Don’t Blame the Model

Discover three post-hoc methods for closing the gap between confidence and accuracy.

The following article originally appeared on the Asimov’s Addendum Substack and is being reposted here with the author’s permission. Bill Gurley has an excellent article on what he calls open source strategy, which we recommend reading. There is a lot to debate about his concluding argument in particular: that open-weight models are central to keeping the AI market […]

DeepMind's shift to 'world models' could redefine AI's role in robotics and scientific discovery, emphasizing causality over language processing. The post Google DeepMind CEO Demis Hassabis says language models can’t understand reality, pushes for ‘world models’ appeared first on Crypto Briefing.

This story of David and Goliath is an iconic biblical narrative about the power of faith and courage against overwhelming odds. But the story can also give us a conceptual […] The post The David and Goliath Paradigm: Comparing Small and Large Language Models appeared first on AIwire.

Modern language models are trained on data with extremely uneven token distributions. A small number of words appear in almost every sentence, while many rare but meaningful tokens occur only occasionally. This creates a hidden optimization challenge: parameters associated with common tokens receive constant gradient updates, while parameters tied to rare tokens may go hundreds […] The post Stochastic Gradient Descent (SGD’s) Frequency Bias and How Adam Fixes It  appeared first on MarkTechPost.

From tokenisation to evaluation :  how modern language models actually work in practice The post The Must-Know Topics for an LLM Engineer appeared first on Towards Data Science.

These ares seven unconventional uses of LLMs that go far beyond usual chat interface and conversations.

Merge LLMs easily with Unsloth Studio's no-code GUI and combine models without retraining.