ChatGPT | 0 articles | Tech News, Tutorials & Expert Insights

27 Sep 2023

11 min read

ChatGPT for Data Analysis

27 Sep 2023

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20764

article-image-decoding-complex-code-with-chatgpt

Dan MacLean

14 Nov 2023

7 min read

Decoding Complex Code with ChatGPT

Dan MacLean

14 Nov 2023

7 min read

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

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20185

article-image-chatgpt-as-a-debugging-tool

M.T. White

22 Aug 2023

14 min read

ChatGPT as a Debugging Tool

M.T. White

22 Aug 2023

14 min read

IntroductionNo matter the technology or application debugging is a major part of software development. Every developer who has ever written a program of any significant size knows that the application is going to have some kind of defect in it and probably won’t build the first few times it is run. In short, a vast amount of time and energy is spent debugging software. In many cases, debugging code can be more challenging than writing the code in the first place. With the advent of systems like ChatGPT, spending hours debugging a piece of code may be a thing of the past, at least for relatively small code blocks. This tutorial is going to explore prompts that we can use to have ChatGPT troubleshoot defective code for us. ExpectationsBefore we can explore troubleshooting with ChatGPT, we need to first set some realistic expectations. To begin, ChatGPT works off a series of inputs known as prompts. For ChatGPT to fix a code block, you’ll first have to submit the code and the issue as a prompt. At first glance, this may not seem like a big deal; however, modern applications are conglomerates of many smaller components that rely on each other to function correctly. On top of that, many of these “smaller” components may be, and usually are, composed of hundreds, if not thousands of lines of code or more. This means that a defect may not stem from the current code block but from a service or line of code, some were no one may consider. As such, if the root of the defect is not inputted into the prompt, ChatGPT may not be of much use. This means that to properly use ChatGPT as a troubleshooting tool, it is important to at least have a clue as to where the offending code is; this is because pasting in thousands, if not millions, of lines of code, is impractical. Ultimately, ChatGPT, at least the web version, cannot be used as a troubleshooting tool without a person who is knowledgeable in both the codebase, debugging, and coding guiding it. With that, let’s start troubleshooting!Troubleshooting with ChatGPTBefore we begin troubleshooting, let’s define the bugs we’re going to look at in this example. Though there are many different types of software defects, this tutorial is going to use ChatGPT to troubleshoot compilation and logical errors as examples. As such, we’re going to define these two terms with the following:Compilation Error: Any error that will prevent the source code from compiling.Logic Error: A software defect that gives an unanticipated result. For these examples, we’re going to use C and Python as the programming languages. However, due to the nature of ChatGPT, the prompts can be modified to accommodate other languages. We’re also going to assume that the program can fit in a single file; that is, we’re going to have ChatGPT analyze code snippets. For now, let’s start by exploring some compilation errors!Compilation ErrorTo begin with, let’s take a look at a very simple Hello World program in C. Consider the following code,#include <stdio.h> int main(){ printf("Hello World") }If you look at the code, you will see that there is a missing semicolon on the print line. This is an obvious issue that the compiler will pick up, but for grins, let’s see what ChatGPT has to say. For this example, we’re going to use the following prompt:Why will this C code not compile? #include <stdio.h> int main(){ printf("Hello World") }This prompt will output the following response:The C code you provided will not compile because there is a missing semicolon (;) at the end of the printf statement in the main function. Here's the corrected code: #include <stdio.h> int main() { printf("Hello World"); return 0; } In C, semicolons are used to terminate statements, including function calls like printf(). The absence of a semicolon after printf("Hello World") causes a compilation error. Adding the semicolon at the end resolves this issue. As can be seen, ChatGPT instantly caught the error. Now, this is a very simple error that basic tools such as the compiler would normally catch. Now, let’s explore how ChatGPT can troubleshoot a program in Python. As many developers know, Python has a unique syntax compared to C and other C-like languages such as C++, C#, or Java. Instead of using curly braces, Python uses indentation to denote hierarchy which can oftentimes lead to problems that are hard to diagnose, especially for the inexperienced. As such, consider the following code block:some_number = 2 if some_number <= 2: print("some number is less than 2")An experienced Python developer would probably catch this issue quickly. However, for this example, we’re going to let ChatGPT troubleshoot the code. For this code block, we’re going to use the following prompt:What is the syntax error with this Python code block?some_number = 2if some_number <= 2:print("some number is less than 2")This prompt will output the following response:The syntax error in the given Python code block is that the print statement is not indented properly. In Python, indentation is crucial to define the structure and hierarchy of code blocks. Here's the corrected version: some_number = 2 if some_number <= 2: print("some number is less than or equal to 2") In this corrected code, the print statement is indented with four spaces (or a tab) to align it with the if statement. This ensures that it is part of the code block executed when the condition is true.In this output, ChatGPT gave us the correct syntax as well as an explanation of the problem and solution. Similar to the C code, these are problems that are usually picked up by the IDE or the compilation system. However, it is not that uncommon to have to make code modifications in a plain text editor or to get tracebacks that aren’t very useful. In these cases, ChatGPT could be an awesome tool to use to quickly fix issues. Now that we’ve explored compilation issues let’s take a look at logical errors.Logical ErrorsLogical errors are oftentimes the hardest to troubleshoot, and many stem from very simple errors, such as using the wrong variable in a control statement, using the wrong math equation, or anything of the sort. Often these are the hardest issues to troubleshoot because unless they are an oversight, the developer will think their logic is sound. As such, let’s explore how ChatGPT can be used to clarify a developer’s logic. Consider the following C code:#include <stdio.h> int main(){ int a = 2; int b = 3; if (b > a){ printf("b < a"); }else{ printf("a > b"); } return 0; }The purpose of this code is to show which value is greater, a or b. However, when the code is run with the current values, it will return the following output:Figure 1 – C Program OutputObviously, there is a logic error in the code. To troubleshoot it, let’s use the following prompt in ChatGPT:This code is producing the wrong results, it is saying that b is less than a when the inverse is true. What is wrong with this code #include <stdio.h>int main(){ int a = 2; int b = 3; if (b > a){ printf("b < a"); }else{ printf("a > b"); } return 0;}When the prompt is executed, it will return the following:The issue with the given C code is the incorrect comparison operator in the printf statements. The condition inside the if statement checks if b is greater than a, but the printf statements incorrectly state the opposite. To fix this, you need to update the printf statements to reflect the correct condition. Here's the corrected code: #include <stdio.h> int main() { int a = 2; int b = 3; if (b > a) { printf("b > a"); } else { printf("a >= b"); } return 0; } Now the code will output "b > a" when b is indeed greater than a, and "a >= b" otherwise.Again, ChatGPT gave a very clear and concise diagnosis and solution. In short, the conditional statement in the if statement was wrong. Now, let’s look at a snippet in Python. Consider the following code:def testFunction(): sum = 0 for i in range(10): sum += i print(i) testFunction()In short, if you tried to run this code, nothing would happen. A Python developer would probably catch this error quickly, but let’s see what ChatGPT has to say. To start, let's use the following prompt:This function will not executedef testFunction():sum = 0 for i in range(10):sum += i print(i)testFunction()After running this prompt, you should get something akin to the following:The Python function you provided has a recursive call to itself (testFunction()) within its own body. This recursive call causes an infinite recursion, which results in a "RecursionError: maximum recursion depth exceeded" error. If you intended to have a recursive function, you should define the base case that stops the recursion. However, in this case, it seems like you want to call the testFunction() only once, without recursion. To fix the code, you can remove the testFunction() call from within the function body. Here's the corrected version: def testFunction(): sum = 0 for i in range(10): sum += i print(i) testFunction() Now the function will execute correctly, iterating through the numbers 0 to 9 and printing each number on a separate line, without causing any recursion errors.This time the diagnosis isn’t as clear because there are a couple of errors that could be the issue. The first is that ChatGPT thinks this might be a recursive function, in which case, ChatGPT suggests putting in an exit for the function. However, if you read the output a little closer, it suggests that the developer is trying to call the function. In this case, it says that the function call is embedded in the function itself and provides the correct working code. As such, it diagnosed and troubleshot the code.Key TakeawaysChatGPT is an excellent way to troubleshoot code. It should be noted that the code in this tutorial was relatively simple and short. With that, ChatGPT is excellent at troubleshooting small snippets, for example, methods or maybe even whole classes. However, for extremely complex problems, that is, problems that require many lines of code to be examined, ChatGPT may not be the optimal tool because all those lines have to be inputted into the prompt. Considering the problem, ChatGPT may get confused with the code, and complex prompts may have to be engineered to find the problem. However, if you have a rough idea of where the defect originates from, like which class file, it may be worthwhile to run the code through ChatGPT. If nothing else, it probably will give you a fresh perspective and, at the very least, point you in the right direction. The key to using ChatGPT as a troubleshooting tool is giving it the proper information. As we saw with the compilation and logic errors, a compilation error only needed the source code; however, that prompt could have been optimized with a description of the problem. On the other hand, to get the most out of logic errors, you’re going to want to include the following at a minimum: The programming language The code (At least the suspected offending code) A description of the problem Any other relevant informationSo far, the more information you provide to ChatGPT, the better the results are, but as we saw, a short description of the problem took care of the logic errors. Now, you could get away without specifying the problem, but when it comes to logical errors, it is wise to at least give a short description of the problem. ChatGPT is not infallible, and as we saw with the Python function, ChatGPT wasn’t too sure if the function was meant to be recursive or not. This means, much like a human, it needs to know as much about the problem as it can to accurately diagnose it.SummaryIn all, ChatGPT is a great tool for troubleshooting code. This tool would be ideal for compilation errors when tracebacks are not useful or not available. In terms of it being a tool for troubleshooting logical errors, ChatGPT can also be very useful. However, more information will be required for ChatGPT to accurately diagnose the problems. Again, the examples in this tutorial are very simple and straightforward. The goal was to simply demonstrate what kind of prompts can be used and the results of those inputs. However, as was seen with the Python function, a complex code block can and probably will confuse the AI. This means that as a user, you have to provide as detailed information as you can to ChatGPT. It is also important to remember that no matter how you use the system, you will still need to use critical thinking and detective work yourself to hunt down the problem. ChatGPT is by no means a replacement for human developers, at least not yet. This means it is important to think of ChatGPT as another set of eyes on a problem and not a one-stop solution for a problem. 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

