As AI systems become capable enough to matter, I think the question of whether we actually understand them becomes urgent in a new way. This talk works through four candidate answers — understanding as explanation, as mechanism, as control, and as process — and argues that each one, on its own, isn't enough.
Michael Sparks - Software Sustainability Institute
The Research Software Quality Toolkit (RSQKit; https://everse.software/RSQKit/), developed by the EVERSE project, lists curated best practices for improving the quality of research software. It is intended for researchers, research software engineers, as well as those running research infrastructures involving software or engaged in research software policy and funding.
Modern cloud applications increasingly rely on low-latency communication, yet end-host bottlenecks remain a major barrier to achieving predictable performance. In this talk, we examine the problem of slow receivers at end-hosts, where limitations in CPU scheduling, networking stacks, and system interfaces can significantly degrade both latency and throughput in cloud VMs.
Lotem Peled-Cohen (Technion - Israel Institute of Technology)
This talk presents my PhD research, supervised by Prof. Roi Reichart, exploring the intersection of Large Language Models (LLMs) and Alzheimer’s and related dementias. I begin by presenting our survey and perspective paper, in which we map the field’s current state and identify critical research gaps, such as data scarcity and the need for LLM-based simulation.
AI evolves rapidly: top models are superseded by the next generation within months, if not weeks. Benchmarks see similarly rapid turnover. Once released, many benchmarks become "solved" within months or at most a few years, no longer able to measure the frontier of AI. New benchmarks quickly take their place. Yet, even though benchmarks and models change so constantly, some fundamental issues of evaluation remain surprisingly enduring.
Scientific discovery emerges not from isolated reasoning, but from the intersection of diverse epistemic traditions. This talk proposes that the modern AI ecosystem, a structured network of heterogeneous reasoning agents spanning approximate and rigorous inference, constitutes a new form of collaborative intelligence for scientific inquiry. Drawing on Simon's conception of reasoning as adaptive search, we argue that such ecosystems do not merely accelerate known reasoning pathways, but create conditions under which genuinely novel representations may emerge.
Scientific discovery emerges not from isolated reasoning, but from the intersection of diverse epistemic traditions. This talk proposes that the modern AI ecosystem, a structured network of heterogeneous reasoning agents spanning approximate and rigorous inference, constitutes a new form of collaborative intelligence for scientific inquiry. Drawing on Simon's conception of reasoning as adaptive search, we argue that such ecosystems do not merely accelerate known reasoning pathways, but create conditions under which genuinely novel representations may emerge.
Scientific discovery emerges not from isolated reasoning, but from the intersection of diverse epistemic traditions. This talk proposes that the modern AI ecosystem, a structured network of heterogeneous reasoning agents spanning approximate and rigorous inference, constitutes a new form of collaborative intelligence for scientific inquiry. Drawing on Simon's conception of reasoning as adaptive search, we argue that such ecosystems do not merely accelerate known reasoning pathways, but create conditions under which genuinely novel representations may emerge.
Luke Gilbert, PhD, Associate Professor of Urology, University of California, San Francisco
Abstract: The ability to precisely manipulate endogenous gene expression enables exploration of gene function and establishment of causal relationships. This lecture will discuss CRISPR tools for turning genes on and off from a research and therapeutics perspective. I will also describe our CRISPRi approach for large-scale mapping of genetic interactions (GI) in the context of environmental perturbations.
Monotonicity is a common and often necessary assumption in biomedical research. In multiplex assays, biomarker expression is expected to have a monotonic association with disease outcome; similarly, in dose-finding studies, the probability of a response or toxicity outcome is expected to increase with dose.
In this talk we will explore a zero-player game based on an information isolation constraint. The dynamics of the game emerge from a “no-barber” selection principle that prohibits external structure. The aim is for the game to avoid impredictive-style inconsistencies. Motivated by the selection principle we will derive a “selected" trajectory in the game that consists of a second-order constrained maximum entropy production along the information geometry.
Kirsty Pringle - Software Sustainability Institute; EPCC, University of Edinburgh
Research Software Engineers (RSEs) collaborate with researchers to develop and maintain software, helping to embed best practices that improve reliability and reduce inefficiencies in research workflows.
As awareness grows of the environmental impact of computational research, a new specialism - Green RSE - is beginning to emerge.
Green RSEs integrate sustainability into software development, ensuring environmental considerations are addressed alongside performance and usability.
Abstract: Neural networks have shown remarkable performance across data domains, especially in regimes of increasing compute budgets. However, fundamental insights into how neural networks process information, share representations and traverse loss landscapes remain uncertain. In this work, we quantify the functional impact of distribution matching, facilitated by knowledge sharing mechanisms such as knowledge distillation, under student-teacher optimisation strategies.
This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.
If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/5dHfs6vJrrvTbqst5. Sign-up is possible from May 21 midday (12pm) until May 25 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by May 27 midday.
AthenaZero is a bimanual manipulator designed to maximize control authority while minimizing inertia. By utilizing quasi-direct drive actuation and transmission remotization techniques, the system achieves an effective endpoint mass comparable to that of a human. Trading off trajectory tracking stiffness as compared to conventional high-impedance manipulators, this architecture reduces reflected inertia by an order of magnitude.
Dr Anna Breger, Project Leader, University of Cambridge
In this talk we discuss the potential of non-invasive imaging and machine learning techniques for the reconstruction of degraded medieval music notation. Our examples include manuscripts and fragments that suffer from different kinds of degradations rendering parts of the notation illegible. Such degradations may happen due to chemical or physical damage, for example from iron-gall acidity or from deliberate erasure.
