AI can predict how you’ll respond to a survey. But that’s not the same as understanding you

Silicon sampling’ promises easy answers to tough questions about human behaviour – but the reality is more complicated.

OpenAI releases new voice models for more natural live conversations

OpenAI today released new conversational models, called GPT-Live-1 and GPT-Live-1 mini, claiming that they sound more natural ...
Eos

Comparing Machine Learning Models of Raindrop Formation

Better simulations of raindrop formation could help improve climate and weather models. This newsletter rocks. Get the most ...

David Vs. Goliath: Why Small Language Models Are Quietly Winning Where It Matters Most

In enterprise AI, the models that win won't be the largest. They'll be the ones that know exactly what they're built for.​​ ...
New Electronics

Keysight and WIN Semiconductors introduce MMIC design workflow to reduce tapeout risk

Keysight Technologies and WIN Semiconductors have launched a joint design workflow aimed at helping gallium nitride (GaN) ...
Communications of the ACM

Can We Understand How Large Language Models Reason?

Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how ...
GitHub

FlowWM stochastic world modeling via flow matching in DINOv3 feature space, with the FuturePerception (Waymo) benchmark.

FlowWM stochastic world modeling via flow matching in DINOv3 feature space, with the FuturePerception (Waymo) benchmark. - ...
JD Supra

AI in recruitment: the ICO’s wake-up call for vendors

Artificial intelligence has quietly reshaped recruitment. From sourcing, screening to selection of candidates, AI tools promise speed and scale in an ever-globalised workforce landscape following the ...
ahajournals.org

The Impact of Sex and Gender on Stroke

Women face a disproportionate burden of stroke mortality and disability. Biologic sex and sociocultural gender both contribute to differences in stroke risk factors, assessment, treatment, and ...
IEEE

Flow-Based Generative Models as Iterative Algorithms in Probability Space: An intuitive mathematical framework [Special Issue on the Mathematics of Deep Learning]

Abstract: Flow-based generative models have emerged as a powerful class of deep generative models, offering exact likelihood estimation, invertible sample transformations, and reliable and efficient ...
GitHub

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

Sep. 07, 2021: We add an online Colob demo for easy comparison of HCFlow and SRFlow. Sep.06, 2021: See our recent work SwinIR: Transformer-based image restoration Aug. 17, 2021: See our recent work ...