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19911

article-image-getting-started-with-the-chatgpt-api

Martin Yanev

21 Sep 2023

9 min read

Getting Started with the ChatGPT API

Martin Yanev

21 Sep 2023

9 min read

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, Building AI Applications with ChatGPT APIs, by Martin Yanev. 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'll walk you through the essential steps to get started with ChatGPT, from creating your OpenAI account to accessing the ChatGPT API. Whether you're a seasoned developer or a curious beginner, you'll learn how to harness the capabilities of ChatGPT, understand tokens, and pricing, and explore its versatility in various NLP tasks. Get ready to unlock the potential of ChatGPT and embark on a journey of seamless communication with AI.Creating an OpenAI AccountBefore using ChatGPT or the ChatGPT API, you must create an account on the OpenAI website, which will give you access to all the tools that the company has developed. To do that, you can visit https://chat.openai.com, where you will be asked to either login or sign up for a new account, as shown in Figure 1.1: OpenAI Welcome WindowSimply click the Sign up button and follow the prompts to access the registration window (see Figure 1.2). From there, you have the option to enter your email address and click Continue, or you can opt to register using your Google or Microsoft account. Once this step is complete, you can select a password and validate your email, just like with any other website registration process.After completing the registration process, you can begin exploring ChatGPT’s full range of features. Simply click the Log in button depicted in Figure 1.1 and enter your credentials into the Log In window. Upon successfully logging in, you’ll gain full access to ChatGPT and all other OpenAI products. With this straightforward approach to access, you can seamlessly explore the full capabilities of ChatGPT and see firsthand why it’s become such a powerful tool for natural language processing tasks.OpenAI Registration WindowNow we can explore the features and functionality of the ChatGPT web interface in greater detail. We’ll show you how to navigate the interface and make the most of its various options to get the best possible results from the AI model.ChatGPT Web InterfaceThe ChatGPT web interface allows users to interact with the AI model. Once a user registers for the service and logs in, they can enter text prompts or questions into a chat window and receive responses from the model. You can ask ChatGPT anything using the Send a message… text field. The chat window also displays previous messages and prompts, allowing users to keep track of the conversation’s context, as shown in the below figure:ChatGPT Following Conversational ContextIn addition to that, ChatGPT allows users to easily record the history of their interactions with the model. Users’ chat logs are automatically saved, which can later be accessed from the left sidebar for reference or analysis. This feature is especially useful for researchers or individuals who want to keep track of their conversations with the model and evaluate its performance over time. The chat logs can also be used to train other models or compare the performance of different models. You are now able to distinguish and use the advancements of different ChatGPT models. You can also use ChatGPT from the web, including creating an account and generating API keys. The ChatGPT API is flexible, customizable, and can save developers time and resources, making it an ideal choice for chatbots, virtual assistants, and automated content generation. In the next section, you will learn how to access the ChatGPT API easily using Python.Getting Started with the ChatGPT APIThe ChatGPT API is an application programming interface developed by OpenAI that allows developers to interact with Generative Pre-trained Transformer (GPT) models for natural language processing (NLP) tasks. This API provides an easy-to-use interface for generating text, completing prompts, answering questions, and carrying out other NLP tasks using state-of-the-art machine learning models.The ChatGPT API is used for chatbots, virtual assistants, and automated content generation. It can also be used for language translation, sentiment analysis, and content classification. The API is flexible and customizable, allowing developers to fine-tune the model’s performance for their specific use case. Let’s now discover the process of obtaining an API key. This is the first step to accessing the ChatGPT API from your own applications.Obtaining an API KeyTo use the ChatGPT API, you will need to obtain an API key. This can be obtained from OpenAI. This key will allow you to authenticate your requests to the API and ensure that only authorized users can access your account.To obtain an API key, you must access the OpenAI Platform at https://platform.openai. com using your ChatGPT credentials. The OpenAI Platform page provides a central hub for managing your OpenAI resources. Once you have signed up, you can navigate to the API access page: https:// platform.openai.com/account/api-keys. On the API access page, you can manage your API keys for the ChatGPT API and other OpenAI services. You can generate new API keys, view and edit the permissions associated with each key, and monitor your usage of the APIs. The page provides a clear overview of your API keys, including their names, types, and creation dates, and allows you to easily revoke or regenerate keys as needed.Click on the +Create new secret key button and your API key will be created: Creating an API KeyAfter creating your API key, you will only have one chance to copy it (see below figure). It’s important to keep your API key secure and confidential, as anyone who has access to your key could potentially access your account and use your resources. You should also be careful not to share your key with unauthorized users and avoid committing your key to public repositories or sharing it in plain text over insecure channels.Saving an API KeyCopying and pasting the API key in our applications and scripts allows us to use the ChatGPT API. Now, let’s examine the ChatGPT tokens and their involvement in the OpenAI pricing model.API Tokens and PricingWhen working with ChatGPT APIs, it’s important to understand the concept of tokens. Tokens are the basic units of text used by models to process and understand the input and output text.Tokens can be words or chunks of characters and are created by breaking down the text into smaller pieces. For instance, the word “hamburger” can be broken down into “ham,” “bur,” and “ger,” while a shorter word such as “pear” is a single token. Tokens can also start with whitespace, such as “ hello” or “ bye”.The number of tokens used in an API request depends on the length of both the input and output text. As a rule of thumb, one token corresponds to approximately 4 characters or 0.75 words in English text. It’s important to note that the combined length of the text prompt and generated response must not exceed the maximum context length of the model. Table 1.1 shows the token limits of some of the popular ChatGPT models.API model token limitsTo learn more about how text is translated into tokens, you can check out OpenAI’s Tokenizer tool. The tokenizer tool is a helpful resource provided by OpenAI for understanding how text is translated into tokens. This tool breaks down text into individual tokens and displays their corresponding byte offsets, which can be useful for analyzing and understanding the structure of your text.You can find the tokenizer tool at https://platform.openai.com/tokenizer. To use the tokenizer tool, simply enter the text you want to analyze and select the appropriate model and settings.The tool will then generate a list of tokens, along with their corresponding byte offsets (see below figure).The Tokenizer ToolThe ChatGPT API pricing is structured such that you are charged per 1,000 tokens processed, with a minimum charge per API request. This means that the longer your input and output texts are, the more tokens will be processed and the higher the cost will be. Table 1.2 displays the cost of processing 1,000 tokens for several commonly used ChatGPT models.ChatGPT API Model PricingImportant noteIt is important to keep an eye on your token usage to avoid unexpected charges. You can track your usage and monitor your billing information through the Usage dashboard at https:// platform.openai.com/account/usage.As you can see, ChatGPT has an easy-to-use interface that allows developers to interact with GPT models for natural language processing tasks. Tokens are the basic units of text used by the models to process and understand the input and output text. The pricing structure for the ChatGPT API is based on the number of tokens processed, with a minimum charge per API request.ConclusionIn conclusion, this article has provided a comprehensive overview of the essential steps to embark on your journey with OpenAI and ChatGPT. We began by guiding you through the process of creating an OpenAI account, ensuring you have seamless access to the myriad tools offered by the company. We then delved into the ChatGPT web interface, showing you how to navigate its features effectively for productive interactions with the AI model. Moreover, we explored the ChatGPT API, highlighting its versatility and use cases in various NLP tasks. Understanding tokens and pricing was demystified, allowing you to make informed decisions. As you embark on your ChatGPT journey, you are well-equipped with the knowledge to harness its potential for your unique needs. Happy exploring!Author BioMartin Yanev is an experienced Software Engineer who has worked in the aerospace and industries for over 8 years. He specializes in developing and integrating software solutions for air traffic control and chromatography systems. Martin is a well-respected instructor with over 280,000 students worldwide, and he is skilled in using frameworks like Flask, Django, Pytest, and TensorFlow. He is an expert in building, training, and fine-tuning AI systems with the full range of OpenAI APIs. Martin has dual master's degrees in Aerospace Systems and Software Engineering, which demonstrates his commitment to both practical and theoretical aspects of the industry.

