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  • So, John-Green-Bot, you know when you let me use your computer the other day?

  • Well, I went on YouTube and it was like seeing a completely different website. There were

  • videos about restoring old VCRs and different kinds of cassette tapes, and ads for motor

  • oil?!

  • John-Green-bot: Yes, Jabril! I love learning about other machines.

  • Jabril: Okay, but do you even know what humans are watching these days? What about those

  • Boston Dynamics videos?

  • John-Green-bot: No. The humans in those videos are so mean to the robots! What about Epic

  • Computation Battles of History, or MKB-AI, or Robot Appétit?

  • Jabril: …… what?

  • INTRO

  • Hi, I'm Jabril and welcome to Crash Course AI! Recommender systems are a type of AI that

  • try to understand our brains and make useful recommendations to us.

  • This kind of AI can guide the things we watch by recommending YouTube videos or shows on

  • Netflix for example.

  • On Amazon, it's recommending items to buy, when I search on Google, it's recommending

  • relevant and interesting links. And everywhere online, advertisement servers are trying to

  • recommend products and services.

  • Recommender systems combine supervised learning and unsupervised learning techniques to learn

  • about us.

  • And because we're so complicated, recommending stuff to us is a tough problem that can produce

  • lots of unexpected results.

  • Maybe we get caught in an online bubble and only see tweets from our friends and people

  • who think like us. Maybe we miss a new TV show because streaming sites don't think

  • we'd like it. Or maybe that creepy thing happens where you're talking to your friends

  • about supercomputers and then every single ad you see for the next day is for supercomputers?!?

  • AI that make recommendations can really change what version of the internet we all see. But

  • to understand the benefits and drawbacks of these algorithms, we have to understand where

  • they get their data and how they work.

  • As an example, let's focus on an algorithm that could recommend YouTube videos. Because

  • The Algorithmis a really big deal if YouTube is your job, and everyone's talking

  • about the mysterious changes behind the algorithm anyway.

  • Three common approaches are content-based recommendation, social recommendation, and

  • personalized recommendations.

  • Content-based recommendations look at the content of the videos, not the audience.

  • Like, for example, our algorithm may decide to recommend more recent videos, or videos

  • that are made by someone on a list ofquality creators.” But this means someone has to

  • decide whoquality creatorsare, or program an AI that tries to predict creator

  • quality.

  • On the other hand, social recommendations pay attention to the audience.

  • YouTube is on the internet so we can use social ratings such aslikesorviews

  • orwatch timeto decide what people are watching and should be recommended. But

  • not everybody likes the same stuff, so maybe pure popularity isn't the way to go.

  • Different people have different preferences, so our AI can incorporate that with personalized

  • recommendations.

  • If you like this Crash Course video, maybe we'd recommend other Crash Course videos

  • or videos from my channel. But the problem with personalized recommendations is that

  • it might be difficult to stumble onto new interesting stuff.

  • So, to get the best of all worlds, recommender systems generally use collaborative filtering,

  • which combines all three of these recommenders.

  • When we see a recommendation on YouTube, it could be because that video is similar to

  • other videos that we've watched and liked and other people who have similar tastes watched

  • and liked that video. Or (especially if you're new to Youtube) that video might be recommended

  • because it's popular and lots of people are watching and liking it.

  • Collaborative filtering combines several of the techniques we've already talked about

  • in Crash Course AI. It uses unsupervised learning to find similar people or content, and it

  • tries to use data from those things to predict how we would feel about something we haven't

  • even seen yet.

  • To see how collaborative filtering works, let's use a simple example.

  • In this table, YouTube channels are represented as columns. So, here, one column represents

  • CrashCourse, one is Jabrils, one is The Best of BattleBots, one is The Art Assignment,

  • and so on.

  • Specific users that watch YouTube videos are represented as rows. So this row is John-Green-bot,

  • this one is me, these two are a couple random folks, this one is our producer Brandon, and

  • so on.

  • Each cell in the table corresponds to whether the user subscribes to a specific channel

  • or not. 1 means they've watched at least one video and subscribed, 0 means they've

  • watched at least one video and didn't subscribe, and the cell is empty if they haven't seen

  • any videos.

  • If we look at John-Green-bot's row, he subscribes to Crash Course and Jabrils, so those cells

  • have a 1. He saw The Best of Battlebots and did not subscribe, because of all the robot-on-robot

  • violence, so that's a 0. And he's never seen The Art Assignment so there's no information

  • in that cell.

  • To recommend new channels for John-Green-bot, our collaborative filtering AI needs to predict

  • how likely he is to subscribe to a channel he's never seen before. In this case, let's

  • see if The Art Assignment ends up in his recommendations.

  • To make a prediction, the algorithm needs to look at which other people have subscribed

  • to the Art Assignment. And because YouTube tastes are very personal, instead of looking

  • at all other users, our algorithm will focus on finding the users who are most similar

  • to John-Green-Bot.

  • Finding similar things is a classic unsupervised learning problem. Our AI can look at all the

  • rows, cluster together similar users, and then pick some of those that are most similar

  • to John-Green-Bot, and who have seen The Art Assignment.

