AI_Distilled #1: Scikit-LLM, Privacy in ML Models, and ChatGPT Copilot in Windows 11

Welcome to the first issue of our newsletter — a treat for anyone interested in AI including developers, engineers, AI practitioners, and enthusiasts who live and breathe AI/ML, LLMs, NLP, Generative AI, and all related fields! This is a newsletter from Packt that combines original ideas and curated content for you. Deep dives, industry developments, interesting tools, and tools, all in one place. If it’s not the right area for you, please click on the unsubscribe button at the footer of this email.

In this edition, we’ll examine differential privacy approaches in ML models, take a look at Scikit-LLM, that allows developers to seamlessly integrate language models into scikit-learn for enhanced text analysis, and explore why Microsoft is emphasizing the importance of causal inference in ML.

We’ll learn how to quickly deploy your own ChatGPT-based apps and explore the concept of AI hallucinations, where AI attempts to overreach itself in misleading (and often terrifying) ways. More in today’s issue:

• TechWave: AI/GPT News & Analysis
• Secret Knowledge: AI/LLM Resources
• Masterclass: AI/LLM Tutorials
• Industry Experts Speak
• HackHub: Trending AI Tools

Coding your new app? Working on the next GPT breakthrough? Trying to reduce ML inference latency? We’re here to help you stay updated and make sense of the rapidly changing AI landscape!

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Until next time!

Kartikey Pandey, Vidhu Jain

Editor-in-Chiefs

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⚡ TechWave: AI/GPT News & Analysis

• ChatGPT iOS App Released: OpenAI has released the much-awaited ChatGPT app for iOS. The app will sync your search history across all your devices and supports voice input for hands-free operation on your iPhone on the go. You can download the app here. The app is being initially rolled out in the US and will be available in more regions in the coming days. OpenAI has promised an Android version soon.
• Built-in ChatGPT-driven Copilot Will be Added to Windows 11: Microsoft is adding a new ChatGPT-driven Copilot feature in Windows 11 that can be used alongside other Windows apps. It will be available to Windows Insiders in June. It can change Windows settings, rearrange windows with Snap Layouts, summarize and rewrite documents, open apps, and more. There will also be a dedicated button for Windows Copilot on the taskbar.
• Scikit-LLM: Scikit-LLM allows developers to seamlessly integrate language models into scikit-learn for enhanced text analysis tasks. Scikit-LLM is still under development and it plans to add support for more models and fine-tuning. It features zero-shot text classification, multi-label zero-shot text classification, and text vectorization. GitHub repo here.
• New Image-to-Speech Generative AI App: AWS architects designed a novel web application, an image-to-speech Generative AI solution that empowers individuals with visual challenges to comprehend images through image captioning, facial recognition, and text-to-speech, leveraging Amazon SageMaker and Hugging Face. The website creates detailed descriptions of uploaded images and reads them out in a nature-sounding voice. The team used Hugging Face’s OFA model, a unified multi-model pre-trained model.
• dreamGPT Puts LLM Hallucinations to Creative Use: A new GPT-based tool turns LLM hallucination on its head and uses it to foster divergent thinking to generate unique insights and ideas for inspiration (think poetry). Contrary to conventional tools that use LLM models to solve specific challenges, dreamGPT ‘dreams’ of new ideas and amalgamates them to create novel concepts. You can access the GitHub repo here.
• Google Launches AI Product Studio: Google has started to deploy more generative technologies to their products. Google says Product Studio gives merchants the ability to create product imagery for free and get more value from the images they already have. You can create new imagery without the added cost of new photoshoots.

