Chip Huyen, author of AI Engineering, discusses AI model size trends and efficiency improvements with host Matt Turck on The MAD Podcast.
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Chip Huyen argues post-training is what differentiates frontier AI models
“So, so I do think that post-training is what makes this, like, really big lab models are, like, different.”
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Evaluation is the single biggest bottleneck holding back enterprise AI adoption
“So, so I do think that evaluation is the biggest bottleneck for AI adoptions, because unless, like, if we can, like, if we can, like, develop a more reliable way to evaluate the application, that application is not going to get adopted. Like, or maybe, maybe i…”
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Chip Huyen warns million-token context capacity does not imply efficient processing
“Just because a model, I think the second reason may be actually more important at least for now is that just because a model can fit in a million con token context doesn't mean that it can process that million token efficiently.”
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Chip Huyen argues human-generated plans are poor training data for AI agents
“When we ask humans to generate like what they consider the best plan for an actions, for a task, it's actually like not quite the best plan for AI, because what is what is easy or efficient for humans is not the same as easy and efficient for AI, right?”
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Huyen: Legacy systems prevent US companies from matching Chinese online learning
“So a lot of American, American internet companies are like a lot older than the average, like the new, like Chinese internet company. So it means it's like American internet companies have legacy systems that you from like, 20 years ago. And just have to build…”
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Chip Huyen: AI becomes harder to evaluate as intelligence increases
“As a more intelligent AI becomes like the harder it is to evaluate it.”