AI_Distilled #27: AI Breakthroughs & Open-Source Pioneers

👋 Hello ,

Welcome to another AI_Distilled! This edition brings you key stories on AI, ML, NLP, Gen AI, and more. Our mission is to keep you informed, empowering your skill advancement. Before we embark on the need-to-know updates, let’s take a moment to observe an important perspective from an industry leader. 

“We’re now seeing a major second wave…let’s acknowledge that without open source, how would AI have made the tremendous progress it has over the last decade” -Jensen Huang, NVIDIA CEO 

Amidst the uncertainty surrounding Sam Altman's removal and reinstatement at OpenAI, the open-source community emerges as a potential beneficiary. Also, as OpenAI pauses new signups for ChatGPT Plus, enterprises are anticipated to seek stability and long-term impact by turning to open-source AI models such as Llama, Mistral, Falcon, and MPT for their AI application development needs. Both proprietary and open-source models will play roles, but the latter's contributions are crucial for advancing AI technology's impact on work and life. 

In this week’s edition, we’ll talk about Google DeepMind unveiling an advanced AI music generation model and experiments, Meta releasing Emu Video and Emu Edit, major breakthroughs in generative AI research, Microsoft Ignite 2023 bringing new AI expansions and product announcements, and Galileo's Hallucination Index identifying GPT-4 as the best LLM for different use cases. 

We’ve also got you your fresh dose of AI secret knowledge and tutorials including how to implement emerging practices for society-centered AI, how to speed up and improve LLM output with skeleton-of-thought, getting started with Llama 2 in 5 steps, and how to build an AI assistant with real-time web access in 100 lines of code using Python and GPT-4. 

Also, don't forget to check our expert insights column, which covers the interesting concepts of data architecture from the book 'Modern Data Architecture on AWS'. It's a must-read!  

Stay curious and gear up for an intellectually enriching experience! 

📥 Feedback on the Weekly Edition

Hey folks!

After the stunning OpenAI DevDay, many of us were eager to embark on creating our custom GPT magic. But let's chat about the recent hiccups: the pause on ChatGPT-4 new sign-ups and the shift or reformation in OpenAI's leadership. It's got us all wondering about the future of our handy tools. 

Quick question: Ever tried ChatGPT's Advanced Data Analysis? Now that it's temporarily on hold for new users, it's got us thinking, right? Share your take on these changes in the comments. Your thoughts count! 

We're turning the spotlight on you – some of the best insights will be featured in our next issue for our 38K-strong AI-focused community. Don't miss out on the chance to share your views! 🗨️✨ 

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! 

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 

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⚡ TechWave: AI/GPT News & Analysis

🔳 Sam Altman Is Reinstated as OpenAI’s Chief Executive: OpenAI reinstated CEO Sam Altman, reversing his ouster amid a board shake-up. The revamped board, led by Bret Taylor, includes Lawrence Summers and Adam D'Angelo, with Microsoft's support. Negotiations involved concessions, including an independent investigation into Altman's leadership. Some outgoing members sought to curb Altman's power. Altman's removal sparked a campaign by allies and employees for his return. The board initially stood by its decision but ultimately reinstated Altman for a fresh start. 

🔳 Google DeepMind Unveils Advanced AI Music Generation Model and Experiments: Google DeepMind introduces Lyria, an advanced AI music generation model, and collaborates with YouTube on two experiments, "Dream Track" and "Music AI tools," revolutionizing music creation. Lyria excels in maintaining musical continuity, while the experiments support artists and producers in crafting unique soundtracks and enhancing the creative process. 

🔳 Meta Unveils Emu Video and Emu Edit: Advancements in Generative AI Research: Meta has unveiled two major advancements in generative AI: Emu Video, a text-to-video platform using diffusion models for high-quality content generation, and Emu Edit, an image editing tool for precise control. Human evaluations favor Emu Video over previous models, showcasing substantial progress in creative and effective generative AI tools. 

🔳 Google's AI Search Feature Expands to 120+ Countries: Google's Search Generative Experience (SGE) has expanded to 120+ countries, offering generative AI summaries and language support for Spanish, Portuguese, Korean, and Indonesian. Users can ask follow-up questions and get interactive definitions. The update will initially roll out in the US before expanding globally, enhancing natural language interactions in search results. 

