Leon X.

Organizations have insufficient technical capabilities for the speed and scale of their ML model implementations. My colleague Sumit Agarwal just published a very helpful research with a capability view and sample vendors for defining a modular, flexible and scalable MLOps solution. https://lnkd.in/gnB_T9bq #MLOps #LLMOps #AI

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Interested in #GenAI and its impact on #softwaredevelopment? Read EQengineered's Principal Technical Consultant/Architect Ed Lyons' recent essay. #techinsights #softwareengineering #digitaltransformation #GenAI #LLMs #EQengineered

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Sharing the First part of my article on Unleashing Discovery-based Insights and Analytics using Vectorized Data Lakes alongside the Traditional Data Lakes. https://lnkd.in/gMCwzJMA The next part will show a more cost-effective and feasible implementation of Leveraging Vectorized Data Lakes for Un-Leashing New Discovery-based Insights & Analytics, apart from the Traditional Insights and Analytics.

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AI next frontier is possibly developing and scaling the reasoning layer, where AI systems possess hard problem-solving and higher order cognitive intelligence. Came across this good article from Sequoia.

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Just published my latest article: "Transforming Business Operations with LLMs: A Path to a Production Ecosystem." a breakdown of the key components necessary for integrating LLMs into core business operations, including Human In The Loop (HITL), Feedback, Evaluation, and Decision Executor subsystems. Learn how these elements work together to start building an AI platform.

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Very cool article by Christoph Molnar on the notion of inductive bias (here for Random Forests). The concept of inductive bias is often overlooked by practitioners, who assume this is a purely theoretical notion. On the contrary! This is one of the most fundamental aspects to consider when picking a model, interpreting its predictions or troubleshooting an underperforming system. #inductivebias #cart #trees #machinelearning #troubleshooting

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The first step to delivering trusted AI is having the right type of data. Explore the dark age of data W/ @rwang0 on episode 1 of The AI Forecast w/ @xthestreams. Watch it here: https://buff.ly/4fBpC0V @cloudera #Data #AI

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We’re excited to bring to customers Stability AI’s newest model, Stable Diffusion 3.5 Large. Trained on Amazon SageMaker HyperPod and now available in SageMaker JumpStart, this new cutting-edge image generation model empowers customers to generate high-quality images from text descriptions with unprecedented ease, flexibility, and creative potential. The model excels at creating diverse, high-quality images across a wide range of styles. By adding Stable Diffusion 3.5 Large to SageMaker JumpStart, we’re taking another significant step towards democratizing access to advanced AI technologies and enabling businesses of all sizes to harness the power of generative AI. All the details are in our latest blog. https://lnkd.in/gijPiEV4

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The emergence of enterprise AI products interweaving with operational workflows is an exciting development. It will be fascinating to see how this integration generates value for customers. https://lnkd.in/dcfYgzrf

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Very insightful work by Anthropic’s interpretability team. And an amazing paper, with outstanding writing and figures. The idea is very simple: interpret LLMs by leveraging sparse autoencoders as surrogate models of the MLP of transformer blocks, which allow one to disambiguate the superposition of features captured by a single neuron. A simple idea, but a very careful and complex execution, as it is often the case in our line of work. The paper goes into many details and provide a large array of insights, although the gist of the implementation remains obfuscated due to the closed source nature of Claude. Too bad, because this is the kind of work that we need to better understand and eventually trust LLMs. This is demonstrated by the authors in the section ‘Influence on Behavior’, where they show that clamping some features to either high or low value during inference is “remarkably effective at modifying model outputs in specific, interpretable ways”. Hopefully this kind of work is going to be replicated and generalized to open-weights models, such that we have new ways to steer their behavior. https://lnkd.in/eVym7f_f #interpretability #xai #explainableai #steerableai #anthropic #claude #anthropic

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Dive into the architecture of OCI’s Generative AI Agent service, Retrieval-Augmented Generation (RAG) to enhance the contextual accuracy of AI agents, addressing the limitations of traditional LLMs and reducing hallucinations.

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This is a great move that shows Amazon is serious about competing with Microsoft in this space. Now they just need to work on their AI stack. Bedrock is a fantastic foundation (no pun intended!) and I'm using it in production, but the higher-abstraction offerings like Q Business need work. Hope this will also drive down the cost of Claude inference even lower than it already is. On the Anthropic side, let’s hope this accelerates their ability to innovate on the user experience. Despite having comparable foundational models, OpenAI has been able to differentiate themselves significantly with unique features like search, voice mode, and code interpreter, the latter of which I’d argue should be table stakes by now. Anthropic, now would be a great time to introduce an amazing EDU offering that undercuts OpenAI’s pricing model! #ai #highereducation

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A must read for AI enthusiasts from Manish Bhide AI Governance is crucial in the Generative AI era to prevent incidents like the airline company's chatbot hallucinating and promising a non-existent discount. By adopting AI Governance principles, enterprises can ensure responsible AI adoption, mitigate risks, and maintain regulatory compliance.

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🚀 Excited to share a new blog post on Amazon Science: "How the Lean Language Brings Math to Coding and Coding to Math"! This post is an adaptation of the keynote I delivered at the International Conference on Computer Aided Verification (CAV) in July 2024. It explores how the Lean language is bridging the gap between formal mathematics and software development, offering new possibilities for both fields. https://lnkd.in/grzRHDHg

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The LLaMA-based o1 Project from Shanghai AI Lab just released a approach that combines Monte Carlo Tree Search (MCTS) with Self-Play Reinforcement Learning and AlphaGo Zero’s strategy to enhance AI’s reasoning abilities. 📊 Key Results: In the AIME2024 Benchmark, outperforming other commercial models except o1-preview and o1-mini. 🔑 Highlights: - Successfully solves Olympiad-level problems with significantly improved accuracy. - Combines exploration and self-refinement to tackle strategic reasoning challenges. 🔗 Read the full paper and access the code. -> https://lnkd.in/g8-VGZcG #AI #LLMs #MCTS #MathematicalReasoning #MachineLearning #AIResearch

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Awesome to see the Meta #Llama 3.2 models on #Azure #AI Model Catalog through managed compute inferencing #MaaS and soon on serverless API deployments while MACC Eligible providing customers with Enhanced Security and Compliance, Content Safety Integration, Simplified Assessment of LLM flows, Client integration, and Simplified Deployment and Inference. https://lnkd.in/g4Zpb3Dm

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Small models becoming smarter! Phi-3 reinforces the importance of data quality.

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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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Key meta-question at the #OpenForumAcademy discussing research projects around #OpenSource/ open science/data/government: How does your FOSS products research project take into account non-GitHub projects? How much of the open source world isn't on GitHub at all? One answer: The Penumbra Of Open Source

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