Google Research introduced PhotoScan, an investigational deep-learning system that estimates body-fat percentage, Android-to-Gynoid fat ratio and Visceral-to-Subcutaneous fat ratio from standard 2D smartphone photos. The system was pre-trained on more than 35,000 UK Biobank participant records and fine-tuned using a cohort of 677 adults. It is intended to estimate body-composition markers associated with insulin resistance, which is commonly defined by a HOMA-IR score above 2.9 and can precede type 2 diabetes by years. In five-fold cross-validation on the PhotoBIA cohort, PhotoScan produced a mean absolute error of 2.15 for body-fat percentage, compared with 2.91 for smartwatch bioelectrical-impedance analysis. Independent validation in the MetabolicMosaic cohort produced errors of 2.13 for body-fat percentage, 0.085 for the Android-to-Gynoid ratio and 0.085 for the Visceral-to-Subcutaneous ratio. A gradient-boosting model using demographics plus PhotoScan metrics reached an AUROC of 0.760 for identifying insulin resistance, versus 0.692 for demographics alone and 0.773 for demographics plus DXA scan data. Adding smartwatch bioelectrical-impedance data did not improve insulin-resistance classification. Google describes PhotoScan as a research prototype rather than a clinical screening product.

The GitHub repository ryanzhou/deepseek-v4-flash-mi300x provides configuration and patches for running DeepSeek-V4-Flash-0731 on an AMD MI300X. It includes a Docker Compose stack, SHA-256-pinned file overlays, reference diffs, tuning tables, and runs checkpoints without additional weight quantization or offload.

PyTorch serves as a reference language for deep learning, providing a common framework of APIs and conventions. Reference implementations prioritize clarity over performance, leading to some confusion regarding the term "reference language" in the context of PyTorch.

Large language models can perform tasks such as writing essays, solving math problems, and generating code, but their internal reasoning processes are not fully understood. Researchers are exploring ways to make these models and deep neural networks more mechanistically interpretable.

Ilya Sutskever provided John Carmack with a curated reading list of 30 foundational AI and deep learning papers. The list includes full papers and plain-language explanations of complex terms.

DeepSpec is an AI tool designed for formal verification of software systems. It utilizes the DSpark framework to enhance the reliability and security of software through formal methods.

AI systems can surpass humans in various tasks but utilize different cognitive processes, allowing for the detection of AI agents and bots. Despite advancements in AI, CAPTCHAs remain effective in certain scenarios, as visual language models can recognize specific objects but may struggle with more complex tasks that require human-like reasoning.

A scientific theory of deep learning is emerging that characterizes key properties and statistics related to the training process, hidden representations, final weights, and performance of neural networks. The research consolidates various ongoing studies in deep learning theory.

Talos is a custom FPGA-based hardware accelerator designed specifically for executing Convolutional Neural Networks with high efficiency. It reimagines deep learning inference at the circuit level rather than merely reimplementing existing software logic in hardware.

DjVu is a file format that offers superior efficiency for storing books and mathematical papers compared to PDF. It incorporates innovations that enhance text processing and storage, particularly beneficial for deep learning applications.