How-To Tutorials - ChatGPT

IntroductionHave you ever wished you could just listen to the summary of a long YouTube video instead of watching the whole thing? Well, you're in luck! In this article, I’ll be showcasing a fun little Python project that I’ve been working on, which allows you to do just that.Don’t get me wrong: YouTube is a great resource for learning about new technologies and keeping you up to date with the latest news. And best of all: it’s free. But sometimes, I tend to lose track of time in the myriad of videos out there, fast forwarding through long talks only to find out in the end that the information I’m looking for is not in the video ☹Well, if you often find yourself in a similar situation, here’s a potential tool you might like. This little script downloads the audio from a YouTube video, transcribes it, summarizes it using AI and finally generates a new audio file with the summary. And all this magic is done using the OpenAI GPT-3.5-turbo API and some cool AWS services (S3, Transcribe, and Polly). In less than 80 lines of code.For those who might be unfamiliar with these APIs, here is their purpose in the script:OpenAI's GPT-3.5-turbo provides programmatic access to the same advanced language model used by ChatGPT. Its purpose in the script is summarizing the transcribed video content.AWS S3 is a storage service where we temporarily store the audio file from the YouTube video and the transcript. We have to use an S3 bucket because it is required by AWS Transcribe and AWS Polly.AWS Transcribe is used to convert the audio file into text.AWS Polly is a service that turns text into lifelike speech. We use it to generate an audio file of the summary.Logic Diagram DisclaimerBefore you start using these services, be aware that both AWS and OpenAI have usage quotas and costs associated with them. Make sure to familiarize yourself with these to avoid any unexpected charges. You’ll probably fall well within the limits of your Amazon account’s free tier unless you start summarizing hundreds of videos.Also, you might consider adding error handling in the code. To keep it short I’ve skipped it from this demo.You can download the Python file for this code from GitHub here.Configuring the APIsMake sure you store your OpenAI API Key and AWS Credentials in your local environment variables for secure and efficient access. The code works on the assumption that both the OpenAI API keys and AWS credentials are valid and have been already stored on your local environment variables. Alternatively, you can store your AWS ACCESS KEY and SECRET ACCESS KEY in %USERPROFILE%\.aws\credentialsMore info on that here: https://docs.aws.amazon.com/sdkref/latest/guide/creds-config-files.htmlFor the code to function properly make sure the AWS credentials you are using have the following permissions:AmazonS3FullAccess: This is required to create and delete S3 buckets, upload files to the buckets and delete objects within the buckets.AmazonTranscribeFullAccess: This is needed to start transcription jobs and get the transcription job results.AmazonPollyFullAccess: This is necessary to synthesize speech from text.The most convenient and safe approach to grant the necessary permissions is though the AWS Management Console by attaching the relevant policies to the user or role associated with the credentials. RequirementsI’ve used Python v3.11. Make sure you first install all the requirements or update them to the latest version if already installed.pip install pytube pip install openai pip install boto3 pip install requests pip install python-dotenvThe CodeLet’s break it down snippet by snippet. Setup and Import Statementsimport os import boto3 import requests import openai import uuid from pytube import YouTube Downloading the Audio from YouTubeThe download_audio function uses the pytube library to download the audio from a YouTube video. The audio file is saved locally before being uploaded to S3 by the main function. Here’s a complete documentation for pytube: https://pytube.io/en/latest/def download_audio(video_id): yt = YouTube(f'https://www.youtube.com/watch?v={video_id}') return yt.streams.get_audio_only().download(filename=video_id) Transcribing Audio to TextThe transcribe_audio function uses AWS Transcribe to convert the audio into text. The UUID (Universally Unique Identifier) module is used to generate a unique identifier for each transcription job. The benefit of using UUIDs here is that every time we run the function, a new unique job name is created. This is important because AWS Transcribe requires job names to be unique. Here’s the complete documentation of AWS Transcribe: https://docs.aws.amazon.com/transcribe/latest/dg/what-is.htmldef transcribe_audio(s3, bucket, file_name): transcribe = boto3.client('transcribe') job_name = f"TranscriptionJob-{uuid.uuid4()}" transcribe.start_transcription_job( TranscriptionJobName=job_name, Media={'MediaFileUri': f"s3://{bucket}/{file_name}"}, MediaFormat='mp4', LanguageCode='en-US' ) while True: status = transcribe.get_transcription_job(TranscriptionJobName=job_name) if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']: break return status['TranscriptionJob']['Transcript']['TranscriptFileUri'] if status['TranscriptionJob']['TranscriptionJobStatus'] == 'COMPLETED' else None Summarizing the TranscriptThe summarize_transcript function leverages OpenAI's GPT-3.5-turbo to summarize the transcript. Notice the simple prompt I’ve used for this task. I’ve tried to keep it very short in order to save more tokens for the actual transcript. It can definitely be improved and tweaked according to your preferences. For a complete documentation of the OpenAI API check out this link: https://platform.openai.com/docs/api-reference/introductiondef summarize_transcript(transcript): response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a knowledge curator helping users to understand the contents of video transcripts."}, {"role": "user", "content": f"Please summarize the following transcript: '{transcript}'"} ] ) return response['choices'][0]['message']['content'].strip() Synthesizing Speech from TextThe synthesize_speech function uses AWS Polly to convert the summarized text back into audio. If you prefer other voices or want to tweak different parameters such as speed, language, or dialect, here’s the complete documentation on how to use Polly: https://docs.aws.amazon.com/polly/index.htmldef synthesize_speech(s3, bucket, transcript_uri): transcript_data = requests.get(transcript_uri).json() transcript = ' '.join(item['alternatives'][0]['content'] for item in transcript_data['results']['items'] if item['type'] == 'pronunciation') summary = summarize_transcript(transcript) summary_file_name = f"summary_{uuid.uuid4()}.txt" s3.put_object(Body=summary, Bucket=bucket, Key=summary_file_name) polly = boto3.client('polly') response = polly.synthesize_speech(OutputFormat='mp3', Text=summary, VoiceId='Matthew', Engine='neural') mp3_file_name = f"speech_{uuid.uuid4()}.mp3" with open(mp3_file_name, 'wb') as f: f.write(response['AudioStream'].read()) return mp3_file_name The Clean-up of the S3 BucketTo keep our storage in check and avoid littering the cloud, it’s best to clean up all objects from the bucket. We’ll be able to delete the bucket completely once the audio summary has been downloaded locally.Remember, we only needed the S3 bucket because it was required by AWS Transcribe and Polly.def delete_all_objects(bucket_name): s3 = boto3.resource('s3') bucket = s3.Bucket(bucket_name) bucket.objects.all().delete() The Main FunctionAnd finally, the main function, which ties everything together. It specifies the YouTube video to summarize (which you can obviously change to any another video ID), sets up the necessary AWS services and calls the functions defined above in the correct order. It also makes sure to clean up by deleting the S3 bucket after use.def main(): video_id = 'U3PiD-g7XJM' #change to any other Video ID from YouTube bucket = f"bucket-{uuid.uuid4()}" file_name = f"{video_id}.mp4" openai.api_key = os.getenv('OPENAI_API_KEY') s3 = boto3.client('s3') s3.create_bucket(Bucket=bucket) print ("Downloading audio stream from youtube video...") audio_file = download_audio(video_id) print ("Uploading video to S3 bucket...") s3.upload_file(audio_file, bucket, file_name) print("Transcribing audio...") transcript_uri = transcribe_audio(s3, bucket, file_name) print("Synthesizing speech...") mp3_file_name = synthesize_speech(s3, bucket, transcript_uri) print(f"Audio summary saved in: {mp3_file_name}\n") delete_all_objects(bucket) s3.delete_bucket(Bucket=bucket) if __name__ == "__main__": main() And that's it! With this simple tool you can now convert any YouTube video into a summarized audio file.So, sit back, relax and let AI do the work for you.Enjoy! About the AuthorAndrei Gheorghiu is an experienced trainer with a passion for helping learners achieve their maximum potential. He always strives to bring a high level of expertise and empathy to his teaching.With a background in IT audit, information security, and IT service management, Andrei has delivered training to over 10,000 students across different industries and countries. He is also a Certified Information Systems Security Professional and Certified Information Systems Auditor, with a keen interest in digital domains like Security Management and Artificial Intelligence.In his free time, Andrei enjoys trail running, photography, video editing and exploring the latest developments in technology.You can connect with Andrei on:LinkedIn: https://www.linkedin.com/in/gheorghiu/Twitter: https://twitter.com/aqg8017

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions IntroductionIn this article, we will focus on how marketers can leverage ChatGPT, looking at the main use cases of ChatGPT in this domain, and how marketers can leverage it as a valuable assistant.We will learn how ChatGPT can assist in the following activities: Marketers’ need for ChatGPTNew product development and the go-to-market strategyBy the end of this article, you will be able to leverage ChatGPT for marketing-related activities and to boost your productivity.Technical requirementsYou will need an OpenAI account to access ChatGPT and DALL-E.All the code and prompts within this chapter are available in the GitHub repository of this bookhere: https://github.com/PacktPublishing/The-Ultimate-Guide-to-ChatGPTand-OpenAI.Marketers’ need for ChatGPTMarketing is probably the domain where ChatGPT and OpenAI models’ creative power can be leveraged in their purest form. They can be practical tools to support creative development in terms of new products, marketing campaigns, search engine optimization, and so on. Overall, marketers automate and streamline many aspects of their work, while also improving the quality and effectiveness of their marketing efforts.Here is an example. One of the most prominent and promising use cases of ChatGPT in marketing is personalized marketing. ChatGPT can be used to analyze customer data and generate personalized marketing messages that resonate with individual customers. For example, a marketing team can use ChatGPT to analyze customer data and develop targeted email campaigns that are tailored to specific customer preferences and behavior. This can increase the likelihood of conversion and lead to greater customer satisfaction. By providing insights into customer sentiment and behavior, generating personalized marketing messages, providing personalized customer support, and generating content, ChatGPT can help marketers deliver exceptional customer experiences and drive business growth.This is one of many examples of ChatGPT applications in marketing. In the following sections, we will look at concrete examples of end-to-end marketing projects supported by ChatGPT.New product development and the go-to-market strategyThe first way you can introduce ChatGPT into your marketing activity might be as an assistant in new product development and go-to-market (GTM) strategy.In this section, we will look at a step-by-step guide on how to develop and promote a new product. You already own a running clothing brand called RunFast and so far you have only produced shoes, so you want to expand your business with a new product line. We will start by brainstorming ideas to create a GTM strategy. Of course, everything is supported by ChatGPT:Brainstorming ideas: The first thing ChatGPT can support you with is brainstorming and drafting options for your new product line. It will also provide the reasoning behind each suggestion. So, let’s ask what kind of new product line I should focus on:Figure 7.1 – Example of new ideas generated by ChatGPTOut of the three suggestions, we will pick the first one, because of the reason ChatGPT suggested it—it is indeed a complementary product for our running shoes, so we will proceed with that one.Product name: Now that we have our idea fixed in mind, we need to think of a catchy name for it. Again, I will ask ChatGPT for more options so that I can then pick my favorite one:Figure 7.2 – A list of potential product namesSprintSoles sounds good enough for me – I’ll go ahead with that one.Generating catchy slogans: On top of the product name, I also want to share the intent behind the name and the mission of the product line, so that my target audience is captured by it. I want to inspire trust and loyalty in my customers and for them to see themselves reflected in the mission behind my new product line.Figure 7.3 – A list of slogans for my new product nameGreat – now I’m satisfied with the product name and slogan that I will use later on to create a unique social media announcement. Before doing that, I want to spend more time on market research for the target audience.Figure 7.4 – List of groups of target people to reach with my new product lineIt’s important to have in mind different clusters within your audience so that you can differentiate the messages you want to give. In my case, I want to make sure that my product line will address different groups of people, such as competitive runners, casual runners, and fitness enthusiasts.Product variants and sales channels: According to the preceding clusters of potential customers, I could generate product variants so that they are more tailored toward specific audiences:Figure 7.5 – Example of variants of the product lineSimilarly, I can also ask ChatGPT to suggest different sales channels for each of the preceding groups:Figure 7.6 – Suggestions for different sales channels by ChatGPTStanding out from the competition: I want my product line to stand out from the competition and emerge in a very saturated market – I want to make it unique. With this purpose in mind, I asked ChatGPT to include social considerations such as sustainability and inclusivity. Let’s ask ChatGPT for some suggestions in that respect:Figure 7.7 – Example of outstanding features generated by ChatGPTAs you can see, it was able to generate interesting features that could make my product line unique.Product Description: Now it’s time to start building our GTP plan. First of all, I want to generate a product description to put on my website, including all the earlier unique differentiators.Figure 7.8 – Example of description and SEO keywords generated by ChatGPTFair price: Another key element is determining a fair price for our product. As I differentiated among product variants for different audiences (competitive runners, casual runners, and fitness enthusiasts), I also want to have a price range that takes into account this clustering.Figure 7.9 – Price ranges for product variantsWe are almost there. We have gone through many new product development and go-to-market steps, and in each of them, ChatGPT acted as a great support tool.As one last thing, we can ask ChatGPT to generate an Instagram post about our new product, including relevant hashtags and SEO keywords. We can then generate the image with DALL-E!Figure 7.10 – Social media post generated by ChatGPTAnd, with the special contribution of DALL-E, here is the final result:Figure 7.11 – Instagram post entirely generated by ChatGPT and DALL-EOf course, many elements are missing here for complete product development and go-to-market. Yet, with the support of ChatGPT (and the special contribution of DALL-E – you can try DALL-E on your own at https://openai.com/product/dall-e-2, we managed to brainstorm a new product line and variants, potential customers, catchy slogans, and finally, generated a pretty nice Instagram post to announce the launch of SprintSoles!ConclusionIn this article, we explored ways in which ChatGPT can be used by marketers to enhance their marketing strategies. We learned that ChatGPT can help in developing new products as well as defining their go-to-market strategy.The importance of ChatGPT for marketers lies in its potential to revolutionize the way companies engage with their customers. By leveraging the power of NLP, ML, and big data, ChatGPT allows companies to create more personalized and relevant marketing messages, improve customer support and satisfaction, and ultimately, drive sales and revenue.Author BioValentina Alto graduated in 2021 in data science. Since 2020, she has been working at Microsoft as an Azure solution specialist, and since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industry. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on modern data platforms, data mesh frameworks, IoT and real-time analytics, Azure Machine Learning, Azure Cognitive Services (including Azure OpenAI Service), and Power BI for dashboarding. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications and has authored a book on the fundamentals of machine learning with Python.

