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  • SPEAKER: This is CS50.

  • [MUSIC PLAYING]

  • DAVID MALAN: Hello world.

  • This is the CS50 podcast.

  • My name is David Malan.

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

  • BRIAN YU: Hi everyone.

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

  • He's headed out west.

  • 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

  • poll that CS50 conducted.

  • So let's dive right in.

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

  • of the media and so forth.

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

  • machine learning is and what its relationship to artificial intelligence

  • is.

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

  • BRIAN YU: Yeah, of course.

  • 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

  • of times using a for loop, for example.

  • 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

  • to learn to do something on its own.

  • 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?

  • BRIAN YU: Yeah, there is.

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

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

  • or intelligently.

  • 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.

  • DAVID MALAN: OK.

  • 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

  • learning, specifically these days.

  • 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,

  • for instance.

  • 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

  • to answer these questions?

  • BRIAN YU: Yeah, absolutely.

  • 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

  • than, say, a human?

  • BRIAN YU: Yeah, it's a good question.

  • 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,

  • trying to make these algorithms better.

  • Right now, they're pretty accurate.

  • 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?

  • DAVID MALAN: OK, all right.

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

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

  • was always related to, like, games.

  • Like, chess was a big one.

  • 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,"

  • quote, unquote, I'm pretty sure it was mostly just random at the time.

  • But there's certainly been some games that

  • are ever more sophisticated where it's actually

  • really hard to beat the computer or really easy to beat the computer,

  • depending on the settings you choose.

  • So how are those kinds of games implemented when

  • there's a computer playing the human?

  • BRIAN YU: Yeah, so this an area, a very development

  • in the last couple of decades that 30 years ago was unimaginable probably

  • that a computer could beat a human at chess, for example.

  • But now, the best computers can easily beat the best humans.

  • No question about it.

  • And one of the ways that you do this is via form of machine learning known

  • as reinforcement learning.

  • And the idea of this is just letting a computer learn from experience.

  • So if you want to train a computer to be good at chess,

  • you could try and give it instructions about you

  • thinking of strategies yourself as the human and telling the computer.

  • But then the computer can only ever be as good as you are.

  • But in reinforcement learning, what we do is,

  • you let the computer play a bunch of chess games.

  • And when the computer loses, it's able to learn from that experience,

  • figure out what to do and then in the future, know to do less of that.

  • And if the computer wins, then whatever it did to get to that position,

  • it can do more of that.

  • And so you imagine just having a computer play millions

  • and millions and millions of games.

  • And eventually, it starts to build up this intelligence, so to speak,

  • of knowing what worked and what didn't work.

  • And so in the future of being able to get better and a better

  • at playing this game.

  • DAVID MALAN: So is this all that different from even the human

  • and the animal world where, like, if humans

  • have tried to domesticate animals or pets where you sort of reinforce

  • good behavior positively and negatively reinforce, like, bad behavior?

  • I mean, is that essentially what we're doing with our computers?

  • BRIAN YU: Yeah, it's inspired by the same idea.

  • And when a computer does something right or does something in the works,

  • you give the computer a reward, so to speak, is what people actually call it.

  • And then there's the penalty if the computer isn't able to perform as well.

  • And so you just train the computer algorithm to maximize that reward,

  • whether that reward is the result of like winning a game of chess or a robot

  • being able to move a certain number of paces.

  • And the result is that with enough training,

  • you end up with a computer that can actually perform the task.

  • DAVID MALAN: Fascinating.

  • So I feel like another buzzword these days is, like,

  • smart city where somehow, cities are using computer science

  • and using software more sophisticatedly.

  • And I gather that you can even use this kind of reinforcement

  • learning for, like, traffic lights, even in our human world?

  • BRIAN YU: Yeah.

  • So traffic lights traditionally are just controlled by a timer

  • that after a certain number of seconds, the traffic light switches.

  • But recently, there's been growth in, like, AI-controlled traffic lights

  • where you have traffic lights that are connected to radar and cameras.

  • And that can actually see, like, when the cars

  • are approaching in different places--

  • what times of day they tend to approach.