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19413

Merlyn Shelley

31 Aug 2023

14 min read

AI_Distilled #15: OpenAI Unveils ChatGPT Enterprise, Code Llama by Meta, VulcanSQL from Hugging Face, Microsoft's "Algorithm of Thoughts”, Google DeepMind's SynthID

Merlyn Shelley

31 Aug 2023

14 min read

👋 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.

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19155

article-image-harnessing-chatgpt-and-gpt-3

Deborah A. Dahl

16 Oct 2023

8 min read

Harnessing ChatGPT and GPT-3

Deborah A. Dahl

16 Oct 2023

8 min read

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, Natural Language Understanding with Python, by Deborah A. Dahl. Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systemsIntroductionIn the world of artificial intelligence, ChatGPT stands as a versatile conversational agent, adept at handling generic information interactions. While customization can be a challenge at present, ChatGPT offers a unique avenue for developers and AI enthusiasts alike. Beyond chat-based dialogue, it holds the potential to streamline the often time-consuming process of generating training data for conventional applications. In this article, we delve into the capabilities of ChatGPT and explore the journey of fine-tuning GPT-3 for specific use cases. By the end, you'll be equipped to harness the power of these language models, from data generation to AI customization, in your projects. Let's embark on this exciting AI journey together.ChatGPTChatGPT (https://openai.com/blog/chatgpt/) is a system that can interact with users about generic information in a very capable way. Although at the time of writing, it is hard to customize ChatGPT for specific applications, it can be useful for other purposes than customized natural language applications. For example, it can very easily be used to generate training data for a conventional application. If we wanted to develop a banking application using some of the techniques discussed earlier in this book, we would need training data to provide the system with examples of how users might ask the system questions. Typically, this involves a process of collecting actual user input, which could be very time-consuming. ChatGPT could be used to generate training data instead, by simply asking it for examples. For example, for the prompt give me 10 examples of how someone might ask for their checking balance, ChatGPT responded with the sentences in Figure 11.3:Figure 11.3 – GPT-3 generated training data for a banking applicationMost of these seem like pretty reasonable queries about a checking account, but some of them don’t seem very natural. For that reason, data generated in this way always needs to be reviewed. For example, a developer might decide not to include the second to the last example in a training set because it sounds stilted, but overall, this technique has the potential to save developers quite a bit of time.Applying GPT-3Another well-known LLM, GPT-3, can also be fine-tuned with application-specific data, which should result in better performance. To do this, you need an OpenAI key because using GPT-3 is a paid service. Both fine-tuning to prepare the model and using the fine-tuned model to process new data at inference time will incur a cost, so it is important to verify that the training process is performing as expected before training with a large dataset and incurring the associated expense.OpenAI recommends the following steps to fine-tune a GPT-3 model.1. Sign up for an account at https://openai.com/ and obtain an API key. The API key will be used to track your usage and charge your account accordingly.2. Install the OpenAI command-line interface (CLI) with the following command:! pip install --upgrade openaiThis command can be used at a terminal prompt in Unix-like systems (some developers have reported problems with Windows or macOS). Alternatively, you can install GPT-3 to be used in a Jupyter notebook with the following code:!pip install --upgrade openaiAll of the following examples assume that the code is running in a Jupyter notebook:1. Set your API key:api_key =<your API key> openai.api_key = api_key2. The next step is to specify the training data that you will use for fine-tuning GPT-3 for your application. This is very similar to the process of training any NLP system; however, GPT-3 has a specific format that must be used for training data. This format uses a syntax called JSONL, where every line is an independent JSON expression. For example, if we want to fine-tune GPT-3 to classify movie reviews, a couple of data items would look like the following (omitting some of the text for clarity):{"prompt":"this film is extraordinarily horrendous and i'm not going to waste any more words on it . ","completion":" negative"} {"prompt":"9 : its pathetic attempt at \" improving \" on a shakespeare classic . 8 : its just another piece of teen fluff . 7 : kids in high school are not that witty . … ","completion":" negative"} {"prompt":"claire danes , giovanni ribisi , and omar epps make a likable trio of protagonists , …","completion":" negative"}Each item consists of a JSON dict with two keys, prompt and completion. prompt is the text to be classified, and completion is the correct classification. All three of these items are negative reviews, so the completions are all marked as negative.It might not always be convenient to get your data into this format if it is already in another format, but OpenAI provides a useful tool for converting other formats into JSONL. It accepts a wide range of input formats, such as CSV, TSV, XLSX, and JSON, with the only requirement for the input being that it contains two columns with prompt and completion headers. Table 11.2 shows a few cells from an Excel spreadsheet with some movie reviews as an example:promptcompletionkolya is one of the richest films i’ve seen in some time . zdenek sverak plays a confirmed old bachelor ( who’s likely to remain so ) , who finds his life as a czech cellist increasingly impacted by the five-year old boy that he’s taking care of …positivethis three hour movie opens up with a view of singer/guitar player/musician/ composer frank zappa rehearsing with his fellow band members . all the rest displays a compilation of footage , mostly from the concert at the palladium in new york city , halloween 1979 …positive`strange days’ chronicles the last two days of 1999 in los angeles . as the locals gear up for the new millenium , lenny nero ( ralph fiennes ) goes about his business …positiveTable 11.2 – Movie review data for fine-tuning GPT-3To convert one of these alternative formats into JSONL, you can use the fine_tunes.prepare_ data tool, as shown here, assuming that your data is contained in the movies.csv file:!openai tools fine_tunes.prepare_data -f ./movies.csv -qThe fine_tunes.prepare_data utility will create a JSONL file of the data and will also provide some diagnostic information that can help improve the data. The most important diagnostic that it provides is whether or not the amount of data is sufficient. OpenAI recommends several hundred examples of good performance. Other diagnostics include various types of formatting information such as separators between the prompts and the completions.After the data is correctly formatted, you can upload it to your OpenAI account and save the filename:file_name = "./movies_prepared.jsonl" upload_response = openai.File.create( file=open(file_name, "rb"), purpose='fine-tune' ) file_id = upload_response.idThe next step is to create and save a fine-tuned model. There are several different OpenAI models that can be used. The one we’re using here, ada, is the fastest and least expensive, and does a good job on many classification tasks:openai.FineTune.create(training_file=file_id, model="ada") fine_tuned_model = fine_tune_response.fine_tuned_modelFinally, we can test the model with a new prompt:answer = openai.Completion.create( model = fine_tuned_model, engine = "ada", prompt = " I don't like this movie ", max_tokens = 10, # Change amount of tokens for longer completion temperature = 0 ) answer['choices'][0]['text']In this example, since we are only using a few fine-tuning utterances, the results will not be very good. You are encouraged to experiment with larger amounts of training data.ConclusionIn conclusion, ChatGPT and GPT-3 offer invaluable tools for AI enthusiasts and developers alike. From data generation to fine-tuning for specific applications, these models present a world of possibilities. As we've seen, ChatGPT can expedite the process of creating training data, while GPT-3's customization can elevate the performance of your AI applications. As the field of artificial intelligence continues to evolve, these models hold immense promise. So, whether you're looking to streamline your development process or take your AI solutions to the next level, the journey with ChatGPT and GPT-3 is an exciting one filled with untapped potential. Embrace the future of AI with confidence and innovation.Author BioDeborah A. Dahl is the principal at Conversational Technologies, with over 30 years of experience in natural language understanding technology. She has developed numerous natural language processing systems for research, commercial, and government applications, including a system for NASA, and speech and natural language components on Android. She has taught over 20 workshops on natural language processing, consulted on many natural language processing applications for her customers, and written over 75 technical papers. This is Deborah’s fourth book on natural language understanding topics. Deborah has a PhD in linguistics from the University of Minnesota and postdoctoral studies in cognitive science from the University of Pennsylvania.

