vtt/episode_035_small.vtt

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 The following is a conversation with Jeremy Howard.

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 He's the founder of Fast AI, a research institute dedicated

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 to making deep learning more accessible.

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 He's also a distinguished research scientist

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 at the University of San Francisco,

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 a former president of Kegel, as well as a top breaking

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 competitor there.

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 And in general, he's a successful entrepreneur,

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 educator, researcher, and an inspiring personality

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 in the AI community.

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 When someone asked me, how do I get

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 started with deep learning?

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 Fast AI is one of the top places I point them to.

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 It's free.

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 It's easy to get started.

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 It's insightful and accessible.

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 And if I may say so, it has very little BS.

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 It can sometimes dilute the value of educational content

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 on popular topics like deep learning.

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 Fast AI has a focus on practical application

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 of deep learning and hands on exploration

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 of the cutting edge that is incredibly

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 both accessible to beginners and useful to experts.

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 This is the Artificial Intelligence Podcast.

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 If you enjoy it, subscribe on YouTube,

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 give it five stars on iTunes, support it on Patreon,

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 or simply connect with me on Twitter.

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 Alex Friedman, spelled F R I D M A N.

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 And now, here's my conversation with Jeremy Howard.

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 What's the first program you ever written?

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 First program I wrote that I remember

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 would be at high school.

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 I did an assignment where I decided

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 to try to find out if there were some better musical scales

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 than the normal 12 tone, 12 interval scale.

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 So I wrote a program on my Commodore 64 in BASIC

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 that searched through other scale sizes

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 to see if it could find one where there

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 were more accurate harmonies.

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 Like mid tone?

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 Like you want an actual exactly 3 to 2 ratio,

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 where else with a 12 interval scale,

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 it's not exactly 3 to 2, for example.

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 So that's well tempered, as they say.

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 And BASIC on a Commodore 64.

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 Where was the interest in music from?

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 Or is it just technical?

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 I did music all my life, so I played saxophone and clarinet

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 and piano and guitar and drums and whatever.

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 How does that thread go through your life?

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 Where's music today?

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 It's not where I wish it was.

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 For various reasons, couldn't really keep it going,

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 particularly because I had a lot of problems with RSI,

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 with my fingers.

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 And so I had to cut back anything that used hands

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 and fingers.

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 I hope one day I'll be able to get back to it health wise.

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 So there's a love for music underlying it all.

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 Sure, yeah.

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 What's your favorite instrument?

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 Saxophone.

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 Sax.

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 Baritone saxophone.

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 Well, probably bass saxophone, but they're awkward.

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 Well, I always love it when music is

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 coupled with programming.

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 There's something about a brain that

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 utilizes those that emerges with creative ideas.

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 So you've used and studied quite a few programming languages.

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 Can you give an overview of what you've used?

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 What are the pros and cons of each?

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 Well, my favorite programming environment almost certainly

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 was Microsoft Access back in the earliest days.

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 So that was a special basic for applications, which

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 is not a good programming language,

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 but the programming environment is fantastic.

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 It's like the ability to create user interfaces and tied data

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 and actions to them and create reports and all that.

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 As I've never seen anything as good.

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 So things nowadays like Airtable, which

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 are like small subsets of that, which people love for good reason.

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 But unfortunately, nobody's ever achieved anything like that.

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 What is that, if you could pause on that for a second?

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 Oh, Access.

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 Access.

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 Is it a fundamental database?

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 It was a database program that Microsoft produced,

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 part of Office, and it kind of withered.

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 But basically, it lets you in a totally graphical way

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 create tables and relationships and queries

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 and tie them to forms and set up event handlers and calculations.

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 And it was a very complete, powerful system designed

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 for not massive scalable things, but for useful little applications

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 that I loved.

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 So what's the connection between Excel and Access?

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 So very close.

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 So Access was the relational database equivalent,

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 if you like.

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 So people still do a lot of that stuff

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 that should be in Access in Excel because they know it.

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 Excel's great as well.

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 But it's just not as rich a programming model as VBA

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 combined with a relational database.

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 And so I've always loved relational databases.

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 But today, programming on top of relational databases

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 is just a lot more of a headache.

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 You generally either need to kind of,

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 you need something that connects, that runs some kind

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 of database server, unless you use SQLite, which

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 has its own issues.

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 Then you kind of often, if you want

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 to get a nice programming model, you

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 need to create an ORM on top.

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 And then, I don't know, there's all these pieces tied together.

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 And it's just a lot more awkward than it should be.

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 There are people that are trying to make it easier,

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 so in particular, I think of Fsharp, Don Syme, who him

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 and his team have done a great job of making something

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 like a database appear in the type system,

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 so you actually get tab completion for fields and tables

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 and stuff like that.

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 Anyway, so that was kind of, anyway,

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 so that whole VBA Office thing, I guess,

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 was a starting point, which is your miss.

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 And I got into Standard Visual Basic, which

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 that's interesting, just to pause on that for a second.

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 And it's interesting that you're connecting programming

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 languages to the ease of management of data.

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 So in your use of programming languages,

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 you always had a love and a connection with data.

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 I've always been interested in doing useful things for myself

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 and for others, which generally means getting some data

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 and doing something with it and putting it out there again.

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 So that's been my interest throughout.

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 So I also did a lot of stuff with Apple script

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 back in the early days.

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 So it's kind of nice being able to get the computer

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 and computers to talk to each other and to do things for you.

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 And then I think that one night, the programming language

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 I most loved then would have been Delphi, which

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 was Object Pascal created by Anders Halsberg, who previously

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 did Turbo Pascal and then went on to create.net

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 and then went on to create TypeScript.

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 Delphi was amazing because it was like a compiled, fast language

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 that was as easy to use as Visual Basic.

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 Delphi, what is it similar to in more modern languages?

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 Visual Basic.

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 Visual Basic.

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 Yeah, that a compiled, fast version.

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 So I'm not sure there's anything quite like it anymore.

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 If you took C Sharp or Java and got rid of the virtual machine

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 and replaced it with something, you could compile a small type

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 binary.

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 I feel like it's where Swift could get to with the new Swift

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 UI and the cross platform development going on.