This article is an excerpt from the book, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. This book will help harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI.In the world of natural language processing, ChatGPT stands as a powerful tool for various text-related tasks. From generating creative and coherent text to providing translations and editing assistance, ChatGPT offers a wide range of functionalities. In this article, we will explore how to harness the capabilities of ChatGPT to accomplish tasks such as generating engaging content, translating text between languages, and receiving helpful suggestions for editing. With practical examples and step-by-step instructions, we will unlock the potential of ChatGPT as a versatile text companion for developers and content creators alike.As a language model, ChatGPT is particularly suited for generating text based on users’ instructions. For example, you   could ask ChatGPT to generate emails, drafts, or templates that target a specific audience:Figure 1: Example of an email generated by ChatGPTAnother example might be asking ChatGPT to create a pitch structure for a presentation you have to prepare: Figure 2 – Slideshow agenda and structure generated by ChatGPTYou can also generate blog posts or articles about trending topics this way. Here is an example:Figure 3 – blog post with relevant tags and SEO  by ChatGPTWe can even get ChatGPT to reduce the size of the post to make it fit for a tweet. Here is how we can do this: Figure 4 – ChatGPT shrinks an article into a Twitter postFinally, ChatGPT can also generate video or theatre scripts, including the scenography and the suggested editing. The following figure shows an example of a theatre dialog between a person and ChatGPT:  Figure 5– Theatre dialog with scenography generated by ChatGPTI only provided a truncated version to keep you in suspense regarding the ending…Improving writing skills and translationSometimes, rather than generating new content, you might want to revisit an existing piece of text. It this be for style improvement purposes, audience changes, language translation, and so on.Let’s look at some examples. Imagine that I drafted an email to invite a customer of mine to a webinar. I wrote two short sentences. Here, I want ChatGPT to improve the form and style of this email since the target audience will be executive-level:Figure 6 – Example of an email revisited by ChatGPT to target an executive audienceNow, let’s ask the same thing but with a different target audience: Figure 6 – Example of the same email with a different audience, generated by ChatGPTChatGPT can also give you some feedback about your writing style and structure.Imagine, for example, that you wrote a script with scenography for your YouTube channel. You included the speech as well as images, clips, and video editing activities. You also know that your typical audience is between 15 and 25 years old. You want feedback on your script and ask for this from ChatGPT: Figure 7 – Example of ChatGPT providing feedback on a video script As you can see, not only was ChatGPT able to give me feedback about the writing style, but also it suggested how I could improve the scenography of the whole video, by including more visuals.Again, imagine you wrote an introduction for an essay titled The History of Natural Language Processing and you want some feedback about the writing style and its consistency with the title: Figure 8 – Example of ChatGPT giving feedback on an introduction for an essayLet’s also ask ChatGPT to make concrete examples of the attention-grabbing anecdote it talked about in its response: Figure 9 – Example of ChatGPT elaborating on something it mentioned I’m also interested in knowing whether my introduction was consistent with the title or whether I’m taking the wrong direction:Figure 10 – ChatGPT provides feedback about the consistency of the introduction with the titleI was impressed by this last one. ChatGPT was smart enough to see that there was no specific mention of the history of NLP in my introduction. Nevertheless, it sets up the expectation about that topic to be treated later on. This means that ChatGPT also has expertise in terms of how an essay should be structured and it was very precise in applying its judgment, knowing that it was just an introduction.It is also impressive to note how the model can give different feedback, depending on the context. With the video script, ChatGPT’s feedback took into account that the final consumption of that content would have been on screen. On the other hand, the essay’s introduction lives in a more formal and academic context, with a specific structure, that ChatGPT was able to capture once more.Last but not least, ChatGPT is also an excellent tool for translation. It knows at least 95 languages (if you have doubts about whether your language is supported, you can always ask ChatGPT directly). Here, however, there is a consideration that might arise: what is the added value of ChatGPT for translation when we already have cutting-edge tools such as Google Translate?To answer this question, we have to consider some key differentiators and how we can leverage ChatGPT’s embedded translations capabilities:ChatGPT can capture the intent. This means that you could also bypass the translation phase since it is something that ChatGPT can do in the backend. For example, if you write a prompt to produce a social media post in French, you could write that prompt in any language you want – ChatGPT will automatically detect it (without the need to specify it in advance) and understand your intent:Figure 11 – Example of ChatGPT generating an output in a language that is different from the inputChatGPT can capture the more refined meaning of particular slang or idioms. This allows for a translation that is not literal so that it can preserve the underlying meaning. Namely, let’s consider the British expression It’s not my cup of tea, to indicate something that is not the type of thing you like. Let’s ask both ChatGPT and Google Translate to translate it into Italian: Figure 12 – Comparison between ChatGPT and Google Translate while translating from English into ItalianAs you can see, ChatGPT can provide several Italian idioms that are equivalent to the original one, also in their slang format. On the other hand, Google Translate performed a literal translation, leaving behind the real meaning of the idiom. As with any other task, you can always provide context to ChatGPT. So, if you want your translation to have a specific slang or style, you can always specify it in the prompt. Or, even funnier, you can ask ChatGPT to translate your prompt with a sarcastic touch:  Figure 5.20 – Example of ChatGPT translating a prompt with a sarcastic touch.The original content from: OpenAI’s Wikipedia page: https://it.wikipedia.org/wiki/OpenAISummaryIn conclusion, ChatGPT is able not only to generate new text but also to manipulate existing material to tailor it to your needs. It has also proven to be very precise at translating between languages, also keeping the jargon and language-specific expressions intact.Author BioValentina Alto graduated in 2021 in Data Science. Since 2020 she has been working in Microsoft as Azure Solution Specialist and, since 2022, she focused on Data&AI workloads within the Manufacturing and Pharmaceutical industry. She has been working on customers’ projects closely with system integrators to deploy cloud architecture with a focus on datalake house and DWH, data integration and engineering, IoT and real-time analytics, Azure Machine Learning, Azure cognitive services (including Azure OpenAI Service), and PowerBI for dashboarding. She holds a BSc in Finance and an MSc degree in Data Science from Bocconi University, Milan, Italy. Since her academic journey she has been writing Tech articles about Statistics, Machine Learning, Deep Learning and AI on various publications. She has also written a book about the fundamentals of Machine Learning with Python. LinkedIn  Medium 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionIn an era defined by the fusion of technology and human interaction, ChatGPT stands at the forefront as a groundbreaking creation. This marvel of machine learning, developed by OpenAI, has transcended mere algorithms to become a conversational AI that possesses the ability to engage, assist, and inspire. As a professional writer deeply immersed in both the realms of language and artificial intelligence, I am excited to delve into the capabilities of ChatGPT and explore its potential impact on a world increasingly reliant on Natural Language Processing (NLP). In this article, we will not only unveil the astonishing abilities of ChatGPT but also shed light on the burgeoning significance of NLP across diverse industries.Accessing GPT APIThe ChatGPT API provides a streamlined way to integrate the power of ChatGPT into applications and services. It operates through a simple yet effective mechanism: users send a list of messages as input, with each message having a 'role' (system, user, or assistant) and 'content' (the text of the message). The conversation typically begins with a system message to set the AI's behavior, followed by alternating user and assistant messages.The API returns a model-generated message as output, which can be easily extracted from the API response. To access this functionality, developers can obtain API keys through the OpenAI platform. These keys grant access to the API, enabling developers to harness the capabilities of ChatGPT within their applications and projects seamlessly.ChatGPT for various NLP tasks1. Sentiment Analysis with ChatGPTUsing ChatGPT for sentiment analysis is a straightforward yet powerful application. To perform sentiment analysis, you can send a message to ChatGPT with user or assistant roles and ask it to determine the sentiment of a piece of text. Here's an example in Python using the OpenAI Python library:import openai openai.api_key = "YOUR_API_KEY" def analyze_sentiment(text):    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=[            {"role": "user", "content": f"Analyze the sentiment of the following text: '{text}'"}        ]    )      sentiment = response['choices'][0]['message']['content']      return sentiment text_to_analyze = "I absolutely love this product!" sentiment_result = analyze_sentiment(text_to_analyze) print(f"Sentiment: {sentiment_result}") Potential Applications:1. Social Media Monitoring: ChatGPT's sentiment analysis can be invaluable for businesses and brands aiming to track public sentiment about their products or services on social media platforms. By analyzing user-generated content, companies can gain real-time insights into how their brand is perceived and promptly respond to both positive and negative feedback.2. Customer Feedback Analysis: ChatGPT can assist in automating the process of analyzing customer reviews and feedback. It can categorize comments as positive, negative, or neutral, helping businesses identify areas for improvement and understand customer sentiment more comprehensively.3. Market Research: Researchers can leverage ChatGPT's sentiment analysis capabilities to process large volumes of text data from surveys, focus groups, or online forums. This aids in identifying emerging trends, gauging public opinion, and making data-driven decisions.By integrating ChatGPT's sentiment analysis into these and other applications, organizations can harness the power of natural language understanding to gain deeper insights into the opinions, emotions, and attitudes of their audience, leading to more informed and effective decision-making.2. Language Translation with ChatGPTChatGPT can be harnessed for language translation tasks with ease. It's a versatile tool for converting text from one language to another. Here's a Python code example demonstrating how to use ChatGPT for language translation:import openai openai.api_key = "YOUR_API_KEY" def translate_text(text, source_language, target_language):    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=[            {"role": "user", "content": f"Translate the following text from {source_language} to {target_language}: '{text}'"}        ]    )      translation = response['choices'][0]['message']['content']      return translation source_text = "Hello, how are you?" source_language = "English" target_language = "French" translated_text = translate_text(source_text, source_language, target_language) print(f"Translated Text: {translated_text}") Relevance in Multilingual Content Creation and Internationalization:1. Multilingual Content Creation: In an increasingly globalized world, businesses and content creators need to reach diverse audiences. ChatGPT's language translation capabilities facilitate the creation of multilingual content, enabling companies to expand their market reach and engage with customers in their native languages. This is crucial for marketing campaigns, websites, and product documentation.2. Internationalization: For software and apps aiming to go international, ChatGPT can assist in translating user interfaces and content into multiple languages. This enhances the user experience and makes products more accessible to a global user base.3. Cross-Cultural Communication: ChatGPT can help bridge language barriers in real-time conversations, facilitating cross-cultural communication. This is beneficial in customer support, online chat, and international business negotiations.By leveraging ChatGPT's language translation capabilities, organizations and individuals can enhance their global presence, foster better communication across languages, and tailor their content to a diverse and international audience. This, in turn, can lead to increased engagement, improved user satisfaction, and broader market opportunities.3. Text Summarization with ChatGPTChatGPT can be a valuable tool for generating concise and coherent text summaries from lengthy articles or documents. It leverages its natural language processing capabilities to extract the most important information and present it in a condensed form. Here's a Python code example illustrating how to use ChatGPT for text summarization:import openai openai.api_key = "YOUR_API_KEY" def generate_summary(text, max_tokens=50):    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=[            {"role": "user", "content": f"Summarize the following text: '{text}'", "role": "assistant", "content": f"Please summarize the following text to around {max_tokens} tokens:"}        ]    )      summary = response['choices'][0]['message']['content']      return summary document_text = SAMPLE_TEXT summary_result = generate_summary(document_text) print(f"Summary: {summary_result}")Applications in Content Curation and Information Extraction:1. Content Curation: Content creators, marketers, and news aggregators can use ChatGPT to automatically summarize news articles, blog posts, or research papers. This streamlines the process of identifying relevant and interesting content to share with their audience.2. Research and Study: Researchers and students can employ ChatGPT to condense lengthy academic papers or reports into more manageable summaries. This helps in quickly grasping the key findings and ideas within complex documents.3. Business Intelligence: In the corporate world, ChatGPT can be employed to summarize market reports, competitor analyses, and industry trends. This enables executives and decision-makers to stay informed and make strategic choices more efficiently.By integrating ChatGPT's text summarization capabilities into various applications, users can enhance their ability to sift through and distill vast amounts of textual information, ultimately saving time and improving decision-making processes.4. Question Answering with ChatGPTChatGPT excels at answering questions, making it a versatile tool for building chatbots, virtual assistants, and FAQ systems. It can provide informative and context-aware responses to a wide range of queries. Here's a Python code example illustrating how to use ChatGPT for question answering:import openai openai.api_key = "YOUR_API_KEY" def ask_question(question, context):    response = openai.ChatCompletion.create(        model="gpt-3.5-turbo",        messages=[            {"role": "user", "content": f"Context: {context}"},            {"role": "user", "content": f"Question: {question}"}        ]    )      answer = response['choices'][0]['message']['content']      return answer context = "The Eiffel Tower is a famous landmark in Paris, France. It was completed in 1889 and stands at 324 meters tall." question = "When was the Eiffel Tower built?" answer_result = ask_question(question, context) print(f"Answer: {answer_result}")Use in Chatbots, FAQs, and Virtual Assistants:1. Chatbots: ChatGPT can serve as the core intelligence behind chatbots, responding to user inquiries and engaging in natural conversations. Businesses can use chatbots for customer support, lead generation, and interactive marketing, delivering real-time assistance to users.2. FAQ Systems: Implementing ChatGPT in FAQ systems allows users to ask questions in a more natural and conversational manner. It ensures that users receive accurate and context-aware responses from a repository of frequently asked questions.3. Virtual Assistants: Virtual assistants powered by ChatGPT can assist users in various tasks, such as scheduling appointments, providing information, and even helping with language translation or summarization. They can be integrated into websites, applications, or devices to enhance user experiences.By harnessing ChatGPT's question-answering capabilities, organizations can create intelligent and responsive digital agents that deliver efficient and accurate information to users, improving customer satisfaction and user engagement across a wide range of applications.Ethical ConsiderationsAI and NLP technologies, like ChatGPT, raise ethical concerns, primarily concerning bias and misuse. Biases in training data can lead to unfair or discriminatory responses, while misuse can involve generating harmful content or misinformation. To responsibly use ChatGPT, consider:1. Bias Mitigation: Carefully curate and review training data to minimize biases. Implement debiasing techniques and provide guidelines for human reviewers to ensure fairness.2. Transparency: Be transparent about the AI's capabilities and limitations. Avoid giving it false identities or promoting misleading information.3. Content Moderation: Implement strong content moderation to prevent misuse. Regularly monitor and fine-tune the AI's responses to ensure they align with ethical standards.4. User Education: Educate users on the nature of AI-generated content, promoting critical thinking and responsible consumption.By proactively addressing these ethical concerns and adhering to guidelines, we can harness AI and NLP technologies like ChatGPT for positive, inclusive, and responsible outcomes.ConclusionIn conclusion, ChatGPT is a remarkable AI tool that showcases the transformative potential of Natural Language Processing (NLP). Key takeaways include its capabilities in sentiment analysis, language translation, text summarization, question answering, and chatbot development. However, ethical considerations like bias and misuse are critical and must be addressed responsibly. I encourage readers to harness ChatGPT and NLP in their projects, emphasizing transparency, bias mitigation, and responsible usage. By doing so, we can unlock the vast possibilities of these technologies while fostering fairness, accuracy, and positive impact across various domains. Explore, innovate, and shape a future where language and AI empower us all.Author BioBhavishya Pandit is a Data Scientist at Rakuten! He has been extensively exploring GPT to find use cases and build products that solve real-world problems.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions.IntroductionIn the ever-evolving landscape of digital marketing, staying competitive and meeting customer expectations is paramount. This article explores the revolutionary potential of ChatGPT in enhancing multiple aspects of marketing. From refining A/B testing strategies to elevating SEO optimization techniques and harnessing sentiment analysis for measuring customer satisfaction, ChatGPT emerges as a pivotal tool. A/B testing for marketing comparisonAnother interesting field where ChatGPT can assist marketers is A/B testing.A/B testing in marketing is a method of comparing two different versions of a marketing campaign, advertisement, or website to determine which one performs better. In A/B testing, two variations of the same campaign or element are created, with only one variable changed between the two versions. The goal is to see which version generates more clicks, conversions, or other desired outcomes.An example of A/B testing might be testing two versions of an email campaign, using different subject lines, or testing two versions of a website landing page, with different call-to-action buttons. By measuring the response rate of each version, marketers can determine which version performs better and make data-driven decisions about which version to use going forward.A/B testing allows marketers to optimize their campaigns and elements for maximum effectiveness, leading to better results and a higher return on investment.Since this method involves the process of generating many variations of the same content, the generative power of ChatGPT can definitely assist in that.Let’s consider the following example. I’m promoting a new product I developed: a new, light and thin climbing harness for speed climbers. I’ve already done some market research and I know my niche audience. I also know that one great channel of communication for that audience is publishing on an online climbing blog, of which most climbing gyms’ members are fellow readers.My goal is to create an outstanding blog post to share the launch of this new harness, and I want to test two different versions of it in two groups. The blog post I’m about to publish and that I want to be the object of my A/B testing is the following:Figure – An example of a blog post to launch climbing gearHere, ChatGPT can help us on two levels:The first level is that of rewording the article, using different keywords or different attention grabbing slogans. To do so, once this post is provided as context, we can ask ChatGPT to work on the article and slightly change some elements:Figure – New version of the blog post generated by ChatGPTAs per my request, ChatGPT was able to regenerate only those elements I asked for (title, subtitle, and closing sentence) so that I can monitor the effectiveness of those elements by monitoring the reaction of the two audience groups.The second level is working on the design of the web page, namely, changing the collocation of the image rather than the position of the buttons. For this purpose, I created a simple web page for the blog post published in the climbing blog (you can find the code in the book’s GitHub repository at https://github.com/PacktPublishing/The-Ultimate-Guideto-ChatGPT-and-OpenAI/tree/main/Chapter%207%20-%20ChatGPT%20 for%20Marketers/Code):Figure  – Sample blog post published on the climbing blogWe can directly feed ChatGPT with the HTML code and ask it to change some layout elements, such as the position of the buttons or their wording. For example, rather than Buy Now, a reader might be more gripped by an I want one! button.So, lets feed ChatGPT with the HTML source code:Figure – ChatGPT changing HTML codeLet’s see what the output looks like:Figure – New version of the websiteAs you can see, ChatGPT only intervened at the button level, slightly changing their layout, position, color, and wording.Indeed, inspecting the source code of the two versions of the web pages, we can see how it differs in the button sections:Figure – Comparison between the source code of the two versions of the websiteConclusionChatGPT is a valuable tool for A/B testing in marketing. Its ability to quickly generate different versions of the same content can reduce the time to market of new campaigns. By utilizing ChatGPT for A/B testing, you can optimize your marketing strategies and ultimately drive better results for your business.Author BioValentina Alto graduated in 2021 in data science. Since 2020, she has been working at Microsoft as an Azure solution specialist, and since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industry. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on modern data platforms, data mesh frameworks, IoT and real-time analytics, Azure Machine Learning, Azure Cognitive Services (including Azure OpenAI Service), and Power BI for dashboarding. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications and has authored a book on the fundamentals of machine learning with Python.

