![Tensor Logic "Unifies" AI Paradigms [Pedro Domingos]](https://img.youtube.com/vi/4APMGvicmxY/maxresdefault.jpg)
Pedro Domingos introduces Tensor Logic, a new programming language designed to unify the two major paradigms of AI—deep learning's ability to learn from data and symbolic AI's power of logical reasoning—into a single, elegant framework.
Edwin Chen, founder and CEO of Surge AI, discusses his contrarian, bootstrapped approach to building a billion-dollar company, the critical role of high-quality data and 'taste' in training advanced AI models, the pitfalls of current benchmarks, and why Reinforcement Learning environments are the next frontier in AI.
Jenn Wortman Vaughan and Hanna Wallach, co-founders of the Women in Machine Learning (WiML) workshop, reflect on their intersecting careers, the founding and evolution of WiML over 20 years, and their influential research in responsible AI, from interpretability and fairness to the current challenges in generative AI.
Drawing surprising parallels between AI agents and robotics, this talk argues that the agent development community is repeating a key mistake from the self-driving industry: underestimating the difficulty of action and over-focusing on reasoning. It covers essential robotics concepts like DAgger, MDPs, simulation, and the critical importance of a robust offline infrastructure, explaining why perfect reasoning doesn't guarantee successful execution in the real world.
Yusuf Olokoba, founder of Muna, details a compiler-based approach to transform Python AI functions into self-contained native binaries. This talk explores the technical pipeline, including custom AST-based tracing, type propagation, and the strategic use of LLMs for code generation, enabling a universal, OpenAI-style client for running any model on any platform.
This talk introduces Meta-ACE, a learned meta-optimization framework that dynamically orchestrates multiple strategies (context evolution, adaptive compute, hierarchical verification, and more) to maximize AI agent performance. The framework profiles each task to select an optimal strategy bundle, overcoming the single-dimension limitations of previous methods.