  • And so you can begin to, like, train an AI traffic

  • light to be able to predict, all right, when should I

  • be switching lights and maybe even having traffic lights coordinated

  • across multiple intersections across the city to try

  • and figure out what's the best way to flip the lights in order

  • to make sure that people are able to get through those intersections quickly.

  • DAVID MALAN: So that's pretty compelling,

  • because I'm definitely in Cambridge, been, like, in a car

  • and stopped at a traffic light.

  • And there's, like, no one around.

  • And you wish it would just notice either via sensor or timer

  • or whatever that, like, this is clearly not the most efficient use of,

  • like, anyone's time.

  • So that's pretty amazing that it could adapt sort of seamlessly like that.

  • Though, what is the relationship between AI

  • and the buttons that the humans pushed across the street

  • that according to various things I've read

  • are actually placebos and don't actually do anything and in some cases,

  • aren't even connected to wires.

  • BRIAN YU: I'm not actually sure.

  • I've also heard that they may be placebos.

  • I've also heard that, like, the elevator close button is also

  • a placebo that you press that.

  • And it sometimes doesn't actually work.

  • DAVID MALAN: Yes, I've read it even, which not necessarily

  • authoritative source.

  • There is, like, a photo where someone showed

  • a door close button had fallen off.

  • But there was nothing behind it.

  • Now, could have been photoshop.

  • But I think there's evidence of this, nonetheless.

  • BRIAN YU: It might be the case.

  • I don't think there's any AI happening there.

  • But I think it's more just psychology of the people and trying to make people

  • feel better by giving them a button to press.

  • DAVID MALAN: Do you push the button when you run across the street?

  • BRIAN YU: I do usually push the button when I want to cross the street.

  • DAVID MALAN: This is such a big scam, though, on all of us it would seem.

  • BRIAN YU: Do not push the button?

  • DAVID MALAN: No, I do, because just, what if?

  • And actually it's so gratifying, because there's

  • a couple places in Cambridge, Massachusetts

  • where the button legitimately works.

  • When you want to cross the street, you hit the button.

  • Within half a second, it has changed the light.

  • It's the most, like, empowering feeling in the world

  • because that never happens.

  • Even in an elevator, half the time you push it, like, nothing happens,

  • or eventually it does and is very good positive reinforcement

  • to see the traffic lights changing.

  • I'm very well behaved-- the traffic lights as a result.

  • OK, so more recently, I feel like, computers

  • have gotten way better at some technologies that kind of sort of

  • existed when I was a kid, like, handwriting recognition.

  • There was the palm pilot early on, which is

  • like a popular PDA or personal digital assistant, which has now been replaced

  • with Androids and iPhones and so forth.

  • But handwriting recognition is a biggie for machine learning, right?

  • BRIAN YU: Yeah, definitely.

  • And this is an area that's gotten very, very good.

  • I mean, I recently have just started using an iPad.

  • And it's amazing that I can be taking handwritten notes.

  • But then my app will let me, like, search for them by text

  • that it will look at my handwriting, convert it to text

  • so that I can search through it all.

  • It's very, very powerful.

  • And the way that this is often working now

  • is just by having access to a lot of data.

  • So, for example, if you wanted to train a computer

  • to be able to recognize handwritten digits, like, digits on a check

  • that you could deposit virtually now, like,

  • my banking app can deposit checks digitally.

  • What you can do is give the machine learning algorithm

  • a whole bunch of data, basically a whole bunch of pictures

  • of handwritten numbers that people have drawn

  • and labels for them associated with what number it actually is.

  • And so the computer can learn from a whole bunch of examples of here

  • are some handwritten ones, and here are some handwritten twos,

  • and here's some handwritten threes.

  • And so when a new handwritten digit comes along,

  • the computer just learns from that previous data and says,

  • does this look kind of like the ones, or does it look more like the twos?

  • And it can make an assessment as a result of that.

  • DAVID MALAN: So how can we humans are sometimes

  • filling out those little captchas--

  • the little challenges on websites where they're asking us, the humans,

  • to tell them what something says?

  • BRIAN YU: Yeah.

  • Part of the ideas that the captchas are trying to prove to the computer

  • that you are, in fact, human.

