Dr. Fei-Fei Li

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372 appearances 1 recordings 1 series first heard Nov 2025 last heard 7 Nov

Dr. Fei-Fei Li’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Nov 2025 with 1.

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Young and Profiting (YAP) with Hala Taha: Entrepreneurship and Self-Improvement Podcast · Dr. Fei-Fei Li: Turn AI Into Humanity's Greatest Ally, Not Its Biggest Threat | Artificial Intelligence | AI Vault · 7 Nov 2025 podcast

You just give that.

the the documents and that's called self-supervised learning so whether it's supervised with additional labels or supervised without additional label is self-supervised it starts with data now data goes into the algorithm and the algorithm has to have an objective to learn

Typically, in the language model, the objective is to predict the next syllabus as accurately as the training data shows you.

In the case of images with cat labels, for example, is to predict an image that has a cat with the right label cat instead of the wrong label microwave.

And then because it has this objective, during training, if it makes a mistake,

you know, if I didn't predict the next word right or if I labeled the cat wrong, it goes back and iterates and updates its parameters based on the mistake.

It has some mathematical rules or learning rules to update.

And then it just keeps doing that till it, you know, when humans ask it to stop or it no longer updates, you know, whatever stop criteria.

And then you're left with a...

ginormous neural network that's already trained by ginormous amount of data.

And in that neural network, it has all the parameters, the mathematical parameters that's already learned now.

You can take this, and now you have a new sentence come in.

And then it goes through this model.

Because it has all the parameter it has learned, it predicts what I should say given the new sentence, like, hello, Hala, how is your breakfast today?

And it would predict I had a great breakfast today or whatever.

So that's how it's going to be used.

It's because math, the way we do math in human mind is different from the way we do language.

Language has a very clear pattern of sequence to sequence.

Like I say the word how, you know, the word are and you typically follow, but sometimes it doesn't, right?

So I have to learn these patterns.

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