Jul 16, 2026

Learning Genetic Perturbation Effects at Single-Cell Resolution for Virtual Cells

Jiaqi Zhang's talk focuses on advancing causal discovery and perturbational modeling for gene networks, crucial for therapeutic insights. She introduces theoretical guarantees for learning latent causal variables and presents MORPH, a VAE-based framework that predicts the effects of novel and combinatorial gene perturbations by integrating diverse prior biological knowledge and using distributional loss for training. The model has demonstrated success in guiding experimental design, including identifying novel T-cell regulators for cancer immunotherapy, highlighting its potential to accelerate biological discovery and drug development.

Jun 21, 2026

6 Things to Know about AIE World's Fair 2026

Discover the AI Engineering World's Fair 2026, the largest iteration yet, offering an unparalleled deep dive into AI engineering with expanded tracks on auto research, GPU specialization, and new verticals like finance and healthcare. Highlights include an innovative expo experience, exclusive leadership initiatives like the "Token Billionaires Program," and unique side events fostering community, including "Posters on AI" where attendees can defend their tweets. This event is designed to be a curated hub for practical, cutting-edge insights and networking in the AI/ML professional landscape.

Jun 16, 2026

You Might Not Need 50 Diffusion Steps — Ziv Ilan, Nvidia

Ziv Ilan from NVIDIA details how latency in video diffusion models can be drastically reduced to achieve real-time generation. He presents a layered approach combining dynamic quantization for memory and speed, chunk-based caching to skip redundant denoising computations, and, most critically, step distillation—training models to achieve high-quality output in significantly fewer steps. These techniques, packaged in the open-source FastGen repository, offer additive performance gains, enabling real-time video on a single Blackwell B200 GPU.

Mar 27, 2026

François Chollet: ARC-AGI-3, Beyond Deep Learning & A New Approach To ML

François Chollet discusses his contrarian approach to AI, moving beyond scaling LLMs to understand intelligence from first principles. He explains his work on the ARC benchmark series, including the new ARC-AGI V3, designed to measure 'agentic intelligence' and skill acquisition efficiency. He also introduces his lab, Ndea, which is developing a new ML paradigm based on symbolic models, and shares his perspective on the limits of current systems and the future path to AGI.

Dec 13, 2025

The Mathematical Foundations of Intelligence [Professor Yi Ma]

Professor Yi Ma challenges our understanding of intelligence, proposing a unified mathematical theory based on two principles: parsimony and self-consistency. He argues that current large models merely memorize statistical patterns in already-compressed human knowledge (like text) rather than achieving true understanding. This framework re-contextualizes deep learning as a process of compression and denoising, allowing for the derivation of Transformer architectures like CRATE from first principles, paving the way for a more interpretable, white-box approach to AI.

Dec 13, 2025

The Mathematical Foundations of Intelligence [Professor Yi Ma]

Professor Yi Ma presents a unified mathematical theory of intelligence built on two principles: parsimony and self-consistency. He challenges the notion that large language models (LLMs) understand, arguing they are sophisticated memorization systems, and demonstrates how architectures like the Transformer can be derived from the first principle of compression.