Name: 機械学習 - CS50ポッドキャスト、Ep.6 (Machine Learning - CS50 Podcast, Ep. 6)
Uploaded: 2021-01-14T10:19:15.000Z
Duration: 28 min 25 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

And I'm here with CS50's own Colton, no Brian Yu.

DAVID MALAN: So Colton could no longer be here today.

But I'm so thrilled that CS50's own Brian Yu's, indeed,

now with us for our discussion today of machine learning.

This was the most asked about topic in a recent Facebook

Machine learning is certainly all over the place these days in terms

But I'm not sure I've really wrapped my own mind around what

machine learning is and what its relationship to artificial intelligence

Brian, our resident expert, would you mind bring me and everyone up to speed?

Machine learning is sometimes a difficult topic

to really wrap your head around, because it

comes in so many different forms and different shapes.

But, in general, when I think about machine learning, the way

I think about it is how a computer is performing a task.

And usually when we're programming a computer to be able to do a task,

we're giving it very explicit instructions-- do this.

And if this is true, then do that or do this some number

But in machine learning, what we do is, instead of giving the computer

explicit instructions for how to do something,

we, instead, give the computer instructions for how

So instead of giving it instructions for how to perform a task,

we're teaching computer how to learn for itself

and how to figure out how to perform some kind of task on it.

DAVID MALAN: And I do feel like I hear about machine learning

and AI, artificial intelligence, almost always in the same breath.

But is there a distinction between the two?

So artificial intelligence or AI is usually a little bit broader.

It used to describe any situation where a computer is acting rationally

Machine learning is a way of getting computers

to act rationally or intelligently by learning from patterns

and learning from data and being able to learn from experiences.

But there are certainly forms of AI of being able to act intelligently

that don't require the computer to actually be able to learn, for example.

And I feel like I've certainly heard about artificial intelligence, AI,

especially for at least 20 years, if not 30 or 40, especially in the movies

or anytime there's some sort of robotic device.

Like, artificial intelligence has certainly been with us for some time.

But I feel like there's quite the buzz around machine

So what is it that has changed in recent months, recent years that

put this at the top of this poll, even among CS50's own students?

BRIAN YU: Yeah, so a couple of things have changed, certainly.

One has definitely been just an increase in the amount of data

that we have access to-- the big companies that have a lot of data

from people on the internet that are using devices and going on websites,

There's a lot of data that companies have access to.

And as we talk about machine learning, you'll

soon see that a lot of the way that these machine learning algorithms work

is that they depend upon having a lot of data

from which to draw understanding from and to try and analyze

in order to make predictions or draw conclusions, for example.

DAVID MALAN: So, then, is it fair to say,

because I have more familiarity myself with networking and hardware

and so forth that because we just have so much more disk space available to us

now and such higher CPU rates at which machines can operate that that's partly

what's driven this that we now have the computational abilities

I would say that's a big contributing factor.

DAVID MALAN: So if we go down that road, like,

at what point are the algorithms really getting fundamentally

smarter or better, as opposed to the computers just getting so darn

fast that they can just think so many steps ahead

and just come up with a compelling answer to some current problem quicker

And the algorithms that we have right now tend to be pretty good.

But there's a lot of research that's happening

in machine learning right now about like,

Can we make them even more accurate, given the same amount of data?

Or even given less data-- can we make our algorithms

able to be able to perform tasks effectively just as effectively?

Well, so I feel like the type of AI or machine

learning that I grew up with or knew about or heard about

I knew Google made a big splash with Go some years ago-- the game,

not the language-- and then video games more generally.

Like, if you ever wanted to play back in the '80s against the "CPU,"

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機械学習 - CS50ポッドキャスト、Ep.6 (Machine Learning - CS50 Podcast, Ep. 6)

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