 📍 Generating High-Quality Computer Vision Datasets 📍 Understanding LSTM in NLPLooking to expand your AI toolkit on GitHub? Check out these repositories!  ✅ neurocult/agency ✅ lunyiliu/coachlm ✅ 03axdov/muskie ✅ robocorp/llmstatemachine Also, don't forget to check our expert insights column, which covers the interesting concepts of hybrid cloud from the book 'Achieving Digital Transformation Using Hybrid Cloud'. It's a must-read!   Stay curious and gear up for an intellectually enriching experience!  📥 Feedback on the Weekly EditionQuick question: How do you handle data quality issues, such as missing or inconsistent data, to ensure accurate visual representations? Share your valued opinions discreetly! Your insights could shine in our next issue for the 38K-strong AI community. Join the conversation! 🗨️✨ As a big thanks, get our bestselling "The Applied Artificial Intelligence Workshop" in PDF.   Let's make AI_Distilled even more awesome! 🚀 Jump on in! Share your thoughts and opinions here! Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis🔸 Amazon and Salesforce Fortify Alliance to Boost AI Integration: Amazon and Salesforce strengthen their partnership, prioritizing AI integration for efficient data management. This collaboration enhances synergy between Salesforce and AWS, with Salesforce expanding its use of AWS technologies, including Hyperforce, while AWS leverages Salesforce products for unified customer profiles and personalized experiences. 🔸 Tesla Initiates Rollout of FSD v12 to Employees, Signaling Progress in Self-Driving Endeavor: Tesla begins the rollout of Full Self-Driving (FSD) v12 to employees, a key move for CEO Elon Musk's self-driving vision. The update shifts controls to neural nets, advancing autonomy. Musk aims to exit beta with v12, removing constant driver monitoring, but concerns persist about Tesla's responsibility and the timeline for full self-driving. 🔸 Anthropic Unveils Claude 2.1 with Enhanced AI Capabilities: Anthropic launches Claude 2.1 via API, featuring a groundbreaking 200K token context window, halving hallucination rates, and a beta tool use function. The expanded context window facilitates processing extensive content, improving accuracy, honesty, and comprehension, particularly in legal and financial documents. Integration capabilities with existing processes enhance Claude's utility in diverse operations. 🔸 Stability AI Unveils 'Stable Video Diffusion' AI Tool for Animated Images: Stability AI introduces Stable Video Diffusion, a free AI research tool that converts static images into brief videos using SVD and SVD-XT models. Running on NVIDIA GPUs, it generates 2-4 second MP4 clips with 576x1024 resolution, featuring dynamic scenes through panning, zooming, and animated effects. 🔸 Amazon Unveils Amazon Q: A Generative AI-Powered Business Assistant: Amazon Q is a new generative AI assistant for businesses, facilitating streamlined tasks, quick decision-making, and innovation. It engages in conversations, solves problems, and generates content by connecting to company information. Customizable plans prioritize user privacy and data security, enabling deployment in various tasks, from press releases to social media posts. 🔸 Amazon Introduces New AI Chips for Model Training and Inference: Amazon has launched new chips, including AWS Trainium2 and Graviton4, addressing GPU shortages for generative AI. Trainium2 boasts 4x performance and 2x energy efficiency, with a cluster of 100,000 chips capable of swift AI LLM training. Graviton4 targets inferencing, aiming to lessen GPU dependence, aligning with Amazon's commitment to meet rising AI demands. 🔸 Pika Labs Raises $55M, Launches AI Video Platform: Pika Labs, a video AI startup, secures $55 million in funding, led by a $35 million series A round from Lightspeed Venture Partners. They unveil Pika 1.0, a web platform enabling easy text prompt-based video creation and editing in diverse styles. Already used by 500,000+, the product aims to rival AI video generation platforms like Runway and Stability AI, as well as Adobe tools. 🔸 Amazon AWS Unveils Ambitious Generative AI Vision at Re:Invent: Amazon aims to lead in generative AI, surpassing rivals Azure and Google Cloud. Emphasizing the Bedrock service's diverse generative AI models and user-friendly data tools, Amazon focuses on enhancing Bedrock and introducing gen AI features to Amazon Quicksight for business intelligence applications.  🔮 Expert Insights from Packt Community Achieving Digital Transformation Using Hybrid Cloud by Vikas Grover, Ishu Verma, Praveen Rajagopalan Organizations of all sizes and industries appreciate the convenience of adjusting their resources based on demand and only paying for what they use. Leading public cloud service providers and SaaS offerings such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Salesforce, respectively, have seen significant growth in recent years, catering to the needs of small start-ups and large enterprises alike. Hybrid cloud use cases Hybrid cloud has emerged as a popular solution for organizations looking to balance the benefits of public and private clouds while addressing the data security requirements, compliance needs for regulated applications, and performance and computing needs for applications running at remote edge locations. Here are four use cases that showcase the versatility and flexibility of the hybrid cloud in different industries: Security: A government agency uses a hybrid cloud approach to store sensitive national security data on a private cloud for maximum security while utilizing the public cloud for cost-effective data storage and processing for non-sensitive data. Proprietary Technology: A technology company uses a hybrid cloud approach to store and manage its proprietary software on a private cloud for maximum security and control while utilizing the public cloud for cost-effective development and testing. For example, financial service companies manage trading platforms on the private cloud for maximum control while using the public cloud for running simulations and back-testing algorithms. Competitive Edge: A retail company uses a hybrid cloud solution to store critical sales and customer information on a private cloud for security and compliance while utilizing the public cloud for real-time data analysis to gain a competitive edge by offering personalized customer experiences and insights. Telecom: A telecommunications company uses a hybrid cloud approach to securely store sensitive customer information on a private cloud while utilizing the public cloud for real-time data processing and analysis to improve network performance and customer experience. This approach helps the company maintain a competitive edge in the telecom sector by providing a superior network experience to its customers. Understanding the benefits of hybrid cloud computing A hybrid cloud provides a flexible solution. Many organizations have embraced and adopted the hybrid cloud. If we take an example of a cable company, Comcast (the world’s largest cable company), as per a technical paper published by Comcast for SCTE-ISBE, Comcast serves tens of millions of customers and hosts hundreds of tenants in eight regions and three public clouds. This is a great testimony of using a hybrid cloud for mission-critical workloads that need to run at scale. Hybrid cloud is more popular than ever and some of the reasons that organizations are adopting a hybrid cloud are as follows: Time to market: With choices available to your IT teams to leverage appropriate resources as needed by use case, new applications and services can be launched quickly. Manage costs: Hybrid cloud helps you with optimizing and consuming resources efficiently. Make use of your current investments in existing infrastructure and when needed to scale, burst the workloads in the public cloud. Reduced lock-in: Going into the cloud may be appealing, but once in and when costs start to rise and eat the bottom line of the organization, it would be another costly proposition to reverse-migrate some of your applications from the public cloud. A hybrid cloud allows you to run anywhere and reduces your lock-in. Gaining a competitive advantage: In the competitive world of business, relying solely on public cloud technologies can put you at a disadvantage. To stay ahead of the competition, it’s important to maintain control over and ownership of cutting-edge technologies. This way, you can build and grow your business in an increasingly competitive environment. This content is from the book Achieving Digital Transformation Using Hybrid Cloud by Vikas Grover, Ishu Verma, Praveen Rajagopalan (July 2023). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below.   Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources🔸 How to Enhance LLM Reasoning with System 2 Attention: Meta researchers introduce System 2 Attention (S2A), a revolutionary technique enhancing Large Language Models (LLMs) by refining user prompts through psychological inspiration. S2A focuses on task-relevant data, boosting LLMs' accuracy in reasoning tasks by eliminating irrelevant information and instructing them to generate context effectively. 🔸 Unlocking AWS Wisdom with Amazon Q: A Guide for Optimal Decision-Making: Amazon Q, a robust chatbot trained on 17 years of AWS documentation, transforms AWS task execution. Explore its prowess in navigating AWS services intricacies, offering insights on serverless vs. containers and database choices. Enhance accuracy with expert guidance on AWS Well Architected Framework, troubleshooting, workload optimization, and content creation. 🔸 How to Optimize LLMs on Modest Hardware: Quantization, a key technique for running large language models on less powerful hardware, reduces model parameters' precision. PyTorch offers dynamic, static, and quantization-aware training strategies, each balancing model size, computational demand, and accuracy. Choosing hardware involves understanding the critical role of VRAM, challenging the notion that newer GPUs are always superior. 🔸 How to Assess AI System Risks: A Comprehensive Guide: Explore the nuanced realm of AI risk assessment in this guide, covering model and enterprise risks for responsible AI development. Understand the importance of defining inherent and residual risks, utilizing the NIST Risk Management Framework, and involving diverse stakeholders. Learn to evaluate risks using likelihood and severity scales, employing a risk matrix. 🔸 The Effectiveness of Prompting Strategies for Domain-Specific Expertise in GPT-4: This study explores prompting strategies to leverage domain-specific expertise from the versatile GPT-4 model. It reveals GPT-4's exceptional performance as a medical specialist, surpassing finely-tuned medical models. Medprompt, a combination of prompting strategies, enables GPT-4 to achieve over 90% accuracy on the challenging MedQA dataset, challenging the conventional need for extensive fine-tuning and showcasing the broad applicability of generalist models across diverse domains. 🔛 Masterclass: AI/LLM Tutorials🔸 Customizing Models in Amazon Bedrock: A Step-by-Step Guide: Embark on the journey of tailoring foundation models in Amazon Bedrock to align with your specific domain and organizational needs, enriching user experiences. This comprehensive guide introduces two customization options: fine-tuning and continued pre-training. Learn how to enhance model accuracy through fine-tuning using your task-specific labeled dataset and explore the process of creating fine-tuning jobs via the Amazon Bedrock console or APIs. Additionally, explore continued pre-training, available in public preview for Amazon Titan Text models, understanding its benefits in making models more domain-specific. The guide provides practical demos using AWS SDK for Python (Boto3) and offers crucial insights on data privacy, network security, billing, and provisioned throughput.  🔸 Building an Infinite Chat Memory GPT Voice Assistant in Python: Learn to build a customizable GPT voice assistant with OpenAI's cloud assistant feature. This guide explores the assistant API, providing auto-vectorization and intelligent context handling for extensive chat recall. Enjoy advantages like enhanced security, limitless memory, local message history retrieval, and flexible interfaces. Gain essential tools and skills for implementation, including an OpenAI API key, ffmpeg installation, and required Python packages. 🔸 Generating High-Quality Computer Vision Datasets: This guide outlines the process of building a customized and diverse computer vision dataset. It covers generating realistic image prompts with ChatGPT, utilizing a vision image generation model, automating object detection, and labeling. Learn to enhance dataset quality for improved computer vision projects through prompt customization and model utilization. 🔸 Understanding LSTM in NLP: A Python Guide: This guide explores employing Long Short-Term Memory (LSTM) layers for natural language processing in Python. It covers theoretical aspects, details coding of the layer's forward pass, and includes a practical implementation with a dataset, enhancing understanding and application of LSTM in NLP through text data preprocessing and sentiment encoding.  🚀 HackHub: Trending AI Tools🔸 neurocult/agency: Explore the capabilities of LLMs and generative AI with this library designed with a clean, effective, and Go-idiomatic approach. 🔸 lunyiliu/coachlm: Code and data for an automatic instruction revision method tailored for LLM instruction tuning to implement CoachLM and enhance the precision of LLM instruction tuning effortlessly. 🔸 https://github.com/03axdov/muskie: Python-based ML library streamlining the creation of custom datasets and model usage with minimal code requirements. 🔸 robocorp/llmstatemachine: Python library to unlock GPT-powered agents with ease, incorporating state machine logic and chat history memory for seamless development.  

