Neil Desai

Smaller is better The need for powerful #LLMs has fueled their exponential growth. However, large LLMs come with a cost - they require significant computational resources and energy, leading to increased response latency. The largest models today have over 100 billion parameters and can take over 200 gigabytes to load. Industry applications and #AI giants are now shifting their focus towards smaller and faster models, prioritizing efficiency over maximum performance. Learn more:

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One of my favorite reports from Gartner is their Impact Radar where they highlight the technologies and trends with the greatest potential to disrupt a broad cross-section of markets that heavily involves AI in 2024. In the big picture, you will see the "GenAI" in the center where enterprises already seeing real value from AI; but in the outer layers you see Digital Twins that will be boosted with AI maybe to a "Digital World"; a real world replica of our life powered with AI Avatars and synthetic data. Spatial computing will be an area to emerge into Spatial Intelligence will merge with devices so maybe our phones will disappear from our lives to a more embedded future. Check-out this amazing report from Gartner: https://lnkd.in/gjwUtq7W

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Google Cloud and Anthropic made some exciting announcements: 📣 Claude 3 Opus, Anthropic’s most intelligent model, is now generally available on Vertex AI ⚙️ Tool use is available for the Claude 3 model family on Vertex AI, enabling Claude to act as an autonomous agent 📈Provisioned throughput is available for the Claude 3 model family on Vertex AI

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Such initiatives combined with highly vetted datasets are critical to the adoption of LLMs in Healthcare. https://lnkd.in/gE-ihkWT

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AI agents can perceive their environment using large language models to make smart decisions and take action. Check out this tutorial for building a research assistance agent using MongoDB, Fireworks AI, and LangChain. https://lnkd.in/gbT_hxQZ

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Another demonstration of ScienceCast.org #AI enhancements of arXiv postings - free, simple interface https://lnkd.in/e_gXsF_9

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In the latest Wall Street Journal article, Tom Dotan and Deepa Seetharaman explore how tech giants are moving from giant AI models to smaller, cost-effective ones.   Smaller models, like Microsoft’s Phi, are cheaper and specialized, performing many tasks almost as well as their larger counterparts.  #wsj #AI #hpaipc

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*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

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Big news: Anthropic's most intelligent model yet, Claude 3.5 Sonnet, is generally available on Google Cloud’s Vertex AI! Get all the details on the announcement and learn how to get started ⬇️

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In this brilliant article from The Wall Street Journal by Tom Dotan and Berber J. They explore the incredible strategy of Satya Nadella and his team at #microsoft on how he’s transformed the business to run like a startup and dominate the AI race.💥🚀 Bringing in the inimitable Mustafa Suleyman is a power move 🔋 Here’s the article: https://lnkd.in/dpp8qD8x #microsoft #openai #wsj

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Simply amazing and this will revolutionize various industries (life sciences and auto in particularly) in next 5 to 7 years

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Bryan Harris and I had the opportunity to talk to Quartz after our global #GenAI study findings were released. According to Bryan, "he is not surprised that China’s adoption rate of generative AI is higher because the country’s government is 'much more efficient' with issuing directives, and that includes telling organizations to use AI. Meanwhile, the U.S. is 'a little more measured in our approach to generative AI'..." What else did we highlight from the study? Check it out: http://2.sas.com/6045945c1 #GenerativeAI

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More awesome news in simplifying production level GenAI app dev! ✨ With just a single line of code, Astra Vectorize makes it easy to use your data with generative AI, from generating your vectors, storing and indexing them, and them using them for search at lightning speed 💡 You can focus more on the *logic* of your application, rather than the intricacies of aspects such as vector embeddings. Read more here 👇 https://ow.ly/J2LR50RRzhC #MSBuild #GenAI #RAGApplications

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A new study by researchers at Google DeepMind explores the “many-shot” in-context learning (ICL) ability of LLMs that have very long context windows. Their findings show that by fitting hundreds or even thousands of training examples in the prompt, you can improve the model’s abilities in ways that would previously require fine-tuning. #ai #llm #contextwindow

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Here's a demo that was created very quickly using #OpenAI's GPT-4o ("omni") #AI model via the #Azure #OpenAI #Service. The demo shows using the state-of-the-art speech, vision, and reasoning capabilities of this multi-modal model in a web chatbot that interacts with the user in a natural manner (by having a conversation and showing it a product through the laptop's webcam). The chatbot can retrieve relevant information about the product and other products in the inventory through a combination of search over unstructured data (e.g., using #Azure Search) and NL-to-Query (e.g., using a connector to a database). This is an early example towards the direction of the chatbots (and #GenAI apps in general) of the future, in retail, education, healthcare, and many other domains. GPT-4o and all of the capabilities necessary to build your idea are available today at Azure OpenAI Studio! https://oai.azure.com/

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AI is reshaping landscapes, altering industries and transforming our lives and work. With its rapid advancement, it's also causing disruption — for better or worse — in every field imaginable. While it promises efficiency and growth, AI also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? Authority Magazine turned to Reggie Townsend for the answers: http://2.sas.com/6041iPQUL. #TrustworthyAI #ArtificialIntelligence

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New chapter to unleash the power of GenAI!

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