How-To Tutorials - ChatGPT

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionFor anyone new to the world of stocks, looking at the charts and numbers can feel a bit like trying to read a foreign language. But with the right tools and a step-by-step approach, understanding these numbers becomes a whole lot easier. This guide aims to walk beginners through a basic analysis of APPLE's stock data. Here's what will be covered:Step 1. Descriptive Statistics: The first step is to look at APPLE's stock prices. This means seeing the high points, the low points, and understanding how often they change.Step 2. Time Series Analysis: This is all about checking the price of the stock over time. It helps to see if the price is going up, going down, or staying about the same.Step 3. Volatility Analysis: This part helps to see how quickly and by how much the price changes. It's a way to see if the stock is calm or if it has a lot of ups and downs. Step 4. Technical Indicators: These are simple tools that help guess what might happen next with the stock price. It's like trying to guess the end of a story by looking at what happened in the middle. Step 5. Volume Analysis: This is about seeing how many times the stock was bought or sold. It gives a clue about how many people are interested in this stock. With these five easy steps aided by prompts and Python codes, anyone can start to understand the basics of APPLE's stock data. It's a straightforward way to see the story behind the numbers. You can access the data here (Link)Step 1: Descriptive StatisticsUsing descriptive statistics, we assess the stock's central tendencies and dispersion by calculating the mean, median, and standard deviation of prices and volume, and pinpointing the highest and lowest values over the specified period.Prompt 1: As a stock analyst, you are tasked with understanding the nuances of a given stock dataset. Start by loading the dataset that encompasses stock prices, specifically the Open, High, Low, and Close values, alongside trading volume. Compute fundamental descriptive statistics like the mean, median, standard deviation, as well as the maximum and minimum values for each mentioned category. Once done, provide a succinct summary detailing the key findings, highlighting any discernible trends or potential outliers that could be of significance to investors.Response: We'll start by generating basic descriptive statistics for the stock prices (Open, High, Low, Close) and the trading volume.The descriptive statistics for the stock prices and volume are as follows:  The next step is to visualize the dataset as a time seriesStep 2: Time Series AnalysisThrough time series analysis, we visualize stock price trends by plotting Open, High, Low, and Close values, while also examining seasonality, identifying prevailing trends, and detecting any anomalies or outliers in prices or volume.Prompt 2: As a stock analyst, begin by loading the dataset and ensure it includes Date, Open, High, Low, and Close prices. Standardize the "Date" column format and set it as the index. Plot these stock prices against dates, making each distinct. Analyze the graph for evident trends, significant peaks, or troughs, and ponder potential influencing factors.Response: It appears that the dataset contains the columns "Date", "Close/Last", "Volume", "Open", "High", and "Low". The "Date" column has inconsistent formats, so we'll need to standardize it. Additionally, the prices are represented as strings with dollar signs; we'll need to convert them to float values for analysis.I'll begin by:1.      Standardizing the "Date" column format.2.      Converting the price columns to float values.3.      Setting the "Date" column as the index. import pandas as pd # Load the dataset data = pd.read_excel("/mnt/data/HistoricalData_1Yr.xlsx") # Display the first few rows of the dataset data.head() The dataset has been appropriately formatted. The next step is to plot the stock prices (Open, High, Low, and Close/Last) against their respective dates. Each price will be represented with a distinct line for clarity.import matplotlib.pyplot as plt # Plotting the stock prices plt.figure(figsize=(15, 7)) for col in price_columns:    plt.plot(data.index, data[col], label=col) plt.title("Stock Prices Over Time") plt.xlabel("Date") plt.ylabel("Price ($)") plt.legend() plt.grid(True) plt.tight_layout() plt.show()Interpretation: When looking at a stock chart, I first noticed its overall upward movement. Yet, near the end, it seemed to stabilize or maybe even decline a bit. Two clear points caught my attention: a high point in the middle and a low point at the start. Stock prices can change due to company news or broader events, and sometimes, high trading volumes can make prices shift dramatically. It's also important to remember that the general mood of investors can set the direction for a stock's movement. Now let’s look at the volatility to check if the share is tradable or notStep 3: Volatility AnalysisBy calculating daily returns and computing the rolling standard deviation, we can effectively analyze and track the volatility of a financial instrument over time.Prompt 3: As a stock analyst, it's imperative to grasp the volatility of a stock, a crucial aspect for investors. To achieve this, compute the stock's daily returns by measuring the percentage change in the closing price from one day to the subsequent one. Once computed, proceed to plot these daily returns, which will visually represent the stock's volatility throughout the chosen time frame. Your plot, reflecting the daily returns over time, will offer invaluable insights into the stock's stability or fluctuations, helping stakeholders make informed decisions.Response: To grasp the volatility of the stock:We'll compute the stock's daily returns. This is achieved by measuring the percentage change in the closing price from one day to the subsequent one.We'll plot these daily returns over time, which will offer a visual representation of the stock's volatility throughout the chosen time frame.# Compute the daily returns data['Daily Returns'] = data['Close/Last'].pct_change() # Display the first few rows of the daily returns data['Daily Returns'].head()The daily returns have been successfully computed. As expected, the first entry is NaN since there's no previous day to calculate the percentage change from.Now, let's plot these daily returns over time to visually assess the stock's volatility.# Plotting the daily returns plt.figure(figsize=(15, 7)) plt.plot(data.index, data['Daily Returns'], label='Daily Returns', color='blue') plt.axhline(0, color='red', linestyle='--')  # Adding a horizontal line at y=0 for reference plt.title("Daily Returns Over Time") plt.xlabel("Date") plt.ylabel("Daily Return") plt.legend() plt.grid(True) plt.tight_layout() plt.show()Interpretation: When I look at the stock's daily returns, I see its ups and downs around the red zero line, showing its riskiness. Close-to-zero movements mean the stock is steady, while bigger jumps indicate more unpredictability. Different events, like company news or global happenings, can cause these shifts. So, when choosing stocks, it's crucial to know whether you prefer a calm ride or a roller coaster of returns.Since we know every information is inbuilt price data itself let’s analyze it to decode that informationStep 4: Forming Technical IndicatorsThrough the calculation of moving averages, such as the 50-day and 200-day, and the identification of golden or death crosses, coupled with the computation of the RSI, we can gauge market momentum and discern overbought or oversold conditions.Prompt 4: As a seasoned stock analyst, you recognize the importance of technical indicators like moving averages to gauge the potential direction of a stock's momentum. For this analysis, focus on two primary moving averages: the 50-day and the 200-day. By calculating these, you can ascertain pivotal trend indications. Notably, watch for the "Golden Cross," a bullish signal that arises when the 50-day moving average surpasses the 200-day moving average. Conversely, be wary of the "Death Cross," which is a bearish sign indicating potential downtrends when the 50-day moving average drops below the 200-day one. These cross points can serve as critical markers for investors, helping them strategize their market positions.Response: The 50-day and 200-day moving averages are among the most observed technical indicators in stock analysis. These moving averages help to smooth out price data to create a single flowing line, making it easier to identify the direction of the trend.Let's proceed with the following steps:Calculate the 50-day moving average for the closing price.Calculate the 200-day moving average for the closing price.Plot both moving averages alongside the stock's closing price.Identify and annotate potential "Golden Cross" and "Death Cross" points on the plot.Let's begin by calculating the two moving averages.# Calculate the 50-day and 200-day moving averages data['50-day MA'] = data['Close/Last'].rolling(window=50).mean() data['200-day MA'] = data['Close/Last'].rolling(window=200).mean() # Display the first few rows of the moving averages data[['Close/Last', '50-day MA', '200-day MA']].tail() The 50-day and 200-day moving averages have been calculated successfully. To better understand their implications:We'll plot the stock's closing price alongside the two moving averages.We'll highlight and annotate any "Golden Cross" and "Death Cross" occurrences on the chart. Let's visualize the data. # Plotting the closing price and moving averages plt.figure(figsize=(15, 7)) plt.plot(data.index, data['Close/Last'], label='Close Price', color='black', alpha=0.5) plt.plot(data.index, data['50-day MA'], label='50-day MA', color='blue') plt.plot(data.index, data['200-day MA'], label='200-day MA', color='red') # Identifying the Golden Cross and Death Cross golden_cross = data[(data['50-day MA'] > data['200-day MA']) & (data['50-day MA'].shift(1) <= data['200-day MA'].shift(1))] death_cross = data[(data['50-day MA'] < data['200-day MA']) & (data['50-day MA'].shift(1) >= data['200-day MA'].shift(1))] # Annotating the Golden Cross and Death Cross on the plot for date in golden_cross.index:    plt.annotate('Golden Cross', xy=(date, data['200-day MA'].loc[date]),                 xytext=(date, data['200-day MA'].loc[date] + 10),                 arrowprops=dict(facecolor='green', arrowstyle='->'), color='green')   for date in death_cross.index:    plt.annotate('Death Cross', xy=(date, data['200-day MA'].loc[date]),                 xytext=(date, data['200-day MA'].loc[date] - 10),                 arrowprops=dict(facecolor='red', arrowstyle='->'), color='red') # Other plot settings plt.title("Stock Price with 50-day and 200-day Moving Averages") plt.xlabel("Date") plt.ylabel("Price ($)") plt.legend() plt.grid(True) plt.tight_layout() plt.show() Interpretation: The chart shows the stock's closing price with two different line averages: a 50-day (blue) and a 200-day (red). When the 50-day line goes above the 200-day line, marked by green arrows, it usually suggests the stock might go up. On the other hand, red arrows where the 50-day line dips below indicate the stock might drop. These crossing points, known as the "Golden" and "Death" Cross, can help investors gauge the stock's potential direction. Simply put, these averages provide hints on whether the stock is on an upward or downward path. Let’s have a look at the volume of the APPLE stock.Step 5: Volume AnalysisBy analyzing volume spikes in relation to stock price movements and plotting volume over time, we can discern potential patterns and their impact on market dynamics.Prompt 5: In your role as a stock analyst, understanding trading volume's nuances is equally as vital as tracking price movements. Now, turn your attention to plotting the trading volume against time. This visualization can reveal patterns or substantial spikes in trading volume, which might correlate with notable price shifts. Such correlations can often indicate key market events or investor sentiment shifts. The graph you produce, illustrating the trading volume over the period, will provide a clearer picture of the stock's liquidity and the market's interest in it. This, in turn, will guide investment decisions and strategies.Response: Trading volume is a critical metric for stock analysts. It provides insights into the stock's liquidity and the market's interest in it. High trading volumes can indicate strong interest in a stock, and when paired with price movements, can reveal significant market events or shifts in investor sentiment.Let's proceed with the following steps:1. Plot the trading volume against time.2. Highlight and annotate any substantial spikes in trading volume.3. Overlay the stock's closing price to see if there's any correlation between trading volume spikes and significant price shifts.# Plotting the trading volume against time fig, ax1 = plt.subplots(figsize=(15, 7)) # Twin the axes for two different y-axes ax2 = ax1.twinx() # Plotting trading volume on ax1 ax1.bar(data.index, data['Volume'], color='gray', alpha=0.5, label='Volume') ax1.set_xlabel('Date') ax1.set_ylabel('Trading Volume', color='gray') ax1.tick_params(axis='y', labelcolor='gray') ax1.set_title("Trading Volume and Stock Price Over Time") # Plotting closing price on ax2 ax2.plot(data.index, data['Close/Last'], color='blue', label='Close Price') ax2.set_ylabel('Close Price ($)', color='blue') ax2.tick_params(axis='y', labelcolor='blue') # Highlighting and annotating substantial volume spikes average_volume = data['Volume'].mean() high_volume_dates = data[data['Volume'] > 1.5 * average_volume] for date in high_volume_dates.index:    ax1.annotate('High Volume', xy=(date, high_volume_dates['Volume'].loc[date]),                 xytext=(date, high_volume_dates['Volume'].loc[date] + 5e6),                 arrowprops=dict(facecolor='red', arrowstyle='->'), color='red') plt.grid(True) plt.tight_layout() plt.show() Let's begin by visualizing the trading volume against time.Interpretation: The chart displays both the stock's closing price (blue line) and how often it was traded (gray bars). Larger gray bars show days when the stock was traded more frequently. Some days, highlighted with red arrows, even saw an unusually high trading activity. Looking at these spikes alongside the blue line, we can guess if positive or negative news affected the stock. Generally, when a stock is traded often, it's easier to buy or sell without greatly changing its price. This chart helps investors gauge interest in the stock and its stability, supporting smarter investment choices.ConclusionIn conclusion, while the world of stocks might initially seem like an intricate puzzle, it truly isn't as daunting as it first appears. By systematically breaking down APPLE's stock data through the aforementioned steps, even a beginner can gain valuable insights into the dynamics of the stock market. Think of it as assembling the pieces of a story – from setting the scene with descriptive statistics to reaching the climax with volume analysis. And with the added advantage of specific prompts and Python code to guide the way, understanding the ebb and flow of stocks becomes a clear and attainable goal. So, here's to turning those charts and numbers from intimidating to intriguing, and uncovering the fascinating story they hold within. 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 and books. Don't miss out – sign up today!IntroductionFine-tuning with OpenAI is a new feature that might become a crucial aspect of enhancing AI language models for specific tasks and contexts. It holds significant importance as it allows these models to be adapted to perform tasks beyond their initial capabilities in a different way that could be done just with Prompt Engineering. In this article, we will use traditional fine-tuning, which involves training a model on a specialized dataset. The dataset we will be using consists of conversations formatted in a JSON lines structure, where each exchange is a sequence of chat message dictionaries. Each dictionary includes role assignments (system, user, or assistant) and the corresponding content of the message. This approach aims to adapt the model to better understand and generate human-like conversations. Let’s start by taking a look at the different alternatives to adapt a Large Language Model for custom tasks. Fine-tuning versus Prompt Engineering There are two distinct approaches for adapting a model to work with custom data: prompt engineering and traditional fine-tuning. While both methods aim to customize LLMs for specific tasks, they differ in their approaches and objectives. Prompt engineering entails crafting precise input prompts to guide the AI's responses effectively. It involves tailoring the prompts to elicit desired outcomes from the AI. This technique requires developers to experiment with different prompts, instructions, and formats to achieve precise control over the model's behavior. By providing explicit instructions within prompts, developers can elicit specific answers for tasks like code generation or translation. Prompt engineering is particularly valuable when clear guidance is essential, but finding the optimal prompts might require iterative testing. On the other hand, fine-tuning focuses on adapting a pre-trained LLM to perform better on a particular task or context. This process involves training the model on custom datasets that align with the desired application. Fine-tuning allows LLMs to develop a deeper understanding of context and language nuances, making them more adaptable to diverse prompts and human-like conversations. While it offers less direct control compared to prompt engineering, fine-tuning improves the model's ability to generate coherent responses across a broader range of scenarios. In essence, prompt engineering emphasizes precision and specific instruction within prompts, while fine-tuning enhances the LLM's adaptability and comprehension of context. Both prompt engineering and traditional fine-tuning serve as techniques to enhance the AI's conversational abilities. Prompt engineering emphasizes precise instruction, while traditional fine-tuning focuses on training the model to comprehend and generate conversations more effectively. Looking at the Training Data Before training a model, we need to understand the required data format for the OpenAI fine-tuning endpoints. This format utilizes JSON lines and consists of a primary key "messages," followed by an array of dictionaries representing chat messages. These dictionaries collectively form a complete conversation. The expected structure to train an Open AI model looks like this: {"messages": [{"role": "system", "content": "..."}, ...]} {"messages": [{"role": "system", "content": "..."}, ...]} {"messages": [{"role": "system", "content": "..."}, ...]} {"messages": [{"role": "system", "content": "..."}, ...]} Each chat message dictionary includes two essential components: The "role" field: This identifies the source of the message, which can be system, user, or assistant. It indicates the origin of the message. The "content" field: This contains the actual textual content of the message. In this article, we will be using an already available training dataset that complies with this structure within the Hugging Face datasets repository. Before we get this data, let’s first install the datasets package alongside the open ai and langchain modules using pip. !pip install datasets==2.14.4 openai==0.27.9 langchain==0.0.274Next, we can download the dataset using the datasets library and write it into a JSON file.from datasets import load_dataset # data = load_dataset( "jamescalam/agent-conversations-retrieval-tool", split="train" ) data.to_json("conversations.jsonl") To verify the structure of the file, we open it and load it into separate conversations. import json with open('conversations.jsonl', 'r') as f: conversations = f.readlines() # Assuming each line is a JSON string, you can iterate through the lines and load each JSON string parsed_conversations = [json.loads(line) for line in conversations] len(parsed_conversations)We get 270 conversations, and if we want, we can inspect the first element of the list.parsed_conversations[0] In the following code snippet, the OpenAI Python library is imported, and the API key is set using the environment variable. The script then uses the OpenAI API to create a fine-tuning job for GPT-3.5 Turbo. It reads the contents of a JSON Lines file named conversations.jsonl and sets the purpose of the file as 'fine-tune'. The resulting file ID is saved for later use.import openai import os # Set up environment variables for API keys os.environ['OPENAI_API_KEY'] = 'your-key' res = openai.File.create( file=open("conversations.jsonl", "r"), purpose='fine-tune' ) # We save the file ID for later file_id = res["id"] Now we can start the Fine-tuning job. res = openai.FineTuningJob.create( training_file=file_id, model="gpt-3.5-turbo" ) job_id = res["id"]In this part of the code, the fine-tuning job is initiated by calling the Openai.FineTuningJob.create() function. The training data file ID obtained earlier is passed as the training_file parameter, and the model to be fine-tuned is specified as "gpt-3.5-turbo". The resulting job ID is saved for monitoring the fine-tuning progress. Monitoring Fine-Tuning Progress from time import sleep while True:    print('*'*50)    res = openai.FineTuningJob.retrieve(job_id)    print(res)    if res["finished_at"] != None:        ft_model = res["fine_tuned_model"]        print('Model trained, id:',ft_model)        break    else:        print("Job still not finished, sleeping")        sleep(60) . In this section, the code enters a loop to continuously check the status of the fine-tuning job using the openai.FineTuningJob.retrieve() method. If the job has finished indicated by the "finished_at" field in the response, the ID of the fine-tuned model is extracted and printed. Otherwise, if the job is not finished yet, the script pauses or waits for a minute using the "sleep(60)" function before checking the job status again.Using the Fine-Tuned Model for Chat from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import (    ChatPromptTemplate,    SystemMessagePromptTemplate,    AIMessagePromptTemplate,    HumanMessagePromptTemplate, ) from langchain.schema import AIMessage, HumanMessage, SystemMessage chat = ChatOpenAI(    temperature=0.5,    model_name=ft_model ) messages = [    SystemMessage(        content="You are a helpful assistant."    ),    HumanMessage(        content="tell me about Large Language Models"    ), ] chat(messages)  In this last part of the code, the fine-tuned model is integrated into a chat using the LangChain library. A ChatOpenAI instance is created with specified settings, including a temperature of 0.5 and the name of the fine-tuned model (ft_model). A conversation is then simulated using a sequence of messages, including a system message and a human message. The chat interaction is executed using the chat() method. The provided code is a step-by-step guide to set up, fine-tune, monitor, and utilize a chatbot model using OpenAI's API and the LangChain library. It showcases the process of creating, training, and interacting with a fine-tuned model for chat applications. ConclusionIn conclusion, fine-tuning GPT-3.5 and GPT-4 marks a significant leap in customizing AI language models for diverse applications. Whether you opt for precise prompt engineering or traditional fine-tuning, both approaches offer unique strategies to enhance conversational abilities. This step-by-step article demonstrates how to prepare data, initiate fine-tuning, monitor progress, and leverage the fine-tuned model for chat applications.As AI evolves, fine-tuning empowers language models with specialized capabilities, driving innovation across various fields. Developers can harness these techniques to excel in tasks ranging from customer support to complex problem-solving. With the power of fine-tuning at your disposal, the possibilities for AI-driven solutions are limitless, promising a brighter future for AI technology.Author BioAlan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, and Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder of startups, and later on earned a Master's degree from the faculty of Mathematics at the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.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 and books. Don't miss out – sign up today!IntroductionIn the dynamic world of finance, technology continually pushes boundaries. Today, financial institutions seek to enhance customer experiences with a powerful tool: Generative Artificial Intelligence (AI). This cutting-edge technology is revolutionizing finance, reshaping customer interactions, and elevating satisfaction and personalization.Generative AI, known for creative output and data generation, is now making waves in finance. It offers unique opportunities to transform the customer experience. By harnessing Generative AI's capabilities, financial institutions gain valuable insights, provide hyper-personalized solutions, and align offerings with individual needs.This article explores Generative AI's impact on Wealth Management in finance. We uncover innovative applications, from personalized financial product recommendations to intuitive virtual assistants meeting customer needs. Additionally, we discuss the benefits, challenges, and ethical considerations of using Generative AI to enhance customer satisfaction.Customer Pain Points in Wealth ManagementIn the ever-evolving realm of finance, where wealth management and customer service intersect, customers often grapple with a host of challenges that can significantly impact their overall satisfaction. These obstacles stem from various sources and play a pivotal role in shaping customer loyalty. Here, we delve into some prevalent pain points experienced by customers in the finance sector, specifically in the context of wealth management and customer service:1. Lack of Personalization: Many clients seek financial advice and solutions tailored to their distinct goals and circumstances. Yet, conventional wealth management approaches often fall short of delivering this level of customization, leaving customers feeling disconnected and dissatisfied.2. Limited Accessibility: Accessibility issues can arise when clients encounter hurdles in accessing their financial data or communicating with their wealth managers and customer service representatives. Challenges in initiating contact, receiving timely responses, or navigating complex procedures can breed frustration and hinder the customer journey.3. Complex and Confusing Information: Financial matters are inherently intricate, and the use of complex jargon and technicalities can overwhelm customers. When information is not conveyed clearly and effectively, clients may find themselves bewildered, making it arduous to make well-informed decisions.4. Slow and Inefficient Processes: Lengthy processing times, excessive paperwork, and cumbersome procedures can be significant roadblocks in the customer experience. Clients demand streamlined, efficient processes that conserve time and effort, allowing them to manage their wealth seamlessly.5. Inadequate Communication and Transparency: Effective communication stands as the bedrock of trust and robust relationships. Clients place a premium on transparent, proactive communication from their wealth managers and customer service representatives. Inadequate communication or a lack of transparency concerning fees, performance updates, or policy changes can breed dissatisfaction and erode trust.6. Limited Innovation and Technology Adoption: Expectations are on the rise, with clients anticipating financial institutions to embrace technology and provide innovative solutions to enrich their financial management experience. A dearth of technological advancements, such as user-friendly digital platforms and interactive tools, can leave clients feeling underserved and disconnected.Mitigating these recurring customer pain points necessitates a customer-centric approach. This approach should encompass personalized services, streamlined processes, transparent communication, and a wholehearted embrace of innovative technologies. Through active engagement with these pain points, financial institutions can craft superior customer experiences, foster lasting relationships, and set themselves apart in an increasingly competitive landscape.How Generative AI can be used for Wealth Management?Let's dive right into the crux of the matter. Customers look to financial institutions not just for financial guidance but for personalized advice that aligns with their unique wealth aspirations. They place a high premium on financial expertise to help them navigate the path to their financial goals. Traditional wealth management has traditionally excelled in fostering strong client relationships, with each customer paired with a dedicated relationship manager who intimately understands their individual objectives.However, here's where things get interesting: the traditional methods of wealth management sometimes fall short of meeting the sky-high expectations for personalization. The limitations primarily stem from the scarcity of relationship managers, leading to challenges in scalability and sluggish communication. This communication bottleneck occasionally results in misunderstandings due to varying levels of subject comprehension. These roadblocks, unfortunately, can turn customers off, leaving them feeling adrift and dissatisfied.Enter Generative AI, poised to be the game-changer in wealth management. With its ability to sidestep scalability issues, Generative AI emerges as a promising solution. Picture this: every customer is equipped with an AGI-powered Chatbot capable of addressing their queries, understanding their goals, and furnishing personalized financial plans tailored to their specific requirements. It's a potential paradigm shift in customer service that holds the promise of seamless, individualized wealth management experiences.Now let us see the working of a use case. In this article, we will walk through an LLM-powered Chatbot that will answer user queries.Demonstrating a use-case: Context-based LLM-powered chatbot for Financial advice# Importing Dependenciesimport streamlit as st from streamlit_chat import message import openai import os# Mentioning API keyopenai.api_key = 'PASTE-YOUR-KEY' os.environ['OPENAI_API_KEY'] = "PASTE-YOUR-KEY"# Function to return response from GPTdef fun(prompt):    response = openai.ChatCompletion.create(                engine="engine_name",                messages = [                            {'role': 'user',                             'content': prompt}                          ],                temperature=0,                max_tokens=800,                top_p=0.95,                frequency_penalty=0,                presence_penalty=0,                stop=None)    response = response['choices'][0]['message']['content']    return response # Function that checks whether the question asked is out of context or not. Returns True or Falsedef context_check(prompt):    testing_query = f'''    Instructions:    Answer the questions only related to "{context_topics}".       Query:    Study the prompt "{prompt}" and tell whether the user directly or indirectly asking questions related to "{context_topics}".    Give a response only in "True" or "False".       Remember:    1. Do no generate any other output, example, code etc.     2. Answer should be 1 word only. True or False.    '''    response = fun(testing_query)    return response#Returns filtered response after context checkingdef generate_response(prompt):    for topic in context_topics:        if topic not in prompt:            is_contexual = 'False'    instructions = f'''         Instructions:        0. Assume yourself to be an expert in answering Financial queries        1. Answer questions only to the topics mention in: "{context_topics}" at all costs!        2. Be precise and crisp.        3. Answer in short.        '''    is_contexual = context_check(prompt)    if is_contexual == 'True':        prompt += instructions        response = fun(prompt)        return response    elif is_contexual == 'False':        return "Sorry the question asked doesn't follow the guidelines." # Gets the input text from streamlitdef get_text():    input_text = st.text_input("How may I help?", key='input')    return input_text with open('only_reply.txt', 'r') as f:        context_topics = f.read() context_topics = context_topics.split('\n')[:-1] # context_topics = ['Finance', 'Wealth Management', 'Investment', 'Wealth'] st.set_page_config(    page_title="FinBot",    page_icon="💰", )st.write("# Welcome to FinBot💰!") changes = ''' <style> [data-testid = "stAppViewContainer"]    {    background-image:url('https://i.ibb.co/qrrD42j/Screenshot-2023-09-15-at-5-41-25-PM.png');    background-size:cover;    }       div.esravye2 > iframe {        background-color: transparent;    } </style> ''' st.markdown(changes, unsafe_allow_html=True) if 'generated' not in st.session_state:    st.session_state['generated'] = [] if 'past' not in st.session_state:    st.session_state['past'] = []user_input = get_text() if user_input:    output = generate_response(user_input)    js_clear_input = """    <script>    const inputElement = document.querySelector('.stTextInput input');    inputElement.addEventListener('keydown', function(event) {        if (event.key === 'Enter') {            inputElement.value = '';        }    });    </script>    """# Display the JavaScript code st.markdown(js_clear_input, unsafe_allow_html=True)    st.experimental_set_query_params(text_input="")    st.session_state.past.append(user_input)    st.session_state.generated.append(output) if st.session_state['generated']:    for i in range(len(st.session_state['generated'])-1, -1, -1):        message(st.session_state['generated'][i], key=str(i))        message(st.session_state['past'][i], key="user_"+str(i), is_user=True)ScreenshotsBlocking Out of context questionContextual QuestionsConclusionIn conclusion, Generative AI stands as a game-changing force in the realm of wealth management. Its ability to provide personalized financial advice and solutions on a scale previously unattainable is reshaping the landscape of financial services. By leveraging the vast potential of Generative AI, financial institutions can navigate the complexities of modern finance with unparalleled precision.The anticipated impact is profound: clients receive tailored recommendations that align seamlessly with their unique financial goals, risk profiles, and the ever-evolving market dynamics. This, in turn, leads to improved investment outcomes, heightened client satisfaction, and a deepened sense of trust in financial institutions.As we march forward, the synergy between technology and human expertise will continue to define the future of wealth management. Generative AI, as a powerful ally, empowers advisors and clients alike to make informed decisions, optimize portfolios, and nurture enduring financial success. In this dynamic landscape, the marriage of cutting-edge technology and personalized financial guidance promises to usher in an era of unprecedented prosperity and financial well-being for all.Author BioBhavishya Pandit is a Data Scientist at Rakuten! He has been extensively exploring GPT to find use cases and build products that solve real-world problems.

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, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions IntroductionIn this article, we will focus on how marketers can leverage ChatGPT, looking at the main use cases of ChatGPT in this domain, and how marketers can leverage it as a valuable assistant.We will learn how ChatGPT can assist in the following activities: Marketers’ need for ChatGPTNew product development and the go-to-market strategyBy the end of this article, you will be able to leverage ChatGPT for marketing-related activities and to boost your productivity.Technical requirementsYou will need an OpenAI account to access ChatGPT and DALL-E.All the code and prompts within this chapter are available in the GitHub repository of this bookhere: https://github.com/PacktPublishing/The-Ultimate-Guide-to-ChatGPTand-OpenAI.Marketers’ need for ChatGPTMarketing is probably the domain where ChatGPT and OpenAI models’ creative power can be leveraged in their purest form. They can be practical tools to support creative development in terms of new products, marketing campaigns, search engine optimization, and so on. Overall, marketers automate and streamline many aspects of their work, while also improving the quality and effectiveness of their marketing efforts.Here is an example. One of the most prominent and promising use cases of ChatGPT in marketing is personalized marketing. ChatGPT can be used to analyze customer data and generate personalized marketing messages that resonate with individual customers. For example, a marketing team can use ChatGPT to analyze customer data and develop targeted email campaigns that are tailored to specific customer preferences and behavior. This can increase the likelihood of conversion and lead to greater customer satisfaction. By providing insights into customer sentiment and behavior, generating personalized marketing messages, providing personalized customer support, and generating content, ChatGPT can help marketers deliver exceptional customer experiences and drive business growth.This is one of many examples of ChatGPT applications in marketing. In the following sections, we will look at concrete examples of end-to-end marketing projects supported by ChatGPT.New product development and the go-to-market strategyThe first way you can introduce ChatGPT into your marketing activity might be as an assistant in new product development and go-to-market (GTM) strategy.In this section, we will look at a step-by-step guide on how to develop and promote a new product. You already own a running clothing brand called RunFast and so far you have only produced shoes, so you want to expand your business with a new product line. We will start by brainstorming ideas to create a GTM strategy. Of course, everything is supported by ChatGPT:Brainstorming ideas: The first thing ChatGPT can support you with is brainstorming and drafting options for your new product line. It will also provide the reasoning behind each suggestion. So, let’s ask what kind of new product line I should focus on:Figure 7.1 – Example of new ideas generated by ChatGPTOut of the three suggestions, we will pick the first one, because of the reason ChatGPT suggested it—it is indeed a complementary product for our running shoes, so we will proceed with that one.Product name: Now that we have our idea fixed in mind, we need to think of a catchy name for it. Again, I will ask ChatGPT for more options so that I can then pick my favorite one:Figure 7.2 – A list of potential product namesSprintSoles sounds good enough for me – I’ll go ahead with that one.Generating catchy slogans: On top of the product name, I also want to share the intent behind the name and the mission of the product line, so that my target audience is captured by it. I want to inspire trust and loyalty in my customers and for them to see themselves reflected in the mission behind my new product line.Figure 7.3 – A list of slogans for my new product nameGreat – now I’m satisfied with the product name and slogan that I will use later on to create a unique social media announcement. Before doing that, I want to spend more time on market research for the target audience.Figure 7.4 – List of groups of target people to reach with my new product lineIt’s important to have in mind different clusters within your audience so that you can differentiate the messages you want to give. In my case, I want to make sure that my product line will address different groups of people, such as competitive runners, casual runners, and fitness enthusiasts.Product variants and sales channels: According to the preceding clusters of potential customers, I could generate product variants so that they are more tailored toward specific audiences:Figure 7.5 – Example of variants of the product lineSimilarly, I can also ask ChatGPT to suggest different sales channels for each of the preceding groups:Figure 7.6 – Suggestions for different sales channels by ChatGPTStanding out from the competition: I want my product line to stand out from the competition and emerge in a very saturated market – I want to make it unique. With this purpose in mind, I asked ChatGPT to include social considerations such as sustainability and inclusivity. Let’s ask ChatGPT for some suggestions in that respect:Figure 7.7 – Example of outstanding features generated by ChatGPTAs you can see, it was able to generate interesting features that could make my product line unique.Product Description: Now it’s time to start building our GTP plan. First of all, I want to generate a product description to put on my website, including all the earlier unique differentiators.Figure 7.8 – Example of description and SEO keywords generated by ChatGPTFair price: Another key element is determining a fair price for our product. As I differentiated among product variants for different audiences (competitive runners, casual runners, and fitness enthusiasts), I also want to have a price range that takes into account this clustering.Figure 7.9 – Price ranges for product variantsWe are almost there. We have gone through many new product development and go-to-market steps, and in each of them, ChatGPT acted as a great support tool.As one last thing, we can ask ChatGPT to generate an Instagram post about our new product, including relevant hashtags and SEO keywords. We can then generate the image with DALL-E!Figure 7.10 – Social media post generated by ChatGPTAnd, with the special contribution of DALL-E, here is the final result:Figure 7.11 – Instagram post entirely generated by ChatGPT and DALL-EOf course, many elements are missing here for complete product development and go-to-market. Yet, with the support of ChatGPT (and the special contribution of DALL-E – you can try DALL-E on your own at https://openai.com/product/dall-e-2, we managed to brainstorm a new product line and variants, potential customers, catchy slogans, and finally, generated a pretty nice Instagram post to announce the launch of SprintSoles!ConclusionIn this article, we explored ways in which ChatGPT can be used by marketers to enhance their marketing strategies. We learned that ChatGPT can help in developing new products as well as defining their go-to-market strategy.The importance of ChatGPT for marketers lies in its potential to revolutionize the way companies engage with their customers. By leveraging the power of NLP, ML, and big data, ChatGPT allows companies to create more personalized and relevant marketing messages, improve customer support and satisfaction, and ultimately, drive sales and revenue.Author BioValentina Alto graduated in 2021 in data science. Since 2020, she has been working at Microsoft as an Azure solution specialist, and since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industry. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on modern data platforms, data mesh frameworks, IoT and real-time analytics, Azure Machine Learning, Azure Cognitive Services (including Azure OpenAI Service), and Power BI for dashboarding. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications and has authored a book on the fundamentals of machine learning with Python.