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18773

article-image-using-chatgpt-for-data-enrichment

Jyoti Pathak

06 Nov 2023

10 min read

Using ChatGPT For Data Enrichment

Jyoti Pathak

06 Nov 2023

10 min read

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

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18600

article-image-chatgpt-prompting-basics-finding-your-ip-address

Clint Bodungen

18 Oct 2023

6 min read

ChatGPT Prompting Basics: Finding Your IP Address

Clint Bodungen

18 Oct 2023

6 min read

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Cybersecurity Cookbook, by Clint Bodungen. Master ChatGPT and the OpenAI API, and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.IntroductionIn this article, we will explore the basics of ChatGPT prompting using the ChatGPT interface, which is different from the OpenAI Playground we used in the previous recipe. The advantage of using the ChatGPT interface is that it does not consume account credits and is better suited for generating formatted output, such as writing code or creating tables. Getting ready To use the ChatGPT interface, you will need to have an active OpenAI account. If you haven't already, please set up your ChatGPT account. How to do it… In this recipe, we'll guide you through using the ChatGPT interface to generate a Python script that retrieves a user's public IP address. By following these steps, you'll learn how to interact with ChatGPT in a conversation-like manner and receive context-aware responses, including code snippets. Now, let's proceed with the steps in this recipe: 1. In your browser, go to https://chat.openai.com and click “Log in” 2. Log in using your OpenAI credentials. 3. Once you are logged in, you will be taken to the ChatGPT interface. The interface is similar to a chat application, with a text box at the bottom where you can enter your prompts. Figure – The ChatGPT interface 4. ChatGPT uses a conversation-based approach, so you can simply type your prompt as a message and press "Enter" or click the button to receive a response from the model. For example, you can ask ChatGPT to generate a piece of Python code to find the public IP address of a user: Figure – Entering a prompt ChatGPT will generate a response containing the requested Python code, along with a thorough explanation. Figure – ChatGPT response with code 5. Continue the conversation by asking follow-up questions or providing additional information, and ChatGPT will respond accordingly. Figure – ChatGPT contextual follow-up response 6. Run the ChatGPT generated code by clicking on “Copy code”, paste it into your code editor of choice (I personally use Visual Studio Code), save it as a “.py” Python script, and run from a terminal. PS D:\GPT\ChatGPT for Cybersecurity Cookbook> python .\my_ip.py Your public IP address is: Your local network IP address is: 192.168.1.105 Figure – Running the ChatGPT generated script How it works… By using the ChatGPT interface to enter prompts, you can generate context-aware responses and content that continues over the course of an entire conversation like a chatbot. The conversation-based approach allows for more natural interactions and the ability to ask follow-up questions or provide additional context. The responses can even include complex formatting such as code snippets or tables (more on tables later). There’s more… As you become more familiar with ChatGPT, you can experiment with different prompt styles, instructions, and contexts to obtain the desired output for your cybersecurity tasks. You can also compare the results generated through the ChatGPT interface and the OpenAI Playground to determine which approach best fits your needs. Tip:You can further refine the generated output by providing very clear and specific instructions or using roles. It also helps to divide complex prompts into several smaller prompts, giving ChatGPT one instruction per prompt, building on the previous prompts as you go. In the upcoming recipes, we will delve into more advanced prompting techniques that utilize these techniques to help you get the most accurate and detailed responses from ChatGPT. As you interact with ChatGPT, your conversation history is automatically saved in the left panel of the ChatGPT interface. This feature allows you to easily access and review your previous prompts and responses. By leveraging the conversation history feature, you can keep track of your interactions with ChatGPT and quickly reference previous responses for your cybersecurity tasks or other projects. Figure – Conversation history in the ChatGPT interface To view a saved conversation, simply click on the desired conversation in the left panel. You can also create new conversations by clicking on the "+ New chat" button located at the top of the conversation list. This enables you to separate and organize your prompts and responses based on specific tasks or topics. Caution Keep in mind that when you start a new conversation, the model loses the context of the previous conversation. If you want to reference any information from a previous conversation, you will need to include that context in your new prompt. ConclusionIn conclusion, this article has unveiled the power of ChatGPT and its conversation-driven approach, making complex tasks like retrieving your public IP address a breeze. With step-by-step guidance, you've learned to harness ChatGPT's capabilities and enjoy context-aware responses, all while keeping your account credits intact. As you dive deeper into the world of ChatGPT, you'll discover its versatility in various applications and the potential to optimize your cybersecurity endeavors. By mastering ChatGPT's conversational prowess, you're on the path to seamless, productive interactions and a future filled with AI-driven possibilities.Author BioClint Bodungen is a cybersecurity professional with 25+ years of experience and the author of Hacking Exposed: Industrial Control Systems. He began his career in the United States Air Force and has since many of the world's largest energy companies and organizations, working for notable cybersecurity companies such as Symantec, Kaspersky Lab, and Booz Allen Hamilton. He has published multiple articles, technical papers, and training courses on cybersecurity and aims to revolutionize cybersecurity education using computer gaming (“gamification”) and AI technology. His flagship product, ThreatGEN® Red vs. Blue, is the world’s first online multiplayer cybersecurity simulation game, designed to teach real-world cybersecurity.

