AI_Distilled #32: Navigating Industry Updates and Innovations

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👋 Hello ,

“There is not going to be one model to rule them all. You need to be trying out different models, you need a real choice of model providers.”  -Adam Selipsky, CEO, AWS. 

There’s no one-size-fits-all approach in AI development. When you embrace diversity in AI, that’s when it truly shines. There’s also a different side to the coin — the infinitely scalable adaptability of AI to revolutionize field after field, such as when it can help discover promising new sustainable battery materials to potentially reduce reliance on Lithium. 

Welcome back to a new issue of AI Distilled - your one-stop destination for all things AI, ML, NLP, and Gen AI. Let’s get started with the latest news and developments across different industries and sectors: 

AI Launches & Industry Updates: 

Explore the GPT Marketplace

NVIDIA Unveils Innovations in Gaming, AI, and Robotics at CES 2024 

Perplexity AI Secures $73.6M Funding Led by NVIDIA and Jeff Bezos 

OpenAI Set to Launch GPT Store for AI Models and Apps 

Google Faces Multibillion-Dollar Patent Trial Over AI Technology in U.S. 

Google's DeepMind Unveils Advances in Robotic Training with Video and Language Models 

AI in Healthcare: 

Isomorphic Labs Secures $3 Billion AI-Driven Drug Discovery Deals with Eli Lilly and Novartis 

Nabla Secures $24 Million in Series B Funding for AI-Powered Medical Assistant 

AI in Business: 

Deloitte Introduces PairD AI Chatbot for 75,000 Staff in Big Four's Latest Automation Move 

Walmart Revolutionizes Shopping with Generative AI Innovations 

AI in Science & Technology: 

Microsoft and PNNL Harness AI to Discover Promising Battery Material 

German Automakers Pioneer AI Integration in Cars, Elevating Driving Experience 

AI in Finance: 

Rising Concerns as Generative AI Use Grows in Finance, Amplifying Misinformation Risks 

AI in Supply Chain Management: 

Warehousing Industry Leverages Machine Learning to Tackle Disruptions 

We’ve also curated the latest GPT and LLM resources, tutorials, and secret knowledge: 

Explore the Future of AI: A Guide to the Top 9 AI APIs of 2024 

Optimizing LLM Inference with Splitwise: Achieving Efficiency in GPU Usage 

A Comprehensive Guide to Merging LLMs 

AI Drift in Retrieval Augmented Generation 

Finally, don’t forget to check-out our hands-on tips and strategies from the AI community for you to use on your own projects: 

Creating Your Own AI Image Generator App with Generative AI 

Optimizing Code Output with CodeWhisperer 

Mastering Knowledge Graph Construction with KeyBERT, HDBSCAN, and Zephyr-7B-Beta 

How to Craft an Open Source Multi-Modal RAG System 

Looking for some inspiration? Here are some GitHub repositories to get your projects going!

gxnu-zhonglab/odtrack 

DLYuanGod/TinyGPT-V 

intel/intel-extension-for-transformers 

CambioML/pykoi 

📥 Feedback on the Weekly Edition

Take our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition." 

We appreciate your input and hope you enjoy the book! 

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

AI Launches & Industry Updates: 

⭐ Explore the GPT Marketplace: Just two months in, 3 million custom ChatGPTs are already out there! The GPT Store is now open to ChatGPT Plus, Team, and Enterprise users, offering a variety of handy GPTs. Get in on the action at chat.openai.com/gpts! 

⭐ NVIDIA Unveils Innovations in Gaming, AI, and Robotics at CES 2024: NVIDIA unveiled impressive CES 2024 innovations: GeForce RTX 40 SUPER GPUs, AI laptops, generative AI tools. They highlighted RTX GPUs' influence on generative AI, introduced TensorRT acceleration for Stable Diffusion XL and SDXL Turbo, and NVIDIA Avatar Cloud Engine (ACE) Microservices for digital avatars. Getty Images and Nvidia introduced Generative AI by iStock, a text-to-image platform for customized stock photos. 

⭐ Perplexity AI Secures $73.6M Funding Led by NVIDIA and Jeff Bezos: San Francisco's Perplexity AI secures $73.6 million in funding led by IVP, with Nvidia and Jeff Bezos participating, valuing the company at $520 million. Despite serving 500 million queries in 2023, profitability remains elusive, as it competes with Google in the search market. The funds will be used for hiring and product development. 

⭐ OpenAI Set to Launch GPT Store for AI Models and Apps: OpenAI is set to launch the GPT Store, where developers can present custom GPT model applications, following updated policies. The launch, previously delayed, offers diverse, code-free applications. Revenue-sharing details await clarification. 

⭐ Google Faces Multibillion-Dollar Patent Trial Over AI Technology in U.S.: Google is facing a federal jury trial in Boston as Singular Computing alleges patent infringement in its AI processors. Singular seeks up to $7 billion in damages, while Google argues independent development. The trial may last two to three weeks. 