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, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAIIntroductionIn this article, we’ll focus on the enterprise-level applications of OpenAI models and introduce the partnership between OpenAI and Microsoft and Azure OpenAI (AOAI) Service. We will go through the milestones and developments of Microsoft in the field of artificial intelligence (AI), highlighting the journey that brought the Azure cloud into the game of OpenAI, and why this is a game-changer for large organizations. Finally, we will consider the topic of responsible AI and how to make sure your AI system complies with ethical standards.In this article, we will discuss the following topics:The history of the partnership between Microsoft and OpenAI and the introduction of AOAI ServiceThe role of the public cloud in the context of OpenAI models Responsible AITechnical requirementsThe following are the technical requirements for this article: An Azure subscription, which you can create for free here: https://azure.microsoft. com/free/cognitive-services.Access granted to Azure OpenAI in the desired Azure subscription. At the time of writing, access to this service is granted only by application. You can apply for access to Azure OpenAI by completing the form at https://aka.ms/oai/accessAzure OpenAI ServiceAOAI Service is a product of Microsoft that provides REST API access to OpenAI’s powerful language models such as GPT-3.5, Codex, and DALL-E. You can use these models for the very same tasks as OpenAI models, such as content generation, summarization, semantic search, natural language, and code translation.In the context of the Microsoft Azure AI portfolio, AOAI Service is collocated among the following Cognitive Services offerings:Figure - AOAI Service General Availability (GA)As with any other Cognitive Services offering, AOAI offers models that have already been trained and are ready to be consumed.To create your AOAI resource, follow these instructions:1.      Navigate to the Azure portal at https://ms.portal.azure.com.2.      Click on Create a resource.3.      Type azure openai and click on Create.4.      Fill in the required information and click on Review + create.This is shown in the following screenshot:Figure  – Steps to create an AOAI resourceThis process might take a few minutes. Once it is ready, you can directly jump to its user-friendly interface, AOAI Playground, to test your models before deploying them:Figure  – AOAI UI and PlaygroundNote that AOAI Playground looks almost identical to the OpenAI Playground version we saw in Chapter 2. The difference here is that, to use AOAI models, you have to initiate a deployment, which is a serverless compute instance you can attach to a model. You can do so either in Playground or on the resource backend page in the Azure portal:Figure – Creating a new AOAI deployment via Playground (A) or in the Azure portal (B)For example, I created a deployment called text-davinci-003 with an associated textdavinci-003 model:Figure 9.7 – An active deployment of AOAIIn OpenAI Playground, we can test those models either directly via the user interface or by embedding their APIs into our applications. In the next section, we are going to explore how to interact with Playground and try different models’ configurations. In Chapter 10, we will learn how to integrate AOAI’s Models API into enterprise applications.Exploring PlaygroundAOAI Playground is the easiest way to get familiar with the underlying models and start planning which model’s version is the most suitable for your projects. The user interface presents different tabs and workspaces, as shown in the following screenshot:Figure - Overview of AOAI PlaygroundLet’s explore each of them:Playground | Chat: The Chat workspace is designed to be only used with conversational models such as GPT-3.5-turbo (the model behind ChatGPT):Figure – AOAI Chat workspaceIt offers a similar experience to ChatGPT itself, with the possibility to configure your model with additional parameters (as we saw in Chapter 2 with OpenAI Playground). Furthermore, there is an additional feature that makes the Chat workspace very interesting, known as System message:         Figure – Example of System messageSystem message is the set of instructions we give the model to tell it how to behave and interact with us. As for the prompt, System message represents a key component of a model’s configuration since it massively affects model performance.For example, let’s instruct our model to behave as a JSON formatter assistant:Figure – Example of a model acting as a JSON formatter assistantAs you can see from the previous screenshot, the model was able to suggest a JSON file through some simple data, such as name and age, without the need to specify any labels.Playground | Completions: Different from the previous workspace, the Completions workspace offers a sort of white paper where you can interact with your models. While GPT-3.5-turbo is designed for conversational tasks (which means it can be consumed via a chatbot-like interface), the GPT-3 series contains more general-purpose models and can be used for a wide range of language tasks, such as content generation, summarization, and so on.For example, we could ask our model to generate a quiz by giving it a description of the topic and a one-shot example, as shown here:Figure – Example of a GPT model generating a quizFinally, as per the Chat workspace, with Completions, you can configure parameters such as the maximum number of tokens or the temperature (refer to Chapter 2 for a comprehensive list of those parameters and their meanings).Management | Deployments: Within the Deployments tab, you can create and manage new deployments to be associated with AOAI models. They are depicted here:Figure – List of AOAI deploymentsEach deployment can host only one model. You can edit or delete your deployments at any time. As we mentioned previously, a model deployment is the enabler step for using either the Completions or Chat workspace within AOAI Service.Management | Models: Within this tab, you can quickly assess the models that are available within AOAI Service and, among them, those that can be deployed (that is, a model that hasn’t been deployed yet). For example, let’s consider the following screenshot:Figure – List of AOAI modelsHere, we have text-similarity-curie-001. It doesn’t have an associated deployment, so it can be deployed (as the Deployable column shows). On the other hand, text-similarityada-002 already has a deployment, so it is not available anymore. Within this tab, you can also create a custom model by following a procedure called fine-tuning.We explored this in Chapter 2:Figure – Example of model fine-tuningStarting from this guided widget, you can upload your training and validation data to produce a customized model, starting from a base model (namely, text-davinci-002), which will be hosted on a dedicated deployment.NoteIn Chapter 2, we saw that the training dataset should align with a specific format of the following type (called JSONL):{"prompt": "<prompt text>", "completion": "<ideal generated text>"}{"prompt": "<prompt text>", "completion": "<ideal generated text>"}{"prompt": "<prompt text>", "completion": "<ideal generated text>"}...To facilitate this formatting, OpenAI has developed a tool that can format your data into this specific format ready for fine-tuning. It can also provide suggestions on how to modify data so that the tool can be used for fine-tuning. Plus, it accepts various data formats as inputs, including CSV, TXT, and JSON.To use this tool, you can initialize the OpenAI command-line interface (CLI) by running the following command: pip install --upgrade openaiOnce initialized, you can run the tool, as follows:openai tools fine_tunes.prepare_data -f <LOCAL_FILE>Management | File Management: Finally, within the File Management tab, you can govern and upload your training and test data directly from the user interface, as shown here:Figure – Example of uploading a file within AOAI ServiceYou can decide to upload files by selecting Local file or Azure blob or other shared web locations. Once you’ve uploaded your files, you will be able to select them while creating customized models, via the Models tab.Finally, as mentioned in the previous section, each model comes with a REST API that can be consumed in your applications.ConclusionIn this article, we saw how the partnership between OpenAI and Microsoft has brought about a powerful and innovative AI solution for enterprise-level organizations: AOAI. This service combines OpenAI’s cutting-edge technology with Microsoft’s extensive cloud infrastructure to provide businesses with a scalable and customizable platform for building and deploying advanced AI applications.We also dwelled on Microsoft’s strong focus on responsible AI practices and ethics, and how AOAI Service reflects this commitment to responsible AI, with features such as a content filter built into the platform.As AI continues to transform industries and shape our future, the collaboration between OpenAI and Microsoft marks an important milestone in the development of enterprise-level AI solutions. AOAI empowers businesses to harness the power of AI to drive growth and innovation while ensuring ethical and responsible practices.Author BioValentina Alto graduated in 2021 in data science. Since 2020, she has been working at Microsoft as an Azure solution specialist, and since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industry. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on modern data platforms, data mesh frameworks, IoT and real-time analytics, Azure Machine Learning, Azure Cognitive Services (including Azure OpenAI Service), and Power BI for dashboarding. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications and has authored a book on the fundamentals of machine learning with Python.

IntroductionDespite reading numerous forums and blogs, you feel unprepared for the technical questions that interviewers might throw at you. Isn't there a more efficient way?The countdown during a timed coding test.The watching eyes of an interviewer - make you blank out.Leading to regrets about not practicing in a more realistic setting.What if there was a tool available 24/7, that can not only answer your questions but also engage with you, much like an interviewer would?Enter ChatGPT, your personalized interview coach.Using ChatGPT for interview preparation is like having a personal trainer at the gym.While you can definitely exercise on your own, the trainer (or in this case, ChatGPT) guides you:It corrects your mistakes,Provides targeted exercises (solutions)Improve your weak areas.Let’s put ChatGPT to the test with a hands-on exercise:Setting up a mock interview for Senior Java RoleDoint theoretical and coding questionsGetting feedback for our answersBonus: We will also check if it can make my learning entertaining.Here are Little-Known Ways to Ace Your Dev Interview with ChatGPTStep 1: Set your levelChatGPT doesn't know your level, so we need to set proper expectationsStep 2: Setup rehears mock interviewNow, we need to let ChatGPT know that we are not interested in answers, but rather feedback.Here is the full prompt:I want to rehear for the senior java interview Currently, I am junior and have experience only with basics and helloworld examples don't show me the answer yet once I answer, check if I was correctHere is the result:ChatGPTOf course! Let's simulate a senior Java interview scenario. I'll provide questions that are often asked for a senior Java position. Try to answer them to the best of your ability, and then I'll provide feedback.Question 1: Explain the difference between ArrayList and LinkedList in Java.Go ahead and provide your answer when you're ready.UserI think arraylist uses dynamic array while linkedlists stores data in different objects and acts as a queueChatGPTYou've touched on the basics, but let's dive a bit deeper.ArrayList:Internal Structure: As you correctly mentioned, ArrayList uses a dynamic array to store its elements. This means its capacity grows automatically when we add more elements than its initial capacity.Access Time: Because of this structure, accessing an element by its index is fast, i.e., O(1).Insertion/Deletion Time: However, inserting or deleting an element from anywhere except the end of the list takes O(n) time on average, as it may require shifting elements.Memory Overhead: It's relatively low, as you're mostly dealing with the raw array and a few integer counters.LinkedList:Internal Structure: LinkedList uses a doubly-linked list. Each element (node) has a data part and two pointers, one pointing to the next element and the other to the previous element.Access Time: Getting to an element by index isn't as fast, since it requires walking through the list. It's O(n) in the worst case.Insertion/Deletion Time: However, once you have a reference to a node (say via an iterator), adding or removing is O(1), provided you don’t need to search for the node.Memory Overhead: Higher than ArrayList because of the two pointers with each data item.Now, one more nuanced point: ArrayList performs better in scenarios where you are frequently accessing elements via their indices and have fewer insertions/deletions. LinkedList is better suited for operations that involve frequent insertions or deletions from the beginning or middle of the list.You had a good start! Keep refining your understanding, and you'll be on your way to senior-level knowledge. Would you like to proceed with the next question?Step 3: Now, the coding testWe can now instruct ChatGPT to give us coding questiongive me one more question, this time coding questionChatGPTOf course! Here's a simpler coding problem:Question: Implement a method that takes an array of integers and returns the sum of all even numbers in the array.Method Signature:public int sumOfEvens(int[] numbers) { // your code here }Please go ahead and implement this, and I'll review your solution afterward!Userint sum = 0; for (int number : numbers) { if (number % 2 == 0) { sum += number; } } return sum;ChatGPTThat's correct! Your solution is concise and to the point.Here's the complete function for clarity: ….Step 4: Make it funTo spice up the interview a bit, let’s make chatGPT talk in a funny way so we can get some laughs along the wayPrompt:write every sentence in a speaking style of a fast-talking wise guy from 1940's gangster moviesChatGPTAlright, alright! I'll take it down a notch for you. No need to get all flustered.Question 3: Look here, champ, you ever heard of this thing called "Encapsulation"? It's one of those big fancy terms they throw around. Can you give me the lowdown on what it means in Java? Just the basics, ya know?Alright, give it your best shot! And remember, keep your chin up, kid!UserIt's basically means binding variables and methods together into a single unit and preventing them from being accessed by other classesChatGPTHey, not too shabby, kid! You've got the right idea. Encapsulation, in Java and many other object-oriented languages, is about keeping the internal states or data of an object private and only allowing modification through public methods. It's a way to protect the integrity of the data and control how it's accessed or modified. It's like having a vault, see? You don’t just let anyone waltz in and take whatever they want. You got guards, and specific ways to access what's inside.Good job, kiddo! Anything else you wanna test your chops on, or are we square?You can find the full chat on this link:https://chat.openai.com/share/b9f8af45-1187-4fa9-a884-4565f37d2884LimitationsKeep in mind that ChatGPT is not always correct, even though it can sound convincing.If you are unsure, always double-check your solution with a compiler for coding answers.ConclusionChatGPT is your ticket to mastering technical interviews.Using ChatGPT ensures you get direct, concise, and often up-to-date explanations on specific problems, making your interview preparation more efficient.Engage, learn, and get instant feedback to always stay a step ahead.As the next step, try it yourself and practice.Remember:Inform ChatGPT about your current skill level.If you are not happy with the questions or topic, give ChatGPT more context.Lastly, make it more fun.Author BioJakov Semenski is an IT Architect working at IBMiX with almost 20 years of experience.He is also a ChatGPT Speaker at the WeAreDevelopers conference and shares valuable tech stories on LinkedIn.