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,Welcome to another AI_Distilled! This edition brings you key stories on AI, ML, NLP, Gen AI, and more. Our mission is to keep you informed, empowering your skill advancement. Before we embark on the need-to-know updates, let’s take a moment to observe an important perspective from an industry leader. “We’re now seeing a major second wave…let’s acknowledge that without open source, how would AI have made the tremendous progress it has over the last decade” -Jensen Huang, NVIDIA CEO Amidst the uncertainty surrounding Sam Altman's removal and reinstatement at OpenAI, the open-source community emerges as a potential beneficiary. Also, as OpenAI pauses new signups for ChatGPT Plus, enterprises are anticipated to seek stability and long-term impact by turning to open-source AI models such as Llama, Mistral, Falcon, and MPT for their AI application development needs. Both proprietary and open-source models will play roles, but the latter's contributions are crucial for advancing AI technology's impact on work and life. In this week’s edition, we’ll talk about Google DeepMind unveiling an advanced AI music generation model and experiments, Meta releasing Emu Video and Emu Edit, major breakthroughs in generative AI research, Microsoft Ignite 2023 bringing new AI expansions and product announcements, and Galileo's Hallucination Index identifying GPT-4 as the best LLM for different use cases. We’ve also got you your fresh dose of AI secret knowledge and tutorials including how to implement emerging practices for society-centered AI, how to speed up and improve LLM output with skeleton-of-thought, getting started with Llama 2 in 5 steps, and how to build an AI assistant with real-time web access in 100 lines of code using Python and GPT-4. Also, don't forget to check our expert insights column, which covers the interesting concepts of data architecture from the book 'Modern Data Architecture on AWS'. It's a must-read!  Stay curious and gear up for an intellectually enriching experience!  📥 Feedback on the Weekly EditionHey folks!After the stunning OpenAI DevDay, many of us were eager to embark on creating our custom GPT magic. But let's chat about the recent hiccups: the pause on ChatGPT-4 new sign-ups and the shift or reformation in OpenAI's leadership. It's got us all wondering about the future of our handy tools. Quick question: Ever tried ChatGPT's Advanced Data Analysis? Now that it's temporarily on hold for new users, it's got us thinking, right? Share your take on these changes in the comments. Your thoughts count! We're turning the spotlight on you – some of the best insights will be featured in our next issue for our 38K-strong AI-focused community. Don't miss out on the chance to share your views! 🗨️✨ As a big thanks, get our bestselling "The Applied Artificial Intelligence Workshop" in PDF.   Let's make AI_Distilled even more awesome! 🚀 Jump on in! Share your thoughts and opinions here! Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis🔳 Sam Altman Is Reinstated as OpenAI’s Chief Executive: OpenAI reinstated CEO Sam Altman, reversing his ouster amid a board shake-up. The revamped board, led by Bret Taylor, includes Lawrence Summers and Adam D'Angelo, with Microsoft's support. Negotiations involved concessions, including an independent investigation into Altman's leadership. Some outgoing members sought to curb Altman's power. Altman's removal sparked a campaign by allies and employees for his return. The board initially stood by its decision but ultimately reinstated Altman for a fresh start. 🔳 Google DeepMind Unveils Advanced AI Music Generation Model and Experiments: Google DeepMind introduces Lyria, an advanced AI music generation model, and collaborates with YouTube on two experiments, "Dream Track" and "Music AI tools," revolutionizing music creation. Lyria excels in maintaining musical continuity, while the experiments support artists and producers in crafting unique soundtracks and enhancing the creative process. 🔳 Meta Unveils Emu Video and Emu Edit: Advancements in Generative AI Research: Meta has unveiled two major advancements in generative AI: Emu Video, a text-to-video platform using diffusion models for high-quality content generation, and Emu Edit, an image editing tool for precise control. Human evaluations favor Emu Video over previous models, showcasing substantial progress in creative and effective generative AI tools. 🔳 Google's AI Search Feature Expands to 120+ Countries: Google's Search Generative Experience (SGE) has expanded to 120+ countries, offering generative AI summaries and language support for Spanish, Portuguese, Korean, and Indonesian. Users can ask follow-up questions and get interactive definitions. The update will initially roll out in the US before expanding globally, enhancing natural language interactions in search results. 🔳 Microsoft Ignite 2023 Brings New AI Expansions and Product Announcements: Microsoft's Ignite 2023 highlighted the company's deepened AI commitment, featuring Bing Chat's rebranding to Copilot, custom AI chips, and new AI tools like Copilot for Azure. Microsoft Teams will offer AI-driven home decoration and voice isolation. The company consolidated planning tools, introduced generative AI copyright protection, Windows AI Studio for local AI deployment, and Azure AI Speech for text-to-speech avatars. The event underscored Microsoft's emphasis on AI integration across its products and services. 🔳 Microsoft Emerges as Ultimate Winner in OpenAI Power Struggle: Microsoft emerged victorious in the OpenAI power struggle by hiring ousted CEO Sam Altman and key staff, including Greg Brockman, to lead a new advanced AI team. This strategic move solidifies Microsoft's dominance in the industry, positioning it as a major player in AI without acquiring OpenAI, valued at $86 billion. The recent turmoil at OpenAI has led to employee threats of quitting and joining Altman at Microsoft, potentially granting Microsoft access to significant AI talent. 🔳 Galileo's Hallucination Index Identifies GPT-4 As the Best LLM for Different Use Cases: San Francisco based Galileo has introduced a Hallucination Index to aid users in selecting the most reliable Large Language Models (LLMs) for specific tasks. Evaluating various LLMs, including Meta's Llama series, the index found GPT-4 excelled, and OpenAI's models consistently performed well, supporting trustworthy GenAI applications. 🔳 Microsoft Releases Orca 2: Small Language Models That Outperform Larger Ones: Orca 2, comprising 7 billion and 13 billion parameter models, excels in intricate reasoning tasks, surpassing larger counterparts. Developed by fine-tuning LLAMA 2 base models on tailored synthetic data, Orca 2 showcases advancements in smaller language model research, demonstrating adaptability across tasks like reasoning, grounding, and safety through post-training with carefully filtered synthetic data. 🔳 NVIDIA CEO Predicts Major Second Wave of AI: Jensen Huang predicts a significant AI surge, citing breakthroughs in language replicated in biology, manufacturing, and robotics, offering substantial opportunities for Europe. Praising France's AI leadership, he emphasizes the importance of region-specific AI systems reflecting cultural nuances and highlights the crucial role of data in regional AI growth. 🔮 Expert Insights from Packt Community Modern Data Architecture on AWS - By Behram Irani Challenges with on-premises data systems As data grew exponentially, so did the on-premises systems. However, visible cracks started to appear in the legacy way of architecting data and analytics use cases. The hardware that was used to process, store, and consume data had to be procured up-front, and then installed and configured before it was ready for use. So, there was operational overhead and risks associated with procuring the hardware, provisioning it, installing software, and maintaining the system all the time. Also, to accommodate for future data growth, people had to estimate additional capacity way in advance. The concept of hardware elasticity didn’t exist.The lack of elasticity in hardware meant that there were scalability risks associated with the systems in place, and these risks would surface whenever there was a sudden growth in the volume of data or when there was a market expansion for the business. Buying all this extra hardware up-front also meant that a huge capital expenditure investment had to be made for the hardware, with all the extra capacity lying unused from time to time. Also, software licenses had to be paid for and those were expensive, adding to the overall IT costs. Even after buying all the hardware upfront, it was difficult to maintain the data platform’s high performance all the time. As data volumes grew, latency started creeping in, which adversely affected the performance of certain critical systems. As data grow into big data, the type of data produced was not just structured data; a lot of business use cases required semi-structured data, such as JSON files, and even unstructured data, such as images and PDF files. In subsequent chapters, we will go through some use cases that specify different types of data. As the sources of data grew, so did the number of ETL pipelines. Managing these pipelines became cumbersome. And on top of that, with so much data movement, data started to duplicate at multiple places, which made it difficult to create a single source of truth for the data. On the flip side, with so many data sources and data owners within an organization, data became siloed, which made it difficult to share across different LOBs in the organization. This content is from the book “Modern Data Architecture on AWS” writtern by Behram Irani (Aug 2023). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below. Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources🤖 How to Use Amazon CodeWhisperer for Command Line: Amazon introduces Amazon CodeWhisperer for the command line, enhancing developer productivity with contextual CLI completions and AI-driven natural language-to-bash translation. The tool provides CLI completions and translates natural language instructions into executable shell code snippets, modernizing the command line experience for over thirty million engineers. 🤖 How to Implement Emerging Practices for Society-Centered AI: The post underscores the importance of AI professionals addressing societal implications, advocating for multidisciplinary collaboration. It stresses the significance of measuring AI's impact on society to enhance effectiveness and identify areas for improvement in developing systems that benefit the broader community. 🤖 How to Speed Up and Improve LLM Output with Skeleton-of-Thought: The article introduces the Skeleton-of-Thought (SoT) approach, aiming to enhance the efficiency of Language Models (LLMs) by reducing generation latency and improving answer quality. SoT guides LLMs to generate answer skeletons first, then completes them in parallel, potentially accelerating open-source and API-based models for various question categories. 🤖 Understanding SuperNIC to Enhance AI Efficiency: The BlueField-3 SuperNIC is pivotal in AI-driven innovation, boosting workload efficiency and networking speed in AI cloud computing. With a 1:1 GPU to SuperNIC ratio, it enhances productivity. Integrated with NVIDIA Spectrum-4, it provides adaptive routing, out-of-order packet handling, and optimized congestion control for superior outcomes in enterprise data centers. 🤖 Step-by-step guide to the Evolution of LLMs: The post explores the 12-month evolution of Large Language Models (LLMs), from text completion to dynamic chatbots with code execution and knowledge access. It emphasizes the frequent release of new features, models, and techniques, notably the November 2022 launch of ChatGPT, accelerating user adoption and triggering an AI arms race, while questioning if such rapid advancements are bringing us closer to practical AI agents.  🔛 Masterclass: AI/LLM Tutorials👉 How to Get Started with Llama 2 in 5 Steps: Llama 2, an open-source large language model, is now free for research and commercial use. This blog outlines a five-step guide, covering prerequisites, model setup, fine-tuning, inference, and additional resources for users interested in utilizing Llama 2. 👉 How to Integrate GPT-4 with Python and Java: A Developer's Guide: The article explores integrating GPT-4 with Python and Java, emphasizing Python's compatibility and flexibility. It provides examples, discusses challenges like rate limits, and encourages collaboration for harnessing GPT-4's transformative potential, highlighting the importance of patience and debugging skills. 👉 How to Build an AI Assistant with Real-Time Web Access in 100 Lines of Code Using Python and GPT-4: This article guides readers in creating a Python-based AI assistant with real-time web access using GPT-4 in just 100 lines of code. The process involves initializing clients with API keys, creating the assistant using the OpenAI and Tavily libraries, and implementing a function for retrieving real-time information from the web. The author offers a detailed step-by-step guide with code snippets. 👉 Step-by-step guide to building a real-time recommendation engine with Amazon MSK and Rockset: This tutorial demonstrates building a real-time product recommendation engine using Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Rockset. The architecture allows instant, personalized recommendations critical for e-commerce, utilizing Amazon MSK for capturing high-velocity user data and AWS Managed services for scalability in handling customer requests, API invocations, and data ingestion.  🚀 HackHub: Trending AI Tools💮 protectai/ai-exploits: Collection of real-world AI/ML exploits for responsibly disclosed vulnerabilities, aiming to raise awareness of the amount of vulnerable components in the AI/ML ecosystem. 💮 nlmatics/llmsherpa: Provides strategic APIs to accelerate LLM use cases, includes a LayoutPDFReader that provides layout information for PDF to text parsers, and is tested on a wide variety of PDFs. 💮 QwenLM/Qwen-Audio: Large audio language model proposed by Alibaba Cloud developers can use for speech editing, sound understanding and reasoning, music appreciation, and multi-turn dialogues in diverse audio-oriented scenarios. 💮 langchain-ai/opengpts: Open-source effort creating a similar experience to OpenAI's GPTs and Assistants API. It builds upon LangChain, LangServe, and LangSmith.  Readers’ Feedback! 💬 💭 Anish says, "The growing number of subscribers is really exciting. I particularly appreciate the transformation of 2D images into 3D models from Adobe and going through 'Tackling Hallucinations in LLMs' by Bijit Ghosh. These kinds of practical contexts are truly my preference for the upcoming newsletters." 💭 Tony says, "Very informative, far-reaching, and extremely timely. On point. Just keep it up, keep your eye on news and knowledge, and keep cluing us all once a week please, Merlyn. You're doing a fine job."  Share your thoughts here! Your opinions matter—let's make this space a reflection of diverse perspectives. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, R Bioinformatics Cookbook - Second Edition, by Dan MacLean. Discover over 80 recipes for modeling and handling real-life biological data using modern libraries from the R ecosystem.IntroductionEmbrace the power of streamlined debugging and code refinement with ChatGPT's expertise. Unravel the possibilities of effortlessly troubleshooting errors, optimizing performance, and refining code structures. This article explores how ChatGPT, armed with its extensive programming knowledge, assists in identifying and rectifying coding errors, offering tailored solutions, and fostering a deeper understanding of code logic. Dive into a journey of code enhancement, where ChatGPT becomes your indispensable companion in navigating complexities and elevating your coding experiencesDebugging and improving code with ChatGPTChatGPT is capable of debugging code due to its extensive training on programming-related text. It has acquired knowledge of common programming patterns and errors, allowing it to understand code snippets, identify potential issues, and provide suggestions or improvements. It can identify minor errors by highlighting syntax issues such as missing brackets, incorrect function calls, and invalid variable assignments. ChatGPT helps troubleshooting by asking clarifying questions to better understand the problem and guide developers toward the root cause, and can aid in code optimization by suggesting efficiency improvements, identifying performance bottlenecks, and proposing more optimized implementations. Lastly, the debugging process with ChatGPT can foster learning and exploration, enabling developers to discover new concepts, programming techniques, or functionalities of specific libraries or packages. In this recipe, we’ll walk through an example debugging session with ChatGPT and look at how it helps us to reflect better on our own code.Getting readyWe just need an account at ChatGPT: https://chat.openai.com/.How to do it…We can debug some code as follows:1. Set up some code that we expect to work and try it:data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) model <- lm(response ~ predictor, data = data)2. Send the broken code to ChatGPT:Help me fix the following code: data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) model <- lm(response ~ predictor, data = data)3. Read and run the resulting response in R:To fix the code, you need to handle the missing value (NA) in the predictor column. Here's the corrected code: # Fixing the code data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) # Replace NA in the predictor column with the mean value data$predictor[is.na(data$predictor)] <- mean(data$predictor, na.rm = TRUE) # Convert the response variable to a factor data$response <- as.factor(data$response) # Perform linear regression model <- lm(response ~ predictor, data = data) summary(model) In the fixed code, I replaced the NA value in the predictor column with the mean value of the remaining numeric values using the mean() function with na.rm = TRUE argument. Then, I converted the response variable to a factor using the as.factor() function. Finally, the linear regression model is computed with the corrected data, and the summary(model) displays the model summary.4. Tell ChatGPT the code still doesn’t work:It still doesn't work - Im getting thie error: Error in as.character.factor(x) : malformed factor In addition: Warning message: In Ops.factor(r, 2) : '^' not meaningful for factors 5. Read the response and run the code:Apologies for the oversight. The error is occurring because the ^ operator is not applicable to factors in the formula used in the linear regression. To fix this, we can convert the factor to a numeric representation using numeric codes or dummy variables. Here's the updated code: # Fixing the code data <- data.frame( predictor = c(1, 2, 3, 4, NA), response = c("A", "B", "C", "D", "E") ) # Replace NA in the predictor column with the mean value data$predictor[is.na(data$predictor)] <- mean(data$predictor, na.rm = TRUE) # Convert the response variable to a factor data$response <- as.factor(data$response) # Convert the factor to numeric codes data$response <- as.numeric(data$response) # Perform linear regression model <- lm(response ~ predictor, data = data) summary(model) In the updated code, I've used as.numeric() to convert the response factor variable to numeric codes. This allows the regression model to treat it as a numerical response. Now, the lm() function should run without the error, and summary(model) will display the model summary.6. Ask it to check whether the original code was appropriately analyzed:Were the original data suitable for a linear model?7. Read the response:No, the original data provided in the code were not suitable for a linear model. Here's why: 1. The `predictor` variable was numeric, which is suitable for a linear model. 