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.