IntroductionImagine constructing a house without a blueprint.Sounds chaotic, right?Similarly, diving into software coding without a UML or sequence diagram is like building that house blindly.Just as architects rely on blueprints, developers can rely on diagrams to build a clear project architecture that guides during the coding phase.It paints a clear roadmap of what needs to be developed.It ensures everyone is on the same page.It saves time during the execution phase.Unfortunately, this phase is often overlooked.It takes time, and you quickly get overwhelmed with so many tools and ways to sketch out diagrams.Now, imagine you can quickly draft diagrams, even during team meetings, so you can visualize complex workflows on the fly.This is what we will cover today.I will share a step-by-step guide on the Little-Known secret of automating diagram creation with ChatGPT (and it can be done all with a free version)Here's how step-by-step:Step 1: Create a sample applicationThe first thing we need is an application.For this example, I will use the following prompt to generate a Java Rest application for borrowing technical books.Create a Java rest application based on Spring Boot that allows borrowing technical books to users admins have dedicated role that are able to add books, remove books users and admins can log in by using Google OAuth as an authentication provider As the first step, show me the directory structureSince I am not interested in code but still curious about how the project would look, I have added.As the first step, show me the directory structure.ChatGPT will also give an explanation for each class.If this is something you are not interested in, just add the word "only".As the first step, show me only the directory structure.Using ASCII art, ChatGPT will present a directory structure.Hint: Sometimes chatGPT will "glitch" meaning it won't give you the full result, so just hit "continue" to get the entire output.Step 2: Quick and dirty way to create a diagram in ChatGPTNow, it's time to visualize the UML diagram.Ask ChatGpt to create a UML and sequence diagram.Prompt:*Create a UML and Sequence diagram using ASCII*You will get an ASCII representation.Do you see the problem?First, there is the limitation of using text to visualize.Second, good luck with maintaining these.Step 3: Using the right tools to create UMLTo get things further, let's use the proper tools.There are many tools on the market you can use to create UML. However, not all can be represented as text.PlantUML and MermaId are both tools that use specific diagram notations in which we can render our final diagram.Personally, I prefer MermaId as it is simpler to useNow we can use the same prompt, but this time, ask for output for MermaidUsing generated directory structure, generate UML diagram for MermaId toolNow copy the output to the MermaId tool: https://mermaid.live/classDiagram    class Application {        + main(args: String[]): void    }    class WebSecurityConfig {        <<configuration>>    }    class OAuth2Config {        <<configuration>>    }    class BookController {        + addBook(bookDTO: BookDTO): ResponseEntity<?>        + removeBook(bookId: Long): ResponseEntity<?>    }    class UserController {        + addUser(userDTO: UserDTO): ResponseEntity<?>        + removeUser(userId: Long): ResponseEntity<?>    }    class AuthController {        + loginUser(): ResponseEntity<?>    }    class Book {        - id: Long        - title: String        - author: String        - available: boolean    }    class User {        - id: Long        - username: String        - password: String        - email: String        - roles: Set<UserRole>    }    class UserRole {        <<enumeration>>        - USER        - ADMIN    }    class BookRepository {        <<interface>>    }    class UserRepository {        <<interface>>    }    class BookService {        + addBook(bookDTO: BookDTO): Book        + removeBook(bookId: Long): void    }    class UserService {        + addUser(userDTO: UserDTO): User        + removeUser(userId: Long): void    }    class UserDetailsServiceImpl {        <<service>>    }    class BookDTO {        - title: String        - author: String    }    class UserDTO {        - username: String        - password: String        - email: String    }    Application --|> WebSecurityConfig    Application --|> OAuth2Config    BookController --|> Book    UserController --|> User    AuthController --|> User    Book -- BookRepository    User -- UserRepository    BookController --|> BookService    UserController --|> UserService    UserService -- UserDetailsServiceImpl    BookController --> BookDTO    UserController --> UserDTOAnd here is the final result.Step 4: Create sequence diagramA sequence diagram, also known as a swimlane diagram, is a type of diagram that visually shows request flow.Imagine an actual swimming pool divided into lanes. Each lane is dedicated to a specific swimmer (e..g class file)Now, use the following prompt to get the swimlane diagram.Create a sequence diagram for Mermaid.Copy the output to Mermaid.Here is the output:sequenceDiagram    participant User as User    participant AuthController as "Auth Controller"    participant OAuth2Config as "OAuth2 Config"    participant UserDetailsServiceImpl as "User Details Service"    participant GoogleOAuth as "Google OAuth Provider"    participant UserDatabase as "User Database"    User ->> AuthController: Initiate Login    AuthController ->> OAuth2Config: Forward to OAuth2 Provider    OAuth2Config ->> GoogleOAuth: Send OAuth Request    GoogleOAuth -->> OAuth2Config: Receive OAuth Response    OAuth2Config -->> AuthController: Receive OAuth Response    AuthController ->> UserDetailsServiceImpl: Load User Details    UserDetailsServiceImpl ->> UserDatabase: Retrieve User Info    UserDatabase -->> UserDetailsServiceImpl: User Info    UserDetailsServiceImpl -->> AuthController: User Details    AuthController -->> User: Authentication SuccessfulHere is the full conversation with ChatGPT 3.5:https://chat.openai.com/share/70157733-da64-4a12-b15b-3607f4d1afe9Step 5: Making things even more fasterEverything we have done can now be compiled into a Mega prompt, compile all the inputs into a 1 mega prompt, and just replace the content under App descriptions.For a given app description APP DESCRIPTION START Spring boot rest application that stores reservations APP DESCRIPTION END Create directory structure, then create UML and sequence diagram for Mermaid tool using it's own syntaxBonus: ChatGPT Plus users onlyIf you are on a ChatGTP Plus subscription, you get several benefits apart from the obvious GPT4.First, ChatGPT4 itself gives you a nice text output, including some nice emojis.Prompt:Create a Java rest application based on Spring Boot that allows borrowing technical books to users admins have dedicated role that are able to add books, remove books users and admins can log in by using Google OAuth as an authentication provider As the first step, show me the directory structure with file names, use emojis to represent different content typeSecond, to speed up chart creation, you can use 2 plugins:GitHub plugin “AskTheCode” (which lets you scan the GitHub repository)Draw plugin “Mermaid Chart” -> generates diagrams for you and displays images directly as part of the chat.ConclusionPretty powerful, huh?Traditional methods of creating UML and sequence diagrams are much more time-consuming.Imagine how much time we just saved.By using this approach, you'll not only save time but get valuable insight into your architecture.Feel free to use these prompts, tweak them, and make them your own.If you want to get systems like these, please connect and reach out to me over Linkedin.Author bioJakov Semenski is an IT Architect working at IBMiX with almost 20 years of experience.He is also a ChatGPT Speaker at WeAreDevelopers conference and shares valuable tech stories on LinkedIn

👋 Hello ,“Artificial intelligence is one of the most profound things we're working on as humanity. It is more profound than fire or electricity.” -Sundar Pichai, Google CEO  Pichai's AI-fire analogy signifies a transformative era; AI and ML will revolutionize education, medicine, and more, reshaping human progress. OpenAI has begun promoting the use of ChatGPT in education, which shouldn’t really come as a surprise as students the world over have been experimenting with the technology. Get ready to dive into the latest AI developments in this edition, AI_Distilled #16, including Baidu launching Ernie chatbot following Chinese government approval, X's Privacy Policy Reveals Plan to Use Public Data for AI Training, Meta releasing FACET Dataset to evaluate AI model fairness, Google’s new Multislice for scalable AI training on cloud TPUs, and Qualcomm's focus on AI and auto amidst NVIDIA's chip dominance. Watch out also for our handpicked collection of fresh AI, GPT, and LLM-focused secret knowledge and tutorials from around the web covering Liquid Neural Networks, Serverless Machine Learning with Amazon Redshift ML, implementing effective guardrails for LLMs, Navigating Generative AI with FMOps and LLMOps, and using Microsoft’s new AI compiler quartet. What do you think of this issue and our newsletter? Please consider taking the short survey below to share your thoughts and you will get a free PDF of the “The Applied Artificial Intelligence Workshop” eBook upon completion. Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt  ⚡ TechWave: AI/GPT News & AnalysisMeta Releases FACET Dataset to Evaluate AI Model Fairness: Meta has launched FACET (FAirness in Computer Vision EvaluaTion), a dataset designed to assess the fairness of AI models used for image and video classification, including identifying people. Comprising 32,000 images with 50,000 labeled individuals, FACET includes demographic and physical attributes, allowing for deep evaluations of biases against various classes. Despite previous concerns about Meta's responsible AI practices, the company claims FACET is more comprehensive than previous bias benchmarks. However, concerns have been raised about the dataset's origins and the compensation of annotators. Meta has also released a web-based dataset explorer tool for FACET. You can read the full paper here. Baidu Launches Ernie Chatbot Following Chinese Government Approval: Chinese tech giant Baidu has unveiled its chatbot, Ernie Bot, after receiving government clearance, along with other AI firms. Ernie Bot is now accessible for download via app stores or Baidu's website. Similar to its rival, ChatGPT, users can engage Ernie Bot for queries, market analysis assistance, marketing slogan ideas, and document summaries. While it's accessible globally, registration requires a Chinese number, and the app is only in Chinese on US Android and iOS stores. Baidu has also introduced a plug-in market for Ernie Bot, which quickly garnered over 1 million users within 19 hours of launch. CEO Robin Li expressed plans for further AI-native apps aimed at exploring generative AI's core abilities. Google Introduces Multislice for Scalable AI Training on Cloud TPUs: Google has unveiled Multislice, a comprehensive large-scale training technology that facilitates straightforward, cost-effective, and nearly linear scaling to tens of thousands of Cloud Tensor Processing Units (TPUs) chips. Traditionally, a training run was restricted to a single slice, which meant a maximum of 3072 TPU v4 chips could be used. With Multislice, training can span multiple slices across pods through data center networking, eliminating these limitations. This innovation offers benefits such as efficient scaling for massive models, enhanced developer productivity, automatic compiler optimizations, and cost-efficiency. It promises to revolutionize AI infrastructure by enabling near-linear scaling for AI supercomputing. OpenAI Promotes Use of ChatGPT in Education: OpenAI is encouraging educators to utilize ChatGPT in classrooms. The company showcased six educators, primarily at the university level, using ChatGPT for various purposes, such as role-playing in debates, aiding translation for English-as-a-second-language students, and fact-checking. Despite some schools banning ChatGPT due to concerns about academic integrity, OpenAI believes it can be a valuable tool in education. However, it emphasizes the importance of maintaining human oversight in the assessment process. X's Privacy Policy Reveals Plan to Use Public Data for AI Training: In an update to its privacy policy, X (formerly Twitter) has informed users that it will now collect biometric data, job histories, and education backgrounds. However, another section of the policy reveals a broader plan: X intends to utilize the data it gathers, along with publicly available information, to train its machine learning and AI models. This revelation has attracted attention, particularly due to the connection with X owner Elon Musk's ambitions in the AI market through his company xAI. Musk confirmed the privacy policy change, emphasizing that only public data, not private messages, would be used for AI training.   Qualcomm's Focus on AI and Auto Amidst NVIDIA’s Chip Dominance: As NVIDIA takes the lead as the world's largest fabless chip company, Qualcomm is strategically positioning itself in the AI realm. The company has unveiled in-vehicle generative AI capabilities, expanded into two-wheelers, and forged a partnership with Amazon Web Services. Qualcomm's CEO, Cristiano Amon, believes that generative AI, currently reliant on cloud resources, will transition to local execution, enhancing performance and cost-efficiency. Diversification is also a priority, with Qualcomm's chips powering various smart devices, especially in the automotive sector. Amid uncertainty about its future relationship with Apple, Qualcomm aims to maintain its dominance through innovations in AI and auto tech. InteRecAgent, A Fusion of Language Models and Recommender Systems Introduced: Researchers from the University of Science and Technology of China, in collaboration with Microsoft Research Asia, have introduced InteRecAgent, a cutting-edge framework. This innovation seeks to combine the interactive capabilities of LLMs with the domain-specific precision of traditional recommender systems. Recommender systems play a vital role in various digital domains, but they often struggle with versatile interactions. On the other hand, LLMs excel in conversations but lack domain-specific knowledge. InteRecAgent introduces the "Candidate Memory Bus" to streamline recommendations for LLMs and a "Plan-first Execution with Dynamic Demonstrations" strategy for effective tool interaction. adidas Utilizes AI and NVIDIA RTX for Photorealistic 3D Content: Sportswear giant adidas is partnering with Covision Media, an Italian startup, to revolutionize their online shopping experience. Covision employs AI and NVIDIA RTX technology to develop 3D scanners that allow businesses to create digital twins of their products with stunning realism. This technology can quickly generate 3D scans, capturing textures, colors, and geometry, resulting in lifelike images. adidas is among the first to adopt this technology for automating and scaling e-commerce content production, enhancing their Virtual Try-On feature and replacing traditional product photography with computer-generated content.  🔮 Expert Insights from Packt CommunityServerless Machine Learning with Amazon Redshift ML - By Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet JoshiData analysts and developers use Redshift data with machine learning (ML) models for tasks such as predicting customer behavior. Amazon Redshift ML streamlines this process using familiar SQL commands. A conundrum arises when attempting to decipher these data silos – a formidable challenge that hampers the derivation of meaningful insights essential for organizational clarity. Adding to this complexity, security and performance considerations typically prevent business analysts from accessing data within OLTP systems. The hiccup is that intricate analytical queries weigh down OLTP databases, casting a shadow over their core operations. Here, the solution is the data warehouse, which is a central hub of curated data, used by business analysts and data scientists to make informed decisions by employing the business intelligence and machine learning tools at their disposal. These users make use of Structured Query Language (SQL) to derive insights from this data trove. Here’s where Amazon Redshift Serverless comes in. It’s a key option within Amazon Redshift, a well-managed cloud data warehouse offered by Amazon Web Services (AWS). With cloud-based ease, Amazon Redshift Serverless lets you set up your data storage without infrastructure hassles or cost worries. You pay based on what you use for compute and storage. Amazon Redshift Serverless goes beyond convenience, propelling modern data applications that seamlessly connect to the data lake. The above content is extracted from the book Serverless Machine Learning with Amazon Redshift ML written by Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi and published in Aug 2023. To get a glimpse of the book's contents, make sure to read the free chapter provided here, or if you want to unlock the full Packt digital library free for 7 days, try signing up now! To learn more, click on the button below. Keep Calm, Start Reading! 🌟 Secret Knowledge: AI/LLM ResourcesUnderstanding Liquid Neural Networks: A Primer on AI Advancements: In this post, you'll learn how liquid neural networks are transforming the AI landscape. These networks, inspired by the human brain, offer a unique and creative approach to problem-solving. They excel in complex tasks such as weather prediction, stock market analysis, and speech recognition. Unlike traditional neural networks, liquid neural networks require significantly fewer neurons, making them ideal for resource-constrained environments like autonomous vehicles. These networks excel in handling continuous data streams but may not be suitable for static data. They also provide better causality handling and interpretability. Navigating Generative AI with FMOps and LLMOps: A Practical Guide: In this informative post, you'll gain valuable insights into the world of generative AI and its operationalization using FMOps and LLMOps principles. The authors delve into the challenges businesses face when integrating generative AI into their operations. You'll explore the fundamental differences between traditional MLOps and these emerging concepts. The post outlines the roles various teams play in this process, from data engineers to data scientists, ML engineers, and product owners. The guide provides a roadmap for businesses looking to embrace generative AI. AI Compiler Quartet: A Breakdown of Cutting-Edge Technologies: Explore Microsoft’s groundbreaking "heavy-metal quartet" of AI compilers: Rammer, Roller, Welder, and Grinder. These compilers address the evolving challenges posed by AI models and hardware. Rammer focuses on optimizing deep neural network (DNN) computations, improving hardware parallel utilization. Roller tackles the challenge of memory partitioning and optimization, enabling faster compilation with good computation efficiency. Welder optimizes memory access, particularly vital as AI models become more memory-intensive. Grinder addresses complex control flow execution in AI computation. These AI compilers collectively offer innovative solutions for parallelism, compilation efficiency, memory, and control flow, shaping the future of AI model optimization and compilation.  💡 MasterClass: AI/LLM Tutorials Exploring IoT Data Simulation with ChatGPT and MQTTX: In this comprehensive guide, you'll learn how to harness the power of AI, specifically ChatGPT, and the MQTT client tool, MQTTX, to simulate and generate authentic IoT data streams. Discover why simulating IoT data is crucial for system verification, customer experience enhancement, performance assessment, and rapid prototype design. The article dives into the integration of ChatGPT and MQTTX, introducing the "Candidate Memory Bus" to streamline data testing. Follow the step-by-step guide to create simulation scripts with ChatGPT and efficiently simulate data transmission with MQTTX.  Revolutionizing Real-time Inference: SageMaker Unveils Streaming Support for Generative AI: Amazon SageMaker now offers real-time response streaming, transforming generative AI applications. This new feature enables continuous response streaming to clients, reducing time-to-first-byte and enhancing interactive experiences for chatbots, virtual assistants, and music generators. The post guides you through building a streaming web application using SageMaker real-time endpoints for interactive chat use cases. It showcases deployment options with AWS Large Model Inference (LMI) and Hugging Face Text Generation Inference (TGI) containers, providing a seamless, engaging conversation experience for users. Implementing Effective Guardrails for Large Language Models: Guardrails are crucial for maintaining trust in LLM applications as they ensure compliance with defined principles. This guide presents two open-source tools for implementing LLM guardrails: Guardrails AI and NVIDIA NeMo-Guardrails. Guardrails AI offers Python-based validation of LLM responses, using the RAIL specification. It enables developers to define output criteria and corrective actions, with step-by-step instructions for implementation. NVIDIA NeMo-Guardrails introduces Colang, a modeling language for flexible conversational workflows. The guide explains its syntax elements and event-driven design. Comparing the two, Guardrails AI suits simple tasks, while NeMo-Guardrails excels in defining advanced conversational guidelines. 🚀 HackHub: Trending AI Toolscabralpinto/modular-diffusion: Python library for crafting and training personalized Diffusion Models with PyTorch.  cofactoryai/textbase: Simplified Python chatbot development using NLP and ML with Textbase's on_message function in main.py. microsoft/BatteryML: Open-source ML tool for battery analysis, aiding researchers in understanding electrochemical processes and predicting battery degradation. facebookresearch/co-tracker: Swift transformer-based video tracker with Optical Flow, pixel-level tracking, grid sampling, and manual point selection. explodinggradients/ragas: Framework evaluates Retrieval Augmented Generation pipelines, enhancing LLM context with external data using research-based tools. 

