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“Once in a while, technology comes along that is so powerful and so broadly applicable that it accelerates the normal march of economic progress. And like a lot of economists, I believe that generative AI belongs in that category.” - Andrew McAfee, Principal Research Scientist, MIT Sloan School of Management
This vividly showcases the kaleidoscope of possibilities Gen AI unlocks as it emerges from its cocoon, orchestrating a transformative symphony across realms from medical science to office productivity. Take Google’s newly released AlphaCode 2, for example, which achieves human-level proficiency in programming, or Meta’s AudioBox, which pioneers next-generation audio production.
Welcome to AI_Distilled #30, your ultimate guide to the latest advancements in AI, ML, NLP, and Gen AI.
Stay curious and gear up for an intellectually enriching experience!
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Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!
A quick heads-up: Our team is taking a well-deserved holiday break to recharge and return with fresh ideas. So, there'll be a pause in our weekly updates for the next two weeks. We're excited to reconnect with you in the new year, brimming with new insights and creativity. Wishing you a fantastic holiday season! See you in 2024!
Cheers,
Merlyn Shelley
Editor-in-Chief, Packt
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⭐ Microsoft Forms Historic Alliance with Labor Unions to Address AI Impact on Workers: Microsoft is partnering with the American Federation of Labor and Congress of Industrial Organizations, a coalition of 60 labor unions representing 12.5 million workers. They plan to discuss AI's impact on jobs, offer AI training to workers, and encourage unionization with "neutrality" terms. The goal is to improve worker collaboration, influence AI development, and shape policies for frontline workers' tech skills.
⭐ Meta’s Audiobox Advances Unified Audio Generation with Enhanced Controllability: Meta researchers have unveiled Audiobox, an advanced audio generation model addressing limitations in existing models. It prioritizes controllability, enabling unique styles via text descriptions and precise management of audio elements. Audiobox excels in speech and sound generation, achieving impressive benchmarks like 0.745 similarity on Librispeech for text-to-speech and 0.77 FAD on AudioCaps for text-to-sound using description and example-based prompts.
⭐ Europe Secures Deal on World's First Comprehensive AI Rules:EU negotiators have achieved a historic agreement on the first-ever comprehensive AI rules, known as the Artificial Intelligence Act. It addresses key issues, such as generative AI and facial recognition by law enforcement, aiming to establish clear regulations for AI while facing criticism for potential exemptions and loopholes.
⭐Google DeepMind Launches AlphaCode 2: Advancing AI in Competitive Programming Collaboration:Google DeepMind has unveiled AlphaCode 2, a successor to its groundbreaking AI that writes code at a human level. It outperforms 85% of participants in 12 recent Codeforces contests, aiming to collaborate effectively with human coders and promote AI-human collaboration in programming, aiding problem-solving and suggesting code designs.
⭐Stable LM Releases Zephyr 3B: Compact and Powerful Language Model for Edge Devices: Stable LM Zephyr 3B is a 3 billion parameter lightweight language model optimized for edge devices. It excels in text generation, especially instruction following and Q&A, surpassing larger models in linguistic accuracy. It's ideal for copywriting, summarization, and content personalization on resource-constrained devices, with a non-commercial license.
⭐Meta Announces Purple Llama: Advancing Open Trust and Safety in Generative AI: Purple Llama is an initiative promoting trust and safety in generative AI. It provides tools like CyberSec Eval for cybersecurity benchmarking and Llama Guard for input/output filtering. Components are permissively licensed to encourage collaboration and standardization in AI safety tools.
⭐Google Cloud Unveils Cloud TPU v5p and AI Hypercomputer for Next-Gen AI Workloads:Google Cloud has launched the powerful Cloud TPU v5p AI accelerator, addressing the needs of large generative AI models with 2X more FLOPS and 3X HBM. It trains models 2.8X faster than TPU v4 and is 4X more scalable. Google also introduced the AI Hypercomputer, an efficient supercomputer architecture for AI workloads, aiming to boost innovation in AI for enterprises and developers.
⭐Elon Musk's xAI Chatbot Launches on X:Grok, created by xAI, debuts on X (formerly Twitter) for $16/month to Premium Plus subscribers. It offers conversational answers, similar to ChatGPT and Google's Bard. Grok-1 incorporates real-time X data, providing up-to-the-minute information. Elon Musk praises Grok's rebellious personality, though its intelligence remains comparable to other chatbots. Currently text-only, xAI intends to expand Grok's capabilities to include video, audio, and more.
Embark on a captivating journey into geospatial analysis, a field beyond geography enthusiasts! This book reveals how combining geospatial magic with SQL can tackle real-world challenges. Learn to create spatial databases, use SQL queries, and incorporate PostGIS and QGIS into your toolkit.
Key Concepts:
🌍 Foundations:
- Understand the importance of geospatial analysis.
- See how location info enhances data exploration.
🗺️ Tobler's Wisdom:
- Embrace Walter Tobler's second law of geography.
- Explore how external factors impact the area of interest.
🔍 SQL Spatial Data Science:
- Master geospatial analysis with SQL.
- Build databases, write queries, and use handy functions.
🛠️ Toolbox Upgrade:
- Boost skills with PostGIS and QGIS.