  • They're asking you to prove that you're a human.

  • And so they're trying to give you a task that a computer might struggle

  • to do, for instance, like, identify which of these images happened to have,

  • like, traffic lights in them, for example.

  • Although nowadays, computers are getting pretty good at that

  • that they using machine learning techniques

  • they can tell which of them are traffic lights.

  • DAVID MALAN: Yeah, exactly.

  • I would think so.

  • BRIAN YU: And I've also heard people talk about it.

  • I don't don't, action, if this is true that you

  • can use the results of these captchas to actually train machine learning

  • algorithms that when you are choosing which of the images

  • have traffic lights in them, you're training the algorithms that

  • are powering, like, self-driving cars, for instance,

  • to be able to better assess whether there are traffic lights in an image,

  • because you're giving more and more of this data

  • that computers are able to draw from.

  • So we've heard that too.

  • DAVID MALAN: It's interesting how these algorithms

  • are so similar to presumably how humans work, because,

  • like, when you and I learned how to write text, whether it was in print

  • or cursive, like, the teacher just shows us, like, one canonical letter A or B

  • or C. And yet, obviously, like, every kid in the room

  • is probably drawing that A or B or C a little bit differently.

  • And yet, somehow, we humans just kind of know that that's close enough.

  • So is it fair to say, like, computers really are just kind of doing that?

  • They are just being taught what something is

  • and then tolerating variations, thereof?

  • BRIAN YU: Yeah, that's probably about it.

  • One of the inspirations for machine learning

  • really is that the types of things that computers are good at

  • and the types of things that people are good at

  • tend to be very, very different.

  • But, like, computers can very easily do complex calculations, no problem,

  • when we might struggle with it.

  • But a problem, like, identifying that in a picture,

  • is there a bird in the sky or not, for example?

  • That's something that for a long time, computers really struggled to do,

  • whereas, it's easy for a child to be able to look in the sky and tell you

  • if there's a bird there.

  • DAVID MALAN: Oh, I was just going to say, I could do that probably.

  • OK.

  • So if this is supervised learning, and handwriting recognition's one,

  • like, what other types of applications fall under this umbrella?

  • BRIAN YU: Yeah, so handwriting recognition

  • counts as supervised learning, because it's supervised in the sense

  • that when we're providing data to the algorithm,

  • like the handwritten numbers, we're also providing labels for that data, like,

  • saying, this is the number one-- this is the number two.

  • That way, the computer is able to learn from that.

  • But this shows up all over the place.

  • So, for instance, like, your email spam filter

  • that detects automatically which emails are spam

  • and puts them in the spam mailbox, it's trained the same way.

  • You basically give the computer a whole bunch of emails-- some of which

  • you tell the computer these are real emails that are good emails.

  • And here, these are some other emails that are spam emails.

  • And the computer tries to learn the characteristics and the traits of spam

  • email so that when a new email comes about,

  • the computer is able to make a judgment call about,

  • do I think this is a nonspam, or do I think it's a spam email?

  • And so you could get it to classify it in that way.

  • So this kind of classification problem is a big area and supervised.

  • DAVID MALAN: And is that what is happening if you use gmail,

  • and you click on an email and report it as spam,

  • like, you're training gmail to get better at distinguishing?

  • BRIAN YU: Yes.

  • You can think of that as a form of reinforcement learning of the computer

  • learning from experience.

  • DAVID MALAN: Good boy.

  • BRIAN YU: You tell the computer that it got it wrong.

  • And it's now going to try and learn to be better in the future

  • to be able to more accurately predict which emails are spam

  • or not spam based on what you tell it.

  • And gmail has so many users and so many emails

  • that are coming to the inbox every day that you do this enough times.

  • And the algorithm gets pretty good at figuring out whether an email's spam

  • or not.

  • DAVID MALAN: It's a little creepy that my inbox is becoming sentient somehow.

  • OK, so if there's supervised learning, I presume

  • there's also unsupervised learning.

  • Is there?

  • BRIAN YU: Yeah, there absolutely is.

  • So supervised learning requires labels on the data.

  • But sometimes, their data doesn't always have labels.

