The largest ongoing debate about AI is “Are Large Language Models (LLMs) intelligent?” That makes sense, at least: the evidence is ambiguous and the stakes a... Read more ›
Google Photos — The App That Understands Your PicturesMost of us have thousands of photos on our phones. Finding a ... viewing patterns shift, so it adapts to trends, seasons, and changes in your own interests over time.6. Google Maps —... Read more ›
Researchers have developed a new "emotionally aware" AI-based model for classifying mental health conditions, which could help clinicians better diagnose patients' mental health conditions. The Emo-MHC model uses machine learning (ML) and deep learning (DL) techniques to analyze text from sources like doctors' notes, social media posts and online forums to help doctors classify patients' mental health conditions more accurately and quickly than existing models, which could help provide more e... Read more ›
“Neuroscience is what oncology was 20 years ago,” says Cristian Massacesi, Chief Medical Officer and Head of Development at Bristol Myers Squibb. Massacesi joins Bloomberg Intelligence pharmaceuticals analyst Sam Fazeli to discuss Bristol Myers’ push into neuroscience, including Alzheimer’s therapies aimed at tau, a protein tied to brain tangles in the disease. They also explore the company’s oncology pipeline, including PD-L1/VEGF bispecifics, next-generation antibody-drug conjugates and advanc Read more ›
Genome assembly is a computational pipeline designed to reconstruct chromosomes from small sequencing reads. Following their assembly, contiguous sequences (contigs) are arranged into chromosome-long sequences during scaffolding. Hi-C, a long-range linkage information between regions of the genome widely used in recent large sequencing projects, is often required to correctly order contigs. Several tools have been developed to automate this task following either statistical or deep-learning a... Read more ›
This is day 8 of building a neural network from scratch in python. Yesterday we said that learning is just a loop: the network makes a… Read more ›
#Python Merge branch 'main' into export-D108661628 Read more ›
Two models on Hugging Face looked ordinary — but the instant you loaded one, it opened a backdoor to your machine. The trick: a file… Read more ›
Author(s): Yuanran Zhu, Peter Rosenberg, Zhen Huang, Hardeep Bassi, Chao Yang, and Shiwei ZhangHere, the authors introduce a Transformer-based framework for learning the electronic self-energy in strongly correlated systems. A distinctive feature of the approach is the use of complementary, readily generated training datasets spanning weak-, intermediate-, and strong-coupling regimes. The dimension-agnostic Transformer architecture enables system-size generalization, allowing self-energy oper... Read more ›
For someone with a reputation as an AI skeptic, I actually have a lot of time for machine learning approaches, at least when pursued with due humility and attention to detail. Science is a vast sea of data, full of currents, seamounts, hidden reefs, archipelagos, and shorelines, and we need all the help we can get in navigating it. And successes in protein structure prediction are evidence enough that if you have a large enough data set, well-curated and covering a wide range of examples, tha... Read more ›
Artificial Intelligence has transformed from a niche research field into a technology that influences daily life, powering applications… Read more ›
Here’s a fact that surprises most people learning about AI for the first time: the core mathematical idea behind deep neural networks has… Read more ›
This morning I saw , describing a small but effective inpainting model - a model where you can mark regions of an image to remove and the model imagines what should fill the space. The released model , but since it described itself as 0.2B I decided to try and get it running using WebGPU in a browser. TL;DR: I got it working, and you can try the demo at The finished tool Here's a video demo of the finished tool: You can open any image in it (non-square images get letterboxed), highlight areas... Read more ›
IntroductionAn ambitious problem in mechanistic interpretability of neural networks is finding an input for a neural network that produces a certain output (), however this is shown to not have a general computationally tractable solution that works in cases such as when the network acts as a verifier for an NP hard problem. However, a general algorithm might not be necessary in practice, as "Eliciting bad contexts" suggests. We also know that finding the input that maximizes the output of an... Read more ›
Intel and AMD have released the official specification for AI Compute Extensions, or ACE, a standardized instruction set for future x86 processors. Developed through the x86 Ecosystem Advisory Group, these extensions aim to accelerate matrix multiplication and machine learning workloads directly on the CPU. By establishing a common technical standard, the two rivals intend to prevent market fragmentation and provide a consistent target for software developers. <a href=" Read more ›
Esmeralda Whitammer, Sara Wade, Vincent Fortuin, Konstantina Palla, and Theodore Papamarkou write: We are organising a focused workshop on Rethinking the Role of Bayesianism in the Age of Modern AI from October 26 to 30, 2026, bringing together researchers exploring the frontiers of Bayesian machine … → Read more ›
You need to pay attention to the metrics, and how to choose the right ones for imbalanced data, asymmetric costs, modern neural networks… Read more ›
If you told someone you were an “AI Engineer” a few years ago, they probably assumed you were elbow-deep in PyTorch, wrangling massive… Read more ›
Teaching cellular automata to actually do things Read more ›
We’re on a journey to advance and democratize artificial intelligence through open source and open science. Read more ›