AI_Distilled #2: Google Gen AI Search Engine, Microsoft Fabric, NVIDIA DGX Supercomputer, Google MatCha, Succeed in AI

“AI is going to touch literally every single industry. While some worry that AI may take their jobs, someone who’s expert with AI will."

 - Jensen Huang, Founder and CEO, NVIDIA

In a world where AI revolutionizes all industries, fears of job loss fade when you become an AI expert. Embrace the power of AI to unlock boundless opportunities and shape the future!  

Welcome to the second issue of AI_Distilled newsletter — your essential guide to the latest developments in AI/ML, LLMs, GPT, NLP, and Generative AI! In this edition, we’ll start with the latest AI buzz, including Google’s newly launched AI search engine, the unveiling of Microsoft Fabric — a new analytics platform for the AI era, NVIDIA’s cutting-edge DGX supercomputer, scientists’ breakthrough discovery of a lifesaving antibiotic using AI, and Microsoft’s recently released report on AI governance proposing “safety brakes” to ensure critical AI always remain under human control. 

We’ve also got you your fresh dose of AI secret knowledge and tutorials. The AI Product Manager's Handbook, Building your own LLM-powered chatbot in 5 minutes with HugChat and Streamlit, see how Google’s MatCha revolutionizes Computer understanding of Visual Language and Chart Reasoning, and discover why self-healing software could become a tangible reality in the era of LLMs.  

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Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content! 

Cheers, 

Merlyn Shelley, 

Editor-in-Chief, Packt 

⚡ TechWave: AI/GPT News & Analysis

• Google Launches its New AI Search Engine: Google has opened access to its new generative AI search capabilities, called Search Labs, the new program lets you access early experiments from Google. Sign up for the waitlist and start testing new Labs experiments, including SGE (Search Generative Experience), Code Tips and Add to Sheets. The enhanced search experience simplifies the search process, helping you grasp a topic more quickly, discover fresh perspectives and valuable insights, and accomplish tasks with greater ease. 
• Microsoft Build Unveils AI-powered Shift in Technology Space: Microsoft Build, the annual flagship event for developers, showcased the major shift in the technology space driven by artificial intelligence (AI). The event highlighted the adoption of AI copilots and plugins across various Microsoft offerings, including Bing, Dynamics 365 Copilot, and Microsoft 365 Copilot. Microsoft also announced the growth of the AI plugin ecosystem, the introduction of Azure AI tooling for developers, initiatives for building responsible AI systems, the unified analytics platform Microsoft Fabric, and collaborations with partners like NVIDIA. Windows 11 will also feature new AI-driven experiences with Windows Copilot. 
• Microsoft Launches Microsoft Fabric, the New Analytics Platform ‘for AI era’: Microsoft Fabric debuts as a comprehensive and integrated analytics platform designed to meet the diverse needs of organizations. This end-to-end solution seamlessly combines various data and analytics tools, including Azure Data Factory, Azure Synapse Analytics, and Power BI, into a single unified product. Fabric empowers data and business professionals to maximize the value of their data, enabling them to delve deeper into insights and enhance decision-making processes.  
• OpenAI Launches $1M Grants Program for Democratic Inputs to AI: OpenAI has announced that it will fund ten grants of $100,000 each, aimed at supporting experiments in establishing a democratic framework for determining the guidelines that govern the behavior of AI systems while staying within legal boundaries. Recognizing that AI’s impact will be “significant” and “far-reaching,” the ChatGPT creator wants decisions concerning how AI behaves to be influenced by diverse public perspectives. The deadline to submit the grant application is June 24, 2023. 
• Microsoft Releases AI Governance Report: Microsoft has published a report titled "Governing AI: A Blueprint for the Future," which outlines guidelines for governments in formulating policies and regulations related to AI. The report emphasizes five key areas for consideration, including the creation of “fail-safe safety brakes” for AI systems that control critical infrastructure including city traffic systems and electrical grids to ensure AI is always under human control. The report highlights Microsoft's commitment to ethical AI practices and how the company is implementing responsible AI principles within its operations. 
• Scientists Harness AI to Unleash Powerful Antibiotic Against Deadly Superbug: Scientists have utilized artificial intelligence (AI) to identify a new antibiotic capable of combating a dangerous superbug. In a study published in Nature Chemical Biology, researchers from McMaster University and MIT discovered a promising antibiotic, named abaucin, through the use of AI algorithms. The superbug in question, Acinetobacter baumannii, poses a severe threat to human health. The AI screening process enabled the identification of several potential antibiotics, with abaucin ultimately proving effective in suppressing the infection in laboratory tests.  
• NVIDIA Unveils DGX GH200 AI Supercomputer to Revolutionize Generative AI and Recommender Systems: NVIDIA has introduced the DGX GH200 AI Supercomputer, a groundbreaking innovation that combines 256 Grace Hopper Superchips into a single, massive GPU, capable of delivering 1 exaflop of performance and 144 terabytes of shared memory. With advanced NVLink interconnect technology and the NVIDIA NVLink Switch System, the DGX GH200 empowers researchers to develop next-generation models for generative AI language applications, recommender systems, and data analytics workloads.

