Artificial intelligence research, products and the companies building it.
Building a neural network is only half the job; knowing when it has learned enough, rather than too much, is the harder skill.
Beyond distillation, a handful of engineering tricks let AI that once needed a data centre run quietly inside a handset or laptop.
The same underlying question, updating what a model knows, produces two very different engineering answers, and the difference matters most when facts change.
Behind every headline about AI learning to spot tumours or translate speech lies the same unglamorous process of guessing, checking and adjusting, millions of times over.
A technique borrowed from decades-old machine learning research explains why the AI running on your phone is quietly catching up with the giants in the data centre.
When an off-the-shelf AI model does not know enough about your world, there are two common fixes, and they solve different problems.
Behind the fluent conversations lies a surprisingly mechanical process of prediction, pattern-matching and statistics, not understanding in any human sense.
Chatbots answer questions, but AI agents are meant to take action in the world, and that shift makes them far harder to build safely and reliably.
Rather than a single AI law, the UK relies on existing regulators, voluntary commitments and international diplomacy to manage the risks of artificial intelligence, a strategy that is increasingly being tested as the technology advances.