IntroductionExploratory data analysis (EDA) refers to the initial investigation of data to discover patterns, identify outliers and anomalies, test hypotheses, and check assumptions with the goal of informing future analysis and model building. It is an iterative, exploratory process of questioning, analyzing, and visualizing data.Some key aspects of exploratory data analysis include:Getting to know the data - Examining individual variables, their values, distributions, and relationships between variables.Data cleaning - Checking and handling missing values, outliers, formatting inconsistencies, etc., before further analysis.Univariate analysis - Looking at one variable at a time to understand its distribution, central tendency, spread, outliers, etc.Bivariate analysis - Examining relationships between two variables using graphs, charts, and statistical tests. This helps find correlations.Multivariate analysis - Analyzing patterns between three or more variables simultaneously using techniques like cluster analysis.Hypothesis generation - Coming up with potential explanations or hypotheses about relationships in the data based on initial findings.Data visualization - Creating graphs, plots, and charts to summarize findings and detect patterns and anomalies more easily.The goals of EDA are to understand the dataset, detect useful patterns, formulate hypotheses, and make decisions on how to prepare/preprocess the data for subsequent modeling and analysis. It is an iterative, exploratory process of questioning, analyzing, and visualizing data.Why ChatGPT for EDA?Exploratory data analysis (EDA) is an important but often tedious process with challenges and pitfalls. The use of ChatGPT saves hours on repetitive tasks. ChatGPT handles preparatory data wrangling, exploration, and documentation - freeing you to focus on insights. Its capabilities will only grow through continued learning. Soon, it may autonomously profile datasets and propose multiple exploratory avenues. ChatGPT is the perfect on-demand assistant for solo data scientists and teams seeking an effortless boost to the EDA process. The drawback of ChatGPT is it can only handle small datasets. There are a few methods like handling smaller datasets and generating Python code to do the necessary analysis.The following table provides detailed challenges/pitfalls during EDA:Challenge/PitfallDetailsGetting lost in the weedsSpending too much time on minor details without focusing on the big picture. This leads to analysis paralysis.Premature conclusionsDrawing conclusions without considering all possible factors or testing different hypotheses thoroughly.BiasPersonal biases, preconceptions or domain expertise can skew analysis in a particular direction.Multiple comparisonsTesting many hypotheses without adjusting for Type 1 errors, leading to false discoveries.DocumentationFailing to properly document methods, assumptions, and thought processes along the way.Lack of focusJumping randomly without a clear understanding of the business objective.Ignoring outliersNot handling outliers appropriately, can distort analysis and patterns.Correlation vs causationIncorrectly inferring causation based only on observed correlations.OverfittingFinding patterns in sample data that may not generalize to new data.Publication biasOnly focusing on publishable significant or "interesting" findings.Multiple rolesWearing data analyst and subject expert hats, mixing subjective and objective analysis. With ChatGPT, get an AI assistant to be your co-pilot on the journey of discovery. ChatGPT can provide EDA at various stages of your data analysis within the limits that we discussed earlier. The following table provides different stages of data analysis with prompts (these prompts either generate the output or Python code for you to execute separately):Type of EDAPromptSummary StatisticsDescribe the structure and summary statistics of this dataset. Check for any anomalies in variable distributions or outliers.Univariate AnalysisCreate histograms and density plots of each numeric variable to visualize their distributions and identify any unusual shapes or concentrations of outliers.Bivariate AnalysisGenerate a correlation matrix and heatmap to examine relationships between variables. Flag any extremely high correlations that could indicate multicollinearity issues.Dimensionality ReductionUse PCA to reduce the dimensions of this high-dimensional dataset and project it into 2D. Do any clusters or groupings emerge that provide new insights?ClusteringApply K-Means clustering on the standardized dataset with different values of k. Interpret the resulting clusters and check if they reveal any meaningful segments or categories.Text AnalysisSummarize the topics and sentiments discussed in this text column using topic modeling algorithms like LDA. Do any dominant themes or opinions stand out?Anomaly DetectionImplement an isolation forest algorithm on the dataset to detect outliers independently in each variable. Flag and analyze any suspicious or influential data points.Model PrototypingQuickly prototype different supervised learning algorithms like logistic regression, decision trees, random forest on this classification dataset. Compare their performance and feature importance.Model EvaluationGenerate a correlation matrix between predicted vs actual values from different models. Any low correlations potentially indicate nonlinear patterns worth exploring further.Report GenerationAutogenerate a Jupyter notebook report with key visualizations, findings, concentrations, and recommendations for the next steps based on the exploratory analyses performed.How do we feed data to ChatGPT for EDA?Describe your dataset through natural language prompts, and ChatGPT instantly runs analyses to find hidden insights. No need to write code - let the AI do the heavy lifting! For this article, let’s use the CSV file available at: (https://media.githubusercontent.com/media/datablist/sample-csv-files/main/files/organizations/organizations-1000.csv) (http://tinyurl.com/mphebj4k)Here are some examples of how ChatGPT can be used for exploratory data analysis:Prompts:Describe the structure and summary statistics of this CSV file: [Pasted URL or file contents]What variable types are in this DataFrame? Import Pandas and show column data types.Generate a correlation matrix and heatmap for these variables.Check for missing values in each column and calculate the percentage missing.Create a histogram to visualize the distribution of this numeric variable.Compare the means of this categorical variable across groups using a boxplot.In the below example, I pasted two hundred rows and see the prompt I gave.This resulted in the following.Look at the column details it provided. Asking for the list of categorical and numerical variables in table format produces the below:Asking for a statistical summary of numerical columns would produce the following:Python Code generation:For certain aspects of EDA, ChatGPT produces Python code with pandas library import and the result will be shown like this:import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # Load the CSV file from the URL into a DataFrame url = "https://media.githubusercontent.com/media/datablist/sample-csv-files/main/files/organizations/organizations-1000.csv" df = pd.read_csv(url) # Display basic information about the DataFrame print("Basic Info About the DataFrame:") print(df.info()) # Display the first few rows of the DataFrame print("\nFirst Few Rows of the DataFrame:") print(df.head()) # Summary statistics of numerical columns print("\nSummary Statistics of Numerical Columns:") print(df.describe()) # Check for missing values print("\nMissing Values:") print(df.isnull().sum()) # Visualize data # Example: Histogram of a numerical column (replace 'col_name' with the column name you want to plot) # plt.hist(df['col_name'], bins=20) # plt.xlabel('X-axis Label') # plt.ylabel('Y-axis Label') # plt.title('Histogram of col_name') # plt.show() # You can create more visualizations and explore relationships between columns as needed. # Correlation matrix heatmap (for numerical columns) correlation_matrix = df.corr() plt.figure(figsize=(10, 8)) sns.heatmap(correlation_matrix, annot=True, cmap="coolwarm", fmt=".2f") plt.title("Correlation Matrix Heatmap") plt.show()Running this in Spyder (Anaconda UI) produces the following output without a single error: EDA on large datasets with millions of recordsAs mentioned earlier in this article, ChatGPT is very powerful for its size, but there are still limitations since it runs on general consumer hardware rather than massive server clusters. Here are a few things to keep in mind regarding its capabilities with large datasets:ChatGPT works best for datasets under 50-100MB in size. It can handle some operations on larger files up to 1GB, but performance will degrade.For initial exploration of very large datasets, ChatGPT is still useful. It can quickly summarize dimensions, types, distributions, outliers, etc., to help shape hypotheses.Advanced analytics like complex multi-variable modeling may not be feasible on the largest datasets directly in ChatGPT.However, it can help with the data prep - filtering, aggregations, feature engineering, etc. to reduce a large dataset into a more manageable sample for detailed analysis.Integration with tools that can load large datasets directly (e.g., BigQuery, Spark, Redshift) allows ChatGPT to provide insights on files too big to import wholesale.As AI capabilities continue advancing, future versions powered by more computing may be able to handle larger files for a broader set of analytics tasks.ConclusionChatGPT revolutionizes Exploratory Data Analysis (EDA) by streamlining the process and making it accessible to a wider audience. EDA is crucial for understanding data, and ChatGPT automates tasks like generating statistics, visualizations, and even code, simplifying the process.ChatGPT's natural language interface enables users to interact with data using plain language, eliminating the need for extensive coding skills. While it excels in initial exploration and data preparation, it may have limitations with large datasets or complex modeling tasks. ChatGPT is a valuable EDA companion, empowering data professionals to uncover insights and make data-driven decisions efficiently. ChatGPT's role in data analytics is expected to expand as AI technology evolves, offering even more support for data-driven decision-making.Author BioRama Kattunga has been working with data for over 15 years at tech giants like Microsoft, Intel, and Samsung. As a geek and a business wonk with degrees from Kellogg and two technology degrees from India, Rama uses his engineering know-how and strategy savvy to get stuff done with analytics, AI, and unlocking insights from massive datasets. When he is not analyzing data, you can find Rama sharing his thoughts as an author, speaker, and digital transformation specialist. Moreover, Rama also finds joy in experimenting with cooking, using videos as his guide to create delicious dishes that he can share with others. This diverse range of interests and skills highlights his well-rounded and dynamic character. LinkedIn

IntroductionMeet ChatGPT: OpenAI's marvelously verbose chatbot, trained on a veritable Everest of text and code. Think of it as your go-to digital polymath, fluent in language translation, a whiz at whipping up creative content, and ever-eager to dispense knowledge on everything from quantum physics to quinoa recipes. Ready to dial in the healthcare lens? This article is your rollercoaster ride through the trials, triumphs, and tangled ethical conundrums of ChatGPT in medicine. From game-changing potential to challenges as stubborn as symptoms, we've got it all. So whether you're a seasoned healthcare pro or a tech-savvy newbie, buckle up. Will ChatGPT be healthcare's new MVP or get benched? Stick around, and let's find out together. Doctor in Your Pocket? Unpacking the Potential of ChatGPT in Healthcare Modern healthcare always seeks innovation to make things smoother and more personal. Enter ChatGPT. While not a stand-in for a doctor, this text-based AI is causing ripples from customer service to content. Below are various scenarios where ChatGPT can be leveraged in its original form or fine-tuned APIs. Pre-Consultation Screeners - ChatGPT-Enabled Triage Before conversational AI, healthcare looked into computational diagnostic aids like the 1960s' Dendral, initially for mass spectrometry, inspiring later medical systems. The 1970s brought MYCIN, designed for diagnosing bacterial infections and suggesting antibiotics. However, these early systems used inflexible "if-then" rules and lacked adaptability for nuanced human interaction. Fast-forward to today's more sophisticated digital triage platforms, and we still find remnants of these rule-based systems. While significantly more advanced, many of these platforms operate within the bounds of scripted pathways, leading to linear and often inflexible patient interactions. This rigidity can result in an inadequate capture of patient nuances, a critical aspect often needed for effective medical triage. The ChatGPT Advantage: Flexibility and Natural Engagement ChatGPT is a conversational agent with the capacity for more flexible, natural interactions due to its advanced Natural Language Understanding (NLU). Unlike conventional scanners with limited predefined pathways, ChatGPT can adapt to a broader range of patient inputs, making the pre-consultation phase more dynamic and patient-centric. It offers: Adaptive Questioning: Unlike traditional systems that follow a strict query pathway, ChatGPT can adapt its questions based on prior patient responses, potentially unearthing critical details. Contextual Understanding: Its advanced NLU allows it to understand colloquial language, idioms, and contextual cues that more rigid systems may miss. Data Synthesis: ChatGPT's ability to process and summarise information can result in a more cohesive pre-consultation report for healthcare providers, aiding in a more effective diagnosis and treatment strategy. Using LLMs bots like ChatGPT offers a more dynamic, flexible, and engaging approach to pre-consultation screening, optimising patient experience and healthcare provider efficacy. Below is a sample code that you can use to play around: import openai import os # Initialize OpenAI API Client api_key = os.environ.get("OPENAI_API_KEY")  # Retrieve the API key from environment variables openai.api_key = api_key  # Set the API key # Prepare the list of messages messages = [ {"role": "system", "content": "You are a pre-consultation healthcare screener. Assist the user in gathering basic symptoms before their doctor visit."}, {"role": "user", "content": "I've been feeling exhausted lately and have frequent headaches."} ] # API parameters model = "gpt-3.5-turbo"  # Choose the appropriate engine max_tokens = 150  # Limit the response length # Make API call response = openai.ChatCompletion.create( model=model, messages=messages ) # Extract and print chatbot's reply chatbot_reply = response['choices'][0]['message']['content'] print("ChatGPT: ", chatbot_reply) And here is the ChatGPT response: Mental Health Companionship The escalating demand for mental health services has increased focus on employing technology as supplemental support. While it is imperative to clarify that ChatGPT is not a substitute for qualified mental health practitioners, the platform can serve as an initial point of contact for individuals experiencing non-critical emotional distress or minor stress and anxiety. Utilizing advanced NLU and fine-tuned algorithms, ChatGPT provides an opportunity for immediate emotional support, particularly during non-operational hours when traditional services may be inaccessible. ChatGPT can be fine-tuned to handle the sensitivities inherent in mental health discussions, thereby adhering to ethically responsible boundaries while providing immediate, albeit preliminary, support. ChatGPT offers real-time text support, serving as a bridge to professional help. Its advanced NLU understands emotional nuances, ensuring personalized interactions. Beyond this, ChatGPT recommends vetted mental health resources and coping techniques. For instance, if you're anxious outside clinical hours, it suggests immediate stress management tactics. And if you're hesitant about professional consultation, ChatGPT helps guide and reassure your decision. Let us now see, how by just changing the prompt we can use the same code as that of ChatGPT enabled triage to build a mental health companion: messages = [ {        "role": "system",        "content": "You are a virtual mental health companion. Your primary role is to provide a supportive environment for the user. Listen actively, offer general coping strategies, and identify emotional patterns or concerns. Remember, you cannot replace professional mental health care, but can act as an interim resource. Always prioritise the user's safety and recommend seeking professional help if the need arises. Be aware of various emotional and mental scenarios, from stress and anxiety to deeper emotional concerns. Remain non-judgmental, empathetic, and consistently supportive."    }, {    "role": "user",    "content": "I've had a long and stressful day at work. Sometimes, it just feels like everything is piling up and I can't catch a break. I need some strategies to unwind and relax." } ] And here is the golden advice from ChatGPT:  Providing immediate emotional support and resource guidance can be a preliminary touchpoint for those dealing with minor stress and anxiety, particularly when conventional support mechanisms are unavailable. Virtual Health Assistants  In the evolving healthcare landscape, automation and artificial intelligence (AI) are increasingly being leveraged to enhance efficiency and patient care. One such application is the utilization of Virtual Health Assistants, designed to manage administrative overhead and provide informational support empathetically. The integration of ChatGPT via OpenAI's API into telehealth platforms signifies a significant advancement in this domain, offering capabilities far surpassing traditional rule-based or keyword-driven virtual assistants. ChatGPT boasts a customizable framework ideal for healthcare, characterized by its contextual adaptability for personalized user experiences, vast informational accuracy, and multi-functional capability that interfaces with digital health tools while upholding medical guidelines. In contrast, traditional Virtual Health Assistants, reliant on rule-based systems, suffer from scalability issues, rigid interactions, and a narrow functional scope. ChatGPT stands out by simplifying medical jargon, automating administrative chores, and ensuring a seamless healthcare journey—bridging pre-consultation to post-treatment, all by synthesizing data from diverse health platforms. Now, let's explore how tweaking the prompt allows us to repurpose the previous code to create a virtual health assistant. messages = [ {    "role": "system",    "content": "You are a Virtual Health Assistant (VHA). Your primary function is to assist users in navigating the healthcare landscape. Offer guidance on general health queries, facilitate appointment scheduling, and provide informational insights on medical terminologies. While you're equipped with a broad knowledge base, it's crucial to remind users that your responses are not a substitute for professional medical advice or diagnosis. Prioritise user safety, and when in doubt, recommend that they seek direct consultation from healthcare professionals. Be empathetic, patient-centric, and uphold the highest standards of medical data privacy and security in every interaction." }, {    "role": "user",    "content": "The doctor has recommended an Intestinal Perforation Surgery for me, scheduled for Sunday. I'm quite anxious about it. How can I best prepare mentally and physically?" } ] Straight from ChatGPT's treasure trove of advice:  So there you have it. Virtual Health Assistants might not have a medical degree, but they offer the next best thing: a responsive, informative, and competent digital sidekick to guide you through the healthcare labyrinth, leaving doctors free to focus on what really matters—your health. Key Contributions Patient Engagement: Utilising advanced Natural Language Understanding (NLU) capabilities, ChatGPT can facilitate more nuanced and personalised interactions, thus enriching the overall patient experience. Administrative Efficiency: ChatGPT can significantly mitigate the administrative load on healthcare staff by automating routine tasks such as appointment scheduling and informational queries. Preventative Measures: While not a diagnostic tool, ChatGPT's capacity to provide general health information and recommend further professional consultation can contribute indirectly to early preventative care. Potential Concerns and Solutions Data Security and Privacy: ChatGPT, in its current form, does not fully meet healthcare data security requirements. Solution: For HIPAA compliance, advanced encryption, and secure API interfaces must be implemented. Clinical Misinformation: While ChatGPT can provide general advice, there are limitations to the clinical validity of its responses. Solution: It is critical that all medical advice provided by ChatGPT is cross-referenced with up-to-date clinical guidelines and reviewed by medical professionals for accuracy. Ethical Considerations: The impersonal nature of a machine providing health-related advice could potentially result in a lack of emotional sensitivity. Solution: Ethical guidelines must be established for the algorithm, possibly integrating a 'red flag' mechanism that alerts human operators when sensitive or complex issues arise that require a more nuanced touch. Conclusion ChatGPT, while powerful, isn't a replacement for the expertise of healthcare professionals. Instead, it serves as an enhancing tool within the healthcare sector. Beyond aiding professionals, ChatGPT can increase patient engagement, reduce administrative burdens, and help in preliminary health assessments. Its broader applications include transcribing medical discussions, translating medical information across languages, and simplifying complex medical terms for better patient comprehension. For medical training, it can mimic patient scenarios, aiding in skill development. Furthermore, ChatGPT can assist in research by navigating medical literature, and conserving crucial time. However, its capabilities should always be seen as complementary, never substituting the invaluable care from healthcare professionals. Author BioAmita Kapoor is an accomplished AI consultant and educator with over 25 years of experience. She has received international recognition for her work, including the DAAD fellowship and the Intel Developer Mesh AI Innovator Award. She is a highly respected scholar with over 100 research papers and several best-selling books on deep learning and AI. After teaching for 25 years at the University of Delhi, Amita retired early and turned her focus to democratizing AI education. She currently serves as a member of the Board of Directors for the non-profit Neuromatch Academy, fostering greater accessibility to knowledge and resources in the field. After her retirement, Amita founded NePeur, a company providing data analytics and AI consultancy services. In addition, she shares her expertise with a global audience by teaching online classes on data science and AI at the University of Oxford. 