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

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn a world where technology is progressing at an exponential rate, the concept of a personal assistant is no longer confined to high-profile executives with hectic schedules. Today, due to the incredible advancements in artificial intelligence (AI), each one of us has the chance to take advantage of a personal assistant's services, even for tasks that may have appeared beyond reach just a few years ago. Imagine having an entity that can aid you in conducting research, examining your daily financial expenditures, organizing your travel itinerary, and much more. This entity is known as AI and, more precisely, it is embodied in AgentGPT.You have likely heard of AI's incredible capabilities, ranging from diagnosing diseases to defeating world-class chess champions. While AI has undoubtedly made significant strides, here's the caveat: unless you possess technical expertise, devising the workflow to fully utilize AI's potential can be an intimidating endeavor. This is where the concepts of "tools" and "agents" become relevant, and AgentGPT excels in this domain.An "agent" is essentially the mastermind behind your AI assistant. It's the entity that “thinks”, strategizes, and determines how to achieve your objectives based on the available "tools." These "tools" represent the skills your agent possesses, such as web searching, code writing, generating images, retrieving knowledge from your personal data, and a myriad of other capabilities. Creating a seamless workflow where your agent utilizes these tools effectively is no simple task. It entails connecting the agent to the tools, managing errors that may arise, devising prompts to guide the agent, and more.Fortunately, there's a game-changer in the world of AI personal assistants, and it goes by the name of AgentGPT. Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn how to utilize AgentGPT to build your personal assistant!What is AgentGPT?AgentGPT is an open-source project that streamlines the intricate process of creating and configuring AI personal assistants. This powerful tool enables you to deploy Autonomous AI agents, each equipped with distinct capabilities and skills. You can even name your AI, fostering a sense of personalization and relatability. With AgentGPT, you can assign your AI any mission you can conceive, and it will strive to accomplish it.The magic of AgentGPT lies in its ability to empower your AI agent to think, act, and learn. Here's how it operates:Select the Tools: You start by selecting the tools for the agent. It can be web searching, code writing, generating images, or even retrieving knowledge from your personal dataSetting the Goal: You then need to define the goal you want your AI to achieve. Whether it's conducting research, managing your finances, or planning your dream vacation, the choice is yours.Task Generation: Once the goal is set and the tools are selected, your AI agent "thinks" about the tasks required to accomplish it. This involves considering the available tools and formulating a plan of action.Task Execution: Your AI agent then proceeds to execute the tasks it has devised. This can include searching the web for information, performing calculations, generating content, and more.Learning and Adaptation: As your AI agent carries out its tasks, it learns from the results. If something doesn't go as planned, it adapts its approach for the future, continuously improving its performance.In a world where time is precious and efficiency is crucial, AgentGPT emerges as a ray of hope. It's a tool that empowers individuals from all walks of life to harness the might of AI to streamline their daily tasks, realize their goals, and amplify their productivity. Thus, whether you're a business professional seeking to optimize your daily operations or an inquisitive individual eager to explore the boundless possibilities of AI, AgentGPT stands ready to propel you into a new era of personalized assistance.Initialize AgentGPTTo build your own personal assistant with AgetnGPT, you can just follow the following simple instructions. Or even, you can also just go to the website and try the demo.Open Your Terminal: You can usually access the terminal from a 'Terminal' tab or by using a shortcut.Clone the Repository: Copy and paste the following command into your terminal and press Enter. This will clone the AgentGPT repository to your local machine.a. For Max/Linux usersgit clone https://github.com/reworkd/AgentGPT.git cd AgentGPT ./setup.sh                b. For Windows usersgit clone https://github.com/reworkd/AgentGPT.git cd AgentGPT ./setup.batFollow Setup Instructions: The setup script will guide you through the setup process. You'll need to add the appropriate API keys and other required information as instructed.Access the Web Interface: Once all the services are up and running, you can access the AgentGPT web interface by opening your web browser and navigating to http://localhost:3000.Build Your Own Assistant with AgentGPTLet’s start with an example of how to build your own assistant. First and foremost, let’s select the tools for our agent. Here, we’re selecting image generation, web search, and code writing as the tools. Once we finish selecting the tools, we can define the goal for our assistant. AgentGPT provides three templates for us:ResearchGPT: Create a comprehensive report of the Nike companyTravelGPT: Plan a detailed trip to HawaiiPlatformerGPT: Write some code to make a platformer gameNote that we can also create our own assistant name with a specific goal apart from these three templates. For now, let’s select the PlatformerGPT template.Once the goal is defined, then the agent will generate all tasks required to accomplish the goal. This involves considering the available tools and formulating a plan of action.Then, based on the generated tasks, the Agent will execute each task and learn through the results of each of the tasks.This process will continue until the goal is achieved, or in this case, until the Agent succeeds in writing the code for a platformer game. If something doesn't go as planned, it adapts its approach for the future, continuously improving its performance.ConclusionCongratulations on keeping up to this point! Throughout this article, you have learned what AgentGPT is capable of and how to build your own personal assistant with it. I wish the best for your experiment in creating your personal assistant 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.

This article is the second part of a series of articles, please refer to Part 1 for learning how to create a simple portfolio website using ChatGPT in a few stepsIn my last blog, we created a basic personal portfolio app for Olivia with ChatGPT. We’ll use this as the starting point for our next adventure. We’re going to be adding a simple chatbot to our website and configuring it to deal with certain questions. Of course, we could do this ourselves, but let’s see how we can utilize ChatGPT to accomplish this task faster. We’re going to do so with just four prompts! Let’s start with the end result, so you know what we’re working towards:Image 1: End result of the project In the right bottom corner, you see a pop-up that says Ask me anything. Well, you can’t ask it anything, but it can answer the following common questions:Where can I contact you?Can I contact you through Twitter?Can I contact you through LinkedIn?Where can I see your projects? Of course, we can elaborate and do even more with it. We actually wanted to use a free API for this, and we were stuck with CDN options due to the nature of our project. Luckily, we have a digital friend who has some options for us. Let’s see how this started.Deciding which service to use (with ChatGPT)It’s best to be quite descriptive when prompting. This is the first prompt that was used to start implementing the chatbot:I have an existing portfolio site, using HTML/CSS/JS. I'm not using any package managers, so any requirements will have to use a CDN. I want to add a chatbot to my site using a Natural Language AI. If it is possible I'd want the API I'll use to be free. Can you give me some options as to how I can achieve this? ChatGPT had some ideas: Certainly! There are several options available for adding a chatbot with Natural Language AI to your existing portfolio site using HTML/CSS/JS and integrating it through a CDN. Here are a few options that offer free plans: 1. IBM Watson Assistant: IBM Watson Assistant is a powerful NLP service that allows you to build chat bots. You can integrate it into your website using the Watson Assistant API. IBM offers a free tier that provides a limited number of API requests per month. You can find more information and documentation on the IBM Watson Assistant website. 2. Dialogflow (formerly API.AI): Dialogflow, now part of Google Cloud, offers a free tier that allows you to create conversational agents and integrate them into your website. Dialogflow provides a JavaScript library that you can use to communicate with the API. You can find documentation and examples on the Dialogflow website. 3. Wit.ai: Wit.ai, which is also owned by Facebook, provides a free NLP service for building chatbots. You can integrate it into your website using the Wit.ai API. Wit.ai offers a JavaScript SDK that you can use to interact with the API. You can find more information on the Wit.ai website. 4. Microsoft Azure Bot Service: Microsoft Azure Bot Service offers a free tier that allows you to create and deploy chatbots. You can use the Bot Service REST API to communicate with your bot from your website. Microsoft provides documentation and examples on its Azure Bot Service website. Remember to review the terms and limitations of the free plans for each service to ensure they meet your requirements. Additionally, be aware that some services may have usage limitations or require you to upgrade to a paid plan for higher usage levels or advanced features. Choose the option that best suits your needs, and refer to the respective documentation for implementation details specific to each service. After this, it was research time. This actually took a little while. IBM wasn’t properly explored, because the sign-up failed due to the Dutch valid phone number not passing validation. Azure seemed great, but a bit too advanced for what we needed right now. The chatbot service chosen was Wit.ai. On there we can sign up, get an access token and specify how to deal with certain inputs. After figuring that out and making a little code start ourselves. It was time to start adjusting the code with ChatGPT. Prompting for codeEnter the following prompt, to begin with: Alright, I set up a wit.ai project, and it seems to work. Now I have the following snippet:   <section id="chat-bot">    <h2>Ask me anything!</h2>    <input id="chat-prompt" type="text">  </section> I'd like to create a chatbot with this. I can write the responses myself, but can you for now create some JavaScript code that sends an HTTP request to the wit.ai server and parses the result? For now, just print it to the console. It gave us a great place to start and some additional implementation guidelines. We’ve only included the code snippet here:// Function to send the user's message to Wit.ai and parse the response async function sendMessage(message) { // Replace 'YOUR_WITAI_ACCESS_TOKEN' with your actual Wit.ai access token const accessToken = 'YOUR_WITAI_ACCESS_TOKEN'; const apiUrl = `https://api.wit.ai/message?v=20210525&q=${encodeURIComponent(message)}`; try {    const response = await fetch(apiUrl, {      headers: {        Authorization: `Bearer ${accessToken}`      }    });    if (response.ok) {      const data = await response.json();      console.log('Wit.ai Response:', data);      // Process the data and handle the response as needed      // For example, extract intents, entities, or generate appropriate responses    } else {      console.log('Request to Wit.ai failed with status:', response.status);    } } catch (error) {    console.log('An error occurred while communicating with Wit.ai:', error); } } // Event listener for handling user input const inputElement = document.getElementById('chat-prompt'); inputElement.addEventListener('keydown', async (event) => { if (event.key === 'Enter') {    const message = event.target.value.trim();    if (message) {      console.log('User Input:', message);      await sendMessage(message);      event.target.value = ''; // Clear the input field    } } }); It also instructed us to take the following steps:Replace YOUR_WITAI_ACCESS_TOKEN with our actual access tokenPress enter after typing your question and it then logs the response to the console Make sure to put the JavaScript code in the <script> tag This is what it looked like:Image 2: practical output after the first few prompts This was already quite good, but of course, we don’t want the response in the console, but displayed in the chat window. And the same goes for our previous questions, it should become a chat. Let’s see if we can move toward that.Making it into an actual conversationIt’s always a good idea to be specific in your prompt. You can see the following prompt does quite a bit of the heavy lifting and explains clearly what type of work is needed from ChatGPT. This helps to achieve the result: I've modified the HTML a little bit:   <section id="chat-bot">    <ol id="chat-history"></ol>    <h2>Ask me anything!</h2>    <input id="chat-prompt" type="text">  </section> Can you write some logic which can populate the `chat-history` element? I need 3 things: - A history as a JavaScript object- A function which can generate the content from the history- A function which can add the question, and the response, to the history in JavaScript For now just print the top intent and all the entities. This did quite a bit of the work already. You can see the result below, the history and the answers of the two questions asked appear on top of the input box.Image 3: Answers and history appearing on top of the Input box                        It’s no secret that I’m not a UX or design expert, but even I can see that this is not ideal. So let’s see if we can improve the design.Asking ChatGPT to Style the ChatbotLet’s see if we can improve the design. Normally chatbots are not on the top left, but the bottom right. Also, it would be nice if the history could go inside the box. Of course, we want to use the same orange as for the rest of our portfolio. Here’s the prompt: Can you generate some CSS to style the element? I'd like the following: - The chat should be at the bottom right of the page.- The chat should not impact the rest of the page.- Each question and response should have a chat bubble.- The theme color is #f37041.This gave us the CSS and some context as output. Here’s the CSS:#chat-bot { position: fixed; bottom: 20px; right: 20px; z-index: 9999; background-color: #f37041; color: #fff; font-family: Arial, sans-serif; border-radius: 5px; padding: 10px; box-shadow: 0 2px 5px rgba(0, 0, 0, 0.3); } #chat-history { list-style: none; margin: 0; padding: 0; max-height: 200px; overflow-y: auto; } .question, .response { display: inline-block; max-width: 80%; padding: 8px; border-radius: 10px; margin-bottom: 5px; } .question { background-color: #fff; color: #333; text-align: right; } .response { background-color: #f37041; color: #fff; text-align: left; } #chat-prompt { width: 100%; padding: 8px; margin-top: 10px; border: none; border-radius: 5px; } #chat-prompt:focus { outline: none; } #chat-prompt::placeholder { color: #ccc; } And this did quite the trick. Here’s what it looks like now: Image4: The final output If you’d like to see all the responses and the versions of the personal portfolio after each step, you can check out this GitHub repo: https://github.com/BrightBoost/portfolio-with-ai-prompt-chatgptSummaryIn conclusion, integrating a basic chatbot into your personal portfolio website can significantly enhance user engagement and provide a unique interactive experience. By leveraging GPT prompts, you can effortlessly develop a conversational AI agent that understands and responds to user queries. Adding this functionality not only showcases your technical skills but also demonstrates your ability to create dynamic and user-centric web experiences. So go ahead, implement a chatbot and elevate your portfolio website to the next level. Happy prompting!Author Bio Maaike van Putten is an experienced software developer and Pluralsight, LinkedIn Learning, Udemy, and Bright Boost instructor. She has a passion for software development and helping others get to the next level in their career.LinkedInTraining courses   