- Handle data questions and excel in spatial analysis.
Decode geospatial secrets—perfect for analysts and devs seeking location-based insights!
⭐A Primer on Enhancing Output Accuracy Using Multiple LLMs: Explore using chain-of-thought prompts with LLMs like GPT-4 and PaLM2 for varied responses. Learn the "majority-vote/quorum" technique to enhance accuracy by combining responses from different LLMs using AIConfig for streamlined coordination, improving output reliability and minimizing errors.
⭐Navigating Responsible AI: A Comprehensive Guide to Impact Assessment: This article introduces the RAI impact assessment, emphasizing aligning AI with responsible principles. It mentions Microsoft's tools like the Responsible AI Standard, v2, RAI Impact Assessment Template, and Guide. The approach involves identifying use cases, stakeholders, harms, and risk mitigation. It suggests adapting RAI to organizational needs and phased alignment with product releases.
⭐Enhancing RAG-Based Chatbots: A Guide to RAG Fusion Implementation:In the fourth installment of this tutorial series, the focus is on implementing RAG Fusion, a technique to improve Retrieval-Augmented Generation (RAG) applications. It involves converting user queries into multiple questions, searching for content in a knowledge base, and re-ranking results. The tutorial aims to enhance semantic search in RAG applications.
⭐ Evaluating Retrieval-Augmented Generation (RAG) Applications with RAGAs Framework:The article discusses challenges in making a production-ready RAG application, highlighting the need to assess retriever and generator components separately and together. It introduces the RAGAs framework for reference-free evaluation using LLMs, offering metrics for component-level assessment. The article provides a guide to using RAGAs for evaluation, including prerequisites, setup, data preparation, and conducting assessments.
🔛 Masterclass: AI/LLM Tutorials
⭐ Creating a Vision Chatbot: A Guide to LLaVA-1.5, Transformers, and Runhouse:Discover how to build a multimodal conversational model using LLaVA-1.5, Hugging Face Transformers, and Runhouse. The post introduces the significance of multimodal conversational models, blending language and visual elements. It emphasizes the limitations of closed-source models, showcasing open-source alternatives. The tutorial includes Python code available on GitHub for deploying a vision chat assistant, providing a step-by-step guide. LLaVA-1.5, with its innovative visual embeddings, is explained, highlighting its lightweight training and impressive performance. The tutorial's implementation code, building a vision chatbot, is made accessible through standardized chat templates, and the Runhouse platform simplifies deployment on various infrastructures.
⭐Fine-Tuning LLMs: A Comprehensive Guide: Explore the potential of fine-tuning OpenAI’s LLMs to revolutionize tasks such as customer support chatbots and financial data analysis. Learn how fine-tuning enhances LLM performance on specific datasets and discover use cases in customer support and finance. The guide walks you through the step-by-step process of fine-tuning, from preparing a training dataset to creating and using a fine-tuned model. Experience how fine-tuned LLMs, exemplified by GPT-3.5 Turbo, can transform natural language processing, opening new possibilities for diverse industries and applications.
⭐Building a Web Interface for LLM Interaction with Amazon SageMaker JumpStart: Embark on a comprehensive guide to creating a web user interface, named Chat Studio, enabling seamless interaction with LLMs like Llama 2 and Stable Diffusion throughAmazon SageMaker JumpStart. Learn how to deploy SageMaker foundation models, set up AWS Lambda, IAM permissions, and run the user interface locally. Explore optional extensions to incorporate additional foundation models and deploy the application using AWS Amplify. This step-by-step tutorial covers prerequisites, deployment, solution architecture, and offers insights into the potential of LLMs, providing a hands-on approach for users to enhance conversational experiences and experiment with diverse pre-trained LLMs on AWS.
⭐Mitigating Hallucinations with Retrieval Augmented Generation:Delve into a step-by-step guide exploring the deployment of LLMs, specifically Llama-2 from Amazon SageMaker JumpStart. Learn the crucial technique of RAG using the Pinecone vector database to counteract AI hallucinations. The primer introduces source knowledge incorporation through RAG, detailing how to set up Amazon SageMaker Studio for LLM pipelines. Discover two approaches to deploy LLMs using HuggingFaceModel and JumpStartModel. The guide further illustrates querying pre-trained LLMs and enhancing accuracy by providing additional context.
🚀 HackHub: Trending AI Tools
⭐ bricks-cloud/BricksLLM: Cloud-native AI gateway written in Go enabling the creation of API keys with fine-grained access controls, rate limits, cost limits, and TTLs for both development and production use.
⭐ kwaikeg/kwaiagents: Comprises KAgentSys-Lite with limited tools, KAgentLMs featuring LLMs with agent capabilities, KAgentInstruct providing finetuning data, and KAgentBench offering over 3,000 human-edited evaluations for testing agent capabilities.
⭐ facebookresearch/Pearl: Production-ready Reinforcement Learning AI agent library from Meta prioritizing long-term feedback, adaptability to diverse environments, and resilience to limited observability.
⭐andvg3/LSDM: Official implementation of a NeurIPS 2023 paper on Language-driven Scene Synthesis using a Multi-conditional Diffusion Model.
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