  • But you still want to be able to take a data set, give it to a computer

  • and get the computer to tell you something interesting about it.

  • And so one common example of this is for when you're doing consumer analysis,

  • like, when Amazon is trying to understand its customers, for instance,

  • Amazon might not know all the different categories of customers

  • that there might be.

  • So it might not be able to give them labels already.

  • But you could feed a whole bunch of customer data to an algorithm.

  • And the algorithm could group customers into similar groups, potentially,

  • based on the types of products they're likely to buy, for example.

  • And you might not know in advance how many groups there are

  • or even what the groups are.

  • But the algorithm can get pretty good at clustering people

  • into different groups.

  • So clustering is a big example of unsupervised learning.

  • That's pretty common.

  • DAVID MALAN: So how different is that from just, like, exhaustive search

  • if you sort of label every customer with certain attributes-- what they've

  • bought, what time they've bought it, how frequently they've bought it

  • and so forth?

  • Like, isn't this really just some kind of quadratic problem

  • where you compare every customer's habits against every other customers

  • habits, and you can, therefore, exhaustively

  • figure out what the commonalities are?

  • Like, why is this so intelligent?

  • BRIAN YU: So you could come up with an algorithm

  • to say, like, OK, how close together are two particular customers, for instance,

  • in terms of how many things that they've bought in common, for instance,

  • or when they're buying particular products?

  • But if you've got a lot of different users

  • that all have slightly different habits, and maybe some groups of people share

  • things in common with other groups but then don't share other characteristics

  • in common, it can be tricky to be able to group an entire user base

  • into a whole bunch of different clusters that are meaningful.

  • And so the unsupervised learning algorithms

  • are pretty good at trying to figure out how you would actually

  • cluster those people.

  • DAVID MALAN: Interesting.

  • OK.

  • And so this is true for things I know in radiology, especially these days, like,

  • computers can actually not only read film, so x-rays and other types

  • of images of human bodies.

  • They can actually identify things, like, tumors now,

  • without necessarily knowing what kind of tumor they're looking for.

  • BRIAN YU: Yeah.

  • So one application of unsupervised learning is, like a anomaly detection,

  • given a set of data, which things stand out as anomalous.

  • And so that has a lot of medical applications

  • where if you've got a whole bunch of medical scans or images, for instance,

  • you could have a computer just look at all that data

  • and try and figure out which are the ones that don't quite look right.

  • And that might be worth doctors taking another look,

  • because potentially, there might be a health concern there.

  • You see the exact same type of technology and finance a lot

  • when you're trying to detect, like, which transactions might be

  • fraudulent transactions, for instance.

  • Out of tons of transactions, can you find the anomalies?

  • The things that sort of stand out is not quite like the others.

  • And these unsupervised learning algorithms

  • can be pretty good at picking out those anomalies out of a data set.

  • DAVID MALAN: So what kind of algorithm triggers a fraud alert?

  • Almost every time, I tried to use my credit cards for work.

  • BRIAN YU: That one is, I don't really know what's going on with that.

  • I know the credit card will often trigger an alert if you're outside

  • of an area where you normally are.

  • But the details of how those algorithms are working--

  • I couldn't really tell you.

  • DAVID MALAN: Interesting.

  • Common frustration when we do travel here for work.

  • OK, so it's funny, as you described unsupervised learning,

  • it occurs to me that, like, 10, 15 years ago when

  • I was actually doing my dissertation work for my PhD, which

  • was, long story short, about security and specifically,

  • how you could with software detect sudden outbreaks of internet worms,

  • so malicious software that can spread from one computer to another.

  • The approach we took at the time was to actually look

  • at the system calls-- the low-level functions

  • that software was executing on Windows PCs

  • and look for common patterns of those system calls across systems.

  • And it only occurs to me, like, all these years later that arguably,

  • what we were doing in our team to do this

  • was really a form of machine learning.

  • I just think it wasn't very buzz worthy at the time

  • to say what we were doing was machine learning.

  • But I kind of think I know machine learning in retrospect.

  • BRIAN YU: Yeah, maybe.

