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All digests tagged Foundation Models
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Wells Fargo built a proprietary Foundation Model for transactions, pre-training a transformer on billions of raw customer transactions. This single backbone model improves fraud detection and streamlines multiple downstream financial tasks.
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Simile's AI agents predict human behavior with 85%+ accuracy by simulating complex social interactions and RCTs, moving beyond simple reasoning models.
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Liquid AI details how to build proactive Personal AI agents by creating a 'Personal Context Layer.' This layer ingests fragmented, real-time device signals (audio, location, motion) to enable intelligent, privacy-preserving routing between edge and cloud models.
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The shift to open intelligence means owning the entire AI stack. Customization now involves RL, environments, and controlling data/safety, moving beyond simple API calls.
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Explore the frontier of AI efficiency: From Claude Opus 5.5 and GPT-6 Sol to TypeSafe's Jev AI, learn how structured, calibrated models are revolutionizing automation and specialized scientific computing.
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Dyna Robotics demonstrates how to build generalist, robust robots using a 'research and deployment flywheel.' By combining 200,000+ hours of diverse data and using reward models, their Dyna-1 model achieved a 99.4% success rate in complex tasks like napkin folding.
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Perceptron introduces Embodied Foundation Models, unifying perception, reasoning, and control. By using data sparse Mixture of Experts, they solve context bloat and achieve a new scaling law: 10x video pretraining substitutes for 10x less teleop data.
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Accelerated Understanding is building a foundation model for the physical world, applying LLM universality to physics. They achieve unprecedented scale with neural operators and self-improvement via physical laws.
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Simile AI is building digital twins of humanity by integrating RCTs, interviews, and observational data to model 'social physics,' moving beyond standard LLM capabilities to solve complex societal problems.
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This session covers Diffusion Models, detailing the loss function $\mathcal{L}_{t-1}$ derived from lower bounds. It also explores Foundation Model paradigms, zero-shot learning, and parameter efficiency techniques like Low-Rank Adaptation (LoRA).
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Xaira's X-Cell uses a 4.9B parameter diffusion model to predict cell response to unseen genetic perturbations. By training on massive causal datasets (Perturb-seq), it moves drug discovery from correlation to prediction.
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