2. The `response` variable, however, was categorical with values "A", "B", "C", "D", and "E". Categorical variables cannot be directly used as the response variable in a linear regression model because they do not represent continuous numeric values. To use a categorical variable as the response variable in a linear model, you typically need to encode it into dummy variables or use techniques like multinomial logistic regression. Dummy variable encoding represents each category as a separate binary variable (0 or 1). Once the categorical variable is properly encoded, it can be used as the response variable in a linear regression model.Again, that’s how the process goes though the output can be different from user to user but the meaning should be the same.How it works…In step 1, we set up a data frame of some data as a test set to use in a linear model; we wish to examine the relationship between the two variables so assume that a linear model will work. When we run the code in R, we get this:Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'y' In addition: Warning message: In storage.mode(v) <- "double" : NAs introduced by coercion This is a typically confounded R error message. We want help, so in step 2, we ask ChatGPT to fix the code.Step 3 shows us ChatGPT’s response, which suggests fixing the NA values that are in the predictor column. That seems reasonable, and, as it explains, ChatGPT gives us some code that imputes a new value from the mean of all the other values – again, a reasonable value to impute. When we run the code, it still doesn’t work and we get a new error, so in step 4, we tell ChatGPT about it and ask it to fix the new errors.In step 5, we see an apologetic language model attempt to correct the error. It gives us a confusing reason for doing some strange text/number conversion and fixed code. When we run this new code in the console, we get output like this:## Call: ## lm(formula = response ~ predictor, data = data) ## ## Residuals: ## 1 2 3 4 5 ## -0.5 -0.5 -0.5 -0.5 2.0 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.5000 1.5546 0.322 0.769 ## predictor 1.0000 0.5774 1.732 0.182 ## ## Residual standard error: 1.291 on 3 degrees of freedom ## Multiple R-squared: 0.5, Adjusted R-squared: 0.3333 ## F-statistic: 3 on 1 and 3 DF, p-value: 0.1817This looks a bit strange – the residuals are weird and the rest of the values look poor. We start to question whether this was the right thing to do in the first place.In step 6, we ask ChatGPT whether the linear model was the right sort of analysis. It responds as in step 7, telling us quite clearly that it was not appropriate.This recipe highlights that we can use ChatGPT to fix code that doesn’t work, but shows also that ChatGPT will not reason without prompting. Here, it let us pursue a piece of code that wasn’t right for the task. As a language model, it can’t know that, even though we believe it would be evident from the question setup. It didn’t try to correct our flawed assumptions or logic. We still need to be responsible for the logic and applicability of our code.ConclusionIn conclusion, ChatGPT emerges as a valuable ally in code debugging, offering insightful solutions and guiding developers toward efficient, error-free code. While it excels in identifying issues and suggesting fixes, it's crucial to recognize that ChatGPT operates within the boundaries of provided information. This journey underscores the importance of critical thinking in code analysis, reminding us that while ChatGPT empowers us with solutions, the responsibility for logical correctness and code applicability ultimately rests on the developer. Leveraging ChatGPT's expertise alongside mindful coding practices paves the way for seamless debugging and continual improvement in software development endeavors.Author BioProfessor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now Head of Bioinformatics at the world-leading Sainsbury Laboratory in Norwich, UK where he works on bioinformatics, genomics, and machine learning. He teaches undergraduates, post-graduates, and post-doctoral students in data science and computational biology. His research group has developed numerous new methods and software in R, Python, and other languages with over 100,000 downloads combined.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,Welcome back to a new issue of AI_Distilled - your guide to the key advancements in AI, ML, NLP, and GenAI. Let's dive right into an industry expert’s perspective to sharpen our understanding of the field's rapid evolution. "In the near future, anyone who's online will be able to have a personal assistant powered by artificial intelligence that's far beyond today's technology."  - Bill Gates, Co-Founder, Microsoft. In a recent interview, Gates minced no words when he said software is still “pretty dumb” even in today’s day and age. The next 5 years will be crucial, he believes, as everything we know about computing in our personal and professional lives is on the brink of a massive disruption. Even everyday things as simple as phone calls are due for transformation as evident from Samsung unveiling the new 'Galaxy AI' and real-time translate call feature. In this issue, we’ll talk about Google exploring massive investment in AI startup Character.AI, Microsoft's GitHub Copilot user base surging to over a million, OpenAI launching data partnerships to enhance AI understanding, and Adobe researchers’ breakthrough AI that transforms 2D images into 3D models in 5 seconds.We’ve also got you your fresh dose of AI secret knowledge and tutorials. Explore how to scale multimodal understanding to long videos, navigate the landscape of hallucinations in LLMs, read a practical guide to enhancing RAG system responses, how to generate Synthetic Data for Machine Learning and unlock the power of low-code GPT AI apps.  📥 Feedback on the Weekly EditionWe've hit 6 months and 38K subscribers in our AI_Distilled newsletter journey — thanks to you!  The best part? Our emails are opened by 60% of recipients each week. We're dedicated to tailoring them to enhance your Data & AI practice. Let's work together to ensure they fully support your AI efforts and make a positive impact on your daily work.Share your thoughts in a quick 5-minute survey to shape our content. As a big thanks, get our bestselling "The Applied Artificial Intelligence Workshop" in PDF.  Let's make AI_Distilled even more awesome! 🚀 Jump on in! Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis🔳 Google Explores Massive Investment in AI Startup Character.AI: Google is reportedly in discussions to invest 'hundreds of millions' in Character.AI, an AI chatbot startup founded by ex-Google Brain employees. The investment is expected to deepen the collaboration between the two entities, leveraging Google's cloud services and Tensor Processing Units (TPUs) for model training. Character.AI, offering virtual interactions with celebrities and customizable chatbots, targets a youthful audience, particularly those aged 18 to 24, constituting 60% of its web traffic.  🔳 AI Actions Empowers AI Platforms with Zapier Integration: AI Actions introduces a tool enabling AI platforms to seamlessly run any Zapier action, leveraging Zapier's extensive repository of 20,000+ searches and actions. The integration allows natural language commands to trigger Zapier actions, eliminating obstacles like third-party app authentication and API integrations. Supported on platforms like ChatGPT, GPTs, Zapier, and customizable solutions, AI Actions provides flexibility for diverse applications. 🔳 Samsung Unveils 'Galaxy AI' and Real-Time Translate Call Feature: Samsung declares its commitment to AI with a preview of "Galaxy AI," a comprehensive mobile AI experience that combines on-device AI with cloud-based AI collaborations. The company introduced an upcoming feature, "AI Live Translate Call," embedded in its native phone app, offering real-time audio and text translations on the device during calls. Set to launch early next year, Galaxy AI is anticipated to debut with the Galaxy S24 lineup.  🔳 Google Expands Collaboration with Anthropic, Prioritizing AI Security and Cloud TPU v5e Accelerators: In an intensified partnership, Google announces its extended collaboration with Anthropic, focusing on elevated AI security and leveraging Cloud TPU v5e chips for AI inference. The collaboration, dating back to Anthropic's inception in 2021, highlights their joint efforts in AI safety and research. Anthropic, utilizing Google's Cloud services like GKE clusters, AlloyDB, and BigQuery, commits to Google Cloud's security services for model deployment. 🔳 Microsoft's GitHub Copilot User Base Surges to Over a Million, CEO Nadella Reports: Satya Nadella announced a substantial 40% growth in paying customers for GitHub Copilot in the September quarter, surpassing one million users across 37,000 organizations. Nadella highlights the rapid adoption of Copilot Chat, utilized by companies like Shopify, Maersk, and PWC, enhancing developers' productivity. The Bing search engine, integrated with OpenAI's ChatGPT, has facilitated over 1.9 billion chats, demonstrating a growing interest in AI-driven interactions. Microsoft's Azure revenue, including a significant contribution from AI services, exceeded expectations, reaching $24.3 billion, with the Azure business rising by 29%.  🔳 Dell and Hugging Face Join Forces to Streamline LLM Deployment: Dell and Hugging Face unveil a strategic partnership aimed at simplifying the deployment of LLMs for enterprises. With the burgeoning interest in generative AI, the collaboration seeks to address common concerns such as complexity, security, and privacy. The companies plan to establish a Dell portal on the Hugging Face platform, offering custom containers, scripts, and technical documentation for deploying open-source models on Dell servers.  🔳 OpenAI Launches Data Partnerships to Enhance AI Understanding: OpenAI introduces Data Partnerships, inviting collaborations with organizations to develop both public and private datasets for training AI models. The initiative aims to create comprehensive datasets reflecting diverse subject matters, industries, cultures, and languages, enhancing AI's understanding of the world. Two partnership options are available: Open-Source Archive for public datasets and Private Datasets for proprietary AI models, ensuring sensitivity and access controls based on partners' preferences. 🔳 Iterate Unveils AppCoder LLM for Effortless AI App Development: California-based Iterate introduces AppCoder LLM, a groundbreaking model embedded in the Interplay application development platform. This innovation allows enterprises to generate functional code for AI applications effortlessly by issuing natural language prompts. Unlike existing AI-driven coding solutions, AppCoder LLM, integrated into Iterate's platform, outperforms competitors, producing better outputs in terms of functional correctness and usefulness.  🔳 Adobe Researchers Unveil Breakthrough AI: Transform 2D Images into 3D Models in 5 Seconds: A collaborative effort between Adobe Research and Australian National University has resulted in a groundbreaking AI model capable of converting a single 2D image into a high-quality 3D model within a mere 5 seconds. The Large Reconstruction Model for Single Image to 3D (LRM) utilizes a transformer-based neural network architecture with over 500 million parameters, trained on approximately 1 million 3D objects. This innovation holds vast potential for industries like gaming, animation, industrial design, AR, and VR.  🔮 Expert Insights from Packt Community Synthetic Data for Machine Learning - By Abdulrahman Kerim Training ML models Developing an ML model usually requires performing the following essential steps: Collecting data. Annotating data. Designing an ML model. Training the model. Testing the model. These steps are depicted in the following diagram: Fig – Developing an ML model process. Now, let’s look at each of the steps in more detail to better understand how we can develop an ML model. Collecting and annotating data The first step in the process of developing an ML model is collecting the needed training data. You need to decide what training data is needed: Train using an existing dataset: In this case, there’s no need to collect training data. Thus, you can skip collecting and annotating data. However, you should make sure that your target task or domain is quite similar to the available dataset(s) you are planning to deploy. Otherwise, your model may train well on this dataset, but it will not perform well when tested on the new task or domain. Train on an existing dataset and fine-tune on a new dataset: This is the most popular case in today’s ML. You can pre-train your model on a large existing dataset and then fine-tune it on the new dataset. Regarding the new dataset, it does not need to be very large as you are already leveraging other existing dataset(s). For the dataset to be collected, you need to identify what the model needs to learn and how you are planning to implement this. After collecting the training data, you will begin the annotation process. Train from scratch on new data: In some contexts, your task or domain may be far from any available datasets. Thus, you will need to collect large-scale data. Collecting large-scale datasets is not simple. To do this, you need to identify what the model will learn and how you want it to do that. Making any modifications to the plan later may require you to recollect more data or even start the data collection process again from scratch. Following this, you need to decide what ground truth to extract, the budget, and the quality you want. This content is from the book “Synthetic Data for Machine Learning” written by Abdulrahman Kerim (Oct 2023). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below. Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources🤖 Scaling Multimodal Understanding to Long Videos: A Comprehensive Guide: This guide provides a step-by-step explanation of the challenges associated with modeling diverse modalities like video, audio, and text. Learn about the Mirasol3B architecture, which efficiently handles longer videos, and understand the coordination between time-aligned and contextual modalities. The guide also introduces the Combiner, a learning module to effectively combine signals from video and audio information.  🤖 Mastering AI and ML Workloads: A Guide with Cloud HPC Toolkit: This post, authored by Google Cloud experts, delves into the convergence of HPC systems with AI and ML, highlighting their mutual benefits. They provide instructions on deploying clusters, utilizing preconfigured partitions, and utilizing powerful tools such as enroot and Pyxis for container integration. Discover the simplicity of deploying AI models on Google Cloud with the Cloud HPC Toolkit, fostering innovation and collaboration between HPC and AI communities. 🤖 Mastering the GPT Workflow: A Comprehensive Guide to AI Language Model: From understanding the basics of GPT's architecture and pre-training concept to unraveling the stages of the GPT workflow, including pre-training, fine-tuning, evaluation, and deployment, this guide provides a step-by-step walkthrough. Gain insights into ethical considerations, bias mitigation, and challenges associated with GPT models. Delve into future developments, including model scaling, multimodal capabilities, explainable AI enhancements, and improved context handling.  🤖 Navigating the Landscape of Hallucinations in LLMs: A Comprehensive Exploration: Delve into the intricate world of LLMs and the challenges posed by hallucinations in this in-depth blog post. Gain an understanding of the various types of hallucinations, ranging from harmless inaccuracies to potentially harmful fabrications, and their implications in real-world applications. Explore the root factors leading to hallucinations, such as overconfidence and lack of grounded reasoning, during LLM training.  🤖 Unveiling the Core Challenge in GenAI: Cornell University's Insightful Revelation: Cornell University researchers unveil a pivotal threat in GenAI, emphasizing the crucial role of "long-term memory" and the need for a vector database for contextual retrieval. Privacy issues emerge in seemingly secure solutions, shedding light on the complex challenges of handling non-numerical data in advanced AI models. 🔛 Masterclass: AI/LLM Tutorials👉 Unlocking the Power of Low-Code GPT AI Apps: A Comprehensive Guide. Explore how AINIRO.IO introduces the concept of "AI Apps" by seamlessly integrating ChatGPT with CRUD operations, enabling natural language interfaces to databases. Dive into the intricacies of creating a dynamic AI-based application without extensive coding, leveraging the Magic cloudlet to generate CRUD APIs effortlessly. Explore the significant implications of using ChatGPT for business logic in apps, offering endless possibilities for user interactions. 👉 Deploying LLMs Made Easy with ezsmdeploy 2.0 SDK: This post provides an in-depth understanding of the new capabilities, allowing users to effortlessly deploy foundation models like Llama 2, Falcon, and Stable Diffusion with just a few lines of code. The SDK automates instance selection, configuration of autoscaling, and other deployment details, streamlining the process of launching production-ready APIs. Whether deploying models from Hugging Face Hub or SageMaker Jumpstart, ezsmdeploy 2.0 reduces the coding effort required to integrate state-of-the-art models into production, making it a valuable tool for data scientists and developers. 👉 Enhancing RAG System Responses: A Practical Guide: Discover how to enhance the performance of your Retrieval-Augmented Generation (RAG) systems in generative AI applications by incorporating an interactive clarification component. This post offers a step-by-step guide on improving the quality of answers in RAG use cases where users present vague or ambiguous queries. Learn how to implement a solution using LangChain to engage in a conversational dialogue with users, prompting them for additional details to refine the context and provide accurate responses.  👉 Building Personalized ChatGPT: A Step-by-Step Guide. In this post, you'll learn how to explore OpenAI's GPT Builder, offering a beginner-friendly approach to customize ChatGPT for various applications. With the latest GPT update, users can now create personalized ChatGPT versions, even without technical expertise. The tutorial focuses on creating a customized GPT named 'EduBuddy,' designed to enhance the educational journey with tailored learning strategies and interactive features. 🚀 HackHub: Trending AI Tools💮 reworkd/tarsier: Open-source utility library for multimodal web agents, facilitating interaction with GPT-4(V) by visually tagging interactable elements on a page.  💮 recursal/ai-town-rwkv-proxy: Allows developers to locally run a large AI town using the RWKV model, a linear transformer with low inference costs. 💮 shiyoung77/ovir-3d: Enables open-vocabulary 3D instance retrieval without training on 3D data, addressing the challenge of obtaining diverse annotated 3D categories.  💮 langroid/langroid: User-friendly Python framework for building LLM-powered applications through a Multi-Agent paradigm. 💮 punica-ai/punica: Framework for Low Rank Adaptation (LoRA) to incorporate new knowledge into a pretrained LLM with minimal storage and memory impact. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, R Bioinformatics Cookbook - Second Edition, by Dan MacLean. Discover over 80 recipes for modeling and handling real-life biological data using modern libraries from the R ecosystem.IntroductionHey there, fellow code explorers! Ever found yourself staring at a chunk of complex R code, feeling lost in its jumble of symbols and functions? Well, fear not! This article dives into the exciting realm of decoding such cryptic code with the help of an unexpected ally: ChatGPT. Join me on this journey as we uncover the secrets behind intricate code snippets, aiming to demystify and explain them in plain, everyday language.Interpreting complicated code with ChatGPT assistanceChatGPT can be used to interpret R code by leveraging its natural language processing capabilities and its understanding of the R programming language. It’s important to note that while ChatGPT can assist in interpreting and explaining R code, it’s still just an AI language model and may not have knowledge of the most recent updates or specific domain-specific intricacies. Double-checking information with official documentation and consulting experienced developers or experts is always necessary. It is massively useful for simplifying and getting a good first-level understanding in most cases.In this recipe, we’ll look at how to take an arbitrary bit of complex code and explain it in plain English.Getting readyWe need some code to interpret – we’ll use a function from the besthr Github-hosted package at https://github.com/TeamMacLean/besthr/blob/master/R/functions.R and an account with ChatGPT from https://chat.openai.com/.How to do it…Here’s how to work in a ChatGPT session and get it to explain some code to you:1. Paste the following code into the ChatGPT prompt box:explain this R code to me: #' Convert named columns to factors #' #' \code{factorise_cols} returns a copy of the passed data frame in which all #' explicitly named columns are converted to factors with \ code{as.factor} #' All columns with only NA values are ommitted. #' #' @param df data frame #' @param col_list vector of quoted column names #' @keywords internal #' factorise_cols <- function(df, col_list ){ not_all_na <- function(x) any(!is.na(x)) for ( i in col_list){ df <- df %>% dplyr::mutate(!!(i) := as.factor(!!(i)) ) } df %>% dplyr::select_if(not_all_na) }2. Read the response:This R code defines a function called `factorise_cols` that converts explicitly named columns of a data frame into factors using the `as.factor` function. The function takes two parameters: 1. `df`: This is the data frame that contains the columns to be converted. 2. `col_list`: This is a vector of quoted column names. It specifies which columns in the data frame should be converted to factors. The function uses a helper function called `not_all_na` to filter out columns that have only `NA` values. This helper function checks if there are any non-NA values in a column using the `any` and `is.na` functions. Inside the main function, a loop iterates over each column name in the `col_list`. For each column, the function uses the `mutate` function from the `dplyr` package to create a new column in the data frame (`df`) with the same name, but with the values converted to factors using the `as.factor` function. The `!!(i)` syntax is used to unquote the column name. After the loop, the function uses the `select_if` function from `dplyr` to select only the columns that pass the `not_all_na` condition, effectively removing columns that contain only `NA` values. The resulting data frame is then returned as the output of the function. Overall, this function allows you to convert specific columns of a data frame into factors while excluding columns that contain only `NA` values. 3. Ask for a clarification on a certain point:You said 'The !!(i) syntax is used to unquote the column name.' Can you go into more depth?4. Read the response:Certainly! In the given code, the `!!` (bang-bang) syntax is used for unquoting the column name within the context of the `mutate` function. Let's break it down step by step: 1. In the loop, the variable `i` represents each element (column name) of the `col_list` vector. 2. Within the `mutate` function, the column name `i` needs to be unquoted and evaluated as an expression. This is necessary because `mutate` expects column names to be provided as expressions rather than strings. 3. The `!!` (bang-bang) operator is used to perform this unquoting. It is part of the tidy evaluation framework provided by the `dplyr` package. 4. By wrapping `i` with `!!`, it instructs `mutate` to evaluate `i` as an expression, resulting in an unquoted column name. Interpreting complicated code with ChatGPT assistance To illustrate with an example, suppose `col_list` contains the column names `col1` and `col2`. During the loop iteration, when `i` is `"col1"`, the line `!!(i)` would be evaluated as `!!("col1")`, which unquotes the string to yield `col1`. Similarly, when `i` is `"col2"`, `!!(i)` would be evaluated as `!!("col2")`, resulting in `col2`. This unquoting operation is necessary in this context because the `mutate` function expects column names to be provided as unevaluated expressions so that it can perform the necessary data manipulation operations. The `!!` operator allows us to achieve this unquoting within the `mutate` function call.Note that the response from ChatGPT will be somewhat different depending on context and you may not get exactly what was generated here. That is the nature of the thing, but you should get something with the same meaning.How it works…The code in this example comes from a package called besthr that creates estimation statistic plots for visual scoring data. The tool must take in arbitrary column names and work within the tidyverse, so it uses some advanced coding techniques to allow that to happen. We first go and get the codefrom the repository on GitHub (https://github.com/TeamMacLean/besthr/blob/ master/R/functions.R) and paste that into ChatGPT’s prompt box asking it for an explanation.In step 2, we can see the explanation provided (note that the one you get if you try may be different as the model is not guaranteed to reproduce its predictions). The detail is largely correct; certainly, it is sufficient to give us a clear idea of what the code attempts to do and how it does it.Some parts of the explanation aren’t clear, so in step 3, we ask for clarification of a tricky bit, again by typing into the prompt box. And in step 4, we see a more in-depth description of that part.In this way, we can get a clear and readable, plain English description of the job done by a particular piece of code very quickly.There’s more…Other sites can do this, such as Google’s Bard. ChatGPT Plus – a subscription service –also has special plug-ins that help make working with code much easier..ConclusionWho knew cracking code could be this fun and straightforward? With ChatGPT as our trusty sidekick, we've peeked behind the curtains of intricate R code, unraveling its mysteries piece by piece. Remember, while this AI wizardry is fantastic, a mix of human expertise and official documentation remains your ultimate guide through the coding labyrinth. So, armed with newfound knowledge and a reliable AI companion, let's keep exploring, learning, and demystifying the captivating world of programming together!Author BioProfessor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now Head of Bioinformatics at the world leading Sainsbury Laboratory in Norwich, UK where he works on bioinformatics, genomics, and machine learning. He teaches undergraduates, post-graduates, and post-doctoral students in data science and computational biology. His research group has developed numerous new methods and software in R, Python, and other languages with over 100,000 downloads combined.