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 That's one of my dreams is that we'll hopefully get back

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 to where Delphi was.

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 There is actually a free Pascal project nowadays

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 called Lazarus, which is also attempting

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 to recreate Delphi.

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 They're making good progress.

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 So OK, Delphi, that's one of your favorite programming languages?

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 Well, it's programming environments.

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 Again, say Pascal's not a nice language.

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 If you wanted to know specifically

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 about what languages I like, I would definitely

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 pick Jay as being an amazingly wonderful language.

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 What's Jay?

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 Jay, are you aware of APL?

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 I am not, except from doing a little research on the work

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 you've done.

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 OK, so not at all surprising you're not

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 familiar with it because it's not well known,

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 but it's actually one of the main families of programming

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 languages going back to the late 50s, early 60s.

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 So there was a couple of major directions.

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 One was the kind of lambda, calculus,

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 Alonzo church direction, which I guess kind of Lisbon scheme

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 and whatever, which has a history going back

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 to the early days of computing.

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 The second was the kind of imperative slash

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 OO, algo, similar going on to C, C++, so forth.

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 There was a third, which are called array oriented languages,

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 which started with a paper by a guy called Ken Iverson, which

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 was actually a math theory paper, not a programming paper.

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 It was called Notation as a Tool for Thought.

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 And it was the development of a new type of math notation.

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 And the idea is that this math notation was much more

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 flexible, expressive, and also well defined than traditional

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 math notation, which is none of those things.

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 Math notation is awful.

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 And so he actually turned that into a programming language.

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 Because this was the late 50s, all the names were available.

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 So he called his programming language, or APL.

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 APL, what?

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 So APL is a implementation of notation

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 as a tool for thought, by which he means math notation.

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 And Ken and his son went on to do many things,

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 but eventually they actually produced a new language that

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 was built on top of all the learnings of APL.

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 And that was called J. And J is the most

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 expressive, composable, beautifully designed language

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 I've ever seen.

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 Does it have object oriented components?

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 Does it have that kind of thing?

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 Not really.

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 It's an array oriented language.

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 It's the third path.

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 Are you saying array?

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 Array oriented.

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 Yeah.

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 It needs to be array oriented.

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 So array oriented means that you generally

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 don't use any loops.

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 But the whole thing is done with kind

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 of an extreme version of broadcasting,

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 if you're familiar with that NumPy slash Python concept.

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 So you do a lot with one line of code.

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 It looks a lot like math.

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 Notation is basically highly compact.

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 And the idea is that you can kind of,

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 because you can do so much with one line of code,

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 a single screen of code is very unlikely to,

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 you very rarely need more than that to express your program.

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 And so you can kind of keep it all in your head.

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 And you can kind of clearly communicate it.

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 It's interesting that APL created two main branches, K and J.

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 J is this kind of like open source niche community of crazy

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 enthusiasts like me.

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 And then the other path, K, was fascinating.

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 It's an astonishingly expensive programming language,

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 which many of the world's most ludicrously rich hedge funds

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 use.

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 So the entire K machine is so small,

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 it sits inside level three cache on your CPU.

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 And it easily wins every benchmark I've ever seen

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 in terms of data processing speed.

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 But you don't come across it very much,

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 because it's like $100,000 per CPU to run it.

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 But it's like this path of programming languages

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 is just so much, I don't know, so much more powerful

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 in every way than the ones that almost anybody uses every day.

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 So it's all about computation.

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 It's really focusing on it.

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 Pretty heavily focused on computation.

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 I mean, so much of programming is data processing

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 by definition.

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 And so there's a lot of things you can do with it.

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 But yeah, there's not much work being

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 done on making user interface toolkills or whatever.

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 I mean, there's some, but they're not great.

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 At the same time, you've done a lot of stuff with Perl and Python.

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 So what does that fit into the picture of J and K and APL

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 and Python?

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 Well, it's just much more pragmatic.

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 In the end, you kind of have to end up

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 where the libraries are.

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 Because to me, my focus is on productivity.

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 I just want to get stuff done and solve problems.

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 So Perl was great.

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 I created an email company called Fastmail.

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 And Perl was great, because back in the late 90s, early 2000s,

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 it just had a lot of stuff it could do.

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 I still had to write my own monitoring system

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 and my own web framework and my own whatever,

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 because none of that stuff existed.

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 But it was a super flexible language to do that in.

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 And you used Perl for Fastmail.

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 You used it as a back end.

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 So everything was written in Perl?

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 Yeah.

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 Yeah, everything was Perl.

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 Why do you think Perl hasn't succeeded or hasn't dominated

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 the market where Python really takes over a lot of the

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 tests?

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 Well, I mean, Perl did dominate.

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 It was everything, everywhere.

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 But then the guy that ran Perl, Larry Wall,

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 just didn't put the time in anymore.

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 And no project can be successful if there isn't.

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 Particularly one that started with a strong leader that

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 loses that strong leadership.

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 So then Python has kind of replaced it.

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 Python is a lot less elegant language in nearly every way.

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 But it has the data science libraries.

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 And a lot of them are pretty great.

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 So I kind of use it because it's the best we have.

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 But it's definitely not good enough.

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 What do you think the future of programming looks like?

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 What do you hope the future of programming looks like if we

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 zoom in on the computational fields on data science

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 and machine learning?

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 I hope Swift is successful because the goal of Swift,

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 the way Chris Latna describes it,

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 is to be infinitely hackable.

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 And that's what I want.

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 I want something where me and the people I do research with

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 and my students can look at and change everything

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 from top to bottom.

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 There's nothing mysterious and magical and inaccessible.

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 Unfortunately, with Python, it's the opposite of that

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 because Python is so slow, it's extremely unhackable.

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 You get to a point where it's like, OK, from here on down

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 at C. So your debugger doesn't work in the same way.

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 Your profiler doesn't work in the same way.

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 Your build system doesn't work in the same way.

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 It's really not very hackable at all.

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 What's the part you like to be hackable?

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 Is it for the objective of optimizing training

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 of neural networks, inference of neural networks?

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 Is it performance of the system?

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 Or is there some nonperformance related, just creative idea?

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 It's everything.

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 I mean, in the end, I want to be productive as a practitioner.