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18545

article-image-chatgpt-for-quantum-computing

Anshul Saxena

03 Nov 2023

7 min read

ChatGPT for Quantum Computing

Anshul Saxena

03 Nov 2023

7 min read

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!IntroductionHello there, fellow explorer! So, you've been hearing about this thing called 'quantum computing' and how it promises to revolutionize... well, almost everything. And you're curious about how we can harness its power, right? But there's a twist: you want to use ChatGPT to help guide the process. Intriguing! In this tutorial, I'll take you by the hand, and together, we'll craft some amazing use cases for quantum computing, all with the help of ChatGPT prompts.First, we'll lay down our goals. What exactly do we want to achieve with quantum computing? Maybe it's predicting the weather years in advance, or understanding the deep mysteries of our oceans. Once we have our roadmap, it's time to gather our tools and data. Here's where satellites, weather stations, and another cool tech come in.But data can be messy, right? No worries! We'll clean it up and get it ready for our quantum adventure. And then, brace yourself, because we're diving deep into the world of quantum mechanics. But fear not! With ChatGPT by our side, we'll decode the jargon and make it all crystal clear.The next steps? Designing our very own quantum algorithms and giving them a test run. It's like crafting a recipe and then baking the perfect cake. Once our quantum masterpiece is ready, we'll look at the results, decipher what they mean, and integrate them with existing tools. And because we always strive for perfection, we'll continuously refine our approach, ensuring it's the best it can be.Here's a streamlined 10-step process for modeling complex climate systems using quantum computing:Step 1. Objective Definition: Clearly define the specific goals of climate modeling, such as predicting long-term temperature changes, understanding oceanic interactions, or simulating atmospheric phenomena.Step 2. Data Acquisition: Gather comprehensive climate data from satellites, ground stations, and other relevant sources, focusing on parameters crucial for the modeling objectives.Step 3. Data Preprocessing: Clean and transform the climate data into a format suitable for quantum processing, addressing any missing values, inconsistencies, or noise.Step 4. Understanding Quantum Mechanics: Familiarize with the principles and capabilities of quantum computing, especially as they relate to complex system modeling.Step 5. Algorithm Selection/Design: Choose or develop quantum algorithms tailored to model the specific climate phenomena of interest. Consider hybrid algorithms that leverage both classical and quantum computations.Step 6. Quantum Simulation: Before deploying on real quantum hardware, simulate the chosen quantum algorithms on classical systems to gauge their efficacy and refine them as needed.Step 7. Quantum Execution: Implement the algorithms on quantum computers, monitoring performance and ensuring accurate modeling of the climate system.Step 8. Result Interpretation: Analyze the quantum computing outputs, translating them into actionable climate models, predictions, or insights.Step 9. Integration & Application: Merge the quantum-enhanced models with existing climate research tools and methodologies, ensuring the findings are accessible and actionable for researchers, policymakers, and stakeholders.Step 10. Review & Iteration: Regularly evaluate the quantum modeling process, updating algorithms and methodologies based on new data, quantum advancements, or evolving climate modeling objectives.Using quantum computing for modeling complex climate systems holds promise for more accurate and faster simulations, but it's essential to ensure the approach is methodical and scientifically rigorous.So, are you ready to create some quantum magic with ChatGPT? Let's jump right in!1. Objective DefinitionPrompt: "ChatGPT, can you help me outline the primary objectives and goals when modeling complex climate systems? What are the key phenomena and parameters we should focus on?"2. Data AcquisitionPrompt:"ChatGPT, where can I source comprehensive climate data suitable for quantum modeling? Can you list satellite databases, ground station networks, or other data repositories that might be relevant?"3. Data PreprocessingPrompt:"ChatGPT, what are the best practices for preprocessing climate data for quantum computing? How do I handle missing values, inconsistencies, or noise in the dataset?"4. Understanding Quantum MechanicsPrompt:"ChatGPT, can you give me a primer on the principles of quantum computing, especially as they might apply to modeling complex systems like climate?"5. Algorithm Selection/DesignPrompt:"ChatGPT, what quantum algorithms or techniques are best suited for climate modeling? Are there hybrid algorithms that combine classical and quantum methods for this purpose?"6. Quantum SimulationPrompt:"ChatGPT, how can I simulate quantum algorithms on classical systems before deploying them on quantum hardware? What tools or platforms would you recommend?"7. Quantum Execution Prompt:"ChatGPT, what are the steps to implement my chosen quantum algorithms on actual quantum computers? Are there specific quantum platforms or providers you'd recommend for climate modeling tasks?"8. Result InterpretationPrompt:"ChatGPT, once I have the outputs from the quantum computation, how do I interpret and translate them into meaningful climate models or predictions?"9. Integration & ApplicationPrompt:"ChatGPT, how can I integrate quantum-enhanced climate models with existing research tools and methodologies? What steps should I follow to make these models actionable for the broader research community?"10. Review & IterationPrompt:"ChatGPT, how should I periodically evaluate and refine my quantum modeling approach? What metrics or feedback mechanisms can help ensure the process remains optimal and up-to-date?"These prompts are designed to guide a user in leveraging ChatGPT's knowledge and insights for each step of the quantum computing-based climate modeling process.ConclusionAnd there you have it! From setting clear goals to diving into the intricate world of quantum mechanics and finally crafting our very own quantum algorithms, we've journeyed through the fascinating realm of quantum computing together. With ChatGPT as our trusty guide, we've unraveled complex concepts, tackled messy data, and brewed some quantum magic. It's been quite the adventure, hasn't it? Remember, the world of quantum computing is vast and ever-evolving, so there's always more to explore and learn. Whether you're a seasoned quantum enthusiast or just starting out, I hope this guide has ignited a spark of curiosity in you. As we part ways on this tutorial journey, I encourage you to keep exploring, questioning, and innovating. The quantum realm awaits your next adventure. Until next time, happy quantum-ing!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

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18404

article-image-using-chatgpt-with-text-to-speech

Denis Rothman

04 Jun 2023

7 min read

Using ChatGPT with Text to Speech

Denis Rothman

04 Jun 2023

7 min read

This article provides a quick guide to using the OpenAI API to jump-start ChatGPT. The guide includes instructions on how to use a microphone to speak to ChatGPT and how to create a ChatGPT request with variables. Additionally, the article explains how to use Google gTTS, a text-to-speech tool, to listen to ChatGPT's response. By following these steps, you can have a more interactive experience with ChatGPT and make use of its advanced natural language processing capabilities. We’re using the GPT-3.5-Turbo architecture in this example. We are also running the examples within Google Colab, but they should be applicable to other environments. In this article, we’ll cover: Installing OpenAI, your API key, and Google gTTS for Text-to-SpeechGenerating content with ChatGPTSpeech-to-text ChatGPT's responseTranscribing with WhisperTo understand GPT-3 Transformers in detail, read Transformers for NLP, 2nd Edition 1. Installing OpenAI, gTTS, and your API Key There are a few libraries that we’ll need to install into Colab for this project. We’ll install them as required, starting with OpenAI. Installing and Importing OpenAI To start using OpenAI's APIs and tools, we'll need to install the OpenAI Python package and import it into your project. To do this, you can use pip, a package manager for Python. First, make sure you have pip installed on your system. !pip install --upgrade pipNext, run the following script in your notebook to install the OpenAI package. It should come pre-installed in Colab:#Importing openaitry:import openaiexcept:!pip install openaiimport openai Installing gTTS Next, install Google gTTS a Python library that provides an easy-to-use interface for text-to-speech synthesis using the Google Text-to-Speech API:#Importing gTTStry:from gtts import gTTSexcept:!pip install gTTS from gtts import gTTS API Key Finally, import your API key. Rather than enter your key directly into your notebook, I recommend keeping it in a local file and importing it from your script. You will need to provide the correct path and filename in the code below.from google.colab import drivedrive.mount('/content/drive')f = open("drive/MyDrive/files/api_key.txt", "r")API_KEY=f.readline()f.close()#The OpenAI Keyimport osos.environ['OPENAI_API_KEY'] =API_KEYopenai.api_key = os.getenv("OPENAI_API_KEY") 2. Generating Content Let’s look at how to pass prompts into the OpenAI API to generate responses. Speech to text When it comes to speech recognition, Windows provides built-in speech-to-text functionality. However, third-party speech-to-text modules are also available, offering features such as multiple language support, speaker identification, and audio transcription. For simple speech-to-text, this notebook uses the built-in functionality in Windows. Press Windows key + H to bring up the Windows speech interface. You can read the documentation for more information.Note: For this notebook, press Enter when you have finished asking for a request in Colab. You could also adapt the function in your application with a timed input function that automatically sends a request after a certain amount of time has elapsed. Preparing the Prompt Note: you can create variables for each part of the OpenAI messages object. This object contains all the information needed to generate a response from ChatGPT, including the text prompt, the model ID, and the API key. By creating variables for each part of the object, you can make it easier to generate requests and responses programmatically. For example, you could create a prompt variable that contains the text prompt for generating a response. You could also create variables for the model ID and API key, making it easier to switch between different OpenAI models or accounts as needed.For more on implementing each part of the messages object, take a look at: Prompt_Engineering_as_an_alternative_to_fine_tuning.ipynb.Here’s the code for accepting the prompt and passing the request to OpenAI:#Speech to text. Use OS speech-to-text app. For example, Windows: press Windows Key + H def prepare_message():#enter the request with a microphone or type it if you wish # example: "Where is Tahiti located?" print("Enter a request and press ENTER:") uinput = input("") #preparing the prompt for OpenAI role="user" #prompt="Where is Tahiti located?" #maintenance or if you do not want to use a microphone line = {"role": role, "content": uinput} #creating the message assert1={"role": "system", "content": "You are a helpful assistant."} assert2={"role": "assistant", "content": "Geography is an important topic if you are going on a once in a lifetime trip."} assert3=line iprompt = [] iprompt.append(assert1) iprompt.append(assert2) iprompt.append(assert3) return iprompt#run the cell to start/continue a dialogiprompt=prepare_message() #preparing the messages for ChatGPTresponse=openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=iprompt) #ChatGPT dialogtext=response["choices"][0]["message"]["content"] #response in JSONprint("ChatGPT response:",text) Here's a sample of the output: Enter a request and press ENTER:Where is Tahiti locatedChatGPT response: Tahiti is located in the South Pacific Ocean, specifically in French Polynesia. It is part of a group of islands called the Society Islands and is located approximately 4,000 kilometers (2,500 miles) south of Hawaii and 7,850 kilometers (4,880 miles) east of Australia. 3. Speech-to-text the response GTTS and IPython Once you've generated a response from ChatGPT using the OpenAI package, the next step is to convert the text into speech using gTTS (Google Text-to-Speech) and play it back using IPython audio.from gtts import gTTSfrom IPython.display import Audiotts = gTTS(text)tts.save('1.wav')sound_file = '1.wav'Audio(sound_file, autoplay=True) 4. Transcribing with Whisper If your project requires the transcription of audio files, you can use OpenAI’s Whisper.First, we’ll install the ffmpeg audio processing library. ffmpeg is a popular open-source software suite for handling multimedia data, including audio and video files:!pip install ffmpegNext, we’ll install Whisper:!pip install git+https://github.com/openai/whisper.git With that done, we can use a simple command to transcribe the WAV file and store it as a JSON file with the same name:!whisper 1.wavYou’ll see Whisper transcribe the file in chunks:[00:00.000 --> 00:06.360] Tahiti is located in the South Pacific Ocean, specifically in the archipelago of society[00:06.360 --> 00:09.800] islands and is part of French Polynesia.[00:09.800 --> 00:22.360] It is approximately 4,000 miles, 6,400 km, south of Hawaii and 5,700 miles, 9,200 km,[00:22.360 --> 00:24.640] west of Santiago, Chile.Once that’s done, we can read the JSON file and display the text object:import json with open('1.json') as f: data = json.load(f) text = data['text'] print(text)This gives the following output:Tahiti is located in the South Pacific Ocean, specifically in the archipelago of society islands and is part of French Polynesia. It is approximately 4,000 miles, 6,400 km, south of Hawaii and 5,700 miles, 9,200 km, west of Santiago, Chile. By using Whisper in combination with ChatGPT and gTTS, you can create a fully featured AI-powered application that enables users to interact with your system using natural language inputs and receive audio responses. This might be useful for applications that involve transcribing meetings, conferences, or other audio files. About the Author Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution used worldwide.You can follow Denis on LinkedIn: https://www.linkedin.com/in/denis-rothman-0b034043/Copyright 2023 Denis Rothman, MIT License