Secret Knowledge: AI/LLM Resources

• How to Use Synthetic Control Method (SCM) for Causal Inference: Traditional ML models overlook causal relationships in favor of correlation. Many real-world instances need cause-effect dynamics, for example, when randomized control trials (A/B testing) are not feasible. Causal effects are also more resilient to model drifts. The article shows how you can estimate causal effects using machine learning with a synthetic control group instead of A/B testing. SCM (Synthetic Control Method) involves creating a synthetic control group that closely resembles the target group and using it to estimate the causal effect of an intervention. 
• CRITIC: A New Framework for Self-Correcting AI Language Models: Researchers have introduced a system named CRITIC, which allows large language models (LLMs) to validate and improve their own outputs in a way similar to humans using tools for fact-checking or debugging. The process involves the model producing an initial output, interacting with tools to evaluate it, and then revising the output based on the feedback received, which has been proven to enhance the performance of LLMs in areas like question answering, program synthesis, and reducing toxicity.
• Leveraging Reinforcement Learning to Facilitate Dynamic Planning in Open-Ended Discussions: Dynamic planning is the ability to modify the original plan of a conversation based on its flow, allowing for flexible and engaging interactions. In the context of virtual assistants, dynamic planning enables deeper, multi-turn conversations that adapt to user preferences and goals. Traditional LLMs excel at generating individual responses but lack the capacity for forward planning. Novel RL constructions utilize supervised models, such as RNNs and transformers, to represent dialogue states effectively.
• Differentially Privacy in ML Models: Best Practices and Open Challenges: The article discusses the importance of protecting the privacy of training data in machine learning (ML) models. It introduces the concept of Differential Privacy (DP) which allows for data anonymization in ML models. 
• Get to know the challenges in achieving good utility with differentially private ML techniques and explore the common techniques for obtaining differentially private ML models. The research emphasizes the need for practitioners to choose the right privacy unit, privacy guarantees, and perform hyperparameter tuning effectively. You can read the complete survey paper here.
• VideoLLM: A Tool for Video Analysis using LLMs: Leveraging the power of language processing models, the newly proposed VideoLLM system converts all video inputs into a type of language that can be analyzed more efficiently. By successfully testing on multiple datasets, VideoLLM proves that it can handle various tasks, suggesting that model's reasoning abilities can be effectively used for understanding and analyzing video content. GitHub repo here.

MasterClass: AI/LLM Tutorials

• Quickly build ChatGPT apps in 5 steps with this low-code platform: This tutorial introduces the use of the low-code solution, ToolJet, to rapidly develop ChatGPT apps. Traditionally, creating and integrating apps with ChatGPT required programming expertise and time-consuming development cycles. ToolJet offers seamless integration with OpenAI, enabling developers to quickly build applications that leverage ChatGPT's capabilities in 5 steps:
  • Sign up for a ToolJet account
  • Create a new app
  • Select OpenAI plugin
  • Enter your Organization ID and API Key
  • Fetch OpenAI data
• Prompt Engineering (GitHub Copilot) Beginner’s Guide: This tutorial explores how you can get started with Prompt Engineering using GitHub Copilot and practice writing and iterating on prompts yourself. First, let's start with the basics for folks who are unfamiliar with GitHub Copilot or prompt engineering. Read the full tutorial here ->
• How to Use Alpa and Ray to Efficiently Scale LLM training Across a Large GPU Cluster: This post explores the integration of Alpa.ai and Ray.io frameworks, highlighting their combined capabilities to train a massive 175 billion-parameter JAX transformer model with pipeline parallelism. We delve into the architectures, developer-friendly APIs, scalability, and performance of these frameworks. Both Alpa and Ray enhance developer productivity and optimize model scalability. Alpa's pipeline parallelism efficiently distributes computation across multiple GPUs, relieving developers of cognitive load. Ray provides a distributed computing framework for simplified resource scaling and management across multiple machines.

Industry Experts Speak

“No one in the field has yet solved the hallucination problems”

- Sundar Pichai, Google and Alphabet CEO

LLMs can deliver inaccurate information with a confident tone, often misleading unsuspecting users. Called hallucinations or confabulations, this is one of the major challenges with AI.

“This new generation of AI will remove the drudgery of work and unleash creativity, and today we're sharing our latest Work Trend Index findings as we apply technology to help alleviate digital debt, build AI aptitude, and empower employees”

-Satya Nadella, Microsoft Chairman and CEO

The latest Microsoft Work Trend Index report shows how AI is redefining the future of work. 49% of surveyed employees are fearful AI will replace their jobs while 70% would readily delegate workloads to AI to simplify their professional life and bolster creativity.

HackHub: Trending AI Tools

• StanGirard/Quivr: Quivr calls itself “your second brain in the cloud”. It’s very convenient to dump all your files and thoughts and retrieve unstructured information, powered by generative AI.
• FlowiseAI/Flowise: Drag & drop UI to build your customized LLM flow using LangchainJS.
• Ricklamers/gpt-code-ui: An open-source implementation of OpenAI's ChatGPT Code interpreter.
• Stability-AI/StableStudio: StableStudio is Stability AI's official open-source variant of DreamStudio (user interface for generative AI). It is a web-based application that allows users to create and edit generated images. 
• 0nutation/SpeechGPT: LLM with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-model content following human instructions.