This summer the UnlikelyAI team welcomed 4 interns that multiplied the energy of the team with their fresh ideas, energy, drive, and full charge of the projects they delivered. Over three months, they became an essential part of the team. They developed features that went live, did fresh research into how LLMs behave, a Python package and a drawing-game rivalry the office still cannot settle.

Any AI system in production needs a way to check its outputs. One way of doing that is having LLM-as-Judge, which is when you have one language model evaluate the output of another. This method is becoming more common because it is fast, cheap and it allows to scale the system without having a human reviewing the data and labelling the outputs.

After a decade in which progress in AI has meant progress in capability, what now decides whether these systems are allowed to make high-stakes decisions is not how much they can do, but whether they can be trusted. That shift is the starting point of a new two-part essay series from our founder and CEO, William Tunstall-Pedoe.

In our latest UnlikelyAI webinar, Callum Hackett (Head of Research, UnlikelyAI) and Louis Mahon (Senior Applied Scientist, UnlikelyAI) wrestled with a framework in human psychology that has been applied when designing and making sense of neurosymbolic AI systems, but does that analogy illuminate, or does it constrain?

The global market for AI in financial services was worth around $14.9bn in 2024. On current projections it reaches $50.7bn by 2034. That kind of growth tells you where the money is going but it tells you less about what is actually working.

Today, July 16, is National AI Day. One of the truths of working in this field is that the vocabulary never sits still. You pick up a new term more or less every day, and the moment you feel fluent someone drops one you have to go and look up. So to mark the occasion we lined up a handful of AI terms in the order most people tend to meet them, starting wi...