Our technical team members share their work in agent science and other fields on the forefront of AI.
“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.
