Artificial Intelligence seminars

Robot missions require both collision-free trajectories and logical task ordering, such as collecting a resource before entering a restricted region. Augmented graphs of convex sets combine continuous trajectory optimization and discrete task sequencing in one problem. A layered graph constructed from an exact convex partition of free space selects an optimal task order and trajectory through a shortest-path calculation, exact up to finite Bézier parameterization. Its structure corresponds to Bellman–Held–Karp dynamic programming for the travelling-salesperson problem, retaining singly exponential worst-case complexity and improving exponentially on general temporal-logic tools. A library of specifications covers ordered collection, alternative keys, combined prerequisites, timing and conditional logic with correctness guarantees. Numerical benchmarks demonstrate exponential speedups and near-global optimality. Possible extensions include moving environments, safety-aware planning with conformal prediction sets and a large galactic-survey benchmark.

Jan Kosinski explains how AlphaFold-style methods predict protein complexes and why their reliance on evolutionary information makes interactions between host and pathogen proteins especially difficult. The talk reviews large-scale studies, interpreting both successful predictions and failures rather than treating reported success rates as universal performance. It then examines ways to improve predictions through broader sampling, altered sequence alignments and experimental restraints, including crosslinking mass spectrometry, with influenza A virus as a worked research example. The session is suitable for students and researchers interested in host-pathogen interactions and structural bioinformatics; basic knowledge of protein structures and sequence alignments is useful, but prior AlphaFold experience is unnecessary.

Deep networks are often thought of as black boxes. Their ability to encompass vast swathes of knowledge indeed makes them hard to explain. Yet many of their behaviours — generalization under overparametrization, grokking, OOD failures, neural scaling laws — recur across architectures and scales, and each, however surprising, can be reproduced and explained in controlled settings. I will review these efforts to identify and explain the universal phenomena of deep learning, and suggest that an LLM may amount to a sum of such tractable sub-phenomena, interpolative in nature. Finally, leaving scientific rigor aside, I'll argue that what separates this prosaic picture from the apparent magic of LLMs may well be the industrial scale of compute and human labour behind it, and that AGI in its deeper extrapolative sense may be much further away than claimed.

Ilaria Ferlenghi discusses a career spanning structural biology, cryo-electron microscopy, vaccine research and development, and the growing role of artificial intelligence and machine learning in scientific innovation and entrepreneurship.

Liver toxicity can emerge late in drug development when early assays fail to capture human biology, exposure or injury mechanisms. This webinar introduces Axiom’s hepatic profiling approach, combining multicellular liver systems, high-content imaging, transcriptomics, proteomics and ADME measurements. Its reasoning agent integrates those results with partner assays, chemical-series context, exposure estimates and clinical evidence to assess mechanistic liver-safety risks. Rather than using a single cell-toxicity score, the approach develops interpretable, series-specific structure–toxicity hypotheses, compares analogues, ranks compounds and suggests follow-up experiments or structural changes. Performance on established liver-injury reference compounds and newer molecules is examined through case studies, showing how multimodal evidence can inform medicinal-chemistry decisions and predicted therapeutic index.

Generative AI is changing scholarly publishing. Editors of applied linguistics journals discuss its effects on publication practices and the challenges and opportunities facing their field. The facilitated panel brings together perspectives from RELC Journal, Applied Corpus Linguistics, and English for Specific Purposes to consider how scholarly publishing can respond to these changes.

Representation learning underlies visual search, retrieval and recognition by encoding gallery images and matching them with queries. Most early work assumes a fixed model, but new training data or more expressive architectures can change the feature representation. Recomputing every gallery vector after an update is expensive for collections containing billions of images and may be impossible when the original images are unavailable because of privacy or storage restrictions. Compatible representation learning aims to update the model while retaining usable existing gallery features. The talk examines the foundational compatibility problem, the role of representation stationarity, and new training techniques that use stationarity to learn compatible feature representations.

Dan Meyer considers the questions generative AI raises for mathematics learning after four years of rapid technological development. He argues that its novelty has been accompanied by educational adoption and institutional change falling short of early expectations, including lower-than-anticipated teacher use. The session explores what the technology can do, what students need and what teaching entails, using these questions to examine AI’s contribution to education.

David Persson presents Polar Express, a method for polar decomposition and the matrix sign function motivated by the Muon neural-network optimizer. It uses matrix multiplications suited to GPUs and adapts each polynomial update through a minimax problem to reduce worst-case error. The talk covers convergence, finite-precision implementation in bfloat16 and validation-loss improvements when training GPT-2 on FineWeb data. This is an in-person PACM IDeAS seminar at Princeton.

José Molina Mora presents an integrative study of the core stress-response mechanisms, or perturbome, of Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus. Transcriptomic data are analysed using machine learning, systems biology, functional enrichment and ortholog comparisons to prioritise stress-associated genes. Experimentally determined protein structures are combined with AlphaFold predictions where structural data are missing. Molecular docking and further computational analyses then identify preliminary compounds and conditions that might inhibit selected targets. These results are a proof of concept awaiting experimental validation. The talk connects gene-expression analysis with structural modelling for antimicrobial research and is aimed at researchers, graduate students and professionals in microbiology, bioinformatics and related biomedical fields.