Name: 「機械学習」って何？ コンピューター科学者が5段階のレベルで説明 | 5 Levels | WIRED Japan
Uploaded: 2025-02-04T09:26:17.000Z
Duration: 26 min 9 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Gerund

I'm a computer scientist, and today I've been asked to explain machine learning in five levels of increasing complexity.

To-infinitive

Machine learning gives us the ability to learn things about the world from large amounts of data that we as human beings can't possibly study or appreciate.

So machine learning is when we teach computers to learn patterns from looking at examples in data such that they can recognize those patterns and apply them to new things that they haven't seen before.

So machine learning is a way that we teach computers to learn things about the world by looking at patterns and looking at examples of things.

So can I show you an example of how a machine might learn something?

And what makes a dog, a dog, and a cat, a cat?

Well, dogs are very playful, I think, more than cats.

Cats lick themselves more than dogs, I think.

Do you think, if we look at these pictures, do you think maybe we could say, well, they both have pointy ears, but the dogs have a different kind of body and the cats like to stand up a little different.

A dog.  I think a cat because it's more skinny and also its legs are like really tall and its ears are a little pointy.

This one's a jackal and it's actually a kind of dog, but you made a good guess.

That's what machines do too, they make guesses.

And how did you know that it's not a cat or a dog?

Because cats and dogs, because they walk on their paws and their ears are like right here, not right here, and they don't wear watches.

And so you did something pretty amazing there because we asked the question, is it a cat or a dog?

And you said, I disagree with your question, it's a human.

So machine learning is when we teach machines to make guesses about what things are based on looking at a lot of different examples.

And I build products that use machine learning to learn about the world and make guesses about things in the world.

When we try to teach machines to recognize things like cats and dogs, it takes a lot of examples.

We have to show them tens of thousands or even millions of examples before they can get even close to as good at it as you are.

Yeah, I have after every unit, we have a review and then we have a test.

Are those like the practice problems you do before the test?

Just like everything that's gonna be on the test is on the review.

Which means that in the test, you're not seeing any problems that you don't know how to solve as long as you did all your practice, right?

If you show them a lot of examples and give them practice, they'll learn how to guess.

And then when you give them the test, they should be able to do that.

So we looked at eight pictures and you were able to answer really quickly.

But what would you do if I gave you 10 million examples?

So one of the differences between people and machines is that people might be a little better at this, but can't look at 10 million different things.

So now that we've been talking about machine learning, is this something you wanna learn how to do?

Because I kind of wanna become a spy and we used to do coding, so I may be kind of good at it.

And machine learning is a great way to use all those math skills, all those coding skills, and would be a super cool tool for a spy.

Have you ever heard of machine learning before?

I'm going to assume that it means humans being able to teach machines or robots how to learn themselves.

When we teach machines to learn from data, to build a model from that data or a representation of that, and then to make a prediction.

One of the places we often find machine learning in the real world is in things like recommendation systems.

So do you have an artist you really like?

So I'm gonna look up Melanie Martinez and it says here, if you like Melanie Martinez, one of the other songs you might like is by Aura.

So why do you think Spotify might've recommended that song?

Well, I know that in Melanie Martinez's music, she used a lot of the filtered voice to make it sound very deep and low.

And that's actually a really interesting thing to think about because that creepy vibe is something that you can perceive and I can perceive, but it's actually really hard to describe to a machine.

Pitch of the music, if it's really low or if it's super high, it could know that.

The machine can understand whatever we tell it to understand.

So there might be a person thinking about things like the pitch or the pacing or the tone, or sometimes machines can figure out things about music or images or videos that we don't tell it to discover, but that it can learn from looking at a lot of different examples.

Why do you think companies might use machine learning?

I think things like Facebook or Instagram, they probably use it to target ads.

Sometimes the ads you see are really uncanny.

And I think that's because they're based on so much data.

It's also important to realize that people in aggregate are actually pretty predictable.

Like when we talk to each other, we like to talk about the novel things.

Like here, we're having this conversation.

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「機械学習」って何？コンピューター科学者が5段階のレベルで説明 | 5 Levels | WIRED Japan

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「機械学習」って何？ コンピューター科学者が5段階のレベルで説明 | 5 Levels | WIRED Japan

sort

perceive

potential

struggle

「機械学習」って何？コンピューター科学者が5段階のレベルで説明 | 5 Levels | WIRED Japan