AI

These cuddly, connected companions could disrupt everything from make-believe to bedtime stories. No wonder some lawmakers want them banned.

Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel

Leaders at the tech giant were skeptical of OpenAI—but wary of pushing it into the arms of Amazon, according to emails dating back to 2018.

Today on Uncanny Valley, we’re diving into recent reports that the Trump administration is considering an executive order that would establish some sort of federal oversight over new AI models.

Chrome users were caught off guard by a 4-GB Google AI model baked into Chrome, sparking privacy concerns. The good news: You can easily uninstall it. The bad? You might not want to.

OpenAI’s chatbot has some weird linguistic tics in Chinese that are driving users crazy.

As companies of various sizes adopt graphic processing units (GPU)-based machine learning (ML) training, fine-tuning and inference workloads, the demand for GPU capacity has outpaced industry-wide supply. This imbalance has made GPUs a scarce resource, creating a challenge for customers who need reliable access to GPU compute resources for their ML workloads. When you encounter

Training large language models requires accurate feedback signals, but traditional reinforcement learning (RL) often struggles with reward signal reliability. The quality of these signals directly influences how models learn and make decisions. However, creating robust feedback mechanisms can be complex and error prone. Real-world training scenarios often introduce hidden biases, unintended incentives, and ambiguous success

In a recent interview, Jassy explained the long-term approach to Amazon’s capital expenditures—and why you have to invest in momentous shifts.

We’re in the midst of a fundamental shift in how software gets built and used. AI agents are moving beyond assistants that wait for instructions. They call APIs, access MCP servers, coordinate with other agents, and complete complex multi-step tasks on behalf of users. As agents take on increasingly diverse tasks, the ecosystem around them