🔳 Microsoft Ignite 2023 Brings New AI Expansions and Product Announcements: Microsoft's Ignite 2023 highlighted the company's deepened AI commitment, featuring Bing Chat's rebranding to Copilot, custom AI chips, and new AI tools like Copilot for Azure. Microsoft Teams will offer AI-driven home decoration and voice isolation. The company consolidated planning tools, introduced generative AI copyright protection, Windows AI Studio for local AI deployment, and Azure AI Speech for text-to-speech avatars. The event underscored Microsoft's emphasis on AI integration across its products and services. 

🔳 Microsoft Emerges as Ultimate Winner in OpenAI Power Struggle: Microsoft emerged victorious in the OpenAI power struggle by hiring ousted CEO Sam Altman and key staff, including Greg Brockman, to lead a new advanced AI team. This strategic move solidifies Microsoft's dominance in the industry, positioning it as a major player in AI without acquiring OpenAI, valued at $86 billion. The recent turmoil at OpenAI has led to employee threats of quitting and joining Altman at Microsoft, potentially granting Microsoft access to significant AI talent. 

🔳 Galileo's Hallucination Index Identifies GPT-4 As the Best LLM for Different Use Cases: San Francisco based Galileo has introduced a Hallucination Index to aid users in selecting the most reliable Large Language Models (LLMs) for specific tasks. Evaluating various LLMs, including Meta's Llama series, the index found GPT-4 excelled, and OpenAI's models consistently performed well, supporting trustworthy GenAI applications. 

🔳 Microsoft Releases Orca 2: Small Language Models That Outperform Larger Ones: Orca 2, comprising 7 billion and 13 billion parameter models, excels in intricate reasoning tasks, surpassing larger counterparts. Developed by fine-tuning LLAMA 2 base models on tailored synthetic data, Orca 2 showcases advancements in smaller language model research, demonstrating adaptability across tasks like reasoning, grounding, and safety through post-training with carefully filtered synthetic data. 

🔳 NVIDIA CEO Predicts Major Second Wave of AI: Jensen Huang predicts a significant AI surge, citing breakthroughs in language replicated in biology, manufacturing, and robotics, offering substantial opportunities for Europe. Praising France's AI leadership, he emphasizes the importance of region-specific AI systems reflecting cultural nuances and highlights the crucial role of data in regional AI growth.

🔮 Expert Insights from Packt Community 

Modern Data Architecture on AWS - By Behram Irani 

Challenges with on-premises data systems 

As data grew exponentially, so did the on-premises systems. However, visible cracks started to appear in the legacy way of architecting data and analytics use cases. 

The hardware that was used to process, store, and consume data had to be procured up-front, and then installed and configured before it was ready for use. So, there was operational overhead and risks associated with procuring the hardware, provisioning it, installing software, and maintaining the system all the time. Also, to accommodate for future data growth, people had to estimate additional capacity way in advance. The concept of hardware elasticity didn’t exist.

The lack of elasticity in hardware meant that there were scalability risks associated with the systems in place, and these risks would surface whenever there was a sudden growth in the volume of data or when there was a market expansion for the business. 

Buying all this extra hardware up-front also meant that a huge capital expenditure investment had to be made for the hardware, with all the extra capacity lying unused from time to time. Also, software licenses had to be paid for and those were expensive, adding to the overall IT costs. Even after buying all the hardware upfront, it was difficult to maintain the data platform’s high performance all the time. As data volumes grew, latency started creeping in, which adversely affected the performance of certain critical systems. 

As data grow into big data, the type of data produced was not just structured data; a lot of business use cases required semi-structured data, such as JSON files, and even unstructured data, such as images and PDF files. In subsequent chapters, we will go through some use cases that specify different types of data. 

As the sources of data grew, so did the number of ETL pipelines. Managing these pipelines became cumbersome. And on top of that, with so much data movement, data started to duplicate at multiple places, which made it difficult to create a single source of truth for the data. 

On the flip side, with so many data sources and data owners within an organization, data became siloed, which made it difficult to share across different LOBs in the organization. 

This content is from the book “Modern Data Architecture on AWS” writtern by Behram Irani (Aug 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. 

🌟 Secret Knowledge: AI/LLM Resources

🤖 How to Use Amazon CodeWhisperer for Command Line: Amazon introduces Amazon CodeWhisperer for the command line, enhancing developer productivity with contextual CLI completions and AI-driven natural language-to-bash translation. The tool provides CLI completions and translates natural language instructions into executable shell code snippets, modernizing the command line experience for over thirty million engineers. 

