Unsupervised Learning
topic on 7 shows · 8 statements across 8 episodes · said 1 times in 1 episodes since 2025
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Brockman: OpenAI's 10-year roadmap focused on RL, unsupervised learning, then complexity
“We came up with what I would Really say is almost the technical plan that we have pursued for the past 10 years. Number one, solve reinforcement learning. Number two, solve unsupervised learning. And number three was gradually learn more complicated, in quotes…”
Tworek: Pre-Training on Unlabeled Data Yields Far More Intelligence Than Supervised Mapping
“There are many more bits usually in the targets than in the labels and studying the structure of targets itself. It yields much more learning and much more intelligence than learning the mapping itself. So like spending a whole compute on just learning the dat…”
Movsisian: Feedback Intelligence converts unstructured feedback into actionable engineering steps
“We have developed a novel solution. It's an orchestration of LLMs plus unsupervised learning where we are able to turn this unstructured thumbs up, thumbs down to a list of actions.”
Altman: OpenAI's original goals included never exceeding 120 people
“I believe there were three goals for the For the effort at the time. It was like, figure out how to do unsupervised learning, solve RL, and never get more than a 120 people. Missed on the third one.”
Hennessy: Most interesting unsupervised learning applications are in natural sciences, not business
“And the space where that works, sort of unsupervised learning, is such a small part of the giant ML space. It's relatively small. And most of its interesting applications are in the natural science world, not in real world applications.”
Clark: AI will continue to require supervised learning alongside unsupervised paradigms
“I think there's going to be need for all of it, to be honest. When it comes down to solving a very specific business problem like fraud detection, you don't want the algorithm to learn on its own. Just let a lot of fraud through as you slowly come up with an i…”
Bloomberg limits unsupervised learning to data bootstrapping due to precision needs
“We use unsupervised learning a lot to Sort of bootstrap our data. For example, word to whack, right? It's a perfect unsupervised learning algorithm. We use that a lot to, for query reformulation and all that, but, ah, it's a little risky for something as high …”