  • I mean, it's become so common nowadays just to take anything and just

  • tack on machine learning to it to make it sound fancier or sound cooler

  • than it actually is.

  • DAVID MALAN: Yes.

  • That, and I gather statistics is now called data science, essentially,

  • perhaps, overstating though.

  • So certainly all the rage, though, speaking of trends

  • is, like, self-driving cars.

  • In fact, if I can cite another authoritative Reddit photo--

  • and this one I think actually made the national news.

  • What is it about AI that's suddenly enabling people to literally sleep

  • behind the wheel of a car?

  • BRIAN YU: Well, I don't think people should be doing that quite yet but--

  • DAVID MALAN: But you do eventually.

  • BRIAN YU: Well.

  • So self-driving technology is hopefully going to get better.

  • But right now, we're in sort of a dangerous middle ground

  • that cars are able to do more and more things autonomously.

  • They can change lanes on their own.

  • They can maintain their lane on their own.

  • They can parallel park by themselves, for example.

  • The consumer ones, at least, are certainly not at the place

  • where you could just ignore the wheel entirely and just

  • let them go on their own.

  • But a lot of people are almost treating cars as if they can do that.

  • And so it's a dangerous time, certainly, for these semi-autonomous vehicles.

  • DAVID MALAN: And it's funny you mentioned parallel parking.

  • In a contest between you and a computer, who could parallel park better,

  • do you think?

  • BRIAN YU: The computer would definitely beat me at parallel parking.

  • So I got my driver's license in California.

  • And learning to parallel park is not on the California driving test.

  • So I was not tested on it.

  • I've done it maybe a couple times with the assistance of my parents

  • but definitely not something I feel very comfortable doing.

  • DAVID MALAN: But I feel like when I go to California and San Francisco

  • in very hilly cities, it's certainly common to park diagonally

  • against the curb so not parallel, per se, partly just for the physics of it

  • so that there's less risk of cars presumably rolling down the hill.

  • But I feel like in other flatter areas of California,

  • I have absolutely when traveling, parallel park.

  • So, like, how is this not a thing?

  • [LAUGHS] I mean, it's definitely common.

  • People do parallel park.

  • It's just not required on the test.

  • And so people invariably learn when they need to.

  • But pretty soon after I got my driver's license,

  • I ended up moving across the country to Massachusetts for college.

  • And so once I got to college, I never really had

  • occasion to drive a whole lot.

  • So I just never really did a lot of driving.

  • DAVID MALAN: I will say, I've gotten very comfortable

  • certainly over the years, parallel parking,

  • when I'm parking on the right-hand side of the road,

  • because, of course, in the US, we drive on the left.

  • But it does throw me if it's like a one-way street,

  • and I need to park on the left-hand side, because all of my optics

  • are a little off.

  • So I can appreciate that.

  • So a self-driving car, like a Tesla, is like the off cited example these days.

  • Like, what are the inputs to that problem and like, the outputs,

  • the decisions that are being made by the car just to make this more concrete?

  • BRIAN YU: Yeah.

  • So I guess the inputs are probably at least two broad categories--

  • one input being all of the sensory information around the car

  • that these cars have so many sensors and cameras that

  • are trying to detect what items and objects are around it

  • and trying to figure all of that out.

  • And the second input being presumably a human-entered destination

  • where the user probably is typing into some device on the computer in the car

  • where it is that they actually want to go.

  • And the output, hopefully is that the computers or the car

  • is able to make all of the decisions about when to step on the gas,

  • when to turn the wheel, and all of those actions

  • that it needs to take to get you from point A to point B. I mean,

  • that's the goal of these technologies.

  • DAVID MALAN: Fascinating.

  • It would just really frighten me to see someone on the road

  • not holding the wheel of the car.

  • This is maybe a little more of a California thing.

  • Though, other states are certainly experimenting with this.

  • Or companies in various states are.

  • So my car is old enough that I don't so much have a screen in the car.

  • It's really just me and a bunch of glass mirrors.

  • And it still blows my mind in 2019 when I

  • get into a rental car or friend's car that even just has the LCD

  • screen with a camera in the back that shows you, like, the green, yellow,

  • and red markings.