This article is the first part of a series of articles, please refer to Part 2  for learning how to add a Chatbot to the portfolio website you create in this article!Creating a personal portfolio is a great way for showcasing your skills and accomplishments as a developer or designer. Does that sound like a lot of work? Well… it doesn’t have to be. We can use ChatGPT to generate code snippets and obtain a lot of guidance throughout the process. This way you can build an impressive portfolio website with minimal effort.Here’s what you can do in around 10 prompts: Fig 1: Homepage Fig 2: Portfolio PageFig 3: Contact Page Not bad, right? And it even contains some features: The search functionality works and filters projects based on what you are typing.The testimonials and projects are not hard-coded, but dynamically populated with JavaScript (but not connected to a backend with a database, so for the purpose of this article, they are hard-coded there).Of course, this personal portfolio would need more content. And you could definitely use ChatGPT to generate a bit more content and descriptions for it. You should be adding in some professional pictures and images to complete your portfolio. However, this is a project that you can do in half a day to a day using ChatGPT. The prompt magic The prompts used to create the personal portfolio follow a few best practices that I’ll discuss later. In the overview below, asking to continue after cutting off a response is not included. If you’d like to see all the responses and the versions of the personal portfolio after each step, you can check out this GitHub repo: https://github.com/BrightBoost/brightboost-portfolio-chatgpt Here is the first prompt:I'm making a portfolio site, using HTML/CSS/JS. Can you help me write the initial framework for it?  I need a home page, in which I will introduce me, my passions, aspirations and study background. I also need a call-to-action directing visitors to explore my work.Then I need a portfolio page, in which I showcase my best projects. This will include case studies, and testimonials highlighting my contributions and accomplishments.Finally I need a contact page, which is a minimalist form and social media links in order to facilitate professional connections. And the second:That seems to be close to what I want. However, could you split these into 3 files? One for each page.The third, as you can see, a lot of the heavy lifting in terms of content is done in the prompt here:Can you populate the pages with the following information?Name: Olivia MartinezStudy: Olivia recently graduated from the University of California, Berkeley with a Bachelor's degree in Computer Science.Aspirations: Olivia aspires to become a full-stack software engineer at a tech startup that focuses on environmental sustainability. She hopes to contribute her skills to develop innovative solutions for pressing environmental issues.Hobbies: In her free time, Olivia enjoys hiking, painting, and exploring the latest advancements in machine learning.Example Open Source Projects:- GreenRoute: A web application that optimizes travel routes based on carbon emissions.- EcoClicker: A browser extension that encourages users to reduce their digital carbon footprint.Additional Personal Details: Olivia has been an active volunteer at her local recycling center, where she has helped develop a digital platform to streamline recycling processes. This is what it looked like after this prompt:Fig 4: Homepage after initial promptsFig 5: Portfolio page after promptFig 6: Contact Page after promptThe fourth prompt was quite a challenge and it required going back and forward a bit and testing it until it was good. It was tempting to just modify it, but ChatGPT was supposed to create it here and it did eventually:Can you help me modify the following snippet? ```html      <h2>Portfolio</h2>      <div class="project">        <h3>GreenRoute</h3>        <p>A web application that optimizes travel routes based            on carbon emissions.</p>        <a href="#" class="project-link">View Case Study</a>        <div class="testimonials">          <p>Testimonial 1</p>          <p>Testimonial 2</p>        </div>      </div>       <div class="project">        <h3>EcoClicker</h3>        <p>A browser extension that encourages users to reduce            their digital carbon footprint.</p>        <a href="#" class="project-link">View Case Study</a>        <div class="testimonials">          <p>Testimonial 1</p>          <p>Testimonial 2</p>        </div>      </div>    ``` I'm not satisfied with the look. Could you make the following changes: - Each project is displayed in a card.- The project link looks like a button, in the bottom right.- The title is underlined, and a bit larger.- The page shows 2 columns of cards. Fig 7: Home page after refined promptingAnd here’s the fifth: I need to make sure that footer is always at the bottom of the page, can you provide a CSS snippet to make that work?This also needed second attempt because it wasn’t working. Don’t just say that it doesn’t work, but be specific:It doesn't seem to work. The page only uses about 50% of the screen, so the footer is still in the middle. After this, it looks like: Fig 8: Homepage after footer promptsThis is where things really got cool, but this needed a few tweaks in terms of output. Here was the first prompt to add JavaScript: I'd like to make the portfolio a bit more extendable. Can you write some JavaScript code that generates the portfolio page using an array of objects? For now just put the content directly in code. I forgot a few classes, so let’s prompt again: This works, but you've excluded the classes used in the CSS. As a reminder, this is how a single item should look:** code of the prompt omitted And after this it was good: It seems the 2 column layout is gone. I think this:```html<section id="portfolio"><div class="container" id="portfolio-container"></div></section>```Should contain an element with the class `project-grid` somewhere, which should create a grid. Can you modify the snippet? The last part was on the search bar, which required this prompt:I'd like to add a search bar to the portfolio page. It must search for the text in the title and body. I only want to look for the exact text. After each character it should update the list, filtering out any project that does not match the search text. Then there should be a button to clear the search bar, and show all projects. Can you add this to the JavaScript file? And that’s it! Of course, there are many ways to do this, but this is one way of how you can use ChatGPT to create a personal portfolio. Let’s see some best practices for your ChatGPT prompts, to help you with using it to create your personal portfolio.Best practices for ChatGPT prompts There are some best practices I figured out when working with ChatGPT. Let’s go over them before seeing the prompts used for the personal portfolio.Be specific and clear: Make sure your prompt leaves little room for interpretation. For example, the prompt:Help me with a grid layout.Is not going to help you as much as:For this piece of HTML containing bootstrap cards provide a CSS snippet for a responsive 3-column grid layout with a 20px gap between columns: ** insert your HTML snippet here **Include relevant context and background information: Give the AI enough information to understand the problem or task and help you to its best ability. Don’t ask:How do I convert a date string to a Date object?But ask:  I have a JSON object with date and value properties. How do I convert the date property to a JavaScript Date object?Ask one question at a time: Keep your prompts focused and avoid asking multiple questions in one prompt.Make sure ChatGPT completes its answer before asking the next question: Sometimes it cuts off the result. You can ask it to continue and it will. That’s harder when you’re further down the line.Test the result after every step: Related to the previous tip, but make sure to test the result after every step. This way you can provide feedback on the outcome and it can easily adjust still. Step? Yes! Break down big projects into smaller tasks: Divide your project into manageable steps, and ask the AI to complete each task separately.Bonus tip: You can even ask ChatGPT for help on how to break your project into smaller tasks and make these tasks very detailed. Then go ahead and ask it to do one task at a time.The good news is these tips are actually great interaction tips with humans as well! I bet you’d like to see some of the prompts used to create the personal portfolio, so let’s dive in. Author BioMaaike van Putten is an experienced software developer and Pluralsight, LinkedIn Learning, Udemy, and Bright Boost instructor. She has a passion for software development and helping others get to the next level in their career.You can follow Maaike on:LinkedInTraining Courses

Prompt Engineering and design play a very vital role in controlling the output of the model. Here are some best practices you can use to improve your prompts, as well as some practices you should avoid:Clarity: Use simple sentences and instructions that can easily be understood by ChatGPT. Conciseness: Favor short prompts and short sentences. This can be achieved by chunking your instructions into smaller sentences with clear intentions.Focus: Keep the focus of the prompt on a well-defined topic so that you don’t risk your output being too generic.Consistency: Maintain a consistent tone and language during the conversation so that you can ensure a coherent conversation.“Acting as…”: The hack of letting ChatGPT act as someone or something has proven to be extremely powerful. You can shorten the context you have to provide to the model by simply asking him to act like the person or system you want information from. We’ve already seen the interview-candidate example, where ChatGPT acted as an interviewer for a data scientist position. A very interesting prompt is that of asking ChatGPT to act as a console. Here is an example of it: Figure 1 – Example of ChatGPT acting as a Python console Note that the console, as it would be if it were real, is also reporting the error I made for the cycle, indicating that I was missing the brackets.There is a continuously growing list of Act as prompts you can try in the following GitHub repository: https://github.com/f/awesome-chatgpt-prompts.Considering the few-shot learning capabilities, there are some good tips for leveraging this feature in prompt designing. An ideal conversation is as follows: On the other hand, there are some things you should avoid while designing your prompt:Start with a concise, clear, and focused prompt. This will help you have an overview of the topic you want to discuss, as well as provide food for thought and potential expansion of particular elements. Here’s an exampleFigure 2 – Example of a clear and focused prompt to initiate a conversation with ChatGPTOnce you have identified the relevant elements in the discussion, you can ask ChatGPT to elaborate on them with much more focusFigure 3 – Example of a deep-dive follow-up question in a ChatGPT Sometimes, it might be useful to remember the model and the context in which you are inquiring, especially if the question might apply to various domainsFigure 4 – Example of a reminder about the context in a conversation with ChatGPTFinally, always in mind the limitations we mentioned in previous chapters. ChatGPT may provide partial or incorrect information, so it is always a good practice to double-check. One nice tip you could try is asking the model to provide documentation about its responses so that you can easily find proof of themFigure 5 – Example of ChatGPT providing documentation supporting its previous responses On the other hand, there are some things you should avoid while designing your prompt: Information overload: Avoid providing too much information to ChatGPT, since it could reduce the accuracy of the response.Open-ended questions: Avoid asking ChatGPT vague, open-ended questions. Prompts such as What can you tell me about the world? or Can you help me with my exam? are far too generic and will result in ChatGPT generating vague, useless, and sometimes hallucinated responses.Lack of constraints: If you are expecting an output with a specific structure, don’t forget to specify that to ChatGPT! If you think about the earlier example of ChatGPT acting as an interviewer, you can see how strict I was in specifying not to generate questions all at once. It took several tries before getting to the result since ChatGPT is thought to generate a continuous flow of text.Furthermore, as a general consideration, we still must remember that the knowledge base of ChatGPT is limited to 2021, so we should avoid asking questions about facts that occurred after that date. You can still provide context; however, all the responses will be biased toward the knowledge base before 2021. SummaryIn this article, we get to learn some strong principles that can help you learn how to prompt effectively.  We cover the importance of a good prompt, and all the important Do’s and Don'ts while designing a good prompt with a practical example. About the Author Valentina Alto graduated in 2021 in Data Science. Since 2020 she has been working in Microsoft as Azure Solution Specialist and, since 2022, she focused on Data&AI workloads within the Manufacturing and Pharmaceutical industry. She has been working on customers’ projects closely with system integrators to deploy cloud architecture with a focus on datalake house and DWH, data integration and engineering, IoT and real-time analytics, Azure Machine Learning, Azure cognitive services (including Azure OpenAI Service), and PowerBI for dashboarding. She holds a BSc in Finance and an MSc degree in Data Science from Bocconi University, Milan, Italy. Since her academic journey, she has been writing Tech articles about Statistics, Machine Learning, Deep Learning, and AI in various publications. She has also written a book about the fundamentals of Machine Learning with Python.LinkedInMedium