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 So at the moment, our understanding of deep learning

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 is incredibly primitive.

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 There's very little we understand.

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 Most things don't work very well, even though it works better

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 than anything else out there.

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 There's so many opportunities to make it better.

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 So you look at any domain area like speech recognition

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 with deep learning or natural language processing

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 classification with deep learning or whatever.

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 Every time I look at an area with deep learning,

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 I always see like, oh, it's terrible.

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 There's lots and lots of obviously stupid ways

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 to do things that need to be fixed.

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 So then I want to be able to jump in there and quickly

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 experiment and make them better.

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 Do you think the programming language has a role in that?

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 Huge role, yeah.

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 So currently, Python has a big gap in terms of our ability

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 to innovate particularly around recurrent neural networks

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 and natural language processing because it's so slow.

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 The actual loop where we actually loop through words,

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 we have to do that whole thing in CUDA C.

17:23.760 --> 17:27.600
 So we actually can't innovate with the kernel, the heart,

17:27.600 --> 17:31.560
 of that most important algorithm.

17:31.560 --> 17:33.680
 And it's just a huge problem.

17:33.680 --> 17:36.600
 And this happens all over the place.

17:36.600 --> 17:40.080
 So we hit research limitations.

17:40.080 --> 17:42.840
 Another example, convolutional neural networks, which

17:42.840 --> 17:46.800
 are actually the most popular architecture for lots of things,

17:46.800 --> 17:48.920
 maybe most things in deep learning.

17:48.920 --> 17:50.360
 We almost certainly should be using

17:50.360 --> 17:54.600
 sparse convolutional neural networks, but only like two

17:54.600 --> 17:56.800
 people are because to do it, you have

17:56.800 --> 17:59.920
 to rewrite all of that CUDA C level stuff.

17:59.920 --> 18:04.520
 And yeah, just research, just in practitioners, don't.

18:04.520 --> 18:09.240
 So there's just big gaps in what people actually research on,

18:09.240 --> 18:11.640
 what people actually implement because of the programming

18:11.640 --> 18:13.240
 language problem.

18:13.240 --> 18:17.560
 So you think it's just too difficult

18:17.560 --> 18:23.480
 to write in CUDA C that a higher level programming language

18:23.480 --> 18:30.520
 like Swift should enable the easier,

18:30.520 --> 18:33.160
 fooling around, create stuff with RNNs,

18:33.160 --> 18:34.920
 or sparse convolutional neural networks?

18:34.920 --> 18:35.920
 Kind of.

18:35.920 --> 18:38.520
 Who is at fault?

18:38.520 --> 18:42.320
 Who is at charge of making it easy for a researcher to play around?

18:42.320 --> 18:43.520
 I mean, no one's at fault.

18:43.520 --> 18:45.120
 Just nobody's got a round to it yet.

18:45.120 --> 18:47.080
 Or it's just it's hard.

18:47.080 --> 18:51.800
 And I mean, part of the fault is that we ignored that whole APL

18:51.800 --> 18:55.640
 kind of direction, or nearly everybody did for 60 years,

18:55.640 --> 18:57.720
 50 years.

18:57.720 --> 18:59.920
 But recently, people have been starting

18:59.920 --> 19:04.840
 to reinvent pieces of that and kind of create some interesting

19:04.840 --> 19:07.400
 new directions in the compiler technology.

19:07.400 --> 19:11.760
 So the place where that's particularly happening right now

19:11.760 --> 19:14.920
 is something called MLIR, which is something that, again,

19:14.920 --> 19:18.000
 Chris Lattener, the Swift guy, is leading.

19:18.000 --> 19:20.080
 And because it's actually not going

19:20.080 --> 19:22.160
 to be Swift on its own that solves this problem.

19:22.160 --> 19:24.880
 Because the problem is that currently writing

19:24.880 --> 19:32.360
 a acceptably fast GPU program is too complicated,

19:32.360 --> 19:33.680
 regardless of what language you use.

19:36.480 --> 19:38.680
 And that's just because if you have to deal with the fact

19:38.680 --> 19:43.160
 that I've got 10,000 threads and I have to synchronize between them

19:43.160 --> 19:45.360
 all, and I have to put my thing into grid blocks

19:45.360 --> 19:47.040
 and think about warps and all this stuff,

19:47.040 --> 19:50.720
 it's just so much boilerplate that to do that well,

19:50.720 --> 19:52.240
 you have to be a specialist at that.

19:52.240 --> 19:58.200
 And it's going to be a year's work to optimize that algorithm

19:58.200 --> 19:59.720
 in that way.

19:59.720 --> 20:04.640
 But with things like TensorFlow Comprehensions, and Tile,

20:04.640 --> 20:08.880
 and MLIR, and TVM, there's all these various projects which

20:08.880 --> 20:11.840
 are all about saying, let's let people

20:11.840 --> 20:16.080
 create domain specific languages for tensor

20:16.080 --> 20:16.880
 computations.

20:16.880 --> 20:19.120
 These are the kinds of things we do generally

20:19.120 --> 20:21.640
 on the GPU for deep learning, and then

20:21.640 --> 20:28.280
 have a compiler which can optimize that tensor computation.

20:28.280 --> 20:31.440
 A lot of this work is actually sitting on top of a project

20:31.440 --> 20:36.040
 called Halide, which is a mind blowing project

20:36.040 --> 20:38.880
 where they came up with such a domain specific language.

20:38.880 --> 20:41.240
 In fact, two, one domain specific language for expressing,

20:41.240 --> 20:43.840
 this is what my tensor computation is.

20:43.840 --> 20:46.320
 And another domain specific language for expressing,

20:46.320 --> 20:50.320
 this is the way I want you to structure

20:50.320 --> 20:53.040
 the compilation of that, and do it block by block

20:53.040 --> 20:54.960
 and do these bits in parallel.

20:54.960 --> 20:57.760
 And they were able to show how you can compress

20:57.760 --> 21:02.880
 the amount of code by 10x compared to optimized GPU

21:02.880 --> 21:05.600
 code and get the same performance.

21:05.600 --> 21:08.480
 So these are the things that are sitting on top

21:08.480 --> 21:12.240
 of that kind of research, and MLIR

21:12.240 --> 21:15.160
 is pulling a lot of those best practices together.

