With a few hundred well-curated examples, an LLM can be trained for complex reasoning tasks that previously required thousands of instances.

The rapid rise of edge AI, where models run locally on devices instead of relying on cloud data centers, improves speed, privacy, and cost-efficiency.

In a blog post published today, first spotted by Wired, the researchers found that DeepSeek "failed to block a single harmful prompt ... trained with a fraction of the budgets that other frontier ...

if you mount the kerb during these maneuvers it shows a lack of vehicle control and that would lead to a grade three fail." Improper use of the handbrake is another common way of failing the test.

“Innovation and efficiency, not excessive compute, is the key.” “People have been chasing the wrong rabbit in LLM development, thinking more compute is going to always lead to breakthroughs,” Cohere ...

On Jan. 20, DeepSeek released R1, its first "reasoning" model based on its V3 LLM. Reasoning models are ... That's basically what inference compute or test-time compute is -- copying the smart ...