Experienced analytics, AI, and technology leader with 13+ years of experience across data science, private equity, management consulting, entrepreneurship, and software engineering. Sharing thoughts on these topics.
Explains how options, symbolic STRIPS planning, language grounding, and multi-agent belief models structure long-horizon planning and where abstractions fail.
Explains how agents trade reward for information using dual control, information gain, curiosity, and safe exploration in world-model decision making.
Explains how actor-critic policies train inside learned world models, covering latent rollouts, lambda-returns, value expansion, and model exploitation.
Covers MCTS, UCT, PUCT, belief-space planning, and POMCP for decision-making under uncertainty with a hidden-state maze and tiger problem.
Explains how gradients flow through world-model rollouts, from adjoint backpropagation to action optimization, terminal values, and long-horizon stability.
Explains how sampling-based planning and model predictive control turn black-box world models into controllers, using random shooting, CEM.
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Speaking with investors and operators about choosing problems worth solving, understanding what the technology can really do, and making it work in practice.
Commercial judgment · Technical depth
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Machine learning and math concepts explained step by step so you can understand how it works, without hours of digging through dense textbooks. Free to read online.
Read the World Models Handbook online for free. Learn latent dynamics, model-based reinforcement learning, planning, physical AI, evaluation, and deployment.
Build a small GPT-style language model in PyTorch. Follow each step from embeddings and attention through training, optimization, and text generation.
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