21:15.160 --> 21:17.160
 And now we're starting to see work done

21:17.160 --> 21:21.400
 on making all of that directly accessible through Swift

21:21.400 --> 21:25.040
 so that I could use Swift to write those domain specific

21:25.040 --> 21:25.880
 languages.

21:25.880 --> 21:29.520
 And hopefully we'll get then Swift CUDA kernels

21:29.520 --> 21:31.720
 written in a very expressive and concise way that

21:31.720 --> 21:36.280
 looks a bit like J in APL, and then Swift layers on top

21:36.280 --> 21:38.360
 of that, and then a Swift UI on top of that,

21:38.360 --> 21:42.600
 and it'll be so nice if we can get to that point.

21:42.600 --> 21:48.560
 Now does it all eventually boil down to CUDA and NVIDIA GPUs?

21:48.560 --> 21:50.120
 Unfortunately at the moment it does,

21:50.120 --> 21:52.600
 but one of the nice things about MLIR,

21:52.600 --> 21:56.120
 if AMD ever gets their act together, which they probably

21:56.120 --> 21:59.040
 want, is that they or others could

21:59.040 --> 22:05.000
 write MLIR backends for other GPUs

22:05.000 --> 22:10.320
 or rather tensor computation devices, of which today

22:10.320 --> 22:15.520
 there are increasing number like Graphcore or Vertex AI

22:15.520 --> 22:18.840
 or whatever.

22:18.840 --> 22:22.600
 So yeah, being able to target lots of backends

22:22.600 --> 22:23.960
 would be another benefit of this,

22:23.960 --> 22:26.680
 and the market really needs competition,

22:26.680 --> 22:28.680
 because at the moment NVIDIA is massively

22:28.680 --> 22:33.640
 overcharging for their kind of enterprise class cards,

22:33.640 --> 22:36.720
 because there is no serious competition,

22:36.720 --> 22:39.280
 because nobody else is doing the software properly.

22:39.280 --> 22:41.400
 In the cloud there is some competition, right?

22:41.400 --> 22:45.080
 But not really, other than TPUs perhaps,

22:45.080 --> 22:49.040
 but TPUs are almost unprogrammable at the moment.

22:49.040 --> 22:51.080
 TPUs have the same problem that you can't.

22:51.080 --> 22:51.760
 It's even worse.

22:51.760 --> 22:54.800
 So TPUs, Google actually made an explicit decision

22:54.800 --> 22:57.200
 to make them almost entirely unprogrammable,

22:57.200 --> 22:59.960
 because they felt that there was too much IP in there,

22:59.960 --> 23:02.640
 and if they gave people direct access to program them,

23:02.640 --> 23:04.360
 people would learn their secrets.

23:04.360 --> 23:09.720
 So you can't actually directly program

23:09.720 --> 23:12.120
 the memory in a TPU.

23:12.120 --> 23:16.360
 You can't even directly create code that runs on

23:16.360 --> 23:19.080
 and that you look at on the machine that has the TPU.

23:19.080 --> 23:20.920
 It all goes through a virtual machine.

23:20.920 --> 23:23.680
 So all you can really do is this kind of cookie cutter

23:23.680 --> 23:27.760
 thing of like plug in high level stuff together,

23:27.760 --> 23:31.440
 which is just super tedious and annoying

23:31.440 --> 23:33.920
 and totally unnecessary.

23:33.920 --> 23:40.960
 So tell me if you could, the origin story of fast AI.

23:40.960 --> 23:45.760
 What is the motivation, its mission, its dream?

23:45.760 --> 23:50.040
 So I guess the founding story is heavily

23:50.040 --> 23:51.840
 tied to my previous startup, which

23:51.840 --> 23:53.960
 is a company called Inletic, which

23:53.960 --> 23:58.280
 was the first company to focus on deep learning for medicine.

23:58.280 --> 24:03.240
 And I created that because I saw there was a huge opportunity

24:03.240 --> 24:07.960
 to, there's about a 10x shortage of the number of doctors

24:07.960 --> 24:12.120
 in the world and the developing world that we need.

24:12.120 --> 24:13.840
 I expected it would take about 300 years

24:13.840 --> 24:16.120
 to train enough doctors to meet that gap.

24:16.120 --> 24:20.760
 But I guessed that maybe if we used

24:20.760 --> 24:23.760
 deep learning for some of the analytics,

24:23.760 --> 24:25.760
 we could maybe make it so you don't need

24:25.760 --> 24:27.320
 as highly trained doctors.

24:27.320 --> 24:28.320
 For diagnosis?

24:28.320 --> 24:29.840
 For diagnosis and treatment planning.

24:29.840 --> 24:33.440
 Where's the biggest benefit just before get the fast AI?

24:33.440 --> 24:37.280
 Where's the biggest benefit of AI and medicine that you see

24:37.280 --> 24:39.440
 today and in the future?

24:39.440 --> 24:41.960
 Not much happening today in terms of stuff that's actually

24:41.960 --> 24:42.440
 out there.

24:42.440 --> 24:43.160
 It's very early.

24:43.160 --> 24:45.320
 But in terms of the opportunity, it's

24:45.320 --> 24:51.080
 to take markets like India and China and Indonesia, which

24:51.080 --> 24:58.120
 have big populations, Africa, small numbers of doctors,

24:58.120 --> 25:02.440
 and provide diagnostic, particularly treatment

25:02.440 --> 25:05.160
 planning and triage kind of on device

25:05.160 --> 25:10.360
 so that if you do a test for malaria or tuberculosis

25:10.360 --> 25:12.800
 or whatever, you immediately get something

25:12.800 --> 25:14.840
 that even a health care worker that's

25:14.840 --> 25:20.360
 had a month of training can get a very high quality

25:20.360 --> 25:23.480
 assessment of whether the patient might be at risk

25:23.480 --> 25:27.480
 until OK, we'll send them off to a hospital.

25:27.480 --> 25:31.720
 So for example, in Africa, outside of South Africa,

25:31.720 --> 25:34.080
 there's only five pediatric radiologists

25:34.080 --> 25:35.320
 for the entire continent.

