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Physicists have long viewed life as a non-equilibrium process that fights the 2nd law of thermodynamics by maintaining order. While we understand how extant biological Maxwell Demons work, much less is known about how such Demons come into existence in the first place. Using theoretical and experimental work on DNA copying machinery, we show that the commonly assumed tradeoff between speed and accuracy can be inverted: error correction can actually speed up replication. The key insight is that errors cause `stalling’, i.e., misincorporated bases slow down subsequent steps by factors up to 100,000x. Correcting errors, though costly per base, avoids these long delays and leads to faster overall replication. We support this prediction with data from a large-scale polymerase mutagenesis screen showing that faster polymerases are more accurate. We further show that analogous error-correcting mechanisms, like the dynamic instability of microtubules, can emerge during self-assembly under selection for speed alone. Our work suggests that complex, dissipative error correction can evolve more easily than assumed, as a byproduct of fast replication, even before that accuracy serves any direct function like preserving genetic information.FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.bio

Modern machine learning systems operate in regimes that challenge classical learning-theoretic assumptions. Models are highly overparameterized, trained with simple optimization algorithms, and rely critically on how data is collected and curated. Understanding the limits of learning in these settings requires revisiting both the computational and statistical foundations of learning theory.

 

A central question in learning theory asks which functions are tractably learnable. Classical complexity results suggest strong computational barriers, motivating a focus on “learnable subclasses” defined by properties of the target function. In this talk, I argue for a different perspective by emphasizing the role of the training distribution. Fixing the learning algorithm (e.g. stochastic gradient descent applied to neural networks), I show that allowing a “positive distribution shift”, where training data is drawn from a carefully chosen auxiliary distribution while evaluation remains on the target distribution, can render several classically hard learning problems tractable.

 

Beyond computational considerations, I then study statistical limits of learning in modern, overparameterized models using stochastic convex optimization as a theoretical framework. While classical theory often suggests that successful generalization requires avoiding memorization, I show that memorization is in fact unavoidable: achieving high accuracy requires retaining nontrivial information about the training data and can even enable the identification of individual training examples. These results reveal fundamental privacy–accuracy tradeoffs inherent to accurate learning.

 

Bio:

 

Idan Attias is a postdoctoral researcher at the Institute for Data, Econometrics, Algorithms, and Learning (IDEAL), working with Lev Reyzin (University of Illinois Chicago), Nati Srebro, and Avrim Blum (Toyota Technological Institute at Chicago). He obtained his Ph.D. in Computer Science under the supervision of Aryeh Kontorovich (Ben-Gurion University) and Yishay Mansour (Tel Aviv University and Google Research).

 

His research focuses on the foundations of machine learning theory and data-driven sequential decision-making. His work has been recognized with a Best Paper Award at ICML ’24 and selection as a Rising Star in Data Science (University of California San Diego ’24). His postdoctoral research is supported by an NSF fellowship, and his Ph.D. studies were fully supported by the Israeli Council for Higher Education Scholarship for Outstanding PhD Students in Data Science.

While AI models are becoming an ever-increasing part of our lives, our understanding of their behavior in unexpected situations is drifting even further out of reach. This gap poses significant risks to users, model owners, and society at large.

In the first part of the talk, I will overview my research on detecting unexpected phenomena with and within deep learning models. Specifically, detecting (i) anomalous samples, (ii) unexpected model behavior, and (iii) unexpected security threats. In the second part of the talk, I will dive into my recent research on a specific type of unexpected security threat: attacks on image watermarks. I will review such attacks and present my recent work toward addressing them. I will conclude with a discussion of future research directions.

Bio:

Niv Cohen is a postdoctoral researcher at the school of Computer Science & Engineering at New York University. He received his Ph.D. in Computer Science from the Hebrew University in 2024. His research interests include representation learning, computer vision, and AI safety. He is a recipient of the VATAT Scholarship for Outstanding Postdoctoral Fellows in Data Science and the 2024 Blavatnik Prize for Outstanding Israeli Doctoral Students in Computer Science.

Dear Colleagues,You are cordially invited to a scientific and application focused webinar entitled Vesiculab: Advancing the Extracellular Vesicle Workflow. This webinar will present state of the art approaches for improving reproducibility, analytical rigor, and translational relevance in extracellular vesicle research, with an emphasis on practical solutions for everyday laboratory workflows. The presentation will be delivered by Dr Dimitri Aubert, PhD, CEO of Vesiculab. Scientific topics include:Fast size exclusion chromatography for efficient EV isolation,Total EV staining strategies for in vitro and in vivo studies,Optimized EV sample preparation for analytical and functional assays,Calibration principles for nanoflow cytometry and fluorescence NTA,Best practices for EV handling, storage, and preservation.