Video: the LLM 2.0 Revolution

Video: the LLM 2.0 Revolution

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What if you could build a secure, scalable RAG+LLM system – no GPU, no latency, no hallucinations? In this session, Vincent Granville shares how to engineer high-performance, agentic multi-LLMs from scratch using Python. Learn how to rethink everything from token chunking to sub-LLM selection to create AI systems that are explainable, efficient, and designed for enterprise-scale applications. What you’ll learn: How to build LLM systems without deep neural nets or GPUs Real-time fine-tuning, self-tuning, and context-aware retrieval Best […]

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Charting the future of AI, from safer answers to faster thinking

Charting the future of AI, from safer answers to faster thinking

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Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and capabilities to promote AI usefulness and deployment — from learning when to trust a model that predicts another’s accuracy to more effectively reasoning […]

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Watermarking and Forensics for AI Models, Data, and Deep Neural Networks

Watermarking and Forensics for AI Models, Data, and Deep Neural Networks

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In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source code, covering problems such as noise filtering, high-dimensional curve fitting, and predictive analytics. One of the models featured a promising universal function able to represent any type of smooth response, while leading to fast convergence. Here I explore weight sensitivity and distillation. The words weight and parameter are used […]

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