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Authors from Anthropic, a splinter group from OpenAI > With this fundraise, we’re going to explore the predictable scaling properties of machine learning syste

Authors from Anthropic, a splinter group from OpenAI > With this fundraise, we’re going to explore the predictable scaling properties of machine learning systems, while closely examining the unpredictable ways in which capabilities and safety issues can emerge at scale https://venturebeat.com/2022/06/27/10-new-ai-unicorns-flying-high-in-2022/ https://venturebeat.com/2022/06/27/10-new-ai-unicorns-flying... Estimating confidence in predictions is an important safety issue. It's usually very hard to do correctly for small models. Fortunately large models have read everything so they have less unknown unknowns.

PaulHoule 4y ago
What I’ve found for simpler models is that probability calibration is the difference between a model you can use and ‘nothing more to see folks, please move on…’ I’ve found that calibration frequently isn’t that hard to do, but mostly people don’t do it (e.g. they announce that their model is 99% accurate for a disease that occurs 1 in 10,000 to which the answer is ‘you can beat that accuracy by saying nobody has the disease) or maybe the model sucks (e.g. a calibrated full text retrieval system will never claim its results are better than 70% likely to be relevant.)