25:35.320 --> 25:37.200
 So most countries don't have any.

25:37.200 --> 25:39.240
 So if your kid is sick and they need something

25:39.240 --> 25:41.200
 diagnosed through medical imaging,

25:41.200 --> 25:44.040
 the person, even if you're able to get medical imaging done,

25:44.040 --> 25:48.920
 the person that looks at it will be a nurse at best.

25:48.920 --> 25:52.480
 But actually, in India, for example, and China,

25:52.480 --> 25:54.760
 almost no x rays are read by anybody,

25:54.760 --> 25:59.400
 by any trained professional, because they don't have enough.

25:59.400 --> 26:02.880
 So if instead we had an algorithm that

26:02.880 --> 26:10.080
 could take the most likely high risk 5% and say triage,

26:10.080 --> 26:13.280
 basically say, OK, somebody needs to look at this,

26:13.280 --> 26:16.240
 it would massively change the kind of way

26:16.240 --> 26:20.640
 that what's possible with medicine in the developing world.

26:20.640 --> 26:23.680
 And remember, increasingly, they have money.

26:23.680 --> 26:24.800
 They're the developing world.

26:24.800 --> 26:26.160
 They're not the poor world, the developing world.

26:26.160 --> 26:26.920
 So they have the money.

26:26.920 --> 26:28.480
 So they're building the hospitals.

26:28.480 --> 26:31.960
 They're getting the diagnostic equipment.

26:31.960 --> 26:34.880
 But there's no way for a very long time

26:34.880 --> 26:38.480
 will they be able to have the expertise.

26:38.480 --> 26:39.760
 Shortage of expertise.

26:39.760 --> 26:42.720
 OK, and that's where the deep learning systems

26:42.720 --> 26:46.040
 can step in and magnify the expertise they do have.

26:46.040 --> 26:47.840
 Exactly.

26:47.840 --> 26:54.160
 So you do see, just to linger a little bit longer,

26:54.160 --> 26:58.520
 the interaction, do you still see the human experts still

26:58.520 --> 26:59.840
 at the core of the system?

26:59.840 --> 27:00.480
 Yeah, absolutely.

27:00.480 --> 27:01.720
 Is there something in medicine that

27:01.720 --> 27:03.760
 could be automated almost completely?

27:03.760 --> 27:06.360
 I don't see the point of even thinking about that,

27:06.360 --> 27:08.480
 because we have such a shortage of people.

27:08.480 --> 27:12.160
 Why would we want to find a way not to use them?

27:12.160 --> 27:13.840
 Like, we have people.

27:13.840 --> 27:17.200
 So the idea of, even from an economic point of view,

27:17.200 --> 27:19.800
 if you can make them 10x more productive,

27:19.800 --> 27:21.600
 getting rid of the person doesn't

27:21.600 --> 27:23.880
 impact your unit economics at all.

27:23.880 --> 27:26.680
 And it totally involves the fact that there are things

27:26.680 --> 27:28.760
 people do better than machines.

27:28.760 --> 27:33.120
 So it's just, to me, that's not a useful way

27:33.120 --> 27:34.120
 of framing the problem.

27:34.120 --> 27:36.440
 I guess, just to clarify, I guess I

27:36.440 --> 27:40.560
 meant there may be some problems where you can avoid even

27:40.560 --> 27:42.160
 going to the expert ever.

27:42.160 --> 27:46.160
 Sort of maybe preventative care or some basic stuff,

27:46.160 --> 27:47.800
 the low hanging fruit, allowing the expert

27:47.800 --> 27:51.320
 to focus on the things that are really that.

27:51.320 --> 27:52.960
 Well, that's what the triage would do, right?

27:52.960 --> 28:00.760
 So the triage would say, OK, 99% sure there's nothing here.

28:00.760 --> 28:04.040
 So that can be done on device.

28:04.040 --> 28:05.920
 And they can just say, OK, go home.

28:05.920 --> 28:10.520
 So the experts are being used to look at the stuff which

28:10.520 --> 28:12.240
 has some chance it's worth looking at,

28:12.240 --> 28:15.720
 which most things is not.

28:15.720 --> 28:16.280
 It's fine.

28:16.280 --> 28:19.840
 Why do you think we haven't quite made progress on that yet

28:19.840 --> 28:27.480
 in terms of the scale of how much AI is applied in the method?

28:27.480 --> 28:28.400
 There's a lot of reasons.

28:28.400 --> 28:29.640
 I mean, one is it's pretty new.

28:29.640 --> 28:32.040
 I only started in late 2014.

28:32.040 --> 28:35.920
 And before that, it's hard to express

28:35.920 --> 28:37.760
 to what degree the medical world was not

28:37.760 --> 28:40.720
 aware of the opportunities here.

28:40.720 --> 28:45.520
 So I went to RSNA, which is the world's largest radiology

28:45.520 --> 28:46.240
 conference.

28:46.240 --> 28:50.040
 And I told everybody I could, like,

28:50.040 --> 28:51.800
 I'm doing this thing with deep learning.

28:51.800 --> 28:53.320
 Please come and check it out.

28:53.320 --> 28:56.880
 And no one had any idea what I was talking about.

28:56.880 --> 28:59.640
 No one had any interest in it.

28:59.640 --> 29:05.040
 So we've come from absolute zero, which is hard.

29:05.040 --> 29:09.920
 And then the whole regulatory framework, education system,

29:09.920 --> 29:13.400
 everything is just set up to think of doctoring

29:13.400 --> 29:14.920
 in a very different way.

29:14.920 --> 29:16.400
 So today, there is a small number

29:16.400 --> 29:22.040
 of people who are deep learning practitioners and doctors

29:22.040 --> 29:22.960
 at the same time.

29:22.960 --> 29:25.040
 And we're starting to see the first ones come out

29:25.040 --> 29:26.520
 of their PhD programs.

29:26.520 --> 29:33.960
 So Zach Cahane over in Boston, Cambridge

29:33.960 --> 29:41.040
 has a number of students now who are data science experts,

29:41.040 --> 29:46.400
 deep learning experts, and actual medical doctors.