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionVirtual Personal Assistants are emerging as an important aspect of the rapidly developing Artificial Intelligence landscape. These intelligent, Artificial Intelligence assistants are capable of carrying out a wide range of tasks such as answering questions and providing advice on how to make process more efficient.You're more easily getting your personal assistant built using the ChatGPT service from OpenAI. We'll explore the creation of virtual personal assistants using ChatGPT, complete with hands-on code examples and projected outputs in this advanced guide. Use ChatGPT, the world's most advanced language model created by OpenAI to create a virtual assistant that you can use.Prerequisites before we startThere are certain prerequisites that need to be met before we embark on this journey:OpenAI API Key: You must have an API key from OpenAI if you want to use ChatGPT. You'll be able to get one if you sign up at OpenAI.Python and Jupyter Notebooks: To provide more interactive learning of the development process, it is recommended that you install Python on your machine.OpenAI Python Library: To use ChatGPT, you will first need to download the OpenAI Python library. Using pip, you can install the following:pip install openaiGoogle Cloud Services (optional): If you plan to integrate with voice recognition and text-to-speech services, such as Google Cloud Speech-to-Text and Text-to-Speech, you'll need access to Google Cloud services.Building a Virtual Personal AssistantLet's have a look at the following steps for creating a Virtual Personal Assistant with ChatGPT1. Set up the environmentTo begin, we shall import the required libraries and set up an API key.import openai openai.api_key = "YOUR_OPENAI_API_KEY"2. Basic Text-Based InteractionWe're going to build an easy interaction based on text with our assistant. We will ask ChatGPT a question, and we shall receive an answer.Input code:def chat_with_gpt(prompt):    response = openai.Completion.create(        engine="davinci-codex",        prompt=prompt,        max_tokens=50  # Adjust as needed    )    return response.choices[0].text # Interact with the assistant user_input = input("You: ") response = chat_with_gpt(f"You: {user_input}\nAssistant:") print(f"Assistant: {response}")Output:You: What's the weather like today? Assistant: The weather today is sunny with a high of 25°C and a low of 15°C.We used ‘chat_with_gpt’, for interacting with ChatGPT to generate responses from user input. Users can input questions or comments and the function will send a request to ChatGPT. In the output, the assistant's answer is shown in a conversational format.Example 1: Language TranslationBy making it a language translation tool, we can improve the assistant's abilities. Users can type a word in one language and an assistant will translate it to another.Input Code:def translate_text(input_text, target_language="fr"):    response = chat_with_gpt(f"Translate the following text from English to {target_language}: {input_text}")    return response # Interact with the translation feature user_input = input("Enter the text to translate: ") target_language = input("Translate to (e.g., 'fr' for French): ") translation = translate_text(user_input, target_language) print(f"Translation: {translation}")Output:Enter the text to translate: Hello, how are you? Translate to (e.g., 'fr' for French): fr Translation: Bonjour, comment ça va?To translate English text to the target language using ChatGPT, we are defining a function, ‘translate_text’. Users input text and the target language, which is returned in translation by this function. It uses the ability of ChatGPT to process natural languages in order to carry out accurate translation.Example 2: Code GenerationThe creation of code fragments may also be assisted by our virtual assistant. It is especially useful for developers and programmers who want to quickly solve code problems.Input Code:def generate_code(question):    response = chat_with_gpt(f"Generate Python code to: {question}")    return response # Interact with the code generation feature user_input = input("You: ") generated_code = generate_code(user_input) print("Generated Python Code:") print(generated_code)Output:You: Create a function to calculate the factorial of a number. Generated Python Code: def calculate_factorial(n):    if n == 0:        return 1    else:        return n * calculate_factorial(n - 1)The user provides a question and the function sends a request to ChatGPT to generate code to answer it. In the output, a Python code is displayed.Example 3: Setting RemindersIt's even possible to make use of our Virtual Assistant as an organizer. A reminder of tasks or events can be set by users, which will be handled by an assistant.Input code:def set_reminder(task, time):    response = chat_with_gpt(f"Set a reminder: {task} at {time}.")    return response # Interact with the reminder feature task = input("Task: ") time = input("Time (e.g., 3:00 PM): ") reminder_response = set_reminder(task, time) print(f"Assistant: {reminder_response}")Output:Task: Meeting with the client Time (e.g., 3:00 PM): 2:30 PM Assistant: Reminder set: Meeting with the client at 2:30 PM.The code defines a function, ‘set_reminder’, which can be used to generate reminders based on the task and time. Users input their tasks and time, and the function requests a reminder to be sent to ChatGPT. The output will be printed with the assistant's answer and confirmation of this reminder.ConclusionIn conclusion, we got to know the evolution of Virtual Personal Assistant using ChatGPT throughout this advanced guide. We've started with a basic text-based interaction, followed by three advanced examples: language translation, code generation, and setting reminders. There is no limit to the potential of Virtual Personal Assistants.Integrating your assistant into various APIs, enhancing the ability to understand languages and making it useful for a variety of tasks will allow you to further expand its capabilities. Creating a tailored virtual assistant is now even easier to create and adapted to your individual needs, given the advancement of AI technologies.Author BioSangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She is a Google Crowdsource Influencer and IBM champion learner gold. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!The AI Product Manager's Handbook ($35.99 Value) FREE for a limited time! Gain expertise as an AI product manager to effectively oversee the design, development and deployment of AI products. Master the skills needed to bring tangible value to your organization through successful AI implementation.Seize this exclusive opportunity and grab your copy now before it slips away on November 16th!  👋 Hello ,Step into another edition of AI_Distilled, brimming with updates in AI/ML, LLMs, NLP, GPT, and Gen AI. Our aim is to help you enhance your AI skills and stay abreast of the ever-evolving trends in this domain. Let’s get started with our news and analysis with an industry expert’s opinion. “Unfortunately, we have biases that live in our data, and if we don’t acknowledge that and if we don’t take specific actions to address it then we’re just going to continue to perpetuate them or even make them worse.” - Kathy Baxter, Responsible AI Architect, Salesforce. Baxter made an important point, for data underlies ML models, and errors will simply result in a domino effect that can have drastic consequences. Equally important is how AI handles data privacy, especially when you consider how apps like ChatGPT have now crossed 100 million weekly users. The Apple CEO recently hinted at major investments in responsible AI, which will likely transform smart handheld devices in 2024 with major AI upgrades. In this issue, we’ll talk about OpenAI unveiling major upgrades and features including GPT-4 Turbo model and DALL-E 3 API, Microsoft’s new breakthrough with smaller AI model, Elon Musk unveiling xAI’s "Grok" competing with GPT, and OpenAI launching the GPT Store for user-created custom AI models. We’ve also got you your fresh dose of AI secret knowledge and tutorials on unlocking zero-shot adaptive prompting for LLMs, creating a Python chat web app with OpenAI's API and Reflex, and 9 open source tools to boost your AI app. 📥 Feedback on the Weekly EditionWhat do you think of this issue and our newsletter?Please consider taking the short survey below to share your thoughts and you will get a free PDF of the “The Applied Artificial Intelligence Workshop” eBook upon completion. Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  Ready to level up your coding game?  🚀 Dive into the Software Supply Chain Security Survey and let's talk vulnerabilities, security practices, and all things code!  🤓 Share your insights and stand a chance to snag some epic prizes, including the coveted MX Master 3S, Raspberry Pi 4 Model B 4GB, $5 Udemy gift credits, and more!  🌟 Your code-savvy opinions could be your ticket to tech greatness.  Don't miss out—join the conversation now! 👩‍💻  Interested? Tell us what you think! SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis🔹 OpenAI Unveils Major Upgrades and Features Including GPT-4 Turbo Model, DALL-E 3 API, Crosses 100 million Weekly Users: At its DevDay event, OpenAI announced significant new capabilities and lower pricing for its AI platform. This includes a more advanced GPT-4 Turbo model with 128K context size and multimodal abilities. OpenAI also released new developer products like the Assistants API and DALL-E 3 integration. Additional updates include upgraded models, customization options, expanded rate limits, and the Copyright Shield protection. Together these represent major progress in features, accessibility and affordability. ChatGPT also achieved 100 million weekly users and over two million developers, marking a significant milestone in its growth.  🔹 Stability AI Launches AI-Powered 3D Model Generator: Stability AI debuts Stable 3D, empowering non-experts to craft 3D models through simple descriptions or image uploads. The tool generates editable .obj files, marking the company's entry into the AI-driven 3D modeling landscape. Questions about training data origin and prior copyright controversies arise, highlighting a strategic move amid financial struggles. 🔹 Apple CEO Hints at Generative AI Plans: Apple CEO Tim Cook hinted at significant investments in generative AI during the recent earnings call. While specifics were not disclosed, Cook emphasized responsible deployment over time. Apple's existing AI in iOS and Apple Watch showcases its commitment, with rumors suggesting major AI updates in 2024, solidifying Apple's leadership in the space. 🔹 Microsoft Unveils Breakthrough with Smaller AI Model: Microsoft researchers revealed a major new capability added to their small AI model Phi 1.5. It can now interpret images, a skill previously limited to much larger models like OpenAI's ChatGPT. Phi 1.5 has only 1.3 billion parameters compared to GPT-4's 1.7 trillion, making it exponentially more efficient. This shows less expensive AI can mimic bigger models. Smaller models need less computing power, saving costs and emissions. Microsoft sees small and large models as complementary, optimizing tasks between them. The breakthrough signals wider access to advanced AI as smaller models spread.🔹 OpenAI Unveils GPT Store for User-Created Custom AI Models: OpenAI introduces GPTs, allowing users to build custom versions of ChatGPT for specific purposes with no coding experience required, opening up the AI marketplace. These GPTs can range from simple tasks like recipe assistance to complex ones such as coding or answering specific questions. The GPT Store will soon host these creations, enabling users to publish and potentially monetize them, mirroring the App Store model's success. OpenAI aims to pay creators based on their GPTs' usage, encouraging innovation. However, this move may create challenges in dealing with industry giants like Apple and Microsoft, who have their app models and platforms. 🔹 Elon Musk Drops xAI's Game-Changer: Meet Grok, the LLM with Real-Time Data, Efficiency, and a Dash of Humor! Named after the slang term for "understanding," Grok is intended to compete with AI models like OpenAI's GPT. It's currently available to a limited number of users in the United States through a waitlist on xAI's website. Grok is designed with impressive efficiency, utilizing half the training resources of comparable models. It brings humor and wit to AI interactions, aligning with Musk's goal of creating a "maximum truth-seeking AI." ***************************************************************************************************************************************************************🔮 Expert Insights from Packt Community Machine Learning with PyTorch and Scikit-Learn - By Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili Solving interactive problems with reinforcement learning Another type of machine learning is reinforcement learning. In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on interactions with the environment. Since the information about the current state of the environment typically also includes a so-called reward signal, we can think of reinforcement learning as a field related to supervised learning. However, in reinforcement learning, this feedback is not the correct ground truth label or value, but a measure of how well the action was measured by a reward function. Through its interaction with the environment, an agent can then use reinforcement learning to learn a series of actions that maximizes this reward via an exploratory trial-and-error approach or deliberative planning. Discovering hidden structures with unsupervised learning In supervised learning, we know the right answer (the label or target variable) beforehand when we train a model, and in reinforcement learning, we define a measure of reward for particular actions carried out by the agent. In unsupervised learning, however, we are dealing with unlabeled data or data of an unknown structure. Using unsupervised learning techniques, we are able to explore the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function. Finding subgroups with clustering Clustering is an exploratory data analysis or pattern discovery technique that allows us to organize a pile of information into meaningful subgroups (clusters) without having any prior knowledge of their group memberships. Each cluster that arises during the analysis defines a group of objects that share a certain degree of similarity but are more dissimilar to objects in other clusters, which is why clustering is also sometimes called unsupervised classification. Clustering is a great technique for structuring information and deriving meaningful relationships from data. For example, it allows marketers to discover customer groups based on their interests, in order to develop distinct marketing programs. Dimensionality reduction for data compression Another subfield of unsupervised learning is dimensionality reduction. Often, we are working with data of high dimensionality—each observation comes with a high number of measurements—that can present a challenge for limited storage space and the computational performance of machine learning algorithms. Unsupervised dimensionality reduction is a commonly used approach in feature preprocessing to remove noise from data, which can degrade the predictive performance of certain algorithms. Dimensionality reduction compresses the data onto a smaller dimensional subspace while retaining most of the relevant information. This content is from the book “Machine Learning with PyTorch and Scikit-Learn” writtern by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili (Feb 2022). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below. Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources💡 Enhancing User Experiences with AI-PWAs in Web Development: This article explores the integration of AI and Progressive Web Applications (PWAs) to revolutionize website development. Learn how AI chatbots and generative AI, such as OpenAI's GPT-3, can personalize content and streamline coding. Discover the benefits of combining AI technology with PWAs, including improved user engagement, streamlined content generation, and enhanced scalability.  💡 Boosting Your AI App with 9 Open Source Tools: From LLM queries to chatbots and AI app quality, explore projects like LLMonitor for cost analytics and user tracking, Guidance for complex agent flows, LiteLLM for easy integration of various LLM APIs, Zep for chat history management, LangChain for building powerful AI apps, DeepEval for LLM application testing, pgVector for embedding storage and similarity search, promptfoo for testing prompts and models, and Model Fusion, a TypeScript library for AI applications. These tools can help you optimize and streamline your AI projects, improving user experiences and productivity. 💡 Creating Gen AI-Powered Vector Search Applications with Vertex AI Search: Learn how to harness the power of generative AI and vector embeddings to build user experiences and applications. Vector embeddings are a way to represent various types of data in a semantic space, enabling developers to create applications such as finding relevant information in documents, personalized product recommendations, and more. The article introduces vector search, a service within the Vertex AI Search platform, which helps developers find relevant embeddings quickly. It offers scalability, adaptability to changing data, security features, and easy integration with other AI tools.  🔛 Masterclass: AI/LLM Tutorials🔑 Integrating Amazon MSK with CockroachDB for Real-Time Data Streams: This guide offers a comprehensive step-by-step process for integrating Amazon Managed Streaming for Apache Kafka (Amazon MSK) with CockroachDB, creating a robust and scalable pipeline for real-time data processing. The integration enables various use cases, such as real-time analytics, event-driven microservices, and audit logging, enhancing businesses' ability to provide immediate, personalized experiences for customers. 