⭐ Google's DeepMind Unveils Advances in Robotic Training with Video and Language Models: DeepMind Robotics unveils AutoRT, a system enhancing robot understanding of human intentions using Visual Language Models. It orchestrates 20 robots, suggesting tasks via LLMs and introduces RT-Trajectory with 63% success in 41 tasks using video input. 

AI in Healthcare: 

⭐ Isomorphic Labs Secures $3 Billion AI-Driven Drug Discovery Deals with Eli Lilly and Novartis: London-based Isomorphic, a DeepMind spin-out, forms strategic alliances with Eli Lilly and Novartis, valued at $3 billion. Utilizing AlphaFold 2 AI technology, Isomorphic focuses on accurate protein predictions for innovative drug discovery. 

⭐ Nabla Secures $24 Million in Series B Funding for AI-Powered Medical Assistant: Paris startup Nabla secures $24 million in a Series B funding round led by Cathay Innovation and ZEBOX Ventures. Nabla develops an AI copilot for doctors, streamlining administrative tasks while collaborating with physicians. 

AI in Business: 

⭐ Deloitte Introduces PairD AI Chatbot for 75,000 Staff in Big Four's Latest Automation Move: Deloitte is using a chatbot called PairD to help 75,000 employees in Europe and the Middle East with everyday tasks. While it's convenient, there are concerns about its accuracy, so employees still check its work. Deloitte is also sharing PairD with 800 workers at the charity Scope as part of its AI strategy. 

⭐ Walmart Revolutionizes Shopping with Generative AI Innovations: Walmart introduces generative AI-powered features on iOS, Android, and its website to improve the digital shopping experience. These features provide personalized responses and recommendations, shifting from scrolling to goal-oriented searching for a smoother shopping journey. 

AI in Science & Technology: 

⭐ Microsoft and PNNL Harness AI to Discover Promising Battery Material: Microsoft and PNNL used AI and cloud computing to speed up battery innovation, identifying a safer, efficient solid-state electrolyte with less lithium. Azure Quantum Elements platform screened 32 million candidates in 80 hours, highlighting a material with potential for a 70% reduction in sodium use, advancing sustainable energy solutions. 

⭐ German Automakers Pioneer AI Integration in Cars, Elevating Driving Experience: Leading German automakers like Volkswagen and Mercedes-Benz are revolutionizing the automotive industry with advanced AI integration. Volkswagen unveiled ChatGPT technology, enhancing the driving experience with AI-powered chatbots and IDA voice assistants, while Mercedes-Benz introduced a sophisticated virtual assistant for context-based suggestions, marking a significant leap in interactive AI utilization at CES 2024. 

AI in Finance: 

⭐ Rising Concerns as Generative AI Use Grows in Finance, Amplifying Misinformation Risks: The finance sector's growing use of generative AI is transforming services but raises concerns of misinformation. A study by PYMNTS Intelligence and AI-ID shows 80% of consumers worry about generative AI's misinformation risk. Regulatory guidelines, model explainability tools, and industry cooperation are essential for responsible AI adoption in finance. 

AI in Supply Chain Management: 

⭐ Warehousing Industry Leverages Machine Learning to Tackle Disruptions: Zebra Technologies Corporation's research highlights the warehousing industry's adoption of AI, particularly machine learning (ML), amid challenges like inflation and labor shortages. The report predicts ML, predictive analytics, and mobile dimensioning will dominate by 2028, aiding historical analysis, demand prediction, and automation. Decision-makers aim to boost resilience with 94% planning ML integration within five years. 

🔮 Expert Insights from Packt Community 

The Handbook of NLP with Gensim - By Chris Kuo 

Gensim and its NLP modeling techniques 

Gensim is actively maintained and supported by a community of developers and is widely used in academic research and industry applications. It covers many important NLP techniques that make up the workforce of today’s NLP. 

Last year, I was at a company’s year-end party. The ballroom was filled with people standing in groups with their drinks. I walked around and listened for conversation topics where I could chime in. I heard one group talking about the FIFA World Cup 2022 and another group talking about stock markets. I joined the stock markets conversation. In that short moment, my mind had performed “word extractions,” “text summarization,” and “topic classifications.” These tasks are the core tasks of NLP and what Gensim is designed to do. 

We perform serious text analyses in professional fields including legal, medical, and business. We organize similar documents into topics. Such work also demands “word extractions,” “text summarization,” and “topic classifications.” In the following sections, I will give you a brief introduction to the key models that Gensim offers so you will have a good overview. These models include the following: 

BoW and TF-IDF 

Latent semantic analysis/indexing (LSA/LSI) 

Word2Vec 

Doc2Vec 

Text summarization 

LDA 

Ensemble LDA 

BoW and TF-IDF 

Texts can be represented as a bag of words, which is the count frequency of a word. 

BoW uses the word count to reflect the significance of a word. However, this is not very intuitive. Frequent words may not carry special meanings depending on the type of document. 