  • Let's just say there are 1000 of these specific users, but there are other clusters with thousands

  • of users too that these recommender systems take into consideration.

  • Now, we have a classic supervised learning problem: training an AI to make predictions

  • based on past examples. In this case, we're training an AI to predict a 1 or 0 (subscribe

  • or not) for John-Green-bot based on other users.

  • We can re-adjust the results so that ratings from the cluster of 1000 most similar users

  • are given more weight in the final prediction, compared to those other clusters. And after

  • the predictions are sorted, our AI does predict that John-Green-bot would subscribe

  • to The Art Assignment, so it gets recommended to himalong with some other new channels.

  • Recommender systems that use collaborative filtering AI can take in lots of different

  • data, not just a 1 or a 0, for whether a user subscribed to a YouTube channel or bought

  • a product. A movie rating site might use a one-to-five star rating system. Or a social

  • media AI could keep track of the number of milliseconds a user dwells on a post.

  • Regardless, the basic strategy is the same: use known information from users to predict

  • preferences. And this can get complicated on big websites that gather lots of user information

  • using a combination of different algorithms.

  • The real world is full of a lot of data and there are three key problems that can lead

  • to recommender systems making small or big mistakes.

  • First, datasets that recommender system AIs get are usually very sparse. Most people don't

  • watch most shows or videos -- there just isn't enough time! And even fewer people give social

  • ratings such aslikes.”

  • Doing any kind of analysis with sparse datasets is very computationally intense, which gets

  • expensive, which means some companies are willing to lose some accuracy to reduce costs.

  • Second, there's the cold start problem. When we go on a website for the first time,

  • for example, the AI doesn't know enough about us to provide good personalized recommendations.

  • And third, even if an AI makes statistically likely predictions, that doesn't mean those

  • recommendations are actually useful to us.

  • Online ads run into this failure a lot, where we'll be shown ads for sites we recently

  • visited, or something we just bought. Sure, that's probably something I'm interested

  • in, but I could've figured that out without a recommender system.

  • In a potentially more harmful way, recommender systems don't understand important social

  • context, sostatistically likelyrecommendations can be worrying.

  • Recommendations may stereotype users in a socially uncomfortable way.

  • Like, for example, an AI might assume that because John-Green-Bot is a robot, he really

  • wants to watch WALL-E and Robocop. Just because he's a robot doesn't mean he wants to

  • watch robot stuff.

  • Or recommendations might be inappropriate for certain users, like recommending a video

  • that a parent would consider too violent to their children after they had watched a bunch

  • of NERF War videos.

  • And, on social media, recommendations can trap us in ideological echo chambers, where

  • we tend to only see the opinions from people that agree with us, which can skew our knowledge

  • about the world.

  • This idea that we all see slightly different versions of the internet, and data is constantly

  • being collected about us, can be a little concerning. But understanding how recommender

  • systems work, can help us live more knowledgeable lives, and coexist with AI.

  • When we don't want data added to a recommender system's model of us, we can use a private

  • or incognito browser window and not log into sites. If we open a news homepage this way,

  • we might see what the average human (or robot!) is being recommended.

  • Of course, incognito browsers don't mean total privacy, but this strategy prevents

  • sites from connecting data -- like, for example, my Twitter account with my searches for tiny

  • polo shirts on Google (because I needed to get John-Green-bot a birthday present).

  • Plus, since we spend so much time online, we might want to make the most of it with

  • really personalized recommendations. Soseriously… “like, comment, and subscribe

  • to your favorite creators because as we leave ratings, reviews, and other traces of online

  • activities, recommender systems can learn better models.

  • Recommender systems are a part of the internet as we know it, whether we like it or not.

  • And as AI becomes a bigger part of our lives,these kinds of recommendations will be too. So it's

  • on us to be aware of this technology, so that we know what kind of world we're living

  • in, and the ways AI might influence us every single day.

  • And if you're here to learn how to build recommender systems, my advice would be to

  • think explicitly about the trade-offs that are involved. Deciding how to define the clusters

  • of users or items, can create more or less personalized spaces.

  • In our next episode, we'll work together on some code to build a recommender system,

  • and we'll get some hands-on experience with weighing some of these trade-offs. I'll

  • see ya then.

  • Speaking of recommendations, you should check out Sound Field. Sound Field is a new music

  • education show from PBS Digital Studios that explores the music theory, production, history

  • and culture behind our favorite songs and musical styles. Hosted by two supremely talented

  • musicians, ArthurLABuckner and Nahre Sol, every episode is one part video essay

  • and one part musical performance.

  • So go subscribe to Sound Field! Link in the description below.

  • Crash Course AI is produced in association with PBS Digital Studios! If you want to help

  • keep all Crash Course free for everybody, forever, you can join our community on Patreon.

  • And if you want to check out Sound Field, click the link below.

So, John-Green-Bot, you know when you let me use your computer the other day?

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YouTubeはどうやって見るべきかを知っているのか。クラッシュコースAI #15 (How YouTube knows what you should watch: Crash Course AI #15)

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    林宜悉 に公開 2021 年 01 月 14 日
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