This article is an excerpt from the book, Machine Learning with Microsoft Power BI, by Greg Beaumont. This book is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI.Data description generation plays a vital role in understanding complex datasets, but it can be a time-consuming task. Enter ChatGPT, an advanced AI language model developed by OpenAI. Trained on extensive text data, ChatGPT demonstrates impressive capabilities in understanding and generating human-like responses. In this article, we explore how ChatGPT can revolutionize data analysis by expediting the creation of accurate and coherent data descriptions. We delve into its training process, architecture, and potential applications in fields like research, journalism, and business analytics. While acknowledging limitations, we unveil the transformative potential of ChatGPT for data interpretation and knowledge dissemination. Our first step will be to identify a suitable use case for leveraging the power of GPT models to generate descriptions of elements of FAA Wildlife Strike data. Our objective is to unlock the potential of external data by creating prompts for GPT models that can provide detailed information and insights about the data we are working with. Through this use case, we will explore the value that GPT models can bring to the table when it comes to data analysis and interpretation.For example, a description of the FAA Wildlife Strike Database by ChatGPT might look like this: Figure 1 – OpenAI ChatGPT description of FAA Wildlife Strike Database Within your solution using the FAA Wildlife Strike database, you have data that could be tied to external data using the GPT models. A few examples include additional information about:AirportsFAA RegionsFlight OperatorsAircraftAircraft EnginesAnimal SpeciesTime of Year When the scoring process for a large number of separate rows in a dataset is automated, we can use a GPT model to generate descriptive text for each individual row. It is worth noting that ChatGPT's approach varies from this, as it operates as a chatbot that calls upon different GPT models and integrates past conversations into future answers. Despite the differences in how GPT models will be used in the solution, ChatGPT can still serve as a valuable tool for testing various use cases.When using GPT models, the natural language prompts that are used to ask questions and give instructions will impact the context of the generated text. Prompt engineering is a topic that has surged in popularity for OpenAI and LLMs. The following prompts will provide different answers when using “dogs” as a topic for a GPT query:Tell me about dogs:From the perspective of an evolutionary biologist, tell me about dogs:Tell me the history of dogs:At a third-grade level, tell me about dogs:When planning for your use of OpenAI on large volumes of data, you should test and evaluate your prompt engineering strategy. For this book, the use cases will be kept simple since the goal is to teach tool integration with Power BI. Prompt engineering expertise will probably be the topic of many books and blogs this year. You can test different requests for a description of an FAA Region in the data: Figure 2 – Testing the utility of describing an FAA Region using OpenAI ChatGPT You can also combine different data elements for a more detailed description. The following example combines data fields with a question to ask “Tell me about the Species in State in Month”: Figure 3 – Using ChatGPT to test a combination of data about Species, State, and Month There are many different options to consider. To combine a few fields of data and provide useful context about the data, you decide to plan a use case for describing the aircraft and operator. An example can be tested with the following formula in OpenAI ChatGPT such as “Tell me about the airplane model Aircraft operated by the Operator in three sentences." Here is an example using data from a single row of the FAA Wildlife Strike database: Figure 4 – Information about an airplane in the fleet of an operator as described by OpenAI ChatGPT From a prompt engineering perspective, asking this question for multiple reports in the FAA Wildlife Strike database would require running the following natural language query on each row of data (column names are depicted with brackets): Tell me about the airplane model [Aircraft] operated by [Operator] in three sentences:SummaryThis article explores how ChatGPT expedites the generation of accurate and coherent data descriptions. Unveiling its training process, architecture, and applications in research, journalism, and business analytics, we showcase how ChatGPT revolutionizes data interpretation and knowledge dissemination. Acknowledging limitations, we highlight the transformative power of this AI technology in enhancing data analysis and decision-making. Author BioGreg Beaumont is a Data Architect at Microsoft; Greg is an expert in solving complex problems and creating value for customers. With a focus on the healthcare industry, Greg works closely with customers to plan enterprise analytics strategies, evaluate new tools and products, conduct training sessions and hackathons, and architect solutions that improve the quality of care and reduce costs. With years of experience in data architecture and a passion for innovation, Greg has a unique ability to identify and solve complex challenges. He is a trusted advisor to his customers and is always seeking new ways to drive progress and help organizations thrive. For more than 15 years, Greg has worked with healthcare customers who strive to improve patient outcomes and find opportunities for efficiencies. He is a veteran of the Microsoft data speaker network and has worked with hundreds of customers on their data management and analytics strategies.You can follow Greg on his LinkedIn 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionIn the dynamic world of finance, technology continually pushes boundaries. Today, financial institutions seek to enhance customer experiences with a powerful tool: Generative Artificial Intelligence (AI). This cutting-edge technology is revolutionizing finance, reshaping customer interactions, and elevating satisfaction and personalization.Generative AI, known for creative output and data generation, is now making waves in finance. It offers unique opportunities to transform the customer experience. By harnessing Generative AI's capabilities, financial institutions gain valuable insights, provide hyper-personalized solutions, and align offerings with individual needs.This article explores Generative AI's impact on Wealth Management in finance. We uncover innovative applications, from personalized financial product recommendations to intuitive virtual assistants meeting customer needs. Additionally, we discuss the benefits, challenges, and ethical considerations of using Generative AI to enhance customer satisfaction.Customer Pain Points in Wealth ManagementIn the ever-evolving realm of finance, where wealth management and customer service intersect, customers often grapple with a host of challenges that can significantly impact their overall satisfaction. These obstacles stem from various sources and play a pivotal role in shaping customer loyalty. Here, we delve into some prevalent pain points experienced by customers in the finance sector, specifically in the context of wealth management and customer service:1. Lack of Personalization: Many clients seek financial advice and solutions tailored to their distinct goals and circumstances. Yet, conventional wealth management approaches often fall short of delivering this level of customization, leaving customers feeling disconnected and dissatisfied.2. Limited Accessibility: Accessibility issues can arise when clients encounter hurdles in accessing their financial data or communicating with their wealth managers and customer service representatives. Challenges in initiating contact, receiving timely responses, or navigating complex procedures can breed frustration and hinder the customer journey.3. Complex and Confusing Information: Financial matters are inherently intricate, and the use of complex jargon and technicalities can overwhelm customers. When information is not conveyed clearly and effectively, clients may find themselves bewildered, making it arduous to make well-informed decisions.4. Slow and Inefficient Processes: Lengthy processing times, excessive paperwork, and cumbersome procedures can be significant roadblocks in the customer experience. Clients demand streamlined, efficient processes that conserve time and effort, allowing them to manage their wealth seamlessly.5. Inadequate Communication and Transparency: Effective communication stands as the bedrock of trust and robust relationships. Clients place a premium on transparent, proactive communication from their wealth managers and customer service representatives. Inadequate communication or a lack of transparency concerning fees, performance updates, or policy changes can breed dissatisfaction and erode trust.6. Limited Innovation and Technology Adoption: Expectations are on the rise, with clients anticipating financial institutions to embrace technology and provide innovative solutions to enrich their financial management experience. A dearth of technological advancements, such as user-friendly digital platforms and interactive tools, can leave clients feeling underserved and disconnected.Mitigating these recurring customer pain points necessitates a customer-centric approach. This approach should encompass personalized services, streamlined processes, transparent communication, and a wholehearted embrace of innovative technologies. Through active engagement with these pain points, financial institutions can craft superior customer experiences, foster lasting relationships, and set themselves apart in an increasingly competitive landscape.How Generative AI can be used for Wealth Management?Let's dive right into the crux of the matter. Customers look to financial institutions not just for financial guidance but for personalized advice that aligns with their unique wealth aspirations. They place a high premium on financial expertise to help them navigate the path to their financial goals. Traditional wealth management has traditionally excelled in fostering strong client relationships, with each customer paired with a dedicated relationship manager who intimately understands their individual objectives.However, here's where things get interesting: the traditional methods of wealth management sometimes fall short of meeting the sky-high expectations for personalization. The limitations primarily stem from the scarcity of relationship managers, leading to challenges in scalability and sluggish communication. This communication bottleneck occasionally results in misunderstandings due to varying levels of subject comprehension. These roadblocks, unfortunately, can turn customers off, leaving them feeling adrift and dissatisfied.Enter Generative AI, poised to be the game-changer in wealth management. With its ability to sidestep scalability issues, Generative AI emerges as a promising solution. Picture this: every customer is equipped with an AGI-powered Chatbot capable of addressing their queries, understanding their goals, and furnishing personalized financial plans tailored to their specific requirements. It's a potential paradigm shift in customer service that holds the promise of seamless, individualized wealth management experiences.Now let us see the working of a use case. In this article, we will walk through an LLM-powered Chatbot that will answer user queries.Demonstrating a use-case: Context-based LLM-powered chatbot for Financial advice# Importing Dependenciesimport streamlit as st from streamlit_chat import message import openai import os# Mentioning API keyopenai.api_key = 'PASTE-YOUR-KEY' os.environ['OPENAI_API_KEY'] = "PASTE-YOUR-KEY"# Function to return response from GPTdef fun(prompt):    response = openai.ChatCompletion.create(                engine="engine_name",                messages = [                            {'role': 'user',                             'content': prompt}                          ],                temperature=0,                max_tokens=800,                top_p=0.95,                frequency_penalty=0,                presence_penalty=0,                stop=None)    response = response['choices'][0]['message']['content']    return response # Function that checks whether the question asked is out of context or not. Returns True or Falsedef context_check(prompt):    testing_query = f'''    Instructions:    Answer the questions only related to "{context_topics}".       Query:    Study the prompt "{prompt}" and tell whether the user directly or indirectly asking questions related to "{context_topics}".    Give a response only in "True" or "False".       Remember:    1. Do no generate any other output, example, code etc.     2. Answer should be 1 word only. True or False.    '''    response = fun(testing_query)    return response#Returns filtered response after context checkingdef generate_response(prompt):    for topic in context_topics:        if topic not in prompt:            is_contexual = 'False'    instructions = f'''         Instructions:        0. Assume yourself to be an expert in answering Financial queries        1. Answer questions only to the topics mention in: "{context_topics}" at all costs!        2. Be precise and crisp.        3. Answer in short.        '''    is_contexual = context_check(prompt)    if is_contexual == 'True':        prompt += instructions        response = fun(prompt)        return response    elif is_contexual == 'False':        return "Sorry the question asked doesn't follow the guidelines." # Gets the input text from streamlitdef get_text():    input_text = st.text_input("How may I help?", key='input')    return input_text with open('only_reply.txt', 'r') as f:        context_topics = f.read() context_topics = context_topics.split('\n')[:-1] # context_topics = ['Finance', 'Wealth Management', 'Investment', 'Wealth'] st.set_page_config(    page_title="FinBot",    page_icon="💰", )st.write("# Welcome to FinBot💰!") changes = ''' <style> [data-testid = "stAppViewContainer"]    {    background-image:url('https://i.ibb.co/qrrD42j/Screenshot-2023-09-15-at-5-41-25-PM.png');    background-size:cover;    }       div.esravye2 > iframe {        background-color: transparent;    } </style> ''' st.markdown(changes, unsafe_allow_html=True) if 'generated' not in st.session_state:    st.session_state['generated'] = [] if 'past' not in st.session_state:    st.session_state['past'] = []user_input = get_text() if user_input:    output = generate_response(user_input)    js_clear_input = """    <script>    const inputElement = document.querySelector('.stTextInput input');    inputElement.addEventListener('keydown', function(event) {        if (event.key === 'Enter') {            inputElement.value = '';        }    });    </script>    """# Display the JavaScript code st.markdown(js_clear_input, unsafe_allow_html=True)    st.experimental_set_query_params(text_input="")    st.session_state.past.append(user_input)    st.session_state.generated.append(output) if st.session_state['generated']:    for i in range(len(st.session_state['generated'])-1, -1, -1):        message(st.session_state['generated'][i], key=str(i))        message(st.session_state['past'][i], key="user_"+str(i), is_user=True)ScreenshotsBlocking Out of context questionContextual QuestionsConclusionIn conclusion, Generative AI stands as a game-changing force in the realm of wealth management. Its ability to provide personalized financial advice and solutions on a scale previously unattainable is reshaping the landscape of financial services. By leveraging the vast potential of Generative AI, financial institutions can navigate the complexities of modern finance with unparalleled precision.The anticipated impact is profound: clients receive tailored recommendations that align seamlessly with their unique financial goals, risk profiles, and the ever-evolving market dynamics. This, in turn, leads to improved investment outcomes, heightened client satisfaction, and a deepened sense of trust in financial institutions.As we march forward, the synergy between technology and human expertise will continue to define the future of wealth management. Generative AI, as a powerful ally, empowers advisors and clients alike to make informed decisions, optimize portfolios, and nurture enduring financial success. In this dynamic landscape, the marriage of cutting-edge technology and personalized financial guidance promises to usher in an era of unprecedented prosperity and financial well-being for all.Author BioBhavishya Pandit is a Data Scientist at Rakuten! He has been extensively exploring GPT to find use cases and build products that solve real-world problems.