LSA/LSI 

Latent semantic analysis (LSA) was developed in the 1990s. It's an NLP solution that far surpasses naïve keyword matching and has become an important search engine algorithm. Prior to that, in 1988, an LSA-based information retrieval system was patented (US Patent #4839853, now expired) and named “latent semantic indexing,” so the technique is also called latent semantic indexing (LSI). Gensim and many other reports name LSA as LSI so as not to confuse LSA with LDA. 

This is an excerpt from the book The Handbook of NLP with Gensim - By Chris Kuo and published in OCT ‘23. To see what's inside the book, read the entire chapter here or try a 7-day free trial to access the full Packt digital library. To discover more, click the button below.      

🌟 Secret Knowledge: AI/LLM Resources

⭐ Explore the Future of AI: A Guide to the Top 9 AI APIs of 2024: In this guide, you'll learn how to navigate the dynamic realm of AI APIs, uncovering the capabilities of the top 9 for 2024. Discover Google Cloud Vision AI, an unparalleled eye for accurate image analysis, IBM Watson Assistant, a conversational genius transforming virtual assistance, Amazon Lex, empowering apps with voice commands effortlessly, Azure Cognitive Services, the Swiss Army knife of AI, offering diverse tools, DeepAI, simplifying deep learning for innovation, and decode texts with MonkeyLearn, a text analysis guru, among others. Read the post to explore how these APIs can shape your tech ventures and redefine the future of AI. 

⭐ Optimizing LLM Inference with Splitwise: Achieving Efficiency in GPU Usage: Discover how Splitwise, a technique from Azure Research - Systems, boosts LLM inference efficiency. It separates prompt computation and token-generation phases, optimizing hardware use. This method enhances GPU cluster design, achieving higher throughput, lower costs, and reduced power for efficient LLM deployment. 

⭐ A Comprehensive Guide to Merging LLMs: This comprehensive guide explores merging LLMs using the mergekit library without requiring a GPU. It covers four merging techniques: SLERP, TIES, DARE, and passthrough, with configuration examples. The result is Marcoro14–7B-slerp, a high-performing model featured on the Open LLM Leaderboard. 

⭐ AI Drift in Retrieval Augmented Generation (RAG): This guide delves into AI drift within RAG pipelines, drawing from a real case where a customer faced declining AI responses. It covers the causes (content drift, LLM drift, pipeline algorithm changes) and strategies (content management, API upgrades, internal metrics) to control AI drift. 

🔛 Masterclass: AI/LLM Tutorials

⭐ Creating Your Own AI Image Generator App with Generative AI: Discover how to build a powerful Generative AI Text-to-Image application in this detailed guide. The author shares their journey of seamlessly integrating AI-generated images into a React app, using third-party APIs like SegMind. With a step-by-step walkthrough, you'll explore the code behind the app on GitHub and learn how to choose the right API, integrate it into React, and unleash AI capabilities in web development. Read on to bring dynamic, AI-generated content to your React projects and stay at the forefront of web development innovation. 

⭐ Optimizing Code Output with CodeWhisperer: Unlock the full potential of Amazon CodeWhisperer with this in-depth guide on prompt engineering. Learn how CodeWhisperer accelerates software development by offering code recommendations based on natural language comments. The post provides step-by-step insights on effective prompt engineering in Python, emphasizing best practices such as crafting specific and concise prompts, incorporating additional context, utilizing multiple comments strategically, and understanding CodeWhisperer's capacity for cross-file context. 

⭐ Mastering Knowledge Graph Construction with KeyBERT, HDBSCAN, and Zephyr-7B-Beta: Discover how to leverage LLMs with traditional NLP and ML methods to create knowledge graphs from unstructured text. The author showcases the synergy of KeyBERT, HDBSCAN, and Zephyr-7B-Beta for improved keyword extraction, clustering, and refinement. The guide covers dataset prep, keyword extraction, and LLM integration. 

⭐ How to Craft an Open Source Multi-Modal RAG System: Discover building a Retrieval-Augmented Generation (RAG) system with an Open Source Large Language Multi-Modal (LLMM). Learn the integration of ChromeDB and Hugging Face, covering Clip, data storage, and MLLMs for user chat sessions in a detailed, dependency-free guide. 

🚀 HackHub: Trending AI Tools

⭐ gxnu-zhonglab/odtrack: Efficient video-level tracking pipeline utilizing online token propagation to densely capture contextual relationships and spatio-temporal trajectories across frames.  

⭐ DLYuanGod/TinyGPT-V: Features an efficient Multimodal Large Language Model using small backbones for efficiently incorporating multimodal capabilities into language models. 

⭐ intel/intel-extension-for-transformers: Toolkit to accelerate GenAI/LLM performance on Intel platforms, including Gaudi2, CPU, and GPU, seamlessly compressing Transformer-based models, accessing optimized model packages, and using NeuralChat. 

⭐ CambioML/pykoi: An open-source Python library for LLMs, enhancing them with RLHF, collecting user feedback, fine-tuning with reinforcement learning, comparing models, and creating RAG chatbots efficiently.