🔑 Understanding GPU Workload Monitoring on Amazon EKS with AWS Managed Services: As the demand for GPU-accelerated ML workloads grows, this post offers valuable insights into monitoring GPU utilization on Amazon Elastic Kubernetes Service (EKS) using AWS managed open-source services. Amazon EC2 instances with NVIDIA GPUs are crucial for efficient ML training. The article explains how GPU metrics can provide essential information for optimizing resource allocation, identifying anomalies, and enhancing system performance.  🔑 Unlocking Zero-Shot Adaptive Prompting for LLMs: This study explores LLMs, emphasizing their prowess in solving problems in both few-shot and zero-shot scenarios. It introduces "Consistency-Based Self-Adaptive Prompting (COSP)" and "Universal Self-Adaptive Prompting (USP)" to generate robust prompts for diverse tasks in natural language understanding and generation. 🔑 Exploring Interactive AI Applications with OpenAI's GPT Assistants and Streamlit: This post unveils a cutting-edge Streamlit app integrating OpenAI's GPT models for interactive Wardley Mapping instruction. It details development, emphasizing GPT-4-1106-preview, covering setup, session management, UI configuration, user input, and real-time content generation, showcasing Streamlit's synergy with OpenAI for dynamic applications.  🔑 Utilizing GPT-4 Code Interpreter API for CSV Analysis: A Step-by-Step Guide: Learn to analyze CSV files with OpenAI's GPT-4 Code Interpreter API. The guide covers step-by-step processes, from uploading files via Postman to creating an Assistant, forming a thread, and executing a run. Gain insights for efficient CSV analysis, unlocking data-driven insights and automation power. 🔑 Creating a Python Chat Web App with OpenAI's API and Reflex: In this tutorial, you'll learn how to develop a chat web application in pure Python, utilizing OpenAI's API for intelligent responses. The guide explains how to use the Reflex open-source framework to build both the backend and frontend entirely in Python. The tutorial also covers styling and handling user input, making it easy for those without JavaScript experience to create a chat application with an AI-driven chatbot. By the end, you'll have a functional AI chatbot web app built in Python.  🚀 HackHub: Trending AI Tools📐tigerlab-ai/tiger: Build customized AI models and language applications, bridging the gap between general LLMs and domain-specific knowledge. 📐 langchain-ai/langchain/tree/master/templates: Reference architectures for various LLM use cases, enabling developers to quickly build production-ready LLM applications. 📐 ggerganov/whisper.cpp/tree/master/examples/talk-llama: Uses the SDL2 library to capture audio from the microphone and combines Whisper and LLaMA models for real-time interactions. 📐 explosion/thinc: Lightweight deep learning library for model composition, offering type-checked, functional-programming APIs, and support for PyTorch, TensorFlow, and MXNet. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionChatGPT is a large language model chatbot developed by OpenAI and released on November 30, 2022. It is a variant of the GPT (Generative Pre-training Transformer) language model that is specifically designed for chatbot applications. In the context of conversation, it has been trained to produce humanlike responses to text input.The potential for ChatGPT to revolutionize the way we find information on the Internet is immense. We can give users a more complete and useful answer to their queries through the integration of ChatGPT into search engines. In addition, ChatGPT could help us to tailor the results so that they are of particular relevance for each individual user.Benefits of Integrating ChatGPT into Search EnginesThere are a number of benefits to using ChatGPT for search engines, including:Enhanced User Experience: By allowing users to talk about their questions in the spoken language, ChatGPT offers better user experiences and more relevant search results by enabling natural language interactions.Improvements in Relevance and Context: With ChatGPT, search engines can deliver very relevant and contextually appropriate results even for ambiguous or complex queries because of their understanding of the context and complexity of the query.Increased Engagement: Users are encouraged to actively engage with the search engine through conversation search. When the user receives interactivity as well as conversation answers, they will be more likely to explore their search results further.Time Efficiency: In order to reduce the time that users spend on adjusting their queries, ChatGPT is able to understand user intent at an early stage. The faster access to the information requested will result from this efficiency.Personalization: As part of its chat function, ChatGPT will gather users' preferences and configure the search results to reflect each user's needs in providing a personalized browsing experience.Prerequisites before we startThere are certain prerequisites that need to be met before we embark on this journey:OpenAI API Key: You must have an API key from OpenAI if you want to use ChatGPT. You'll be able to get one if you sign up at OpenAI.Python and Jupyter Notebooks: To provide more interactive learning of the development process, it is recommended that you install Python on your machine.OpenAI Python Library: To use ChatGPT, you will first need to download the OpenAI Python library. Using pip, you can install the following:pip install openaiExample-1: Code Search EngineInput Code:import openai # Set your OpenAI API key openai.api_key = 'YOUR_OPENAI_API_KEY' def code_search_engine(user_query):    # Initialize a conversation with ChatGPT    conversation_history = [        {"role": "system", "content": "You are a helpful code search assistant."},        {"role": "user", "content": user_query}    ]      # Engage in conversation with ChatGPT    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=conversation_history    )    # Extract code search query from ChatGPT response    code_search_query = response.choices[0].message['content']['body']    # Perform code search with the refined query (simulated function)    code_search_results = perform_code_search(code_search_query)    return code_search_results def perform_code_search(query):    # Simulated code search logic    # For demonstration purposes, return hardcoded code snippets based on the query    if "sort array in python" in query.lower():        return [            "sorted_array = sorted(input_array)",            "print(sorted_array)"        ]    elif "factorial in JavaScript" in query.lower():        return [            "function factorial(n) {",            "  if (n === 0) return 1;",            "  return n * factorial(n-1);",            "}",            "console.log(factorial(5));"        ]    else:        return ["No matching code snippets found."] # Example usage user_query = input("Enter your coding-related question: ") code_search_results = code_search_engine(user_query) print("Code Search Results:") for code_snippet in code_search_results:    print(code_snippet) Output:Enter your coding-related question: How to sort array in Python? Code Search Results: sorted_array = sorted(input_array) print(sorted_array) We demonstrate a code search engine. It's the user's query related to coding, and it will refine this query with help of a model that simulates code searching. Examples of usage demonstrate how appropriate code snippets are returned after a refined query like sorting the array in Python.Example-2: Interactive Search AssistantInput Code:import openai # Set your OpenAI API key openai.api_key = 'YOUR_OPENAI_API_KEY' def interactive_search_assistant(user_query):    # Initialize a conversation with ChatGPT    conversation_history = [        {"role": "system", "content": "You are a helpful assistant."},        {"role": "user", "content": user_query}    ]    # Engage in interactive conversation with ChatGPT    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=conversation_history    )      # Extract refined query from ChatGPT response    refined_query = response.choices[0].message['content']['body']    # Perform search with refined query (simulated function)    search_results = perform_search(refined_query)    return search_results def perform_search(query):    # Simulated search engine logic    # For demonstration purposes, just return a placeholder result    return f"Search results for: {query}" # Example usage user_query = input("Enter your search query: ") search_results = interactive_search_assistant(user_query) print("Search Results:", search_results) Output:Enter your search query: Tell me about artificial intelligence Search Results: Search results for: Tell me about artificial intelligence This task takes user search queries, refines them with assistance from the model, and performs a simulated search. In the example usage, it returns a placeholder search result based on the refined query, such as "Search results for: Tell me about artificial intelligence."Example-3: Travel Planning Search EngineInput Code:import openai # Set your OpenAI API key openai.api_key = 'YOUR_OPENAI_API_KEY' class TravelPlanningSearchEngine:    def __init__(self):        self.destination_info = {            "Paris": "Paris is the capital of France, known for its art, gastronomy, and culture.",            "Tokyo": "Tokyo is the capital of Japan, offering a blend of traditional and modern attractions.",            "New York": "New York City is famous for its iconic landmarks, Broadway shows, and diverse cuisine."            # Add more destinations and information as needed        }      def search_travel_info(self, user_query):        # Engage in conversation with ChatGPT        conversation_history = [            {"role": "system", "content": "You are a travel planning assistant."},            {"role": "user", "content": user_query}        ]              # Engage in conversation with ChatGPT        response = openai.ChatCompletion.create(            model="gpt-3.5-turbo",            messages=conversation_history        )        # Extract refined query from ChatGPT response        refined_query = response.choices[0].message['content']['body']        # Perform travel planning search based on the refined query        search_results = self.perform_travel_info_search(refined_query)        return search_results    def perform_travel_info_search(self, query):        # Simulated travel information search logic        # For demonstration purposes, match the query with destination names and return relevant information        matching_destinations = []        for destination, info in self.destination_info.items():            if destination.lower() in query.lower():                matching_destinations.append(info)        return matching_destinations # Example usage travel_search_engine = TravelPlanningSearchEngine() user_query = input("Ask about a travel destination: ") search_results = travel_search_engine.search_travel_info(user_query) print("Travel Information:") if search_results:    for info in search_results:        print(info) else:    print("No matching destination found.")Output:Ask about a travel destination: Tell me about Paris. Travel Information: Paris is the capital of France, known for its art, gastronomy, and culture.If users are interested, they can ask about their destination and the engine refines their query by applying a model's help to return accurate travel information. As an example, information on the destination shall be given by the engine when asking about Paris.ConclusionIn terms of user experience, it is a great step forward that ChatGPT has become integrated into search engines. The search engines can improve understanding of users' intents, deliver high-quality results, and engage them in interactivity dialogues by using the power of speech processing and cognitive conversations. The synergy of ChatGPT and search engines, with the development of technology, will undoubtedly transform our ability to access information in a way that makes online experiences more user-friendly, efficient, or enjoyable. You can embrace the future of search engines by enabling ChatGPT, which means every query is a conversation and each result will be an intelligent answer.Author BioSangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She is a Google Crowdsource Influencer and IBM champion learner gold. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionChatGPT Advanced Data Analysis is an invaluable tool that significantly speeds up the analysis and processing of our data and our files. In the first part of this post, we showcased how to use this feature to generate a CSV file containing randomly generated values. In addition to this, we demonstrated how to utilize different prompts in order to process data stored in files and generate visualizations. In this second part, we’ll build on top of what we learned already and work on more complex examples and scenarios.If you’re looking for the link to the first part, here it is: Getting Started with ChatGPT Advanced Data Analysis- Part 1That said, we will tackle the Example 03, Example 04, and Current Limitations sections in this post:Example 01 — Generating a CSV file (discussed in Part 1)Example 02 — Analyzing an uploaded CSV file, transforming the data, and generating charts  (discussed in Part 1)Example 03 — Processing and analyzing an iPython notebook fileExample 04 — Processing and analyzing the contents of a ZIP fileCurrent LimitationsWhile working on the hands-on examples in this post, you’ll be surprised that ChatGPT Advanced Data Analysis is able to process and analyze various types of files such as Jupyter Notebook (.ipynb) and even ZIP files containing multiple files. As you dive into the features of ChatGPT Advanced Data Analysis, you'll discover that it serves as a valuable addition to your data analysis toolkit. This will help you unlock various ways to optimize your workflow and significantly accelerate data-driven decision-making.Without further ado, let’s begin!Example 03: Processing and analyzing an iPython notebook fileIn this third example, we will process and analyze an iPython notebook file containing blocks of code for deploying machine learning models. Here, we’ll use an existing iPython notebook file I prepared while writing my 1st book “Machine Learning with Amazon SageMaker Cookbook”. If you're wondering what an iPython notebook file is, it's essentially a document produced by the Jupyter Notebook app, which contains both computer code (like Python) and rich text elements (paragraphs, equations, figures, links, etc.). These notebooks are both human-readable documents containing the analysis description and the results (like visualizations) as well as executable code that can be run to perform data analysis. It’s popular among data scientists and researchers for creating and sharing documents that contain live code, equations, visualizations, and narrative text.Now that we have a better idea of what iPython notebook (.ipynb) files are, let’s proceed with our 3rd example:STEP # 01: Open a new browser tab. Navigate to the following link and Download the .ipynb file to your local machine by clicking Download raw file:https://github.com/PacktPublishing/Machine-Learning-with-Amazon-SageMaker-Cookbook/blob/master/Chapter09/03%20-%20Hosting%20multiple%20models%20with%20multi-model%20endpoints.ipynbImage 17 — Downloading the IPython Notebook .ipynb fileTo download the file, simply click the download button highlighted in Image 17. This should download the .ipynb file to your local machine.STEP # 02: Navigate back to the browser tab where you have your ChatGPT session open and create a new chat session by clicking + New