Given the volume, velocity, and diversity of data expanding at an exponential rate in the modern era, it is crucial to utilize this data for data analytics and machine learning projects to generate business insights. Let's discuss in this post how to utilize ChatGPT to develop ETL pipelines considering the growing popularity of ChatGPT in recent years.What is an ETL (Extract, Transform, and Load) Pipeline Data must first be fetched from one or more sources, processed, or transformed in accordance with the requirements, and then loaded into a storage system that can be used directly by end users without the need for data validation or poor data quality. ETL Pipeline also known as a ‘Data Pipeline’ sometimes known as three phases. Image 1: ETL Process Flow (Extract, Transformation, and Load) During the ETL process, first, we fetch the data, and we perform data quality and validation checks on the extracted data. Once data is extracted then data pre and post-processing should be done to transform the data into a usable format. Once data processing is done, the last step is to store the data from where the end user can access this data. Let’s ask ChatGPT to build an ETL pipeline for data engineering.Problem StatementUsing Databricks and Pyspark build an ETL pipeline using 3 layered approaches: Raw, Bronze, and Gold Layers.  Data should be ingested incrementally automatically, and data should be stored in Azure SQL Database. Business Analysts can use this data to derive business insights.1. Input to ChatGPT with the programming language, and file information (location, format, delimiter, headers, output) Image 2 : Input to chatGPT to create ETL Pipeline Here, the input has been given to the ChatGPT to build an ETL pipeline with all the required input. 2. Prerequisites suggested by ChatGPT before using the code are as follows:  Image 3 : Prerequisite to setup ETL Flow Here, ChatGPT first lists down the prerequisites to set up the blob storage, azure data lake storage gen2, and Databricks workspace.3. Importing the necessary libraries and configuring the ADLS Gen2 storage credentials as shown in the preceding figure:Image 4 : ADLS Gen2 configuration for Pyspark NotebookThis code configures ADLS gen2 using the pyspark notebook to connect and use the data using the storage account key. Here, you should replace the storage account name and key with your storage account name and key details. 4. In this step, pyspark notebook creates the schema for the pyspark data frame based on the file information and we must replace adls container name with the actual container name of ADLS Gen2. Image 5 : Create Structured Schema for pyspark dataframe5. This piece of code renames the columns and once the data frame is ready, it will write the content of the dataframe to the delta format. Image 6 : Pyspark Notebook - Data Transformation Logic 6.  Finally, ChatGPT provides guidance on where to run this code and how to set up an automated pipeline using Databricks. Image 7 : Final ChatGPT Output  7. At a first glance, it looks like it worked like a charm. Generated code can be directly used in the Databricks workspace to build an ETL pipeline. But the limitation of this solution is that it is hard coding the file path so it is not a generic code.Image 8 : Pyspark Code Optimization - ChatGPT input  8.     ChatGPT creates a generic code that can be used to trigger the ETL pipeline whenever there is a new source file in the ADLS Gen2 container. Image 9 : ADLS Gen2 configuration for Pyspark Notebook9.     Next step is to configure the ADLS Gen2 to connect to Databricks using the storage account key.Image 10 : Schema Definition for CSV File 10.  Next step is to create a structured schema to use it while creating the pyspark dataframe in the next step: Image 11: Setting up a loop to check new files 11.  As a final step, we will work towards optimizing the PySpark code: Image 12: Optimised Pyspark Code for incremental data loadTo process any file using the ETL pipeline code, the suggestion is to loop the code to continuously poll the storage container location to check if there is any new file and execute the code to process the new file if any.So, we can use this code and set up Databricks notebooks in a couple of minutes to set up an automated data pipeline for the incremental data load.ConclusionIt is getting much easier and more efficient to build ETL data pipelines using the ChatGPT. ChatGPT can also assist to create a generic and optimized code as per our requirements promptly without spending development efforts. Author BioSagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP. Link - Medium , Amazon , LinkedIn 

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

OpenAI's recent release of the ChatGPT Code Interpreter plugin has introduced a groundbreaking addition to their language model, ChatGPT. This plugin combines the power of Large Language Models (LLMs) with traditional programming capabilities, revolutionizing programming workflows and enhancing data analysis processes. By eliminating the need to write code or set up separate environments, the Code Interpreter plugin simplifies the process of leveraging the capabilities of ChatGPT for data analysis. Let's explore how this plugin can be used to analyze Eurostat data and derive valuable insights.Introduction to the ChatGPT Code Interpreter PluginThe ChatGPT Code Interpreter plugin provides a Python interpreter within a secure execution environment. It supports file uploads and downloads, allowing seamless handling of data. The plugin enables persistent code execution within a chat conversation, allowing users to build on previous code executions. Its purpose is to solve mathematical problems, perform data analysis and visualization, and convert files between different formats.Simplifying Programming WorkflowsBefore the introduction of the Code Interpreter plugin, users had to generate code separately and execute it outside of the ChatGPT model. However, with the Code Interpreter, the entire process can be completed within ChatGPT itself. This eliminates the need for external runtime environments and offers an easy-to-use interface for both programmers and non-programmers to access programming capabilities.Analyzing Eurostat Data Using Code InterpreterTo demonstrate the capabilities of the Code Interpreter plugin, let's analyze Eurostat data. Eurostat provides various datasets related to agriculture, trade, energy, and more. We will focus on analyzing livestock production data in this example.Installing the Required LibrariesTo begin, we need to install the eurostat library, which allows us to access Eurostat data in Python. We can use the following command within the Code Interpreter plugin:!pip install eurostatAnd then we need to obtain the table of contents to obtain the necessary data for the analysis.Obtaining Eurostat DataOnce the library is installed, we can proceed to obtain the table of contents (TOC) of the available Eurostat datasets. This will help us identify the datasets relevant to our analysis. Here is the code to retrieve the TOC:import eurostat import pandas as pd toc = eurostat.get_toc() toc_df = pd.DataFrame(toc[1:],columns=toc[0]) toc_df.head().to_string()We obtain a table that contains the next information:title      code     type       last update of data last table structure change data start data end 0                           Balance of payments  MED_ECBP  dataset  2020-05-28T23:00:00+0200    2021-02-08T23:00:00+0100       2005     2018 1       International trade by SITC - 1 000 EUR  MED_ECET  dataset  2020-05-28T23:00:00+0200   2021-02-08T23:00:00+0100       2005     2019 2                     Primary energy production  MED_EG10  dataset  2020-05-28T23:00:00+0200    2021-02-08T23:00:00+0100       2005     2018 3  Secondary energy production (transformation)  MED_EG20  dataset  2016-10-28T23:00:00+0200    2021-02-08T23:00:00+0100       2000     2015 4                            Energy consumption  MED_EG30  dataset  2020-05-28T23:00:00+0200    2021-02-08T23:00:00+0100       2005     2018Let’s save this into a CSV file:toc_df.to_csv('toc_df',index=False)The resulting table will contain information such as the dataset title, code, type, last update of data, and data start/end years.Identifying Relevant DatasetsNext, we can use the uploaded table of contents file to find the dataset codes that are most relevant to our analysis. In this case, we are interested in datasets related to crop production, livestock numbers, fertilizer usage, and economic accounts for agriculture. We can use the following code within the Code Interpreter:I have uploaded a file with Eurostat datasets and I need to find the codes for the datasets that most resemble the next indicators: - Crops production in tonnes by NUTS 2 regions. - Livestock and meat numbers by NUTS 2 regions. - Poultry production or similar by NUTS 2 regions. - Mlik or derivates production or similar by NUTS 2 regions. - Economic Accounts for Agriculture (EAA) by NUTS 2 regions, where we will take the total intermediate consumption as a proxy for input energy. The assistant will now analyze the uploaded and provided this: The uploaded file contains the following columns: - `title`: The title of the dataset. - `code`: The code of the dataset. - `type`: The type of the entry (all seem to be datasets). - `last update of data`: The date of the last update of the dataset. - `last table structure change`: The date of the last structural change of the dataset. - `data start`: The start year of the data. - `data end`: The end year of the data. To find the codes for the datasets that most resemble the indicators you mentioned, I will search the dataset titles for relevant keywords. For each indicator, I'll look for the following keywords: 1. Crop production: "crop", "production", "NUTS 2". 2. Livestock numbers: "livestock", "NUTS 2". 3. Fertilizer usage: "fertilizer", "NUTS 2". 4. Economic Accounts for Agriculture (EAA): "economic accounts", "agriculture", "NUTS 2". Let's start this search. Here are the dataset codes that most resemble your indicators: 1. **Crop production in tonnes by NUTS 2 regions:**    - `APRO_CPNHR`: Crop production in national humidity by NUTS 2 regions (data available from 2000 to 2023)    - `APRO_CPNHR_H`: Crop production by NUTS 2 regions - historical data (data available from 1975 to 1999)    - `APRO_CPSHR`: Crop production in EU standard humidity by NUTS 2 regions (data available from 2000 to 2023) 2. **Livestock numbers by NUTS 2 regions:**    - `TAI06`: Share of main livestock types in total livestock (data available from 2005 to 2016)    - `EF_KVAAREG`: Key farm variables: area, livestock (LSU), labour force (data available from 2005 to 2013)    - `EF_KVECSLEG`: Key variables: area, livestock (LSU), labour force by economic size class (data available from 2005 to 2013)    - `EF_KVFTREG`: Key variables: area, livestock (LSU), labour force by type of farming (data available from 2005 to 2013)    - `EF_LSK_MAIN`: Main livestock indicators by NUTS 2 regions (data available from 2005 to 2020)    - `EF_LS_OVAAREG`: Livestock: number of farms and heads by size of the economic output (data available from 1990 to 2007)    - `EF_LS_OVESU`: Livestock: number of farms and heads by economic size class (data available from 1990 to 2007)    - `EF_LS_OVLSUREG`: Livestock: number of farms and heads by livestock size class (data available from 1990 to 2007)    - `EF_OLSAAREG`: Livestock: number of farms and heads of animals by size of the agricultural area (data available from 2005 to 2013)    - `EF_OLSECSREG`: Livestock: number of farms and heads of animals by economic size class (data available from 2005 to 2013)    - `EF_OLSLSUREG`: Livestock: number of farms and heads of animals by livestock size class (data available from 2005 to 2013)    - `AEI_EF_LS`: Share of main livestock types in total livestock (data available from 2005 to 2016) 3. **Fertilizer usage or similar by NUTS 2 regions:**    - Unfortunately, there were no datasets found that match this indicator. 