“When you can measure what you are speaking about, and express it in numbers, you know something about it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts advanced to the stage of science.” – Lord Kelvin

A new paper explores how to encode specific innate knowledge into AI systems that are agnostic to the sensory modality of their inputs.

A new paper, "Transformer Layers as Painters," co-written by Emergence researchers and Sakana AI, investigates the internal workings of transformers, focusing on how the removal or reorganization of information impacts pretrained models.

We evaluated our Agent-E with the WebVoyager benchmark.

As we’ve seen the rapidly rising impact of LLMs, we’ve also seen the growing importance of “synthetic data,” generated instructional raw text used to train LLMs.