Chat. Make sure to select Advanced Data Analysis under the list of options available under GPT-4:Image 18 — Using Advanced Data AnalysisSTEP # 03: Upload the .ipynb file from your local machine to the new chat session and then run the following prompt:What's the ML instance type used in the example?This should yield the following response:Image 19 — Analyzing the uploaded file and identifying what ML instance type is used in the exampleIf you’re wondering what a machine learning (ML) instance is, you can think of it as a server or computer running specific machine learning workloads (such as training and serving machine learning models). Given that running these ML instances could be expensive, it’s best if we estimate the cost of running these instances!STEP # 04: Next, run the following prompt to locate the block of code where the ML instance type is mentioned or used:Print the code block(s) where this ML instance type is usedImage 20 — Locating the block of code where the ML instance type is usedCool, right? Here, we can see that ChatGPT can help us identify blocks of code using the right set of prompts. Make sure to verify the results and files produced by ChatGPT as you might find discrepancies or errors.STEP # 05: Run the following prompt to update the ML instance used in the previous block of code:Update the code block and use an ml.g5.2xlarge instead.Image 21 — Using ChatGPT to perform code modification instructionsHere, we can see that ChatGPT can easily perform code modification instructions as well. Note that ChatGPT is not limited to simply replacing certain portions of code blocks. It is also capable of generating code from scratch! In addition to this, it is capable of reading blocks of code as well.STEP # 06: Run the following prompt to generate a chart comparing the estimated cost per month when running ml.t2.medium and ml.g5.2xlarge inference endpoint instances:Generate a chart comparing the estimated cost per month when running an ml.t2.medium vs an ml.g5.2xlarge SageMaker inference endpoint instanceImage 22 — Estimated monthly cost comparison of running an ml.t2.medium instance vs ml.g5.2xlarge instanceMake sure to always verify the results and files produced by ChatGPT as you might find discrepancies or errors.Now, let’s proceed with our final example.Example 04: Processing and analyzing the contents of a ZIP fileIn this final example, we will compare the estimated cost per month of running the ML instances in each of the chapters of my book “Machine Learning with Amazon SageMaker Cookbook”. Of course, the assumption in this example is that we’ll be running the ML instances for an entire month. In reality, we’ll only be running these examples for a few seconds (to at most a few minutes).STEP # 01: Navigate to the following link:https://github.com/PacktPublishing/Machine-Learning-with-Amazon-SageMaker-CookbookSTEP # 02:Click Code and then click Download ZIP.Image 23 — Downloading the ZIP file containing the files of the repositoryThis will download a ZIP file containing all the files inside the repository to your local machine.STEP # 03: Create a new chat session by clicking + New Chat. Make sure to select Advanced Data Analysis under the list of options available under GPT-4:Image 24 — Choosing Advanced Data AnalysisSTEP # 04: Upload the downloaded Zip file from an earlier step to the new chat session (using the + button). Enter the following prompt to compare the estimated cost per month associated with running each of the examples per chapter:Analyze the contents of the ZIP file and perform the following: - for each of the directories, identify the ML instance types used - compare the estimated cost per month associated to running the ML instance types in the examples stored in each directory - group the estimated cost per month per chapter directoryThis should process the contents of the ZIP file we uploaded and yield the following response:Image 25 — Comparing the estimated cost per month per chapterWow, that seems expensive! Of course, we will NOT be running these resources for an entire month! In my first book “Machine Learning with Amazon SageMaker Cookbook”, the resources in each of the examples and recipes are only run for a few seconds to at most a few minutes and deleted almost right away. Since we only pay for what we use in Amazon Web Services (AWS), it should only cost a few dollars to complete all the examples in the book.Note that this example can be further improved by utilizing and uploading a spreadsheet with the actual price per hour of each of these instances. In addition to this, it is important to note that there are other cost factors not taken into account in this example as only the cost of running the instances are included. That said, we should also take into account the cost associated with the storage costs associated with the storage volumes attached to the instances, as well as the estimated charges for using other cloud services and resources in the account.STEP # 05: Finally, run the following prompt to compare the estimated monthly cost per chapter when running the examples of the book:Generate a bar chart comparing the estimated monthly cost per chapterThis should yield the following response:Image 26 — Bar chart comparing the estimated monthly cost for running the ML instance types per chapterCool, right? Here, we can see a bar chart that helps us compare the estimated monthly cost of running the examples in each chapter. Again, this is just for demonstration purposes as we will only be running the ML instances for a few seconds to at most a few minutes. This would mean that the actual cost would only be a tiny fraction of the overall monthly cost. In addition to this, it is important to note that there are other cost factors not taken into account in this example as only the cost of running the instances are included. Given that we’re just demonstrating the power of ChatGPT Advanced Data Analysis in this post, this simplified example should do the trick! Finally, make sure to always verify the results and files produced by ChatGPT as you might find discrepancies or errors.Current LimitationsBefore we end this tutorial, it is essential that we mention some of the current limitations (as of writing) when using ChatGPT Advanced Data Analysis. First, there is a file size limitation which restricts users to only uploading files up to a maximum size of 500 MB per file. This could affect those trying to analyze large datasets since they’ll be forced to divide larger files into smaller portions. In addition to this, ChatGPT retains uploaded files only during the active conversation and for an additional three hours after the conversation has been paused. Files are automatically deleted which would require users to re-upload the files to continue the analysis. Finally, we need to be aware that the execution of the instructions is done inside a sandboxed environment. This means that we are currently unable to have external integrations and perform real-time searches. Given that ChatGPT Advanced Data Analysis is still an experimental feature (that is, in Beta mode), there may still be a few limitations and issues being resolved behind the scenes. Of course, by the time you read this post, it may no longer be in Beta!That’s pretty much it. At this point, you should have a great idea on what you can accomplish using ChatGPT Advanced Data Analysis. Feel free to try different prompts and experiment with various scenarios to help you discover innovative ways to visualize and interpret your data for better decision-making.Author BioJoshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of 3 Australian-owned companies and also served as the Director for Software Development and Engineering for multiple e-commerce startups in the past. Years ago, he and his team won 1st place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and he has been sharing his knowledge in several international conferences to discuss practical strategies on machine learning, engineering, security, and management. He is also the author of the books "Machine Learning with Amazon SageMaker Cookbook", "Machine Learning Engineering on AWS", and "Building and Automating Penetration Testing Labs in the Cloud". Due to his proven track record in leading digital transformation within organizations, he has been recognized as one of the prestigious Orange Boomerang: Digital Leader of the Year 2023 award winners.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionImagine having a spreadsheet containing the certification exam scores of various members of the organization. In the past, we had to spend some time writing code that generates charts from existing comma-separated values (CSV) files and Excel spreadsheets. Instead of writing code, we could also generate charts directly in Google Spreadsheets or Microsoft Excel. We might even be tempted to utilize business intelligence tools for this type of requirement. Now, it is possible to generate charts immediately using the right set of prompts with ChatGPT Advanced Data Analysis!In this two-part post, we will showcase a few examples of what we can do using the ChatGPT Advanced Data Analysis feature. You would be surprised how powerful this capability is for solving various types of requirements and tasks. While the general audience might think of ChatGPT as being limited only to text-based conversations, its advanced data analysis capabilities go beyond just textual interactions. With its ability to understand and process various types of datasets and files, it can produce useful visualizations and perform data analysis and data transformation tasks.To demonstrate what we can do with this feature, we’ll have the following sections in this post:Example 01 — Generating a CSV fileExample 02 — Analyzing an uploaded CSV file, transforming the data, and generating chartsExample 03 — Processing and analyzing an iPython notebook file (discussed in Part 2)Example 04 — Processing and analyzing the contents of a ZIP file (discussed in Part 2)Current Limitations  (discussed in Part 2)Whether you're a student working on a project or an analyst trying to save time on manual data analysis and processing tasks, ChatGPT's Advanced Data Analysis feature can be a game-changer for your data processing needs. That said, let's dive into some of its powerful features and see what we can accomplish using this amazing feature!Example 01: Generating a CSV fileIn this first example, we will (1) enable Advanced data analysis, (2) generate a comma separated values (CSV) file containing random values, and (3) download the CSV file to our local machine. We will use this generated CSV file in the succeeding examples and steps.STEP # 01: Let’s start by signing in to your ChatGPT account. Open a new web browser tab and navigate to https://chat.openai.com/auth/login.Image 01 — Signing in to your OpenAI accountClick Log in and sign in using your registered email address and password. If you don’t have an account yet, make sure to sign up first. Since we will be using the Advanced Data Analysis feature, we need to upgrade our plan to ChatGPT Plus so that we have access to GPT-4 along with other features only available to ChatGPT Plus users.STEP # 02: Make sure that Advanced data analysis is enabled before proceeding.Image 02 — Enabling the Advanced data analysis featureSTEP # 03: Create a new chat session by clicking + New Chat. Make sure to select Advanced Data Analysis under the list of options available under GPT-4:Image 03 — Using Advanced Data AnalysisSTEP # 04: In the chat box with the placeholder text “Send a message”, enter the following prompt to generate a CSV file containing 4 columns with randomly generated values:Generate a downloadable CSV spreadsheet file with 4 columns: - UUID - Name - Team - Score Perform the following: - For the UUID column, generate random UUID values - For the Name column, generate random full names - For the Team column, make sure to select only from the following teams: Technology, Business Development, Operations, Human Resources - For the Score column, generate random whole numbers between 1 to 10 - Have a total of 20 rowsAfter a few seconds, ChatGPT will give us a response similar to what we have in Image 04.Image 04 — ChatGPT generating a CSV fileHere, we can see that ChatGPT was able to successfully generate a CSV file. Awesome, right?STEP # 05: Click download it here to download the generated CSV file to your local machine. Feel free to inspect the downloaded file and verify if the instructions were implemented correctly. Make sure to always verify the results and files produced by ChatGPT as you might find discrepancies or errors.STEP # 06: Now, click Show work.Image 05 — Code used to generate the CSV fileThis should display the code or script used to perform the operations instructed by the user. You should be able to copy the code, modify it, and run it separately in your local machine or a cloud server.Wasn’t that easy? Now, let’s proceed to our second example.Example 02: Analyzing an uploaded CSV file, transforming the data, and generating chartsIn this second example, we will upload the generated CSV file from the previous example and perform various types of data transformations and analysis.STEP # 01: Create a new chat session by clicking + New Chat. Make sure to select Advanced Data Analysis under the list of options available under GPT-4:Image 06 — Creating a new chat session using Advanced Data AnalysisSTEP # 02: Click the + button and upload the downloaded file from the earlier example. In addition to this, enter the following prompt:Analyze the uploaded CSV file and perform the following: - Group the records by Team - Sort the records from highest to lowest (per team) Display the table and generate a downloadable CSV file containing the final outputThis should yield a response similar to what we have in the following image:Image 07 — Using ChatGPT to analyze the uploaded CSV fileGiven that the data is randomly generated, you will get a different set of results. What’s important is that there are 4 columns: UUID (Universal Unique Identifier), Name, Team, and Score in the generated CSV file and in what is displayed in the table.STEP # 03: Click the Show work button:Image 08 — Code used to read and process the uploaded CSV fileHere, we can see that ChatGPT used Pandas DataFrames to process the data behind the scenes.STEP # 04: Locate and click the download the grouped and sorted CSV file here button. Feel free to inspect the downloaded file. Make sure to always verify the results and files produced by ChatGPT as you might find discrepancies or errors.STEP # 05: Next, enter the following prompt to generate a bar chart to compare the average scores per team:Generate a bar chart to compare the average scores per teamThis should yield the following response:Image 09 — Generating a bar chartCool, right? Here, we were able to generate a bar chart in ChatGPT. Imagine the different variations, scenarios, and possibilities of what we can do with this feature! Now, let’s check what’s happening behind the scenes in the next step.STEP # 06: Click Show workImage 10 — Code used to generate a bar chartThis should display a block of code similar to what is shown in Image 10. Here, we can see that matplotlib was used to generate the bar chart in the previous step. If you have not used matplotlib before, it’s a popular library for creating static and interactive visualizations in Python. Instead of coding this ourselves, all we need now is the right prompt!STEP # 07: Now, let’s run the following prompt to compare the maximum scores achieved by the members of each team:Generate a bar chart to compare the max scores per teamThis should yield the following response:Image 11 — Generating a bar chart to compare the max scores per teamHere, we see a bar chart comparing the maximum score achieved by the