4. **Economic Accounts for Agriculture (EAA) by NUTS 2 regions:**    - `AGR_R_ACCTS`: Economic accounts for agriculture by NUTS 2 regions (data available from 1980 to 2021)    - `AGR_R_ACCTS_H`: Economic accounts for agriculture by NUTS 2 regions - historical data (data available from 1973 to 2012) Please note that the data ranges for each dataset may not cover the exact period you are interested in. You might need to merge data from multiple datasets or look for additional datasets to fill in any gaps.Based on this, we can retrieve the next datasets:Crop production in tonnes by NUTS 2 regions:APRO_CPNHR: Crop production in national humidity by NUTS 2 regions (data available from 2000 to 2023)APRO_CPSHR: Crop production in EU standard humidity by NUTS 2 regions (data available from 2000 to 2023)Livestock numbers by NUTS 2 regions:EF_LSK_MAIN: Main livestock indicators by NUTS 2 regions (data available from 2005 to 2020)Economic Accounts for Agriculture (EAA) by NUTS 2 regions:AGR_R_ACCTS: Economic accounts for agriculture by NUTS 2 regions (data available from 1980 to 2021)As a next step, we can start downloading one of the datasets using the Eurostat API.Analyzing Livestock Production DataWe will focus on the "Livestock numbers by NUTS 2 regions" dataset (EF_LSK_MAIN) for our analysis. Let's proceed with downloading and analyzing this dataset:# Animal production livestock_prod_code = 'EF_LSK_MAIN' livestock_prod_pars = eurostat.get_pars(livestock_prod_code) print(livestock_prod_pars) par_values = eurostat.get_par_values(livestock_prod_code, 'geo') # filter the regions for germany de_par_values = {    'unit':'LSU',    'so_eur':'TOTAL',    'geo':[p for p in par_values if all([p.startswith('DE'),len(p)==4])]} # Download data for de filtered regions livestock_prod_data = eurostat.get_data_df(livestock_prod_code, filter_pars=de_par_values) print(livestock_prod_data.head().to_string())Which produces the following result:['freq', 'lsu', 'animals', 'farmtype', 'agrarea', 'so_eur', 'unit', 'geo'] freq   lsu animals farmtype  agrarea so_eur unit geo\\TIME_PERIOD  2005  2007  2010  2013  2016  2020 0    A  LSU0   A0010  FT15_SO  HA10-19  TOTAL  LSU            DE11   0.0   0.0   0.0   0.0  None   0.0 1    A  LSU0   A0010  FT15_SO    HA2-4  TOTAL  LSU            DE11   0.0   0.0   NaN   NaN  None   NaN 2    A  LSU0   A0010  FT15_SO  HA20-29  TOTAL  LSU            DE11   0.0   0.0   0.0   0.0  None   0.0 3    A  LSU0   A0010  FT15_SO  HA30-49  TOTAL  LSU            DE11   0.0   0.0   0.0   0.0  None   0.0 4    A  LSU0   A0010  FT15_SO    HA5-9  TOTAL  LSU            DE11   0.0   0.0   0.0   0.0  None   0.0The code above filters the dataset for Germany's NUTS 2 regions, specifically focusing on the unit of measurement (LSU) and the standard output in Euros (TOTAL). The resulting DataFrame provides a glimpse of the livestock production data for different regions and years.Some of the codes are still not meaningful, specifically, the ones related to animals, so let’s obtain some results about it:# Get the values in the dataset livestock_prod_animal_vals = eurostat.get_par_values(livestock_prod_code, 'animals') # Obtain their meaning livestock_prod_animal_codes = [p for p in eurostat.get_dic(livestock_prod_code, 'animals') if p[0] in livestock_prod_animal_vals] livestock_prod_animal_codes = pd.DataFrame(livestock_prod_animal_codes,columns= ['code','meaning']) print(livestock_prod_animal_codes.head().to_string())Which produces the next:code                                meaning 0  A0010                              Livestock 1  A1000  Live horses, asses, mules and hinnies 2  A2000                    Live bovine animals 3  A3100           Live swine, domestic species 4  A4100                             Live sheepNow let’s download both datasets and ask the ChatGPT code interpreter to run some analysis for us. We want to see some basic analysis of the data that use these to run some basic analysis.Visualizing and Analyzing the Livestock Production DataWe can leverage the Code Interpreter plugin to visualize and analyze the downloaded livestock production data. I’ve uploaded the file to the code interpreter and ask it to create an analysis of the data:The result is a very good initial evaluation of the data. The complete response its:The data seems to pertain to livestock production and has the following columns: - `freq`: The frequency of the data - `lsu`: LSU (Livestock Unit) - a reference unit which facilitates the aggregation of livestock from various species and age according to their nutritional or feed requirement. - `animals`: The type of animals - `farmtype`: Type of the farm - `agrarea`: The agricultural area - `so_eur`: Unknown, needs further clarification - `unit`: The unit of measurement - `geo\\TIME_PERIOD`: Geographical region and time period - `2005`, `2007`, `2010`, `2013`, `2016`, `2020`: These seem to be the years for which the data was collected, indicating the measure of the livestock production. To provide an insightful analysis, let's check the basic statistics of the numerical data and count the unique values of the categorical data. The basic statistics of the numerical data (years 2005, 2007, 2010, 2013, 2016, and 2020) are as follows: - For each year, we have a substantial number of entries (count). - The mean (average) varies across the years, with the highest being around 3,250 for the year 2007 and the lowest around 1,759 for the year 2020. - The standard deviation, which measures the dispersion of the data from the mean, is quite large for all years, suggesting a significant variation in the data. - The minimum value is 0 for all years, which indicates that in some cases, there was no livestock production. - The 25th, 50th (median), and 75th percentiles are 0 for many years, indicating that a substantial portion of the data has low or zero values. - The maximum value is quite large for all years, reaching up to around 3,062,740 in 2020, suggesting that some entities had very high livestock production. For the year 2016, there are no data points available. This might be due to missing data or no data collection in that year. Regarding the categorical data: - `freq` has only one unique value, which does not provide any variability. - `lsu`, `animals`, `farmtype`, `agrarea`, and `geo\\TIME_PERIOD` have multiple unique values, indicating different categories within these fields. - `so_eur` and `unit` also have only one unique value each. Next, we can further examine the distribution of the numerical data and the most common categories in the categorical data. We could also investigate the missing values in the dataset.This is a great starting point, now I’ll ask Code Interpreter to run some basic analysis on the data. I’ve uploaded the codes and ask it to join the data and provide some basic visualizations, which results in the next answer:The uploaded file contains codes for different types of livestock and their meanings. Now, let's join this data with the original dataset. After that, we will visualize the livestock production by year and by different categories.Up next it creates a visualization of the data:The image comes with the next description:The bar chart illustrates the total livestock production over the years. Some observations from the plot include: - The total livestock production appears to be highest in the years 2005 and 2007. - There's a noticeable drop in total production in 2010 and 2013. - The year 2016 does not have any data. - There's a slight increase in total production in 2020 compared to 2013. Next, let's analyze the livestock production by different categories, such as the type of animals and the type of farms.Up next it creates a visualization of the joined data by type of animal:It not only provides the visualization but also an analysis of the data:The bar chart shows the top 5 animal types with the highest total livestock production. Some observations from the plot include: - "Livestock" has the highest total production. However, this category may be a general category that includes various types of animals. - "Live bovine animals" and "Live swine, domestic species" have the next highest total production levels. - "Live poultry, domestic species" and "Live sheep" also contribute significantly to the total livestock production. Next, let's analyze the livestock production by different farm types.As we can see, the analysis continues with the next of step of the analysis. First by creating the visualization:And then by providing the resulting analysis:The bar chart shows the top 5 farm types with the highest total livestock production. Some observations from the plot include: - The farm type "FT15_SO" has the highest total livestock production. However, without a proper definition for this farm type, it's hard to interpret what this category entails. - The farm types "FT16_SO", "FT10_SO", "FT14_SO", and "FT08_SO" also have significant levels of livestock production. These visualizations provide a broad overview of the livestock production trends by year, animal type, and farm type. For a more detailed analysis, it would be beneficial to have more context or information about the different categories, especially the farm types.The Code Interpreter plugin is particularly useful for conducting data analysis and visualization tasks. It allows users to directly interact with large datasets within ChatGPT, posing questions and extracting valuable insights without the need for writing code. The plugin's ability to handle substantial amounts of data enables efficient exploration and analysis, enhancing decision-making processes. Furthermore, it provides visual representations of complex datasets, simplifying data comprehension and aiding in the communication of findings.The resulting analyses provide insights into the most common animal types and farm types contributing to livestock production.ConclusionThe Eurostat dataset, integrated with OpenAI's ChatGPT Code Interpreter plugin, streamlines data analysis. This fusion of programming and language models empowers users to extract insights from vast datasets, regardless of coding expertise. With simplified workflows, the plugin caters to programmers and non-programmers alike, enabling data retrieval, filtering, visualization, and basic analysis. This innovation has applications in marketing, finance, HR, and healthcare, democratizing coding and fostering collaboration. The technology's evolution holds the promise of revolutionizing programming and data analysis, empowering users to glean insights from intricate datasets for informed decision-making across industries.Author Bio:Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.LinkedIn