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• ↓ Machine learning, AI and physical models
• ↓ Consultancy and workshops
• ↓ Recent work in industry
• ↓ In practice
• ↓ Background
• ↓ Research
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"Machine learning models are probabilistic models. Treated as such they can be reasoned about: their uncertainties carry meaning, their failure modes become predictable, and the conclusions drawn from them can be defended. Treated as black boxes, none of that holds."

"I work with research groups and companies on that boundary — putting machine learning inside statistical inference that is mathematically sound, and understood by the people who have to rely on the answer."

"Neural networks, including the large language models behind most of what is currently called AI, are probabilistic models, almost always fitted via some form of maximum likelihood estimation. Treating the networks as distributions, rather than as machines that produce answers, makes them possible to interrogate: what distribution has a model actually learned, what are its outputs conditioned on, and what do its inferences mean?"

"However, in their current form, such networks are massively misunderstood and their results mistreated. A softmax score is not the probability that an answer is correct. An ensemble with low variance is not necessarily a well-calibrated one. A model that fits the data can still be the wrong model, and will fail confidently when it is. These are just a few misunderstandings among many, but the results are routinely and dangerously treated as concrete fact."

Key Topics & Focus Areas for charnock.fr:

Machine learning, AI and physical models

Consultancy and workshops

Recent work in industry

In practice

Background

Research