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Artificial Intelligence

Upcoming events

Seminar

MechE Colloquium: From Motion to Mission Planning via Augmented Graphs of Convex Sets

Tyler Summers · The University of Texas at Dallas

Tue, Oct 6 · 10:00 UTC · Hybrid

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.

robotics+2
Seminar

AI-based structural modelling of host-pathogen protein interactions

Jan Kosinski · EMBL Hamburg

Wed, Oct 7 · 13:30 UTC · Online

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.

Seminar

Big Boxes, Not Black Boxes: What we can compute about LLMs, and what it may say about AGI

Zohar Ringel · Hebrew University of Jerusalem

Wed, Oct 7 · 18:00 UTC

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.

Deep learning+2

Recordings

Podcast episode

Open models and the future of Physical AI with NVIDIA

Practical AI

Published Thu, Oct 1

Ming-Yu Liu, NVIDIA vice president of research and head of the Cosmos Lab, joins Practical AI hosts Daniel Whitenack and Chris Benson to discuss AI systems that interact with the physical world. The conversation examines open models, world models and simulation as foundations for robotics and autonomous vehicles, and how research and deployment can advance physical AI. Published 1 October 2026; publisher runtime 47 minutes 19 seconds.

Podcast episode

From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing?

The TWIML AI Podcast

Published Tue, Sep 29

Sam Charrington speaks with Greg Burnham, who leads AI capabilities research at Epoch AI, about the progression from elementary mathematical tasks to difficult research problems, including Navier–Stokes. They examine how advanced systems solve mathematical problems, the roles of persistence and existing human work, and the evidence for new ideas. The conversation also considers how to measure progress when traditional benchmarks become less informative, why improvements appear steady across successive model generations, and the remaining weaknesses in open-ended research, learning from experience and choosing productive scientific questions.

AI capabilities+2
Podcast episode

Chris Manning: Language Is the Real Unlock for Intelligence

The Information Bottleneck

Published Sun, Sep 27

Stanford linguist and computer scientist Chris Manning discusses what linguistics contributed to machine learning and why pragmatics and dialogue remain open problems for language models. The conversation examines early abstraction of verb categories in small transformers, how distributed representations shape learning, and whether language alone can support meaningful representations. It also considers language in world models, diffusion language models, and representation finetuning (ReFT), which steers frozen models through their hidden states. The final discussion asks where knowledge resides and whether concepts occupy linear subspaces.

Podcast episode

From AGENTS.md to Enterprise Deployment

Practical AI

Published Thu, Sep 24

Nick Kuhn joins Daniel Whitenack and Chris Benson to discuss deploying AI agents alongside conventional enterprise applications. Topics include agent build packs, MCP gateways, shared memory, identity, sandboxing and lessons from platform engineering.

Agent deployment+2

Open deadlines

Grant

Start Package Grants - for faculty recruitment Q4 2026

Novo Nordisk Foundation

Deadline Thu, Oct 8

Help Danish research institutions recruit faculty and establish independent research groups in life sciences, medicine and other fields relevant to health or sustainability, including data science and AI. Four-year support is capped at DKK 5 million for assistant professors, DKK 8 million for associate professors and DKK 10 million for full or eligible clinical professors. The institution’s appointment authority must apply for an identified candidate. The deadline is 8 October 2026 at 2 p.m. Copenhagen time.

Support for Brazilian business research and innovation using artificial intelligence, aligned with the Nova Indústria Brasil missions and the Brazilian AI plan. Eligible applications include health-industry innovation. A company-linked coordinator submits one project through Plataforma Integrada Carlos Chagas by 9 October 2026, 23:59 Brasília time. Use the required project template and identify the requested fellowship profiles; researchers are selected after award.

Grant

Mathematical Foundations of Artificial Intelligence (MFAI)

U.S. National Science Foundation

Deadline Fri, Oct 9

NSF support for interdisciplinary collaborations developing mathematical and theoretical foundations for explainable, reliable, sustainable and trustworthy artificial intelligence.

Two-year, full-time postdoctoral appointment investigating commonsense reasoning in multimodal world models. Research combines neural and symbolic methods to represent abstractions, causality, space and time, with the aim of improving human-centred AI. The project involves collaboration across AI, computer vision and reasoning, alongside limited teaching and student supervision. The preferred start is in the first quarter of 2027.

New and updated

Podcast episode

The Kodak moment for drug discovery: AI, adaptation, and what stays human | TPM podcast

Talking Precision Medicine

Published Thu, Sep 17

Rafael Rosengarten and BioPharmaTrend co-founder Andrii Buvailo discuss how to assess progress in AI-assisted drug discovery. They trace the move from individual computational tasks to connected research workflows and question whether counting AI-associated drug candidates captures the technology’s value. Topics include research productivity, trial design, biomarkers, human expertise and the use of patient-derived data, organoids and other representative biological systems to improve translation. The conversation also considers why AI has become polarising and how specialised models may fit into pharmaceutical research.

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.

Seminar

AI-based structural modelling of host-pathogen protein interactions

Jan Kosinski · EMBL Hamburg

Wed, Oct 7 · 13:30 UTC · Online

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.

SC26 connects researchers and practitioners developing high-performance computing, networking, storage and scientific data analysis. The programme includes research presentations, tutorials and workshops at McCormick Place in Chicago, 15–20 November 2026. Registration is open, with early rates through 14 October. A digital participation option is available through the official registration page.

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