Sequential decision problems distill important challenges frequently faced by humans. Through repeated interactions with an uncertain world, unknown statistics need to be learned while balancing ...

Distinct model-free and model-based learning processes are thought to drive both typical and dysfunctional behaviours. Data from two-stage decision tasks have seemingly shown that human behaviour is ...

Model inversion and membership inference attacks create unique risks to organizations that are allowing artificial intelligences to be trained using their data. Companies may wish to begin to evaluate ...

ByteDance’s Doubao Large Model team yesterday introduced UltraMem, a new architecture designed to address the high memory access issues found during inference in Mixture of Experts (MoE) models.

DeepSeek V4 architecture uses sparse attention to cut inference costs 73% at one-million-token contexts, but a NIST ...

A technical paper titled “Yes, One-Bit-Flip Matters! Universal DNN Model Inference Depletion with Runtime Code Fault Injection” was presented at the August 2024 USENIX Security Symposium by ...

Historically, we have used the Turing test as the measurement to determine if a system has reached artificial general intelligence. Created by Alan Turing in 1950 and originally called the “Imitation ...

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. Researchers at Massachusetts Institute of ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Chinese AI model inference startup SiliconFlow has raised more than 2 billion yuan ($294 million) in a Series B funding round ...

Detecting gas molecules through light scattering is fundamentally limited by weak signals and environmental noise. To address this, researchers ...