  • And it beeps when you're getting too close to the car.

  • So is that machine learning when it's detecting something and beeping at you

  • when you're trying to park, for instance?

  • Well, I guess you wouldn't know.

  • BRIAN YU: [LAUGHS] My guess is that's probably not machine learning.

  • It's probably just a pretty simple logic of,

  • like, try and detect what the distance is via some sensor.

  • And if the distance is less than a certain amount,

  • then, beep or something like that.

  • You could try and do it using machine learning.

  • But probably, simple heuristics are good enough for that type of thing--

  • would be my guess.

  • DAVID MALAN: So how should people think about the line between software

  • just being ifs and else ifs and conditions

  • and loops, versus, like, machine learning,

  • which kind of takes things up a notch?

  • BRIAN YU: Yeah.

  • So I guess the line comes when it would be difficult to formally articulate

  • exactly what the steps should be.

  • And driving is a complicated enough task that trying to formally describe

  • exactly what the steps should be for every particular circumstance

  • is going to be extraordinarily difficult, if not impossible.

  • And so then you really need to start to rely

  • on machine learning to be able to answer questions, like,

  • is there a traffic light ahead of me?

  • And is the traffic light green or red?

  • And how many cars are ahead of me, and where are they?

  • Because those are questions that it's harder to just program

  • a definitive answer to just given, like, all the

  • pixels of what the sensor of the front of the car is seeing.

  • DAVID MALAN: So is that also true with this other technology that's

  • in vogue these days of these always listening devices,

  • so like Siri and hey, Google and Alexa?

  • Like, I presume it's relatively easy for companies

  • to support well-defined commands, so a finite set of words or sentences

  • that the tools just to understand.

  • But does AI come into play or machine learning coming into play

  • when you want to support an infinite language,

  • like English or any other spoken language?

  • BRIAN YU: Yes.

  • So certainly when it comes to natural language processing,

  • given the words that I have spoken, can you figure out what it is that I mean?

  • And that's a problem that you'll often use

  • machine learning to be able to try and get at some sense of the meaning for.

  • But even with those predefined commands, if you

  • imagine a computer that only supported a very limited number of fixed commands,

  • we're still giving those commands via voice.

  • And so the computer still needs to be able to translate the sounds that

  • are just being produced in the air that the microphone is picking up

  • on into the actual words that they are.

  • And there's usually machine learning involved there too,

  • because it's not simple to be able to just take the sounds

  • and convert them to words, because different people speak

  • at different paces or have slightly different accents

  • or will speak in slightly different ways.

  • They might mispronounce something.

  • And so being able to train a computer to listen to that

  • and figure out what the words are, that can be tricky too.

  • DAVID MALAN: So that's pretty similar, though, to handwriting recognition?

  • Is that fair to say?

  • BRIAN YU: Probably.

  • You could do it a similar way where you train a computer by giving it

  • a whole bunch of sounds and what they correspond to in getting the computer

  • to learn from all of that data.

  • DAVID MALAN: And so why is it that every time I talk to Google,

  • it doesn't know what song I wanted to play.

  • BRIAN YU: [LAUGHS] Well, this technology is definitely still in progress.

  • There is definitely a lot of room for these technologies to get better.

  • DAVID MALAN: Those are diplomatic.

  • BRIAN YU: [LAUGHS] I mean, Siri on my phone half the time.

  • It doesn't pick up on exactly what I'm trying to ask it.

  • DAVID MALAN: Oh, for me, it feels even worse than that.

  • Like, I can confidently set timers, like set timer for three minutes

  • if I'm boiling some water or something.

  • But I pretty much don't use it for anything else besides that.

  • BRIAN YU: Yeah, I think timers I can do.

  • I used to try to, like, if I needed to send, like, a quick text

  • message to someone, I used to try and say, like,

  • text my mom that I'll be at the airport in 10 minutes.

  • But even then, it's very hit or miss.

  • DAVID MALAN: Well, even with you the other day,

  • I sent you a text message verbally.

  • But I just let the audio go out, because I just

  • have too little confidence in the transcription capabilities

  • of these devices these days.