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18392

article-image-using-openai-python-library-to-interact-with-the-chatgpt-api

Martin Yanev

04 Jun 2023

7 min read

Using OpenAI Python Library to Interact with the ChatGPT API

Martin Yanev

04 Jun 2023

7 min read

Using the ChatGPT API with Python is a relatively simple process. You'll first need to make sure you create a new PyCharm project called ChatGPTResponse as shown in the following screenshot: Fig 1: New Project Window in PyCharm Once you have that setup, you can use the OpenAI Python library to interact with the ChatGPT API. Open a new Terminal in PyCharm, make sure that you are in your project folder, and install the openai package: $ pip install openai Next, you need to create a new Python file in your PyCharm project on the left top corner perform a Right-click on the folder ChatGPTResponse | New | Python File. Name the file app.py and click hit Enter. You should now have a new Python file in your project directory: Fig 2: New Python File To get started, you'll need to import the openai library into your Python file. Also, you'll need to provide your OpenAI API Key. You can obtain an API Key from the OpenAI website by following the steps outlined in the previous sections of this book. Then you'll need to set it as a parameter in your Python code. Once your API Key is set up, you can start interacting with the ChatGPT API. import openaiopenai.api_key = "YOUR_API_KEY" Replace YOUR_API_KEY with the API key you obtained from the OpenAI platform page. Now, you can ask the user a question using the input() function: question = input("What would you like to ask ChatGPT? ") The input() function is used to prompt the user to input a question they would like to ask the ChatGPT API. The function takes a string as an argument, which is displayed to the user when the program is run. In this case, the question string is "What would you Like to ask ChatGPT?". When the user types their question and presses enter, the input() function will return the string that the user typed. This string is then assigned to the variable question. To pass the user question from your Python script to ChatGPT, you will need to use the ChatGPT API Completion function: response = openai.Completion.create( engine="text-davinci-003", prompt=question, max_tokens=1024, n=1, stop=None, temperature=0.8,) The openai.Completion.create() function in the code is used to send a request to the ChatGPT API to generate a completion of the user's input prompt. The engine parameter specifies the ChatGPT engine to use for the request, and in this case, it is set to "text-davinci-003". The prompt parameter specifies the text prompt for the API to complete, which is the user's input question in this case. The max_tokens parameter specifies the maximum number of tokens the response should contain. The n parameter specifies the number of completions to generate for the prompt. The stop parameter specifies the sequence where the API should stop generating the response. The temperature parameter controls the creativity of the generated response. It ranges from 0 to 1. Higher values will result in more creative but potentially less coherent responses, while lower values will result in more predictable but potentially fewer interesting responses. Later in the book, we will delve into how these parameters impact the responses received from ChatGPT.The function returns a JSON object containing the generated response from the ChatGPT API, which then can be accessed and printed to the console in the next line of code.print(response)In the project pane on the left-hand side of the screen, locate the Python file you want to run. Right-click on the app.py file and select Run app.py from the context menu. You should receive a message in the Run window that asks you to write a question to the ChatGPT. Fig 3: Run WindowOnce you have entered your question, press the Enter key to submit your request to the ChatGPT API. The response generated by the ChatGPT API model will be displayed in the Run window as a complete JSON object: { "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "text": "\n\n1. Start by getting in the water. If you're swimming in a pool, you can enter the water from the side, …………. } ], "created": 1681010983, "id": "cmpl-73G2JJCyBTfwCdIyZ7v5CTjxMiS6W", "model": "text-davinci-003", "object": "text_completion", "usage": { "completion_tokens": 415, "prompt_tokens": 4, "total_tokens": 419 }} This JSON response produced by the OpenAI API contains information about the response generated by the GPT-3 model. This response consists of the following fields:The choices field contains an array of objects with the generated responses, which in this case only contains one response object. The text field within the response object contains the actual response generated by the GPT-3 model.The finish_reason field indicates the reason why the response was generated; in this case it was because the model reached the stop condition specified in the API request.The created field specifies the Unix timestamp of when the response was created. The id field is a unique identifier for the API request that generated this response.The model field specifies the GPT-3 model that was used to generate the response. The object field specifies the type of object that was returned, which in this case is text_completion.The usage field provides information about the resource usage of the API request. It contains information about the number of tokens used for the completion, the number of tokens in the prompt, and the total number of tokens used.The two most important parameter from the response is the text field, that contains the answer to the question asked to ChatGPT API. This is why most API users would like to access only that parameter from the JSON object. You can easily separate the text from the main body as follows: answer = response["choices"][0]["text"]print(answer)By following this approach, you can guarantee that the variable answer will hold the complete ChatGPT API text response, which you can then print to verify. Keep in mind that ChatGPT responses can significantly differ depending on the input, making each response unique.OpenAI:1. Start by getting in the water. If you're swimming in a pool, you can enter the water from the side, ladder, or diving board. If you are swimming in the ocean or lake, you can enter the water from the shore or a dock.2. Take a deep breath in and then exhale slowly. This will help you relax and prepare for swimming.Summary In this tutorial, you implemented a simple ChatGPT API response, by sending a request to generate a completion of a user's input prompt/question. You have also learned how to set up your API Key and how to prompt the user to input a question, and finally, how to access the generated response from ChatGPT in the form of a JSON object containing information about the response. About the Author Martin Yanev is an experienced Software Engineer who has worked in the aerospace and medical industries for over 8 years. He specializes in developing and integrating software solutions for air traffic control and chromatography systems. Martin is a well-respected instructor with over 280,000 students worldwide, and he is skilled in using frameworks like Flask, Django, Pytest, and TensorFlow. He is an expert in building, training, and fine-tuning AI systems with the full range of OpenAI APIs. Martin has dual Master's degrees in Aerospace Systems and Software Engineering, which demonstrates his commitment to both practical and theoretical aspects of the industry.https://www.linkedin.com/in/martinyanev/https://www.udemy.com/user/martin-yanev-3/

