The allure of ChatGPT in the AI/ML arena

ChatGPT is a general-purpose chatbot, a natural language processing tool, and is proving itself to be more intelligent than some other AI/ML tools in the market today.

Scrolling through social media you have undoubtedly seen the term ChatGPT.   What is ChatGPT? The Chat Generative Pre-Trained Transformer is a powerful AI tool. Launched by OpenAI in November 2022 it is built on OpenAI’s GPT-3 language model and comes at the end of a year of headline-grabbing advances in AI. The organization behind ChatGPT was co-founded by Elon Musk and Sam Altman. OpenAI is already working on a successor GPT-4 model for its natural language processing.

ChatGPT, the latest in technology known as large language model tools, doesn’t speak and think the way people do. 

That means that even though ChatGPT can explain quantum physics or write a poem on command, a full AI takeover isn’t exactly imminent, according to experts.

Chatbots like ChatGPT are powered by large amounts of data and computing techniques to make predictions to string words together in a meaningful way. They not only tap into a vast amount of information and vocabulary, but also understand words in context. It is able to find insights and relationships in the text you input extracting key phrases, sentiment, and topics from unstructured data. This helps them to dispatch encyclopedic knowledge while mimicking speech patterns. AWS offers its own conversational AI and Chatbot called Amazon Lex. AWS additionally offers a ML service called Amazon Comprehend that can understand and capture insights from information written in your text input. There are many machine learning services in AWS which can work for your ML workloads.

ChatGPT does go beyond a mere chatbot, you can ask it to create a Python program. The response will be full source code in a matter of seconds. This functionality is similar with Amazon CodeWhisperer. ChatGPT’ s coding suggestions work across dozens of programming languages and can assist producing technical articles.

Many noted companies though are finding that using chatbots for real-world services have proved troubling — with odd results. Samantha Delouya from the Insider stated , “I asked ChatGPT to do my work and write an Insider article for me. It quickly generated an alarmingly convincing article filled with misinformation”. We just need to be smart about what we are allowing ChatGPT to do, there is still the human element of edit and review.

ChatGPT uses can seem endless, on fire and even intoxicating, it is still early days with this tool. Companies are still trying to figure out the legal and ethical implications of content turned out by artificial intelligence. Let’s step back for a moment and look at some fun we can have with ChatGPT.

 Let’s entertain some uses of ChatGPT you might not have known were possible:

1. Write a novel
2. Coding for developers
3. Create a game
4. Write a Twitter thread
5. Social media comments and reviews
6. Design furniture
7. Crime fighting unsolved murders laying out the crime, suspects, and clues.
8. Dating help and advice
9. Naming items
10. Interview Prep
11. Gift ideas
12. Explaining complex concepts
13. Translation on the go
14. Fitness planning, suggestions
15. Lesson Plans for teachers

Let us now turn our focus on AWS in the AI/ML area. What AWS Machine Learning Services are available to us? The primary ML platform in AWS is called Amazon SageMaker. We also see AWS services which have machine learning(ML) features; Amazon Aurora Machine Learning, Deep Learning AMIs, RedShift ML and more.

Amazon SageMaker is a full-fledged machine learning platform in AWS with a lot of services, features, and components. We are not just talking about a simple ML service but a fully managed cloud platform with a lot of modules you can use. Build, train, and deploy ML models for your use case with the AWS fully managed infrastructure, tools, and workflows. Amazon SageMaker will allow you to remove manual tasks from each step of the ML process allowing you to easily develop high-quality models.

Let’s now look at the different ML services related to the following use cases.

Computer Vision:

• Amazon Rekognition
• Amazon Lookout for Vision
• AWS Panorama

Automated data extraction and analysis:

• Amazon Textract
• Amazon Augmented AI
• Amazon Comprehend

Language AI:

• Amazon Lex
• Amazon Transcribe
• Amazon Polly

Customer Experience improvement:

• Amazon Kendra
• Amazon Personalize
• Amazon Translate

Business metrics:

• Amazon Forecast
• Amazon Fraud Detector
• Amazon Lookout for Metrics

DevOps:

• Amazon DevOps Guru
• Amazon Code Guru Reviewer
• Amazon Code Guru Profiler
• Amazon CodeWhisperer

Visit us at TAM Training and check out our class schedule to learn more about AI/ML and all the AWS services in our 1-5 day live virtual training.

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