🤖 How to Implement Emerging Practices for Society-Centered AI: The post underscores the importance of AI professionals addressing societal implications, advocating for multidisciplinary collaboration. It stresses the significance of measuring AI's impact on society to enhance effectiveness and identify areas for improvement in developing systems that benefit the broader community. 

🤖 How to Speed Up and Improve LLM Output with Skeleton-of-Thought: The article introduces the Skeleton-of-Thought (SoT) approach, aiming to enhance the efficiency of Language Models (LLMs) by reducing generation latency and improving answer quality. SoT guides LLMs to generate answer skeletons first, then completes them in parallel, potentially accelerating open-source and API-based models for various question categories. 

🤖 Understanding SuperNIC to Enhance AI Efficiency: The BlueField-3 SuperNIC is pivotal in AI-driven innovation, boosting workload efficiency and networking speed in AI cloud computing. With a 1:1 GPU to SuperNIC ratio, it enhances productivity. Integrated with NVIDIA Spectrum-4, it provides adaptive routing, out-of-order packet handling, and optimized congestion control for superior outcomes in enterprise data centers. 

🤖 Step-by-step guide to the Evolution of LLMs: The post explores the 12-month evolution of Large Language Models (LLMs), from text completion to dynamic chatbots with code execution and knowledge access. It emphasizes the frequent release of new features, models, and techniques, notably the November 2022 launch of ChatGPT, accelerating user adoption and triggering an AI arms race, while questioning if such rapid advancements are bringing us closer to practical AI agents. 

🔛 Masterclass: AI/LLM Tutorials

👉 How to Get Started with Llama 2 in 5 Steps: Llama 2, an open-source large language model, is now free for research and commercial use. This blog outlines a five-step guide, covering prerequisites, model setup, fine-tuning, inference, and additional resources for users interested in utilizing Llama 2. 

👉 How to Integrate GPT-4 with Python and Java: A Developer's Guide: The article explores integrating GPT-4 with Python and Java, emphasizing Python's compatibility and flexibility. It provides examples, discusses challenges like rate limits, and encourages collaboration for harnessing GPT-4's transformative potential, highlighting the importance of patience and debugging skills. 

👉 How to Build an AI Assistant with Real-Time Web Access in 100 Lines of Code Using Python and GPT-4: This article guides readers in creating a Python-based AI assistant with real-time web access using GPT-4 in just 100 lines of code. The process involves initializing clients with API keys, creating the assistant using the OpenAI and Tavily libraries, and implementing a function for retrieving real-time information from the web. The author offers a detailed step-by-step guide with code snippets. 

👉 Step-by-step guide to building a real-time recommendation engine with Amazon MSK and Rockset: This tutorial demonstrates building a real-time product recommendation engine using Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Rockset. The architecture allows instant, personalized recommendations critical for e-commerce, utilizing Amazon MSK for capturing high-velocity user data and AWS Managed services for scalability in handling customer requests, API invocations, and data ingestion. 

🚀 HackHub: Trending AI Tools

💮 protectai/ai-exploits: Collection of real-world AI/ML exploits for responsibly disclosed vulnerabilities, aiming to raise awareness of the amount of vulnerable components in the AI/ML ecosystem. 

💮 nlmatics/llmsherpa: Provides strategic APIs to accelerate LLM use cases, includes a LayoutPDFReader that provides layout information for PDF to text parsers, and is tested on a wide variety of PDFs. 

💮 QwenLM/Qwen-Audio: Large audio language model proposed by Alibaba Cloud developers can use for speech editing, sound understanding and reasoning, music appreciation, and multi-turn dialogues in diverse audio-oriented scenarios. 

💮 langchain-ai/opengpts: Open-source effort creating a similar experience to OpenAI's GPTs and Assistants API. It builds upon LangChain, LangServe, and LangSmith. 

Readers’ Feedback! 💬 

💭 Anish says, "The growing number of subscribers is really exciting. I particularly appreciate the transformation of 2D images into 3D models from Adobe and going through 'Tackling Hallucinations in LLMs' by Bijit Ghosh. These kinds of practical contexts are truly my preference for the upcoming newsletters." 

💭 Tony says, "Very informative, far-reaching, and extremely timely. On point. Just keep it up, keep your eye on news and knowledge, and keep cluing us all once a week please, Merlyn. You're doing a fine job."  

Share your thoughts here! Your opinions matter—let's make this space a reflection of diverse perspectives.