  • BRIAN YU: Yeah.

  • Like, the iPhone, now, we'll try to, like, transcribe voicemails for you.

  • Or, at least, it'll make an attempt to so that you can just

  • tap on the voicemail and see a transcription of what's

  • contained in the voicemail.

  • And I really haven't found it very helpful.

  • But it can get like a couple of words.

  • And maybe I'll get a general sense.

  • But it's not good enough for me to really get any meaning out of it.

  • That's tough to listen to voicemail.

  • DAVID MALAN: See, I don't know.

  • I think that's actually use case where it's useful enough usually for me

  • if I can glean who it's from, or what the gist of the messages

  • so then I don't have to actually listen to it in real time.

  • But the problem for me when sending outbound messages

  • is, I want to look like an idiot.

  • They were completely incoherent, because Siri or whatever technology

  • is not transcribing me correctly.

  • OK.

  • But the dream I have, at least-- one of my favorite books

  • ever was Douglas Adams' Hitchhiker's Guide to the Galaxy,

  • where the most amazing technology in that book

  • is called The Babel fish, where it's a little fish that you put in your ear.

  • And it somehow translates all spoken words

  • that you're hearing into your own native language, essentially.

  • So how close are we to being able to talk to another human being who

  • does not speak the same language but seamlessly chat with that person?

  • BRIAN YU: I think we're pretty far away from it.

  • I think Skype, I think, has this feature or, at least,

  • it's a feature that they've been developing where they can try

  • to approximate a real-time translation.

  • And I think I saw a video--

  • DAVID MALAN: I can't even talk successfully to someone in English

  • on Skype.

  • BRIAN YU: Yeah.

  • So I think the demo is pretty good.

  • But I don't think it's, like, commercially available yet.

  • But translation technology has gotten better.

  • But it's certainly still not good.

  • One of my favorite types of YouTube videos that I watch sometimes

  • are people that will, like, take a song and their lyrics of the song

  • and translate it into another language and translate back into English.

  • And the lyrics just get totally messed up,

  • because this translation technology is, it can approximate meaning.

  • But it's certainly far from perfect.

  • DAVID MALAN: That's kind of like playing operator in English where

  • you tell someone something, and they tell someone something,

  • and they tell someone-- someone.

  • And by the time you go around the circle,

  • it is not at all what you originally said.

  • BRIAN YU: Yeah, I think I played that game when I was younger.

  • I think we called it telephone.

  • We call it operator?

  • DAVID MALAN: Yeah.

  • No, actually we probably called it telephone too.

  • Was there an operator involved?

  • Maybe you call operator if you need a hint.

  • Or maybe--

  • BRIAN YU: I don't think you got hints when I was playing.

  • DAVID MALAN: No, I think we had a hint feature where you say, operator.

  • And maybe the person next to you has to tell you again or something.

  • Maybe it's been a long time since I played this too.

  • Fascinating.

  • Well, thank you so much for explaining to me

  • and everyone out there a little bit more about machine learning.

  • If folks want to learn more about ML, what would you suggest they google?

  • BRIAN YU: Yeah.

  • So you can look up basically any of the keywords that we talked about today.

  • You could just look up machine learning.

  • But if you wanted to be more specific, you

  • could look up reinforcement learning or supervised learning or unsupervised

  • learning.

  • If there's any of the particular technologies,

  • you could look those up specifically, like handwriting recognition

  • or self-driving cars.

  • There are a lot of resources available of people

  • that are talking about these technologies

  • and how they work for sure.

  • DAVID MALAN: Awesome.

  • Well, thanks so much.

  • This was Machine Learning on the CS50 podcast.

  • If you have other ideas for topics that you'd love for Brian and I and the team

  • to discuss and explore, do just drop us an email at podcast@cs50.harvard.edu.

  • My name is David Malan.

  • BRIAN YU: I'm Brian Yu.

  • DAVID MALAN: And this was the CS50 Podcast.

SPEAKER: This is CS50.

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機器學習 - CS50播客,Ep.6。 (Machine Learning - CS50 Podcast, Ep. 6)

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    林宜悉 發佈於 2021 年 01 月 14 日
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