29:46.400 --> 29:49.480
 Quite a few doctors have completed our fast AI course

29:49.480 --> 29:54.920
 now and are publishing papers and creating journal reading

29:54.920 --> 29:58.040
 groups in the American Council of Radiology.

29:58.040 --> 30:00.280
 And it's just starting to happen.

30:00.280 --> 30:02.840
 But it's going to be a long process.

30:02.840 --> 30:04.920
 The regulators have to learn how to regulate this.

30:04.920 --> 30:08.720
 They have to build guidelines.

30:08.720 --> 30:12.120
 And then the lawyers at hospitals

30:12.120 --> 30:15.080
 have to develop a new way of understanding

30:15.080 --> 30:18.680
 that sometimes it makes sense for data

30:18.680 --> 30:24.880
 to be looked at in raw form in large quantities

30:24.880 --> 30:27.000
 in order to create world changing results.

30:27.000 --> 30:30.080
 Yeah, there's a regulation around data, all that.

30:30.080 --> 30:33.840
 It sounds probably the hardest problem,

30:33.840 --> 30:36.760
 but it sounds reminiscent of autonomous vehicles as well.

30:36.760 --> 30:38.760
 Many of the same regulatory challenges,

30:38.760 --> 30:40.560
 many of the same data challenges.

30:40.560 --> 30:42.160
 Yeah, I mean, funnily enough, the problem

30:42.160 --> 30:44.880
 is less the regulation and more the interpretation

30:44.880 --> 30:48.200
 of that regulation by lawyers in hospitals.

30:48.200 --> 30:52.560
 So HIPAA was actually designed.

30:52.560 --> 30:56.400
 The P in HIPAA does not stand for privacy.

30:56.400 --> 30:57.640
 It stands for portability.

30:57.640 --> 31:01.200
 It's actually meant to be a way that data can be used.

31:01.200 --> 31:04.400
 And it was created with lots of gray areas

31:04.400 --> 31:06.560
 because the idea is that would be more practical

31:06.560 --> 31:10.480
 and it would help people to use this legislation

31:10.480 --> 31:13.680
 to actually share data in a more thoughtful way.

31:13.680 --> 31:15.320
 Unfortunately, it's done the opposite

31:15.320 --> 31:18.880
 because when a lawyer sees a gray area, they see, oh,

31:18.880 --> 31:22.440
 if we don't know we won't get sued, then we can't do it.

31:22.440 --> 31:26.360
 So HIPAA is not exactly the problem.

31:26.360 --> 31:30.080
 The problem is more that hospital lawyers

31:30.080 --> 31:34.720
 are not incented to make bold decisions

31:34.720 --> 31:36.520
 about data portability.

31:36.520 --> 31:40.480
 Or even to embrace technology that saves lives.

31:40.480 --> 31:42.440
 They more want to not get in trouble

31:42.440 --> 31:44.280
 for embracing that technology.

31:44.280 --> 31:47.840
 Also, it is also saves lives in a very abstract way,

31:47.840 --> 31:49.840
 which is like, oh, we've been able to release

31:49.840 --> 31:52.360
 these 100,000 anonymous records.

31:52.360 --> 31:55.360
 I can't point at the specific person whose life that's saved.

31:55.360 --> 31:57.760
 I can say like, oh, we've ended up with this paper

31:57.760 --> 32:02.200
 which found this result, which diagnosed 1,000 more people

32:02.200 --> 32:04.200
 than we would have otherwise, but it's like,

32:04.200 --> 32:07.360
 which ones were helped, it's very abstract.

32:07.360 --> 32:09.400
 Yeah, and on the counter side of that,

32:09.400 --> 32:13.080
 you may be able to point to a life that was taken

32:13.080 --> 32:14.360
 because of something that was...

32:14.360 --> 32:18.240
 Yeah, or a person whose privacy was violated.

32:18.240 --> 32:20.360
 It's like, oh, this specific person,

32:20.360 --> 32:25.480
 you know, there was deidentified.

32:25.480 --> 32:27.360
 Just a fascinating topic.

32:27.360 --> 32:28.360
 We're jumping around.

32:28.360 --> 32:32.880
 We'll get back to fast AI, but on the question of privacy,

32:32.880 --> 32:38.160
 data is the fuel for so much innovation in deep learning.

32:38.160 --> 32:39.840
 What's your sense on privacy,

32:39.840 --> 32:44.080
 whether we're talking about Twitter, Facebook, YouTube,

32:44.080 --> 32:48.720
 just the technologies like in the medical field

32:48.720 --> 32:53.440
 that rely on people's data in order to create impact?

32:53.440 --> 32:58.840
 How do we get that right, respecting people's privacy

32:58.840 --> 33:03.360
 and yet creating technology that is learned from data?

33:03.360 --> 33:11.480
 One of my areas of focus is on doing more with less data,

33:11.480 --> 33:15.000
 which so most vendors, unfortunately, are strongly

33:15.000 --> 33:20.000
 centred to find ways to require more data and more computation.

33:20.000 --> 33:24.000
 So Google and IBM being the most obvious...

33:24.000 --> 33:26.000
 IBM.

33:26.000 --> 33:30.600
 Yeah, so Watson, you know, so Google and IBM both strongly push

33:30.600 --> 33:35.400
 the idea that they have more data and more computation

33:35.400 --> 33:37.800
 and more intelligent people than anybody else,

33:37.800 --> 33:39.840
 and so you have to trust them to do things

33:39.840 --> 33:42.600
 because nobody else can do it.

33:42.600 --> 33:45.360
 And Google's very upfront about this,

33:45.360 --> 33:48.680
 like Jeff Dain has gone out there and given talks and said,

33:48.680 --> 33:52.840
 our goal is to require 1,000 times more computation,

33:52.840 --> 33:55.120
 but less people.

33:55.120 --> 34:00.600
 Our goal is to use the people that you have better

34:00.600 --> 34:02.960
 and the data you have better and the computation you have better.

34:02.960 --> 34:06.000
 So one of the things that we've discovered is,

34:06.000 --> 34:11.080
 or at least highlighted, is that you very, very, very often

34:11.080 --> 34:13.360
 don't need much data at all.

34:13.360 --> 34:16.160
 And so the data you already have in your organization

34:16.160 --> 34:19.240
 will be enough to get state of the art results.