👋 Hello ,“[AI] will touch every sector, every industry, every business function, and significantly change the way we live and work..this isn’t just the future. We are already starting to experience the benefits right now. As a company, we’ve been preparing for this moment for some time.” -Sundar Pichai, CEO, Google Speaking at the ongoing Google Cloud Next conference, Pichai emphasized how AI is the future, and it’s here already.   Step into the future with AI_Distilled#15, showcasing the breakthroughs in AI/ML, LLMs, NLP, GPT, and Generative AI, as we talk about Nvidia reporting over 100% increase in sales amid high demand for AI chips, Meta introducing Code Llama: a breakthrough in AI-powered coding assistance, OpenAI introducing ChatGPT Enterprise for businesses, Microsoft’s promising new "Algorithm of Thoughts" to enhance AI reasoning, and Salesforce's State of the Connected Customer Report which shows how businesses are facing AI trust gap with customers. Looking for fresh knowledge resources and tutorials? We’ve got your back! Look out for our curated collection of posts on how to use Code Llama, mitigating hallucination in LLMs, Google’s: Region-Aware Pre-Training for Open-Vocabulary Object Detection with Vision Transformers, and making data queries with Hugging Face's VulcanSQL.  We’ve also handpicked some great GitHub repos for you to use on your next AI project! What do you think of this issue and our newsletter? Please consider taking the short survey below to share your thoughts and you will get a free PDF of the “The Applied Artificial Intelligence Workshop” eBook upon completion. Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt   ⚡ TechWave: AI/GPT News & Analysis OpenAI Introduces ChatGPT Enterprise: AI Solution for Businesses: OpenAI has unveiled ChatGPT Enterprise with advanced features. The enterprise-grade version offers enhanced security, privacy, and access to the more powerful GPT-4 model. It includes unlimited usage of GPT-4, higher-speed performance, longer context windows for processing lengthier inputs, advanced data analysis capabilities, customization options, and more, targeting improved productivity, customized workflows, and secure data management. Meta Introduces Code Llama: A Breakthrough in AI-Powered Coding Assistance: Code Llama is a cutting-edge LLM designed to generate code based on text prompts and is tailored for code tasks and offers the potential to enhance developer productivity and facilitate coding education. Built on Llama 2, Code Llama comes in different models, including the foundational code model, Python-specialized version, and an instruct variant fine-tuned for understanding natural language instructions. The models outperformed existing LLMs on code tasks and hold promise for revolutionizing coding workflows while adhering to safety and responsible use guidelines. Nvidia Reports Over 100% Increase in Sales Amid High Demand for AI Chips: Nvidia has achieved record-breaking sales, more than doubling its revenue to over $13.5 billion for the quarter ending in June. The company anticipates further growth in the current quarter and plans to initiate a stock buyback of $25 billion. Its stock value soared by more than 6.5% in after-hours trading, bolstering its substantial gains this year. Nvidia's data center business, which includes AI chips, fueled its strong performance, with revenue surpassing $10.3 billion, driven by cloud computing providers and consumer internet firms adopting its advanced processors. With a surge in its market value, Nvidia joined the ranks of trillion-dollar companies alongside Apple, Microsoft, Alphabet, and Amazon. Businesses Facing AI Trust Gap with Customers, Reveals Salesforce's State of the Connected Customer Report: Salesforce's sixth edition of the State of the Connected Customer report highlights a growing concern among businesses about an AI trust gap with their customers. The survey, conducted across 25 countries with over 14,000 consumers and business buyers, indicates that as companies increasingly adopt AI to enhance efficiency and meet customer expectations, nearly three-quarters of their customers are worried about unethical AI use. Consumer receptivity to AI has also decreased over the past year, urging businesses to address this gap by implementing ethical guidelines and providing transparency into AI applications. Microsoft Introduces "Algorithm of Thoughts" to Enhance AI Reasoning: Microsoft has unveiled a novel AI training method called the "Algorithm of Thoughts" (AoT), aimed at enhancing the reasoning abilities of large language models like ChatGPT by combining human-like cognition with algorithmic logic. This new approach leverages "in-context learning" to guide language models through efficient problem-solving paths, resulting in faster and less resource-intensive solutions. The technique outperforms previous methods and can even surpass the algorithm it is based on.  Google's Duet AI Expands Across Google Cloud with Enhanced Features: Google's Duet AI, a suite of generative AI capabilities for tasks like text summarization and data organization, is expanding its reach to various products and services within the Google Cloud ecosystem. The expansion includes assisting with code refactoring, offering guidance on infrastructure configuration and deployment in the Google Cloud Console, writing code in Google's dev environment Cloud Workstations, generating flows in Application Integration, and more. ̌It also integrates generative AI advancements into the security product line. OpenAI Collaborates with Scale to Enhance Enterprise Model Fine-Tuning Support: OpenAI has entered into a partnership with Scale to provide expanded support for enterprises seeking to fine-tune advanced models. Recognizing the demand for high performance and customization in AI deployment, OpenAI introduced fine-tuning for GPT-3.5 Turbo and plans to extend it to GPT-4. This feature empowers companies to customize advanced models with proprietary data, enhancing their utility. OpenAI assures that customer data remains confidential and is not utilized to train other models. Google DeepMind Introduces SynthID: A Tool to Identify AI-Generated Images: In response to the growing prevalence of AI-generated images that can be indistinguishable from real ones, Google Cloud has partnered with Imagen to unveil SynthID. This newly launched beta version aims to watermark and identify AI-created images. The technology seamlessly embeds a digital watermark into the pixels of an image, allowing for imperceptible yet detectable identification. This tool is a step towards responsible use of generative AI and enhances the capacity to identify manipulated or fabricated images.   ✨ Unleashing the Power of Causal Reasoning with LLMs:Join Aleksander Molak on October 11th and be a part of Packt's most awaited event of 2023 on Generative AI! In AI's evolution, a big change is coming. It's all about Causally Aware Prompt Engineering, and you should pay attention because it's important. LLMs are good at recognizing patterns, but what if they could do more? That's where causal reasoning comes in. It's about understanding not just what's connected but why. Let's distill the essence: - LLMs can outperform causal discovery algorithms on some tasks  - GPT-4 achieves a near-human performance on some counterfactual benchmarks  - This might be the case because the models simply memorize the data, but it's also possible that they build a **meta-SCM** (meta structural causal models) based on the correlations of causal facts learned from the data  - LLMs can reason causally if we allow them to intervene on the test time  - LLMs do not reason very well, when we provide them with verbal description of conditional independence structures in the data (but nor do (most of) humans). Now, catalyze your journey with three simple techniques: Causal Effect Estimation: Causal effect estimate aims at capturing the strength of (expected) change in the outcome variable when we modify the value of the treatment by one unit. In practice, almost any machine learning algorithm can be used for this purpose, yet in most cases we need to use these algorithms in a way that differs from the classical machine learning flow. Confronting Confounding: The main challenge (yet not the only one) in estimating causal effects from observational data comes from confounding. Confounder is a variable in the system of interest that produces a spurious relationship between the treatment and the outcome. Spurious relationships are a kind of illusion. Interestingly, you can observe spurious relationships not only in the recorded data, but also in the real world. Unveiling De-confounding: To obtain an unbiased estimate of the causal effect, we need to get rid of confounding. At the same time, we need to be careful not to introduce confounding ourselves! This usually boils down to controlling for the right subset of variables in your analysis. Not too small, not too large. If you're intrigued by this, I invite you to join me for an in-depth exploration of this fascinating topic at Packt's upcoming Generative AI conference on October 11th. During my power-talk, we'll delve into the question: Can LLMs learn Causally?  REGISTER NOW at Early Bird discounted pricing! *Free eBook on Registration: Modern Generative AI with ChatGPT and OpenAI Models   🔮 Expert Insights from Packt Community The Regularization Cookbook - By Vincent Vandenbussche Regularization serves as a valuable approach to enhance the success rate of ML models in production. Effective regularization techniques can prevent AI recruitment models from exhibiting gender biases, either by eliminating certain features or incorporating synthetic data. Additionally, proper regularization enables chatbots to maintain an appropriate level of sensitivity toward new tweets. It also equips models to handle edge cases and previously unseen data proficiently, even when trained on synthetic data. Key concepts of regularization Let us now delve into a more precise definition and explore key concepts that enable us to better comprehend regularization. Bias and variance Bias and variance are two key concepts when talking about regularization. We can define two main kinds of errors a model can have: Bias is how bad a model is at capturing the general behavior of the data Variance is how bad a model is at being robust to small input data fluctuations Let’s describe those four cases: High bias and low variance: The model is hitting away from the center of the target, but in a very consistent manner Low bias and high variance: The model is, on average, hitting the center of the target, but is quite noisy and inconsistent in doing so High bias and high variance: The model is hitting away from the center in a noisy way Low bias and low variance: The best of both worlds – the model is hitting the center of the target consistently  The above content is extracted from the book The Regularization Cookbook By Vincent Vandenbussche and published in July 2023. To get a glimpse of the book's contents, make sure to read the free chapter provided here, or if you want to unlock the full Packt digital library free for 7 days, try signing up now! To learn more, click on the button below. Keep Calm, Start Reading!  🌟 Secret Knowledge: AI/LLM Resources Google’s RO-ViT: Region-Aware Pre-Training for Open-Vocabulary Object Detection with Vision Transformers: Google's research scientists have unveiled a new method called "RO-ViT" that enhances open-vocabulary object detection using vision transformers. Learn how the technique addresses limitations in existing pre-training approaches for vision transformers, which struggle to fully leverage the concept of objects or regions during pre-training. RO-ViT introduces a novel approach called "cropped positional embedding" that aligns better with region-level tasks.Tiered AIOps: Enhancing Cloud Platform Management with AI: Explore the concept of Tiered AIOps to manage complex cloud platforms. The ever-changing nature of cloud applications and infrastructure presents challenges for complete automation, requiring a tiered approach to combine AI and human intervention. The concept involves dividing operations into tiers, each with varying levels of automation and human expertise. Tier 1 incorporates routine operations automated by AI, Tier 2 empowers non-expert operators with AI assistance, and Tier 3 engages expert engineers for complex incidents. Effective AI-Agent Interaction: SERVICE Principles Unveiled: In this post, you'll learn how to design AI agents that can interact seamlessly and effectively with users, aiming to transition from self-service to "agent-service." The author introduces the concept of autonomous AI agents capable of performing tasks on users' behalf and offers insights into their potential applications. The SERVICE principles, rooted in customer service and hospitality practices, are presented as guidelines for designing agent-user interactions. These principles encompass key aspects like salient responses, explanatory context, reviewable inputs, vaulted information, indicative guidance, customization, and empathy.  How to Mitigate Hallucination in Large Language Models: In this article, researchers delve into the persistent challenge of hallucination in Generative LLMs. The piece explores the reasons behind LLMs generating nonsensical or non-factual responses, and the potential consequences for system reliability. The focus is on practical approaches to mitigate hallucination, including adjusting the temperature parameter, employing thoughtful prompt engineering, and incorporating external knowledge sources. The authors conduct experiments to evaluate different methods, such as Chain of Thoughts, Self-Consistency, and Tagged Context Prompts.    💡 MasterClass: AI/LLM Tutorials How to Use Code Llama: A Breakdown of Features and Usage: Code Llama has made a significant stride in code-related tasks, offering an open-access suite of models specialized for code-related challenges. This release includes various notable components, such as integration within the Hugging Face ecosystem, transformative integration, text generation inference, and inference endpoints. Learn how these models showcase remarkable performance across programming languages, enabling enhanced code understanding, completion, and infilling.  Make Data Queries with Hugging Face's VulcanSQL: In this post, you'll learn how to utilize VulcanSQL, an open-source data API framework, to streamline data queries. VulcanSQL integrates Hugging Face's powerful inference capabilities, allowing data professionals to swiftly generate and share data APIs without extensive backend knowledge. By incorporating Hugging Face's Inference API, VulcanSQL enhances the efficiency of query processes. The framework's HuggingFace Table Question Answering Filter offers a unique solution by leveraging pre-trained AI models for NLP tasks.  Exploring Metaflow and Ray Integration for Supercharged ML Workflows: Explore the integration of Metaflow, an extensible ML orchestration framework, with Ray, a distributed computing framework. This collaboration leverages AWS Batch and Ray for distributed computing, enhancing Metaflow’s capabilities. Know how this integration empowers Metaflow users to harness Ray’s features within their workflows. The article also delves into the challenges faced, the technical aspects of the integration, and real-world test cases, offering valuable insights into building efficient ML workflows using these frameworks. Explore Reinforcement Learning Through Solving Leetcode Problems: Explore how reinforcement learning principles can be practically grasped by solving a Leetcode problem. The article centers around the "Shortest Path in a Grid with Obstacles Elimination" problem, where an agent aims to find the shortest path from a starting point to a target in a grid with obstacles, considering the option to eliminate a limited number of obstacles. Explore the foundations of reinforcement learning, breaking down terms like agent, environment, state, and reward system. The author provides code examples and outlines how a Q-function is updated through iterations.    🚀 HackHub: Trending AI Tools apple/ml-fastvit: Introduces a rapid hybrid ViT empowered by structural reparameterization for efficient vision tasks. openchatai/opencopilot: A personal AI copilot repository that seamlessly integrates with APIs and autonomously executes API calls using LLMs, streamlining developer tasks and enhancing efficiency. neuml/txtai: An embeddings database for advanced semantic search, LLM orchestration, and language model workflows featuring vector search, multimodal indexing, and flexible pipelines for text, audio, images, and more. Databingo/aih: Interact with AI models via terminal (Bard, ChatGPT, Claude2, and Llama2) to explore diverse AI capabilities directly from your command line. osvai/kernelwarehouse: Optimizes dynamic convolution by redefining kernel concepts, improving parameter dependencies, and increasing convolutional efficiency. morph-labs/rift: Open-source AI-native infrastructure for IDEs, enabling collaborative AI software engineering. mr-gpt/deepeval: Python-based solution for offline evaluations of LLM pipelines, simplifying the transition to production. 