Expert Insights from Packt Community

The AI Product Manager's Handbook – By Irene Bratsis

Succeeding in AI – how well-managed AI companies do infrastructure right 

Many large technology companies that depend heavily on ML have dedicated teams and platforms that focus on building, training, deploying, and maintaining ML models. The following are a few examples of options you can take when building an ML/AI program: 

• Databricks has MLflow: MLflow is an open source platform developed by Databricks to help manage the complete ML life cycle for enterprises. It allows you to run experiences and work with any library, framework, or language.  
• Google has TensorFlow Extended (TFX): This is Google’s newest product built on TensorFlow and it’s an end-to-end platform for deploying production-level ML pipelines. It allows you to collaborate within and between teams and offers robust capabilities for scalable, high-performance environments. 
• Uber has Michelangelo: Uber is a great example of a company creating their own ML management tool in-house for collaboration and deployment. Earlier, they were using disparate languages, models, and algorithms and had teams that were siloed. After they implemented Michelangelo, they were able to bring in varying skill sets and capabilities under one system.  

The above content is extracted from the recently published book titled "The AI Product Manager's Handbook," authored By Irene Bratsis and published in Feb 2023. To get a glimpse of the book's contents, make sure to read the free chapter provided here, or if you want to unlock the full Packt digital library free for 7 days, try signing up now! To learn more, click on the button below.     

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Secret Knowledge: AI/LLM Resources

• LLMs Enabling Self-Healing Software that Repair Vulnerabilities Automatically: Researchers have introduced a groundbreaking solution that utilizes Large Language Models (LLMs) and Formal Verification techniques to automatically detect and fix software vulnerabilities. The method involves Bounded Model Checking (BMC) to identify vulnerabilities and generate counterexamples that highlight incorrect system behavior. These counterexamples, along with the source code, are then fed into an LLM engine, which uses a specialized prompt language for code debugging and generation. The repaired code is verified using BMC.  
• Google Research Introduces MatCha to Revolutionize Computer Understanding of Visual Language and Chart Reasoning: MatCha is a groundbreaking pixels-to-text foundation model that aims to improve computer understanding of visual language, including charts and graphs. Training on chart de-rendering and math reasoning tasks, MatCha surpasses previous models in ChartQA performance by over 20% and achieves comparable results in summarization systems with significantly fewer parameters. The research papers on MatCha and DePlot will be presented at ACL2023, and the models and code are available on Google Research's GitHub repository.  
• Dialogue-guided intelligent document processing with foundation models on Amazon SageMaker JumpStart: A dialogue-guided approach to intelligent document processing (IDP) using Amazon SageMaker JumpStart. IDP automates the processing of unstructured data and offers improvements over manual methods. The solution discussed in the article combines OCR, large language models (LLMs), task automation, and external data sources to enhance IDP workflows. Incorporating dialogue capabilities and generative AI technologies, the system becomes more efficient, accurate, and user-friendly.  
• Resolving Code Review Comments with Machine Learning: Google has implemented a machine learning (ML) system to automate and streamline the code review process, reducing the time spent on code reviews. By training a model to predict code edits based on reviewer comments, Google's system suggests code changes to authors, increasing their productivity and allowing them to focus on more complex tasks. The model has been calibrated to achieve a target precision of 50% and has successfully addressed 52% of comments in offline evaluations. 

MasterClass: AI/LLM Tutorials

• Build LLM-powered chatbot in 5 minutes using HugChat and Streamlit: If you’re interested in building a chatbot using Language Models, this is a step-by-step guide on developing an LLM-powered chatbot using HugChat, a Python library that simplifies the integration of LLMs into chatbot applications and Streamlit, a user-friendly framework for creating interactive web applications.  
• Unlock the Potential of Unstructured Data with BigQuery Object Tables: Discover how Google Cloud's BigQuery Object Tables, now generally available, empower AI developers to analyze unstructured data more effectively. Object tables provide a structured record interface for unstructured data stored in Cloud Storage, enabling the use of SQL and AI models for processing and managing diverse data types. You can access Google’s guided lab and tutorials to get started with your project. 
• Vertex AI Embeddings for Text: Grounding LLMs Easily: Explore the concept of grounding and learn about Vertex AI Embeddings for Text and Matching Engine, including its key features. Learn how to build reliable Gen AI services for enterprise use, enabling deep semantic understanding and enhancing user experiences in applications such as search, classification, recommendation, and clustering. You can access the Vertex AI Embeddings for Text API documentation here and see the Stack Overflow semantic search demo on GitHub. 
• Getting Started with Generative AI Studio on Google Cloud: Google Cloud offers Generative AI Studio, a user-friendly console tool for prototyping and testing generative AI models. This article provides step-by-step instructions on using Generative AI Studio through the Google Cloud user interface, without the need for REST API or Python SDK. Further resources are available in the GitHub repository for those interested in learning more about using Generative AI Studio. 

HackHub: Trending AI Tools

• SamurAIGPT/privateGPT: Create a QnA chatbot on your documents without relying on the internet by utilizing the capabilities of local LLMs with complete privacy and security.  
• facebookresearch/fairseq: A sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.  
• iperov/DeepFaceLive: Swap your face from a webcam or the face in the video using trained face models. 
• geohot/tinygrad: Aims to be the easiest deep learning framework to add new accelerators to, with support for both inference and training.  
• OpenGVLab/InternGPT: A pointing-language-driven visual interactive system, allowing you to interact with ChatGPT by clicking, dragging, and drawing using a pointing device.