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18383

article-image-autogpt-a-game-changer-in-ai-automation

Louis Owen

11 Oct 2023

9 min read

AutoGPT: A Game-Changer in AI Automation

Louis Owen

11 Oct 2023

9 min read

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn recent years, we've witnessed a technological revolution in the field of artificial intelligence. One of the most groundbreaking developments has been the advent of Large Language Models (LLMs). Since the release of ChatGPT, people have been both shocked and excited by the capabilities of this AI.Countless experiments have been conducted to push the boundaries and explore the full potential of LLMs. Traditionally, these experiments have involved incorporating AI as part of a larger pipeline. However, what if we told you that the entire process could be automated by the AI itself? Imagine just setting the goal of a task and then sitting back and relaxing while the AI takes care of everything, from scraping websites for information to summarizing content and executing connected plugins. Fortunately, this vision is no longer a distant dream. Welcome to the world of AutoGPT!AutoGPT is an experimental open-source application that showcases the remarkable capabilities of the GPT-4 language model. This program, driven by GPT-4, connects the dots between LLM "thoughts" to autonomously achieve whatever goal you set. It represents one of the first examples of GPT-4 running fully autonomously, effectively pushing the boundaries of what is possible with AI.AutoGPT comes packed with an array of features that make it a game-changer in the world of AI automation. Let's take a closer look at what sets this revolutionary tool apart:Internet Access for Searches and Information Gathering: AutoGPT has the power to access the internet, making it a formidable tool for information gathering. Whether you need to research a topic, gather data, or fetch real-time information, AutoGPT can navigate the web effortlessly.Long-Term and Short-Term Memory Management: Just like a human, AutoGPT has memory. It can remember context and information from previous interactions, enabling it to provide more coherent and contextually relevant responses.GPT-4 Instances for Text Generation: With the might of GPT-4 behind it, AutoGPT can generate high-quality text that is coherent, contextually accurate, and tailored to your specific needs. Whether it's drafting an email, writing code, or crafting a compelling story, AutoGPT has got you covered.Access to Popular Websites and Platforms: AutoGPT can access popular websites and platforms, interacting with them just as a human user would. This opens up endless possibilities, from automating routine tasks on social media to retrieving data from web applications.File Storage and Summarization with GPT-3.5: AutoGPT doesn't just generate text; it also manages files and can summarize content using the GPT-3.5 model. This means it can help you organize and understand your data more efficiently.Extensibility with Plugins: AutoGPT is highly extensible, thanks to its plugin architecture. You can customize its functionality by adding plugins tailored to your specific needs. Whether it's automating tasks in your business or streamlining personal chores, plugins make AutoGPT endlessly adaptable. For more information regarding plugins, you can check the official repo.Throughout this article, we’ll learn how to install AutoGPT and run it on your local computer. Moreover, we’ll also learn how to utilize it to build your own personal investment valuation analyst! Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn all about AutoGPT!Setting Up AutoGPTLet’s go through the process of setting up AutoGPT, whether you choose to use Docker or Git, setting up AutoGPT is pretty straightforward. But before we delve into the technical details, let's start with the most crucial step: obtaining an API key from OpenAI.Getting an API KeyTo use AutoGPT effectively, you'll need an API key from OpenAI. You can obtain this key by visiting the OpenAI API Key page at https://platform.openai.com/account/api-keys. It's essential to note that for seamless operation and to prevent potential crashes, we recommend setting up a billing account with OpenAI.Free accounts come with limitations, allowing only three API calls per minute. A paid account ensures a smoother experience. You can set up a paid account by following these steps:Go to "Manage Account."Navigate to "Billing."Click on "Overview."Setting up AutoGPT with DockerBefore you begin, make sure you have Docker installed on your system. If you haven't installed Docker yet, you can find the installation instructions here. Now, let’s start setting up AutoGPT with Docker.1. Open your terminal or command prompt.2. Create a project directory for AutoGPT. You can name it anything you like, but for this guide, we'll use "AutoGPT".mkdir AutoGPT cd AutoGPT3. In your project directory, create a file called `docker-compose.yml` and populate it with the following contents:version: "3.9" services: auto-gpt: image: significantgravitas/auto-gpt env_file: - .env profiles: ["exclude-from-up"] volumes: - ./auto_gpt_workspace:/app/auto_gpt_workspace - ./data:/app/data - ./logs:/app/logsThis configuration file specifies the settings for your AutoGPT Docker container, including environment variables and volume mounts.4. AutoGPT requires specific configuration files. You can find templates for these files in the AutoGPT repository. Create the necessary configuration files as needed.5. Before running AutoGPT, pull the latest image from Docker Hubdocker pull significantgravitas/auto-gpt6. With Docker Compose configured and the image pulled, you can now run AutoGPT:docker compose run --rm auto-gptThis command launches AutoGPT inside a Docker container, and it's all set to perform its AI-powered magic.Setting up AutoGPT with GitIf you prefer to set up AutoGPT using Git, here are the steps to follow:1. Ensure that you have Git installed on your system. You can download it from https://git-scm.com/downloads.2. Open your terminal or command prompt.3. Clone the AutoGPT repository using Git:git clone -b stable https://github.com/Significant-Gravitas/AutoGPT.git4. Navigate to the directory where you downloaded the repository:cd AutoGPT/autogpts/autogpt5. Run the startup scripta. On Linux/MacOS:./run.shb. On Windows:.\run.batIf you encounter errors, ensure that you have a compatible Python version installed and meet the requirements outlined in the documentation.AutoGPT for Your Personal Investment Valuation AnalystIn our previous article, we explored the exciting use case of building a personal investment news analyst with LLM. However, making sound investment decisions based solely on news articles is only one piece of the puzzle.To truly understand the potential of an investment, it's crucial to dive deeper into the financial health of the companies you're considering. This involves analyzing financial statements, including balance sheets, income statements, and cash flow statements. Yet, the sheer volume of data within these documents can be overwhelming, especially for newbie retail investors.Let’s see how AutoGPT is in action! Once the AutoGPT is up, we’ll be shown a welcome message and it will ask us to give the name of our AI, the role, and also the goals that we want to achieve. In this case, we’ll give the name of AI as “Personal Investment Valuation Analyst”. As for the role and goals, please see the attached image below.After we input the role and the goals, our assistant will start planning all of the things that it needs to do. It will give some thoughts along with the reasoning before creating a plan. Sometimes it’ll also criticize itself with the aim to create a better plan. Once the plan is laid out, it will ask for confirmation from the user. If the user is satisfied with the plan, then they can give their approval by typing “y”.Then, AutoGPT will execute each of the planned tasks. For example, here, it is browsing through the internet with the “official source of Apple financial statements” query.Based on the result of the first task, it learned that it needs to visit the corporate website of Apple, visit the invertor relations page, and then search for the required documents, which are the balance sheet, cashflow statement, and income statement. Look at this! Pretty amazing, right?The process then continues by searching through the investor relations page on the Apple website as planned in the previous step. This process will continue until the goals are achieved, which is to give recommendations to the user on whether to buy, sell, or hold the Apple stock based on valuation analysis.ConclusionCongratulations on keeping up to this point! Throughout this article, you have learned what is AutoGPT, how to install and run it on your local computer, and how to utilize it as your personal investment valuation analyst. Hope the best for your experiment with AutoGPT and see you in the next article!Author BioLouis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects.Currently, Louis is an NLP Research Engineer at Yellow.ai, the world’s leading CX automation platform. Check out Louis’ website to learn more about him! Lastly, if you have any queries or any topics to be discussed, please reach out to Louis via LinkedIn.

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18273

Amita Kapoor

05 Sep 2023

9 min read

ChatGPT for Healthcare

Amita Kapoor

05 Sep 2023

9 min read

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.