IntroductionIn the ever-evolving landscape of artificial intelligence, a groundbreaking collaboration has emerged between Wolfram Alpha and ChatGPT, giving birth to an extraordinary plugin: the AI Advantage. This partnership bridges the gap between ChatGPT's proficiency in natural language processing and Wolfram Alpha's computational prowess. The result? A fusion that unlocks an array of new possibilities, revolutionizing the way we interact with AI. In this hands-on tutorial, we're embarking on a journey to explore the power of the Wolfram Alpha API, demonstrate its integration with Python and ChatGPT, and empower you to tap into this dynamic duo for tasks ranging from complex calculations to real-time data retrieval.Understanding Wolfram Alpha APIImagine having an intelligent assistant at your fingertips, capable of not only understanding your questions but also providing detailed computational insights. That's where Wolfram Alpha shines. It's more than just a search engine; it's a computational knowledge engine. Whether you need to solve a math problem, retrieve real-time data, or generate visual content, Wolfram Alpha has you covered. Its unique ability to compute answers based on structured data sets it apart from traditional search engines.So, how can you tap into this treasure trove of computational knowledge? Enter the Wolfram Alpha API. This API exposes Wolfram Alpha's capabilities for developers to harness in their applications. Whether you're building a chatbot, a data analysis tool, or an educational resource, the Wolfram Alpha API can provide you with instant access to accurate and in-depth information. The API supports a wide range of queries, from straightforward calculations to complex data retrievals, making it a versatile tool for various use cases.Integrating Wolfram Alpha API with ChatGPTChatGPT's strength lies in its ability to understand and generate human-like text based on input. However, when it comes to intricate calculations or pulling real-time data, it benefits from a partner like Wolfram Alpha. By integrating the two, you create a dynamic synergy where ChatGPT can effortlessly tap into Wolfram Alpha's computational engine to provide accurate and data-driven responses. This collaboration bridges the gap between language understanding and computation, resulting in a well-rounded AI interaction.Before we dive into the technical implementation, let's get you set up to take advantage of the Wolfram Alpha plugin for ChatGPT. First, ensure you have access to ChatGPT+. To enable the Wolfram plugin, follow these steps:Open the ChatGPT interface.Navigate to "Settings."Look for the "Beta Features" section.Enable "Plugins" under the GPT-4 options.Once "Plugins" is enabled, locate and activate the Wolfram plugin.With the plugin enabled you're ready to harness the combined capabilities of ChatGPT and Wolfram Alpha API, making your AI interactions more robust and informative.In the next sections, we'll dive into practical applications and walk you through implementing the integration using Python and ChatGPT.Practical Applications with Code ExamplesLet's start by exploring how the Wolfram Alpha API can assist with complex mathematical tasks. Below are code examples that demonstrate the integration between ChatGPT and Wolfram Alpha to solve intricate math problems. In these scenarios, ChatGPT serves as the bridge between you and Wolfram Alpha, seamlessly delivering accurate solutions.Before diving into the code implementation, let's ensure your environment is ready to go. Follow these steps to set up the necessary components:Install the required packages: Make sure you have the necessary Python packages installed. You can use pip to install them:pip install langchain openai wolframalphaNow, let's walk through implementing the code example you provided earlier. This code integrates the Wolfram Alpha API with ChatGPT to provide accurate and informative responses:Wolfram Alpha can solve simple arithmetic queries:# User input question = "Solve for x: 2x + 5 = 15" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response result = response['text'] print("Solution:", result)Or mode complex ones like calculating integrals:# User input question = "Calculate the integral of x^2 from 0 to 5" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response print("Integral:", response) Real-time Data RetrievalIncorporating real-time data into conversations can greatly enhance the value of AI interactions. Here are code examples showcasing how to retrieve up-to-date information using the Serper API and integrate it seamlessly into the conversation:# User input question = "What's the current exchange rate between USD and EUR?" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response print("Exchange Rate:", response)We can also ask for the current weather forecast:# User input question = "What's the weather forecast for London tomorrow?" # Let ChatGPT interact with Wolfram Alpha response = agent_chain.run(input=question) # Extracting and displaying the result from the response print("Weather Forecast:", response)Now we can put everything together into a single block including all the required library imports and use both real time data with Serper and use the reasoning skills of Wolfram Alpha.# Import required libraries from langchain.agents import load_tools, initialize_agent from langchain.llms import OpenAI from langchain.memory import ConversationBufferMemory from langchain.chat_models import ChatOpenAI # Set environment variables import os os.environ['OPENAI_API_KEY'] = 'your-key' os.environ['WOLFRAM_ALPHA_APPID'] = 'your-key' os.environ["SERPER_API_KEY"] = 'your-key' # Initialize the ChatGPT model llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") # Load tools and set up memory tools = load_tools(["google-serper", "wolfram-alpha"], llm=llm) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) # Initialize the agent agent_chain = initialize_agent(tools, llm, handle_parsing_errors=True, verbose=True, memory=memory) # Interact with the agent response_weather = agent_chain.run(input="what is the weather in Amsterdam right now in celcius? Don't make assumptions.") response_flight = agent_chain.run(input="What's a good price for a flight from JFK to AMS this weekend? Express the price in Euros. Don't make assumptions.")ConclusionIn this tutorial, we've delved into the exciting realm of integrating the Wolfram Alpha API with Python and ChatGPT. We've explored how this collaboration empowers you to tackle complex mathematical tasks and retrieve real-time data seamlessly. By harnessing the capabilities of both Wolfram Alpha and ChatGPT, you've unlocked a powerful synergy that's capable of transforming your AI interactions. As you continue to explore and experiment with this integration, you'll discover new ways to enhance your interactions and leverage the strengths of each tool. So, why wait? Start your journey toward more informative and engaging AI interactions today.Author BioAlan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder of startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.LinkedIn 

IntroductionEmbark on an enlightening journey into the ChatGPT stack, a remarkable feat in AI-driven language generation. Unveiling its evolution from inception to a proficient AI assistant, we delve into decoder-only transformers, specialized for crafting Shakespearean verses and informative responses.Throughout this exploration, we dissect the four integral stages that constitute the ChatGPT stack. From exhaustive pretraining to fine-tuned supervised training, we unravel how rewards and reinforcement learning refine response generation to align with context and user intent.In this blog, we will get acquainted briefly with the ChatGPT stack and then implement a simple decoder-only transformer to train on Shakespeare.Creating ChatGPT models consists of four main stages:1.    Pretraining:2.    Supervised Fine Tuning3.    Reward modeling4.    Reinforcement learningThe Pretraining stage takes most of the computational time since we train the language model on trillions of tokens. The following table shows the Data Mixtures used for pretraining of LLaMA Meta Models [0]:The datasets come and mix together, according to the sampling proportion, to create the pretraining data. The table shows the datasets along with their corresponding sampling proportion (What portion of the pre-trained data is the dataset?), epochs (How many times do we train the model on the corresponding datasets?), and dataset size. It is obvious that the epoch of high-quality datasets such as Wikipedia, and Books is high and as a result, the model grasps high-quality datasets better.After we have our dataset ready, the next step is Tokenization before training. Tokenizing data means mapping all the text data into a large list of integers. In language modeling repositories, we usually have two dictionaries for mapping tokens (a token is a sub word. Like ‘wait’, and ‘ing’ are two tokens.) into integers and vice versa. Here is an example:In [1]: text = "it is obvious that the epoch of high .." In [2]: tokens = list(set(text.split())) In [3]: stoi = {s:i for i,s in enumerate(tokens)} In [4]: itos = {i:s for s,i in stoi.items()} In [5]: stoi['it'] Out[5]: 22 In [6]: itos[22] Out[6]: 'it'Now, we can tokenize texts with the following functions:In [7]: encode = lambda text: [stoi[x] for x in text.split()] In [8]: decode = lambda encoded: ' '.join([itos[x] for x in encoded]) In [9]: tokenized = encode(text) In [10]: tokenized Out[10]: [22, 19, 18, 5, ...] In [11]: decode(tokenized) Out[11]: 'it is obvious that the epoch of high ..'Suppose the tokenized variable contains all the tokens converted to integers (say 1 billion tokens). We select 3 chunks of the list randomly that each chunk contains 10 tokens and feed-forward them into a transformer language model to predict the next token. The model’s input has a shape (3, 10), here 3 is batch size and 5 is context length. The model tries to predict the next token for each chunk independently. We select 3 chunks and predict the next token for each chunk to speed up the training process. It is like running the model on 3 chunks of data at once. You can increase the batch size and context length depending on the requirements and resources. Here’s an example:For convenience, we wrote the token indices along with the corresponding tokens. For each chunk or sequence, the model predicts the whole sequence. Let’s see how this works:By seeing the first token (it), the model predicts the next token (is). The context token(s) is ‘it’ and the target token for the model is ‘is’. If the model fails to predict the target token, we do backpropagation to adjust model parameters so the model can predict correctly.During the process, we mask out or hide the future tokens so that the model can’t have access to the future tokens. Because it is kind of cheating. We want the model itself to predict the future by only seeing the past tokens. That makes sense, right? That’s why we used a gray background for future tokens, which means the model is not able to see them.After predicting the second token, we have two tokens [it, is] as context to predict what token comes next in the sequence. Here is the third token (obvious).By using the three previous tokens [it, is, obvious], the model needs to predict the fourth token (that). And as usual, we hide the future tokens (in this case ‘the’).We give [it, is, obvious, that] to the model as the context in order to predict ‘the’. And finally, we give all the sequence as context [it, is, obvious, that, the] to predict the next token.We have five predictions for a sequence with a length of five.After training the model on a lot of randomly selected sequences from the pre-trained dataset, the model should be ready to autocomplete your sequence. Give it a sequence of tokens, and then, it predicts the next token and based on what was predicted plus previous tokens, the model predicts the next tokens one by one. We call it an autoregressive model. That’s it.But, at this stage, the model is not an AI assistant or a chatbot. It only receives a sequence and tries to complete the sequence. That’s how we trained the model. We don’t train it to answer questions and listen to the instructions. We give it context tokens and the model tries to predict the next token based on the context.You give it this:“In order to be irrational, you first need to”And the model continues the sequence:“In order to be irrational, you first need to abandon logical reasoning and disregard factual evidence.”Sometimes, you ask it an instruction:“Write a function to count from 1 to 100.”And instead of trying to write a function, the model answers with more similar instructions:“Write a program to sort an array of integers in ascending order.”“Write a script to calculate the factorial of a given number.”“Write a method to validate a user's input and ensure it meets the specified criteria.”“Write a function to check if a string is a palindrome or not.”That’s where prompt engineering came in. People tried to use some tricks to get the answer to a question out of the model.Give the model the following prompt:“London is the capital of England.Copenhagen is the capital of Denmark.Oslo is the capital of”The model answers like this:“Norway.”So, we managed to get something helpful out of it with prompt engineering. But we don’t want to provide examples every time. We want to ask it a question and receive an answer. To prepare the model to be an AI assistant, we need further training named Supervised Fine Tuning for instructional purposes.In the Supervised Fine-Tuning stage, we make the model instructional. To achieve this goal the model needs training on a high quality 15k-100K of prompt and response dataset. Here’s an example of it: { "instruction": "When was the last flight of Concorde?", "context": "", "response": "On 26 November 2003", "category": "open_qa" }This example was taken from the databricks-dolly-15k dataset that is an open-source dataset for Supervised/Instruction Fine Tuning[1]. You can download the dataset from here. Instructions have seven categorizations including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This is because we want to train the model in different tasks. For instance, the above instruction is open QA, meaning the question is a general one and does not require reasoning abilities. It teaches the model to answer general questions. Closed QA requires reasoning abilities. During Instruction fine-tuning, nothing will change algorithmically. We do the same process as the previous stage (Pretraining). We gave instructions as context tokens and we want the model to continue the sequence with response.We continue this process for thousands of examples and then, the model is ready to be instructional. But that’s not the end of the story of the model behind ChatGPT. OpenAI designed a supervised reward modeling that returns a reward for the sequences that were made by the base model for the same input prompt. They give the model a prompt and run the model four times, for instance, to have four different answers for the same prompt. The model produces different answers each time because of the sampling method they use. Then, the reward model receives the input prompt and the produced answers to get a reward score for each answer. The better the answer, the better the reward score is. The model requires ground-truth scores to be trained and these scores came from labelers who worked for OpenAI. Labelers were given prompt text and model responses and they ranked them from the best to the worst.At the final stage, the ChatGPT uses Reinforcement Learning with Human Feedback (RLHF) to generate responses that get the best scores from the rewarding model. RL is an architecture that tries to find the best way of achieving a goal. The goal can be checkmate in chess or creating the best answer for the input prompt. The RL learning process is like doing an action and getting a reward or penalty for the action. And we do not take actions that end up penalizing. RLHF is what made ChatGPT so good:The PPO-ptx shows the win rate of GPT + RLHF compared to SFT (Supervised Fine-Tuned model), GPT with prompt engineering, and GPT base.ConclusionIn summation, the ChatGPT stack exemplifies AI's potent fusion with language generation. From inception to proficient AI assistant, we've traversed core stages – pretraining, fine-tuning, and reinforcement learning. Decoder-only transformers have enlivened Shakespearean text and insights.Tokenization's role in enabling ChatGPT's prowess concludes our journey. This AI evolution showcases technology's synergy with creative text generation.ChatGPT's ascent highlights AI's potential to emulate human-like language understanding. With ongoing refinement, the future promises versatile conversational AI that bridges artificial intelligence and language's artistry, fostering human-AI understanding.Author BioSaeed Dehqan trains language models from scratch. Currently, his work is centered around Language Models for text generation, and he possesses a strong understanding of the underlying concepts of neural networks. He is proficient in using optimizers such as genetic algorithms to fine-tune network hyperparameters and has experience with neural architecture search (NAS) by using reinforcement learning (RL). He implements models starting from data gathering to monitoring, and deployment on mobile, web, cloud, etc. 