34:19.240 --> 34:22.600
 So like my starting point would be to kind of say around privacy

34:22.600 --> 34:25.760
 is a lot of people are looking for ways

34:25.760 --> 34:28.120
 to share data and aggregate data,

34:28.120 --> 34:29.920
 but I think often that's unnecessary.

34:29.920 --> 34:32.160
 They assume that they need more data than they do

34:32.160 --> 34:35.240
 because they're not familiar with the basics of transfer

34:35.240 --> 34:38.440
 learning, which is this critical technique

34:38.440 --> 34:42.000
 for needing orders of magnitude less data.

34:42.000 --> 34:44.680
 Is your sense, one reason you might want to collect data

34:44.680 --> 34:50.440
 from everyone is like in the recommender system context,

34:50.440 --> 34:54.520
 where your individual, Jeremy Howard's individual data

34:54.520 --> 34:58.600
 is the most useful for providing a product that's

34:58.600 --> 34:59.880
 impactful for you.

34:59.880 --> 35:02.240
 So for giving you advertisements,

35:02.240 --> 35:07.640
 for recommending to you movies, for doing medical diagnosis.

35:07.640 --> 35:11.720
 Is your sense we can build with a small amount of data,

35:11.720 --> 35:16.040
 general models that will have a huge impact for most people,

35:16.040 --> 35:19.120
 that we don't need to have data from each individual?

35:19.120 --> 35:20.560
 On the whole, I'd say yes.

35:20.560 --> 35:26.400
 I mean, there are things like, recommender systems

35:26.400 --> 35:30.960
 have this cold start problem, where Jeremy is a new customer.

35:30.960 --> 35:33.280
 We haven't seen him before, so we can't recommend him things

35:33.280 --> 35:36.520
 based on what else he's bought and liked with us.

35:36.520 --> 35:39.440
 And there's various workarounds to that.

35:39.440 --> 35:41.160
 A lot of music programs will start out

35:41.160 --> 35:44.920
 by saying, which of these artists do you like?

35:44.920 --> 35:46.800
 Which of these albums do you like?

35:46.800 --> 35:49.800
 Which of these songs do you like?

35:49.800 --> 35:51.040
 Netflix used to do that.

35:51.040 --> 35:55.320
 Nowadays, people don't like that because they think, oh,

35:55.320 --> 35:57.400
 we don't want to bother the user.

35:57.400 --> 36:00.560
 So you could work around that by having some kind of data

36:00.560 --> 36:04.240
 sharing where you get my marketing record from Axiom

36:04.240 --> 36:06.360
 or whatever and try to question that.

36:06.360 --> 36:12.360
 To me, the benefit to me and to society

36:12.360 --> 36:16.520
 of saving me five minutes on answering some questions

36:16.520 --> 36:23.520
 versus the negative externalities of the privacy issue

36:23.520 --> 36:24.800
 doesn't add up.

36:24.800 --> 36:26.600
 So I think a lot of the time, the places

36:26.600 --> 36:30.520
 where people are invading our privacy in order

36:30.520 --> 36:35.360
 to provide convenience is really about just trying

36:35.360 --> 36:36.880
 to make them more money.

36:36.880 --> 36:40.760
 And they move these negative externalities

36:40.760 --> 36:44.360
 into places that they don't have to pay for them.

36:44.360 --> 36:48.120
 So when you actually see regulations

36:48.120 --> 36:50.560
 appear that actually cause the companies that

36:50.560 --> 36:52.360
 create these negative externalities to have

36:52.360 --> 36:54.320
 to pay for it themselves, they say, well,

36:54.320 --> 36:56.160
 we can't do it anymore.

36:56.160 --> 36:58.240
 So the cost is actually too high.

36:58.240 --> 37:02.280
 But for something like medicine, the hospital

37:02.280 --> 37:06.440
 has my medical imaging, my pathology studies,

37:06.440 --> 37:08.920
 my medical records.

37:08.920 --> 37:11.920
 And also, I own my medical data.

37:11.920 --> 37:16.960
 So I help a startup called DocAI.

37:16.960 --> 37:19.760
 One of the things DocAI does is that it has an app.

37:19.760 --> 37:26.120
 You can connect to Sutter Health and Labcore and Walgreens

37:26.120 --> 37:29.840
 and download your medical data to your phone

37:29.840 --> 37:33.560
 and then upload it, again, at your discretion

37:33.560 --> 37:36.040
 to share it as you wish.

37:36.040 --> 37:38.440
 So with that kind of approach, we

37:38.440 --> 37:41.160
 can share our medical information

37:41.160 --> 37:44.840
 with the people we want to.

37:44.840 --> 37:45.720
 Yeah, so control.

37:45.720 --> 37:48.240
 I mean, really being able to control who you share it with

37:48.240 --> 37:49.760
 and so on.

37:49.760 --> 37:53.080
 So that has a beautiful, interesting tangent

37:53.080 --> 37:59.360
 to return back to the origin story of FastAI.

37:59.360 --> 38:02.520
 Right, so before I started FastAI,

38:02.520 --> 38:07.160
 I spent a year researching where are the biggest

38:07.160 --> 38:10.400
 opportunities for deep learning.

38:10.400 --> 38:14.080
 Because I knew from my time at Kaggle in particular

38:14.080 --> 38:17.960
 that deep learning had hit this threshold point where it was

38:17.960 --> 38:20.520
 rapidly becoming the state of the art approach in every area

38:20.520 --> 38:21.600
 that looked at it.

38:21.600 --> 38:25.400
 And I'd been working with neural nets for over 20 years.

38:25.400 --> 38:27.440
 I knew that from a theoretical point of view,

38:27.440 --> 38:30.760
 once it hit that point, it would do that in just about every

38:30.760 --> 38:31.600
 domain.

38:31.600 --> 38:34.480
 And so I spent a year researching

38:34.480 --> 38:37.120
 what are the domains it's going to have the biggest low hanging

38:37.120 --> 38:39.400
 fruit in the shortest time period.

38:39.400 --> 38:43.880
 I picked medicine, but there were so many I could have picked.