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18171

article-image-chatgpt-prompts-for-project-managers

Anshul Saxena

23 Oct 2023

10 min read

ChatGPT Prompts for Project Managers

Anshul Saxena

23 Oct 2023

10 min read

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!IntroductionStarting a project requires good tools to keep things running smoothly. That's where our guide, combining ChatGPT with PMBOK, comes in handy. We'll walk you through each step, from beginning your project to making detailed plans. With easy-to-use templates and clear examples, we aim to make things simpler for you. In short, our guide brings together the best of ChatGPT and PMBOK to help you manage projects better. Let's get started!First, let’s have a look at the steps defined under PMBOK for project management planningStep 1: Initiating the Project1. Objective: Set the foundation for your project.2. Actions: - 1.1 Identify the need or problem the project aims to address. - 1.2 Develop the Project Charter: - Define the project's purpose, objectives, and scope. - Identify primary stakeholders. - Outline initial budget estimates. - 1.3 Identify all stakeholders, including those who can influence or are impacted by the project.3. Outcome: A Project Charter that provides a high-level overview and project stakeholders are identified.Step 2: Planning the Project1. Objective: Develop a comprehensive roadmap for your project.2. Actions: - 2.1 Define success criteria. - 2.2 Detail the project's scope and boundaries. - 2.3 List out deliverables. - 2.4 Break down the project into tasks and set timelines. - 2.5 Create a budget, detailing estimated costs for tasks. - 2.6 Develop sub-plans such as: - Human Resource Plan - Quality Management Plan - Risk Management Plan - Procurement Management Plan - 2.7 Document change management procedures.Now let’s have a look at a generic template and an example for each step defined aboveStep 1.1: Initiating the ProjectGeneric Prompt: “As a project manager, I'm looking to address an underlying need or problem within [specific domain/area, e.g., 'our software development lifecycle']. Based on recent data, stakeholder feedback, market trends, and any other relevant information available in this domain, can you help identify the primary challenges or gaps that our project should target? The ultimate goal is to [desired outcome, e.g., 'improve efficiency and reduce bug counts']. Please provide a comprehensive analysis of potential problems and their implications."Prompt Example: “In our organization, managing vendors has become an increasingly complex task, with multiple touchpoints and communication channels. Given the crucial role vendors play in our supply chain and service delivery, there's an urgent need to streamline our vendor management processes. As a software solution is desired, can you help identify the primary requirements, challenges, and functionalities that our vendor management software should address? The primary objective is to enhance vendor communication, monitor performance metrics, ensure contract compliance, and facilitate swift issue resolution. Please provide a detailed analysis that can serve as a starting point for our software development."Response:Step 1.2: Develop the Project CharterGeneric Prompt: “For our objective of [specific domain or objective, e.g., 'customer relationship management'], draft a concise project charter. Address the phases of [list main stages/phases, e.g., 'identifying customer needs and feedback collection'], aiming to [primary goal, e.g., 'enhance customer satisfaction']. Given the importance of [contextual emphasis, e.g., 'customer relationships'], and involving stakeholders like [stakeholders involved, e.g., 'sales teams and customer support'], define a methodology that captures the essence of our goal."Prompt Example: "For our vendor management objective, draft a succinct project charter for a System Development Life Cycle (SDLC). The SDLC should cover phases from identifying vendor needs to termination or renewal processes, with an aim to enhance cost-efficiency and service reliability. Given our organization's growing dependency on vendors and the involvement of stakeholders like procurement and legal teams, outline a process that ensures optimal vendor relationship management."Response:2.1 Define success criteriaGeneric Prompt: "In light of the complexities in project management, having lucid success criteria is paramount. Can you delineate general success criteria pivotal for any project management initiative? This will gauge the project's triumph throughout its lifecycle, aligning with stakeholder aspirations and company objectives.Prompt Example: "Considering the intricacies of crafting vendor management software, establishing precise success criteria is crucial. To align the software with our goals and stakeholder demands, can you list and elaborate on success criteria tailored for this task? These standards will evaluate the software's efficiency throughout its phases, from design to updates. Supply a list specific to vendor management software, adaptable for future refinementsOutput:2.2 Detail the project's scope and boundariesGeneric Prompt: "Given the intricacies of today's projects, clear scope and boundaries are vital. Can you elucidate our project's scope, pinpointing its main objectives, deliverables, and focal areas? Additionally, specify what it won't encompass to avoid scope creep. Offer a clear outline demarcating the project's inclusions and exclusions, ensuring stakeholder clarity on its scope and constraintsPrompt Example: "In light of the complexities in vendor management software development, clear scope and boundaries are essential. Can you describe the scope of our software project, highlighting its main objectives, deliverables, and key features? Also, specify any functionalities it won't include to avert scope creep. Furnish a list that distinctly differentiates the software's capabilities from its exclusions, granting stakeholders a clear perspective."Output: 2.3 & 2.4: List out deliverables & Break down the project into tasks and set timelinesGeneric Prompt: “For our upcoming project, draft a clear roadmap. List the key deliverables encompassing objectives, functionalities, and related documentation. Then, dissect each deliverable into specific tasks and suggest timelines for each. Based on this, provide a structured breakdown suitable for a Gantt chart representation."Prompt Example: "For our vendor management software project, provide a succinct roadmap. Enumerate the key deliverables, encompassing software functionalities and associated documentation. Subsequently, dissect these deliverables into specific tasks, suggesting potential timelines. This breakdown should be structured to facilitate the creation of a Gantt chart for visual timeline representation."Output:2.5 Create a budget, detailing estimated costs for tasksGeneric Prompt: Can you draft a budgetary outline detailing the estimated costs associated with each major task and deliverable identified? This should consider potential costs for [list some generic cost categories, e.g., personnel, equipment, licenses, operational costs], and any other relevant expenditures. A clear financial breakdown will aid in the effective management of funds and ensure the project remains within its financial boundaries. Please provide a comprehensive budget plan suitable for [intended audience, e.g., stakeholders, team members, upper management]."Prompt Example: "Can you draft a budgetary outline detailing the estimated costs associated with each major task and deliverable identified in the project? This should include anticipated costs for personnel, software and hardware resources, licenses, testing, and any other potential expenditures. Remember, a clear financial breakdown will help in managing funds and ensuring the project remains within the set financial parameters. Please provide a comprehensive budget plan that can be presented to stakeholders for approval."Output:2.6 Develop sub-plans such asHuman Resource PlanQuality Management PlanRisk Management PlanProcurement Management PlanGeneric prompt: "In light of the requirements for comprehensive project management, it's crucial to have detailed sub-plans addressing specific areas. Could you assist in formulating a [specific sub-plan, e.g., 'Human Resource'] plan? This plan should outline the primary objectives, strategies, and actionable steps relevant to [specific domain, e.g., 'staffing and team development']. Additionally, consider potential challenges and mitigation strategies within this domain. Please provide a structured outline that can be adapted and refined based on the unique nuances of our project and stakeholder expectations."By replacing the placeholders (e.g., [specific sub-plan]) with the desired domain (Human Resource, Quality Management, etc.), this prompt can be tailored for various sub-plans.By filling in the "[specific project or objective]" placeholder with details pertaining to your specific project, this prompt layout can be tailored to various projects or initiatives.Have a glimpse at the output generated for various sub-plans in the context of the Vendor Management Software projectHuman Resource PlanQuality Management PlanRisk Management PlanProcurement Management Plan2.7 Document change management proceduresGeneric Prompt: “As a project manager, outline a Document Change Management procedure for a project. Ensure you cover change initiation, review, approval, implementation, communication, version control, auditing, and feedback."Prompt Example: "As the project manager of a Vendor Management Software deployment, design a Document Change Management procedure. Keeping in mind the dynamic nature of vendor integrations and software updates, outline the process for initiating, reviewing, approving, and implementing changes in documentation. Also, address communication with stakeholders, version control mechanisms, auditing frequency, and feedback integration from both team members and vendors. Aim for consistency and adaptability in your procedure."Output:ConclusionWrapping things up, effective project planning is foundational for success. Our guide has combined the best of ChatGPT and PMBOK to simplify this process for you. We've delved into creating a clear project roadmap, from setting success markers to managing changes effectively. By detailing scope, listing deliverables, breaking tasks down, budgeting, and designing crucial sub-plans, we've covered the essentials of project planning. Using our straightforward templates and examples, you're equipped to navigate project management with clarity and confidence. As we conclude, remember: proper planning today paves the way for smoother project execution tomorrow. Let's put these tools to work and achieve those project goals!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

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17992

article-image-chatgpt-for-search-engines

Sangita Mahala

10 Nov 2023

10 min read

ChatGPT for Search Engines

Sangita Mahala

10 Nov 2023

10 min read

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

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