IntroductionChatGPT is a revolutionary new technology that is making a large impact on society.  The full impact of ChatGPT cannot be fully known at the time of writing this article because of how novel the technology is.  However, what can be said is that since its introduction many industries have been trying to leverage it and increase productivity.  Simultaneously, everyday people are trying to learn to leverage it as well.  Overall, ChatGPT and similar systems are very new and the full impact of how to leverage them will take some more time to fully manifest.  This article is going to explore how ChatGPT can be used for everyday life by exploring a few use cases.What is ChatGPT?       Before we begin, it is important to understand what ChatGPT is and what it isn’t.  To begin ChatGPT is in a lay sense a super advanced chatbot.  More specifically, ChatGPT is known as a generative AI that uses Natural Language Processing (NLP) to create a dialog between a user and itself.  ChatGPT and similar systems are what are known as Large Language Models (LLMs).  In short, for AI models to work they have to be trained using data.  To train LLMs engineers use vast amounts such as books, articles, journals, and so on.  The result is a system like ChatGPT that has a vast knowledge base on many different subjects.  Before we can explore how to use ChatGPT for everyday life we need to explore how NOT to use ChatGPT. How not to use ChatGPT?ChatGPT is very powerful and can be used for many different things; however, is important to understand that ChatGPT is not a sage nor infallible.  Remember ChatGPT only knows about what it was trained on.  This means if the information it was taught was wrong or outdated so too will be the response it provides.  This means that as of writing this article ChatGPT cannot and should not be used as a replacement for humans.  Even with the answer ChatGPT gives, a decent level of domain knowledge is still required properly format and use its response.  As such, it is important to take suggestions with a certain amount of skepticism.  The best way to think of ChatGPT is as an extremely smart friend with a wide range of knowledge.  Though that friend may be very knowledgeable they can and will be wrong from time to time.  With that how does one use ChatGPT?How to use ChatGPT for everyday life?Using ChatGPT starts with what is called a prompt.  A prompt can be thought of as a query that the user feeds into ChatGPT.  This prompt is kind of like a Google search.  It will be a statement that asks ChatGPT a question or to perform a certain task.  Since ChatGPT used NLP, the prompt does not have to be formatted a certain way nor does it require a specific syntax.  In other words, a prompt can be entered the same way a user would pose the instruction to another person.  However, it is good to provide ChatGPT will enough information to adequately describe the problem.  An example, of a prompt is as follows:How can ChatGPT be used for everyday life?Once the prompt is run ChatGPT will produce text with suggestions, answers, programming software, or whatever was asked for.  That output that is produced is called a response which will have the answer to the request/question. There are a lot of ways ChatGPT can be used for everyday life.  In terms of professional life, ChatGPT can make an excellent assistant.  For most “text-based” jobs, that is jobs that don’t require design such as drawing diagrams, ChatGPT can be an excellent assistant.  For example, suppose a programmer needs a small piece of software for a project, assume they need a SQL query that can pull all the rows between two dates.  The user could use a prompt like the following:Write a SQL query that will pull all rows between January 3 and March 31.This prompt will produce the following response:To pull all rows between January 3 and March 31 from a table, you'll need to use the WHERE clause to filter the results based on the date column. Assuming you have a date column named "date_column" in your table, here's the SQL query: sqlCopy code SELECT * FROM your_table WHERE date_column >= '2023-01-03' AND date_column <= '2023-03-31'; Replace "your_table" with the actual name of your table, and "date_column" with the name of the column that contains the dates you want to filter. The query will select all rows where the "date_column" is on or after January 3, 2023, and on or before March 31, 2023.For those that are not familiar with SQL, the prompt produced a simple query with generic placeholders that will pull the correct data out of a database. Not everyone is a programmer, so how could a non-programmer use ChatGPT as an assistant?  One area in which ChatGPT can be used is by composing emails.  Suppose a salesperson needs to inform Tom, their boss, that a $1 million deal was closed today via email.  They could use a prompt like the following:Write Tom an email saying that the deal with Company X went through, and we made $1 million.The response would be something along the lines of:Subject: Successful Deal with Company X - $1 Million Revenue Achieved Hi Tom, I hope this email finds you well. I am thrilled to share some exciting news with you! The deal with Company X has successfully gone through, and I am delighted to inform you that we have made an astounding $1 million in revenue from this collaboration. This achievement wouldn't have been possible without the hard work and dedication of our entire team. It reflects the trust and confidence our partners have in our capabilities, and I believe it marks a significant milestone for our company's growth and success. I want to express my gratitude to you for your valuable contributions and expertise that played a crucial role in making this deal a reality. Your efforts have been commendable, and I am truly grateful for your commitment to the success of this venture. Let's take a moment to celebrate this triumph together. Your exceptional skills and commitment to excellence have made this accomplishment possible, and I am genuinely proud to have you as part of our team. Looking forward to even more prosperous ventures in the future. Once again, congratulations to you and the entire team! Best regards, [Your Name] [Your Title/Position] [Company Name] [Email] [Phone Number]In this case, all the users would have to do is proofread the email, copy it, and send it.  Now, for applications such as this proofreading is very important.  As stated before, ChatGPT is not a mind reader nor a sage, as such it may produce emails with wrong information, illogical wording, or anything else.  So, it’ll pay to read the message before sending it. Learning ToolA very powerful use of ChatGPT is as a learning tool.  For example, students can use this tool to help them master a new subject, generate practice problems, and even check answers to an extent.  Assume the user needs a few extra practice problems for their English homework, they could enter a prompt like the following:Generate some grammar practice problems for a 1st year English classWhich would yield the following output:Of course! Here are some grammar practice problems suitable for a 1st-year English class: 1. Subject-Verb Agreement: Choose the correct verb form that agrees with the subject in each sentence: a) The cat (purr/purrs) when it is happy. b) My friend and I (is/are) going to the movies tonight. c) Neither the students nor the teacher (understands/understand) the assignment. 2. Pronoun Usage: Select the appropriate pronoun to complete each sentence: a) She invited Sarah and (I/me) to the party. b) My sister and (her/she) are going shopping. c) The book is for (he/him) and his brother. 3. Articles (a/an/the): Insert the correct article (a, an, the, or no article) in each sentence: a) Can you pass me (a/an) apple, please? b) I need to buy (the) new shoes for the dance. c) My friend has (no article) car. 4. Prepositions: Choose the correct preposition to complete each sentence: a) We arrived (in/on/at) the airport fifteen minutes early. b) The cat is sleeping (under/below/above) the table. c) She's going to the store (by/with/for) her mom. 5. Verb Tenses: Identify the correct verb tense used in each sentence: a) They (will be/was) studying for the exam all night. b) By the time we arrived, the party (had started/has started). c) I (am/was) watching a movie right now. 6. Adjective vs. Adverb: Decide whether the word in parentheses should be an adjective or adverb in the sentence: a) She sings (beautiful/beautifully). b) The (happy/happily) children played in the park. c) The (quick/quickly) fox jumped over the fence. 7. Negation: Add the necessary negative word to each sentence: a) I have (no/not) seen him today. b) She does (not/none) like coffee. c) We will (not/none) be attending the party. 8. Passive Voice: Rewrite the following sentences in the passive voice: a) The chef cooked the meal. b) The teacher is explaining the lesson. c) The storm destroyed the old building. 9. Comparative and Superlative Forms: Complete each sentence with the correct comparative or superlative form of the adjective: a) This book is (interesting/more interesting/most interesting) than the last one. b) She is (tall/taller/tallest) than her sister. c) It was the (big/biggest/bigger) cake at the party. 10. Reported Speech: Rewrite the following sentences using reported speech: a) Mary said, "I am going to the store." b) "I will call you later," said John. c) "They have arrived," she said. These grammar practice problems aim to reinforce essential grammar rules and concepts, helping 1st-year English students develop their language skills and proficiency.As can be seen, ChatGPT generated a number of questions that one would usually find in an English 101 class.  Now, if the user wanted a more specific answer that could reword the prompt to accommodate. ChatGPT is also a great teaching tool as well.  Teachers can also use this tool to generate lesson plans, write intros for learning modules, generate problems, and more.  Overall, ChatGPT can be used as an excellent tool for academia in general.Now, it is important to remember that ChatGPT is not all-knowing.  It can only generate responses based on the data that it was trained on.  This means if the user is asking questions about a subject where there is little or no publicly available data the response probably isn’t going to be of the highest quality.  For example, if someone askedHow do I build a working time machine?They’re going to get the following:Building a working time machine, as depicted in science fiction, is currently beyond our technological capabilities and goes against the known laws of physics. Time travel, as popularly imagined, involves traveling backward or forward in time, similar to how we move through space. As of my last update in September 2021, time travel remains a concept explored mainly in theoretical physics, and no practical time machine has been built or proven to be possible. Several theories, such as the theory of relativity, suggest that time travel could be theoretically feasible under certain conditions, but these ideas are still purely theoretical and have not been put into practice. It's important to remember that building a time machine would present enormous scientific, technological, and ethical challenges. It could raise paradoxes, such as the "grandfather paradox," where a time traveler could potentially alter past events and create inconsistencies in history. As of now, time travel remains a fascinating topic in science fiction and theoretical physics, but it is not something that can be practically accomplished with our current understanding and technology. The scientific community continues to explore the nature of time and the universe, but building a working time machine as portrayed in movies and books is not yet a reality.ChatGPT gave some general information on time travel but unfortunately, it didn’t generate any schematics.  Again, this goes back to ChatGPT not being a sage as it only knows what it was “taught”.  Since there is no data on how to build a working time machine ChatGPT could not fully answer the prompt. Now, If one looks at the English question prompt above, one will notice that practice questions were quite broad.  It is common to have to “dig” with ChatGPT.  In other words, a person may have to refine the queriers to get what they need.  To get some practice try re-wording the prompt to generate practice questions for only passive writing.  SummaryChatGPT is a tool, and like any other tool, what it’s used for is up to the user.  As was seen in this article, ChatGPT is an excellent tool for helping a person through their day by generating software, emails, and so on.  ChatGPT can also be used as a great learning or teaching device to help students and teachers generate practice problems, create lesson plans, and so much more.  However, as was stated so many numerous times.  Unless ChatGPT has been trained on something it does not know about it.  This means that asking it things like how to build a time machine or domain specific concepts aren’t going to return quality responses.  Also, even if ChatGPT has been trained on the prompt, it may not always generate a quality response.  No matter the use case, the response should be vetted for accuracy.  This may mean doing a little extra research with the response given, testing the output, or whatever needs to be done to verify the response. Overall, ChatGPT at the time of writing this article is less than a year old.  This means that the full implication of using ChatGPT are not fully understood.  Also, how to fully leverage ChatGPT is not understood yet either.  What can be said is that ChatGPT and similar LLM systems will probably be the next Google.  In terms of everyday use, the only true inhibitors are the user's imagination and the data that was used to train ChatGPT.Author BioM.T. White has been programming since the age of 12. His fascination with robotics flourished when he was a child programming microcontrollers such as Arduino. M.T. currently holds an undergraduate degree in mathematics, and a master's degree in software engineering, and is currently working on an MBA in IT project management. M.T. is currently working as a software developer for a major US defense contractor and is an adjunct CIS instructor at ECPI University. His background mostly stems from the automation industry where he programmed PLCs and HMIs for many different types of applications. M.T. has programmed many different brands of PLCs over the years and has developed HMIs using many different tools.Author of the book: Mastering PLC Programming

IntroductionChatGPT can do many amazing things. ChatGPT can easily troubleshoot code, it can generate source code, and much more.  However, software development and by extension data engineering is comprised of so much more than just text-based programming.  For example, Azure offers a low/no-code tool that can be used to generate machine learning models without having to spend countless hours writing millions of lines of code.  However, there is a caveat to this service; in short, a person has to know what they are doing to use it.  For many building a machine-learning system is a complex task.  This is where ChatGPT comes into play.  ChatGPT can easily guide a user into building the pipeline needed to meet their goals.  What is especially interesting about this notion is that we can use AI to help build other AI!  To demonstrate this we’re going to use ChatGPT as a guiding tool to build our own linear regression model in Azure. ExpectationsAt the time of writing this article, ChatGPT cannot produce actual diagrams; however, what it can do is guide you. This means that you still have to be familiar with the overall gist of technology, but you don’t have to be a data science expert to figure out how to make a model from scratch.  ChatGPT is an excellent tool to get you up and running quickly, but as stated in many other articles throughout the internet, ChatGPT is not a replacement for developers.  On a side note, this tutorial is meant to be interactive.  ChatGPT will only generate a series of steps that must be interpreted. This means that if you don’t know how to carry out a task there are a lot of opportunities to create your own prompts.  With that, let’s start!SetupThis tutorial is going to assume that you have a basic knowledge of Azure and the Azure machine learning services.  However, we’re going to use ChatGPT as a guide from start to finish, so if you’re not sure of what you’re doing you can always modify the prompts or create new ones to help you along.  As an exercise, if you do get stuck it is a good idea to try to write some prompts on your own to get a feel for ChatGPT.  With that let’s start by having ChatGPT guide us in creating the necessary environment for our machine learning pipeline.EnvironmentTo begin, let’s start with getting our environment set up.  To figure out how to do this let’s use the following prompt:What infrastructure will I need to build a no-code Azure machine learning pipeline?Once you run this prompt you should be met with something akin to the following:To build a no-code Azure Machine Learning pipeline, you will need the following infrastructure components: 1.     Azure Subscription: You need an active Azure subscription to access and utilize Azure Machine Learning services. 2.     Azure Machine Learning Workspace: Create an Azure Machine Learning workspace, which serves as the top-level resource for organizing and managing your machine learning assets. The workspace provides a centralized location to store your pipelines, datasets, models, and other resources. 3.     Compute Resources: Azure Machine Learning provides various compute options for running your pipeline. Depending on your workload and requirements, you can choose from options such as Azure Machine Learning Compute, Azure Databricks, or remote VMs. These resources will execute the pipeline steps and perform the required computations. 4.     Data Storage: Azure Machine Learning supports multiple data storage options, such as Azure Blob Storage, Azure Data Lake Storage, or Azure SQL Database. You need a suitable data storage solution to store your input datasets, intermediate data, and any other data required for your pipeline. 5.     Networking and Security: Ensure that your Azure subscription and associated resources are properly secured. Azure provides various security features, such as Virtual Network (VNet) integration, network security groups (NSGs), role-based access control (RBAC), and Azure Active Directory (AAD) authentication, to protect your resources and control access. 6.     Monitoring and Logging: Azure Machine Learning offers monitoring and logging capabilities to track the execution of your pipelines, monitor performance metrics, and troubleshoot issues. You can use Azure Monitor, Azure Log Analytics, or other monitoring tools to gain insights into pipeline performance and health. 7.     Integration with DevOps: If you have a DevOps workflow, you may want to integrate your no-code pipeline with your existing CI/CD processes. Azure Machine Learning can be integrated with Azure DevOps, GitHub Actions, or other tools to automate the deployment and management of your pipelines. Remember to evaluate your specific workload requirements and choose the appropriate infrastructure components accordingly. The exact infrastructure setup will vary depending on factors such as data size, computational needs, security requirements, and integration with existing systems.This prompt generated a lot of needed infrastructure components that are necessary for a production-level pipeline.  However, our pipeline will only be used for practice so all we need to do is follow steps 1-3.  In other words, all we need for this tutorial is an Azure instance, an Azure Machine Learning workspace, and a compute instance.  If you can create these by yourself do so; however, if you can’t try writing your own prompt.  For example, try engineering a prompt that can give you the steps necessary to set up a compute instance.  You could try something like the following:How do I set up a compute instance in Azure?If this prompt doesn’t provide you with the necessary information, try rewording it and giving it more details until you can do it with the provided steps. PipelineThe next step is building out the pipeline.  The pipeline is the logic for the model.  In other words, the pipeline is the algorithm that will load the data for the model, clean the data, train the model, and evaluate the model if necessary.  In short, the best way to think of the pipeline is as a graphical machine learning program.  As stated before, ChatGPT is mostly limited to text-based responses.  However, we can still get step-by-step directions with the following prompt:Build a minimal linear regression model using Azure Machine Learning StudioWhich will output the following response:As an AI language model, I don't have direct access to external tools or platforms like Azure Machine Learning Studio, and my capabilities are limited to providing instructions and guidance. However, I can walk you through the steps to build a minimal linear regression model using Azure Machine Learning Studio. Please note that the steps might slightly vary based on updates to the platform since my last update in September 2021. Make sure to check the official Azure Machine Learning documentation for any changes. Here's a general outline of the steps: 1.     Set up Azure Machine Learning Studio: If you don't have an Azure Machine Learning account, sign up for one. Once you have access to Azure Machine Learning Studio, create a new project or workspace. 2.     Create a new Experiment: In Azure Machine Learning Studio, create a new experiment by clicking on "Experiments" and then "New." Give your experiment a descriptive name. 3.     Data Ingestion: Upload your dataset to Azure Machine Learning Studio. You can do this by clicking on "Datasets" and then "New" to upload your dataset. 4.     Data Cleaning and Preparation: Prepare your data by cleaning and transforming it if needed. This might include removing missing values, encoding categorical variables, and splitting the data into training and testing sets. 5.     Add Linear Regression Module: Drag and drop the "Linear Regression" module from the left panel to the canvas in your experiment. 6.     Connect Data to the Model: Connect the dataset you uploaded earlier to the "Linear Regression" module by clicking and dragging from the dataset to the input of the linear regression module. 7.     Train the Model: Run the experiment to train the linear regression model on your dataset. 8.     Evaluate the Model: After the training is complete, you can use evaluation modules like "Score Model" and "Evaluate Model" to assess the performance of the trained linear regression model. 9.     Deploy the Model (Optional): If you want to use the trained model for predictions in real-world scenarios, you can deploy the model as a web service in Azure Machine Learning Studio. 10. Test the Deployed Model (Optional): Test the deployed model to ensure it is working correctly and providing accurate predictions. Remember, this is just a minimal example of building a linear regression model using Azure Machine Learning Studio. Depending on your dataset and specific use case, you may need to perform additional steps like feature engineering, hyperparameter tuning, and more thorough model evaluation. Always refer to the official documentation and resources from Azure Machine Learning Studio for the most up-to-date and detailed instructions.Analyzing the output, the first two steps are self-explanatory.  The third step will require some preparation since we’re going to use a custom dataset for this tutorial. This tutorial is going to use a dataset that consists of 14 points.  Normally, you’ll want to have a dataset of at least 30 points to ensure normal distribution; however, for this example, we’re going to assume the data has a normal distribution.  With that, to train the model for this tutorial, copy the following data into either a plain text file or an Excel spreadsheet and save it as a .csv file:Once you have the data saved, you’ll need to create a data asset and upload the file to Azure.  If you don’t know the steps to upload the dataset you should try writing another prompt for directions.  If you need more information on this step you could try a prompt like:How do I upload a custom dataset to Azure Machine Learning Studio?Moving on to the rest of the instructions, we can construct a pipeline that resembles the pipeline in Figure 1:Figure 1 – Completed PipelineThis pipeline is, for the most part, a one-to-one representation of the instructions.  However, this pipeline expanded in Step 4.  In this pipeline, we added a Select Columns in the Dataset module to ensure we are only including the necessary data for the model.  From there we ran that data to a Split Data component as was suggested by ChatGPT.  Other than that, the model is exactly as described with the exception of the last two steps which are for deployment and testing; that is, the two steps that were labeled as “optional” by ChatGPT. For this tutorial build the model as is seen in Figure 1 and run it.  After you run the pipeline, you can see how well the model performed. To see the statistics, you can click the Evaluate Model component to see the statistics for the model.  There is a lot of information that can be unpacked, but you can see the scores by navigating to the metrics tab.  If you used the same dataset, your numbers should be around the values that are in Figure 2.Figure 2 – Linear Regression OutputsAt this point, ChatGPT has guided us in building a linear regression model.  Overall, the linear regression model that ChatGPT guided us to build is a very simple model that, all things considered, is fairly accurate. SummaryThis tutorial has been a crash course on how ChatGPT can be used to build no-code solutions in Azure Machine Learning Studios.  What’s incredible about this tutorial is that we used AI to help build another AI system.  However, as was seen in the tutorial, ChatGPT was only a guide.  For graphical systems, ChatGPT, at best, can be used as a guide.  This means that for systems like Azure Machine Learning Studio, a basic understanding of using the system is required.  As such, this means that for graphical systems ChatGPT is best utilized by persons that are knowledgeable of the system but need guidance for the task at hand.  For example, if this were a real project the ideal engineer would be a person that knows how to use Azure Machine Learning Studio but needs help with creating the pipeline logic. In terms of graphical-based programming, ChatGPT is almost a little ironic.  For example, when it comes to text-based programming in Java, Python, or whatever other language ChatGPT can be used to generate working code.  However, due to the current limitation of ChatGPT not being able to generate graphical programs for systems like Azure Machine Learning Studio a person will require a more in-depth knowledge of the system.  As ChatGPT matures this may change but for now, it is best to still have a knowledgeable engineer driving ChatGPT and implementing its solutions.  Overall, ChatGPT is an excellent assistant but will require a person that is knowledgeable of the technology being used. Author BioM.T. White has been programming since the age of 12. His fascination with robotics flourished when he was a child programming microcontrollers such as Arduino. M.T. currently holds an undergraduate degree in mathematics, and a master's degree in software engineering, and is currently working on an MBA in IT project management. M.T. is currently working as a software developer for a major US defense contractor and is an adjunct CIS instructor at ECPI University. His background mostly stems from the automation industry where he programmed PLCs and HMIs for many different types of applications. M.T. has programmed many different brands of PLCs over the years and has developed HMIs using many different tools.Author of the book: Mastering PLC Programming