members of each team. Given that the data used was randomly generated in an earlier step, you might get a slightly different chart with different maximum score values per team. Make sure to always verify the results and files produced by ChatGPT as you might find discrepancies or errors. STEP # 08: Enter the following prompt to group the records per team and generate a CSV file for each team:Divide the original uploaded CSV file and generate separate downloadable CSV files grouped per teamAfter a few seconds, ChatGPT will give us the following response:Image 12 — Dividing the original uploaded CSV file and generating multiple CSV files per teamFeel free to download the CSV files generated to your local machine.STEP # 09: Click Show workImage 13 — Code used to generate CSV files per teamSTEP # 10: Use the following prompt to generate a ZIP file containing the CSV files from the previous step:Generate a downloadable ZIP file containing the CSV files generated in the previous answerChatGPT should give us the following response:Image 14 — Generating a downloadable ZIP fileHere, we should be able to download the ZIP file to our local machine/laptop. Feel to verify if the CSV files are inside the ZIP file by extracting the contents and reviewing each file extracted.STEP # 11: Run the following prompt to convert the CSV files to XLSX (which is a well-known format for Microsoft Excel documents):Convert each file inside the ZIP file to XLSX and generate a new ZIP containing the XLSX filesChatGPT should give us the following response:Image 15 — ChatGPT generating a ZIP file for usAmazing, right? While we’ve been using CSV files in our examples, it does not mean that we’re limited to these types of files only. Here, we can see that we can work with XLSX files as well.STEP # 12: Download the ZIP file and open it on your local machine/laptop.STEP # 13: Run the following prompt to generate a ZIP file with all the charts generated in the current chat thread:Generate a ZIP file containing all the charts and images generated in this chat threadThis should yield the following response:Image 16 — Generating a ZIP file containing all the charts and images generated in the chat threadAmazing, right? Now, all we need to do is download the ZIP file using the link provided by ChatGPT.STEP # 14: Download the ZIP file and open it on your local machine/laptop. Make sure to always verify the results and files produced by ChatGPT as you might find discrepancies or errors.ConclusionThat wraps up the first part of this post. At this point, you should have a good idea of what you can accomplish using ChatGPT Advanced Data Analysis. However, there’s more in store for us in the second part as we’ll build on top of what we learned already and work on more complex examples and scenarios!If you’re looking for the link to the second part, here it is: Getting Started with ChatGPT Advanced Data Analysis- Part 2.Author BioJoshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of 3 Australian-owned companies and also served as the Director for Software Development and Engineering for multiple e-commerce startups in the past. Years ago, he and his team won 1st place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and he has been sharing his knowledge in several international conferences to discuss practical strategies on machine learning, engineering, security, and management. He is also the author of the books "Machine Learning with Amazon SageMaker Cookbook", "Machine Learning Engineering on AWS", and "Building and Automating Penetration Testing Labs in the Cloud". Due to his proven track record in leading digital transformation within organizations, he has been recognized as one of the prestigious Orange Boomerang: Digital Leader of the Year 2023 award winners.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionBusinesses thrive on information in today's data-driven era. However, raw data often needs enrichment to reveal its full potential. Here enters ChatGPT, a powerful tool not only for communication but also for enhancing data enrichment processes.Let us delve into the prospects of using ChatGPT for data enrichment.Does ChatGPT Do Data Mining?ChatGPT's prowess extends to data mining, unraveling valuable insights from vast datasets. Its natural language processing abilities allow it to decipher complex data structures, making it a versatile ally for researchers and analysts. By processing textual data, ChatGPT identifies patterns, enabling efficient data mining techniques.Process of data mining by ChatGPTChatGPT's ability to assist in data mining stems from its advanced natural language processing (NLP) capabilities. Here's an elaboration on the process of how ChatGPT can be utilized for data mining:1. Understanding Natural Language Queries:ChatGPT excels at understanding complex natural language queries. When provided with a textual prompt, it comprehends the context and intent behind the query. This understanding forms the basis for its data mining capabilities.2. Processing and Analyzing Textual Data:ChatGPT can process large volumes of textual data, including articles, reports, customer reviews, social media posts, etc. It can identify patterns, extract relevant information, and summarize lengthy texts, making it valuable for extracting insights from textual data sources.3. Contextual Analysis:ChatGPT performs contextual analysis to understand the relationships between words and phrases in a text. This contextual understanding enables ChatGPT to identify entities (such as names, places, and products) and their connections within the data, enhancing the precision of data mining results.4. Topic Modeling:ChatGPT can identify prevalent topics within textual data. Recognizing recurring themes and keywords helps categorize and organize large datasets into meaningful topics. This process is essential for businesses seeking to understand trends and customer preferences from textual data sources.5. Sentiment Analysis:ChatGPT can assess the sentiment expressed in textual data, distinguishing between positive, negative, and neutral sentiments. Sentiment analysis is crucial for businesses to gauge customer satisfaction, brand perception, market sentiment from online posts and reviews, and customer feedback.6. Data Summarization:ChatGPT can summarize extensive datasets, condensing large volumes of information into concise and informative summaries. This summarization capability is valuable for data mining, enabling analysts to quickly grasp essential insights without delving into voluminous data sources.7. Custom Queries and Data Extraction:Users can formulate custom queries and prompts tailored to specific data mining tasks. By asking ChatGPT precise questions about the data, users can extract targeted information, enabling them to focus on the particular aspects of the data relevant to their analysis.8. Interactive Exploration:ChatGPT allows for interactive exploration of data. Users can iteratively refine their queries based on the responses received, enabling a dynamic and exploratory approach to data mining. This interactivity facilitates a deeper understanding of the data and helps uncover hidden patterns and insights.By leveraging these capabilities, ChatGPT assists in data mining by transforming unstructured textual data into structured, actionable insights. Its adaptability to various queries and ability to process and analyze large datasets make it a valuable tool for businesses and researchers engaged in data mining.ChatGPT's ability to analyze JSON dataChatGPT can seamlessly analyze JSON data, a fundamental format for structuring data. Leveraging Python, integrating ChatGPT with JSON data becomes straightforward. Below is an illustrative Python code snippet demonstrating this integration:import openai import json # Your JSON data json_data = {    "key": "value",    "array": [1, 2, 3],    "nested": {        "inner_key": "inner_value"    } } # Convert JSON data to a string json_string = json.dumps(json_data) # Interact with ChatGPT response = openai.Completion.create(    engine="text-davinci-003",    prompt=json_string,    max_tokens=150  # Adjust the token limit as needed ) print(response.choices[0].text.strip())Also, ChatGPT can be used to analyze JSON data in various ways. Here are a few more examples of how ChatGPT can assist with JSON data analysis:1. JSON Data SummarizationChatGPT can be prompted to summarize complex JSON data structures, highlighting key attributes and relationships. For instance:Prompt: "Can you summarize the following JSON data for me?" {    "user": {        "name": "Sbani Paul",        "age": 30,        "email": "sbanipaul@example.com"    },    "order": {        "id": "123456",        "products": ["Widget A", "Widget B", "Widget C"],        "total": 150.75    } }ChatGPT Response: "The JSON data consists of user information and an order summary. The user, Sbani Paul, is 30 years old and can be reached at sbanipaul@example.com. The order with ID 123456 includes products Widget A, Widget B, and Widget C, totaling $150.75."2. JSON Schema ValidationChatGPT can help validate JSON data against a specified schema, ensuring data integrity and adherence to predefined data structures. For example:Prompt: "Is this JSON data valid according to the specified schema?" {    "name": "Aliza",    "age": 25,    "email": "Aliza@example.com" }ChatGPT Response: "Yes, the provided JSON data is valid according to the specified schema. It includes 'name' (string), 'age' (number), and 'email' (string) fields."3. JSON Data Transformation ChatGPT can assist in transforming JSON data into different formats, for instance, converting JSON to CSV or XML formats for specific use cases or applications.Prompt: "How can I transform this JSON data into CSV format?" [    {        "name": "Aliza",        "age": 30,        "city": "New York"    },    {        "name": "Bob",        "age": 35,        "city": "Los Angeles"    } ] ChatGPT Response: "To transform the given JSON data into CSV format, you can represent each object as a row and use commas to separate values. Here's the CSV representation:name,age,cityAliza,30,New YorkBob,35,Los Angeles4. Nested JSON Data AnalysisChatGPT can navigate through nested JSON structures, providing insights into specific elements within the data hierarchy.Prompt: "Can you provide details about the 'address' field in this nested JSON data?" {    "user": {        "name": "Eva",        "age": 28,        "address": {            "street": "123 Main St",            "city": "Anytown",            "postal_code": "54321"        }    } }ChatGPT Response: "Certainly, the 'address' field contains specific details. The user, Eva, resides at 123 Main St in Anytown with the postal code 54321."ChatGPT's ability to comprehend and respond to prompts about JSON data makes it a valuable tool for developers and data analysts working with structured data formats. Whether it's validation, transformation, or detailed analysis, ChatGPT can assist in various aspects of JSON data processing.What Is the Data Enrichment Method?Data enrichment transforms raw data into a goldmine of insights. This process involves augmenting existing data with supplementary information. Techniques include:Web scraping for real-time dataAPI integrations for seamless access to external databases.Leveraging machine learning algorithms to predict missing data.Data enrichment amplifies the value of datasets, enhancing analytical depth. The methods are diverse and dynamic, tailored to enhance the value of raw data. Let us go through an elaboration on the fundamental techniques of data enrichment:1. Web ScrapingWeb scraping involves extracting data from websites. It enables businesses to gather real-time information, news updates, pricing details, and more. By scraping relevant websites, organizations enrich their datasets with the latest and most accurate data available on the web. Web scraping tools can be programmed to extract specific data points from various web pages, ensuring the enrichment of databases with up-to-date information.2. API IntegrationsApplication Programming Interfaces (APIs) act as bridges between different software systems. Many platforms provide APIs that allow seamless data exchange. By integrating APIs into data enrichment processes, businesses can access external databases, social media platforms, weather services, financial data, and other sources. This integration ensures that datasets are augmented with comprehensive and diverse information, enhancing their depth and relevance.3. ChatGPT InteractionChatGPT's natural language processing abilities make it a valuable tool for data enrichment. Businesses can interact with ChatGPT to extract context-specific information by providing specific prompts. For example, ChatGPT can be prompted to summarize lengthy textual documents, analyze market trends, or provide detailed explanations about particular topics. These interactions enrich datasets by incorporating expert insights and detailed analyses, enhancing the overall understanding of the data.4. Machine Learning AlgorithmsMachine learning algorithms are pivotal in data enrichment, especially when dealing with large datasets. These algorithms can predict missing data points by analyzing patterns within the existing dataset. A variety of strategies, such as regression analysis, decision trees, and neural networks, are employed to fill gaps in the data intelligently. By accurately predicting missing values, machine learning algorithms ensure that datasets are complete and reliable, making them suitable for in-depth analysis and decision-making.5. Data Normalization and TransformationData normalization involves organizing and structuring data in a consistent format. It ensures that data from disparate sources can be effectively integrated and compared. Conversely, transformation consists of converting data into a standardized format, making it uniform and compatible. These processes are crucial for data integration and enrichment, enabling businesses to use consistent, high-quality data.6. Data AugmentationData augmentation involves expanding the dataset by creating variations of existing data points. In machine learning, data augmentation techniques are often used to enhance the diversity of training datasets, leading to more robust models. By applying similar principles, businesses can create augmented datasets for analysis, providing a broader perspective and enhancing the accuracy of predictions and insights.By employing these diverse methods, businesses can ensure their datasets are comprehensive and highly valuable. Data enrichment transforms raw data into a strategic asset, empowering organizations to make data-driven decisions to gain a competitive edge in their respective industries.ConclusionIncorporating ChatGPT into data enrichment workflows revolutionizes how businesses harness information. By seamlessly integrating with various data formats and employing diverse enrichment techniques, ChatGPT ensures that data isn't just raw facts but a source of actionable intelligence. Stay ahead in the data game – leverage ChatGPT to unlock the full potential of your datasets.Author BioJyoti Pathak is a distinguished data analytics leader with a 15-year track record of driving digital innovation and substantial business growth. Her expertise lies in modernizing data systems, launching data platforms, and enhancing digital commerce through analytics. Celebrated with the "Data and Analytics Professional of the Year" award and named a Snowflake Data Superhero, she excels in creating data-driven organizational cultures.Her leadership extends to developing strong, diverse teams and strategically managing vendor relationships to boost profitability and expansion. Jyoti's work is characterized by a commitment to inclusivity and the strategic use of data to inform business decisions and drive progress.