38:43.880 --> 38:47.640
 And so there was a level of frustration for me of like, OK,

38:47.640 --> 38:50.840
 I'm really glad we've opened up the medical deep learning

38:50.840 --> 38:53.880
 world and today it's huge, as you know.

38:53.880 --> 38:58.280
 But we can't do, you know, I can't do everything.

38:58.280 --> 39:00.400
 I don't even know like, like in medicine,

39:00.400 --> 39:02.760
 it took me a really long time to even get a sense of like,

39:02.760 --> 39:05.080
 what kind of problems do medical practitioners solve?

39:05.080 --> 39:06.400
 What kind of data do they have?

39:06.400 --> 39:08.520
 Who has that data?

39:08.520 --> 39:12.480
 So I kind of felt like I need to approach this differently

39:12.480 --> 39:16.200
 if I want to maximize the positive impact of deep learning.

39:16.200 --> 39:19.480
 Rather than me picking an area and trying

39:19.480 --> 39:21.720
 to become good at it and building something,

39:21.720 --> 39:24.480
 I should let people who are already domain experts

39:24.480 --> 39:29.240
 in those areas and who already have the data do it themselves.

39:29.240 --> 39:35.520
 So that was the reason for vast AI is to basically try

39:35.520 --> 39:38.840
 and figure out how to get deep learning

39:38.840 --> 39:41.800
 into the hands of people who could benefit from it

39:41.800 --> 39:45.400
 and help them to do so in as quick and easy and effective

39:45.400 --> 39:47.080
 a way as possible.

39:47.080 --> 39:47.560
 Got it.

39:47.560 --> 39:50.240
 So sort of empower the domain experts.

39:50.240 --> 39:51.320
 Yeah.

39:51.320 --> 39:54.200
 And like partly it's because like,

39:54.200 --> 39:56.280
 unlike most people in this field,

39:56.280 --> 39:59.960
 my background is very applied and industrial.

39:59.960 --> 40:02.480
 Like my first job was at McKinsey & Company.

40:02.480 --> 40:04.640
 I spent 10 years of management consulting.

40:04.640 --> 40:10.240
 I spend a lot of time with domain experts.

40:10.240 --> 40:12.800
 You know, so I kind of respect them and appreciate them.

40:12.800 --> 40:16.440
 And I know that's where the value generation in society is.

40:16.440 --> 40:21.560
 And so I also know how most of them can't code.

40:21.560 --> 40:26.320
 And most of them don't have the time to invest, you know,

40:26.320 --> 40:29.320
 three years in a graduate degree or whatever.

40:29.320 --> 40:33.520
 So it's like, how do I upskill those domain experts?

40:33.520 --> 40:36.080
 I think that would be a super powerful thing,

40:36.080 --> 40:40.200
 you know, the biggest societal impact I could have.

40:40.200 --> 40:41.680
 So yeah, that was the thinking.

40:41.680 --> 40:45.680
 So so much of fast AI students and researchers

40:45.680 --> 40:50.120
 and the things you teach are programmatically minded,

40:50.120 --> 40:51.520
 practically minded,

40:51.520 --> 40:55.840
 figuring out ways how to solve real problems and fast.

40:55.840 --> 40:57.480
 So from your experience,

40:57.480 --> 41:02.040
 what's the difference between theory and practice of deep learning?

41:02.040 --> 41:03.680
 Hmm.

41:03.680 --> 41:07.520
 Well, most of the research in the deep mining world

41:07.520 --> 41:09.840
 is a total waste of time.

41:09.840 --> 41:11.040
 Right. That's what I was getting at.

41:11.040 --> 41:12.200
 Yeah.

41:12.200 --> 41:16.240
 It's it's a problem in science in general.

41:16.240 --> 41:19.600
 Scientists need to be published,

41:19.600 --> 41:21.480
 which means they need to work on things

41:21.480 --> 41:24.040
 that their peers are extremely familiar with

41:24.040 --> 41:26.200
 and can recognize in advance in that area.

41:26.200 --> 41:30.040
 So that means that they all need to work on the same thing.

41:30.040 --> 41:33.040
 And so it really ink and the thing they work on

41:33.040 --> 41:35.640
 is nothing to encourage them to work on things

41:35.640 --> 41:38.840
 that are practically useful.

41:38.840 --> 41:41.120
 So you get just a whole lot of research,

41:41.120 --> 41:43.200
 which is minor advances in stuff

41:43.200 --> 41:44.600
 that's been very highly studied

41:44.600 --> 41:49.280
 and has no significant practical impact.

41:49.280 --> 41:50.840
 Whereas the things that really make a difference

41:50.840 --> 41:52.760
 like I mentioned transfer learning,

41:52.760 --> 41:55.560
 like if we can do better at transfer learning,

41:55.560 --> 41:58.160
 then it's this like world changing thing

41:58.160 --> 42:02.880
 where suddenly like lots more people can do world class work

42:02.880 --> 42:06.760
 with less resources and less data and.

42:06.760 --> 42:08.480
 But almost nobody works on that.

42:08.480 --> 42:10.760
 Or another example, active learning,

42:10.760 --> 42:11.880
 which is the study of like,

42:11.880 --> 42:15.880
 how do we get more out of the human beings in the loop?

42:15.880 --> 42:17.120
 That's my favorite topic.

42:17.120 --> 42:18.520
 Yeah. So active learning is great,

42:18.520 --> 42:21.160
 but it's almost nobody working on it

42:21.160 --> 42:23.800
 because it's just not a trendy thing right now.

42:23.800 --> 42:27.040
 You know what somebody started to interrupt?

42:27.040 --> 42:29.720
 He was saying that nobody is publishing

42:29.720 --> 42:31.520
 on active learning, right?

42:31.520 --> 42:33.440
 But there's people inside companies,

42:33.440 --> 42:36.800
 anybody who actually has to solve a problem,

42:36.800 --> 42:39.600
 they're going to innovate on active learning.

42:39.600 --> 42:42.080
 Yeah. Everybody kind of reinvents active learning

42:42.080 --> 42:43.760
 when they actually have to work in practice

42:43.760 --> 42:46.360
 because they start labeling things and they think,

42:46.360 --> 42:49.280
 gosh, this is taking a long time and it's very expensive.

42:49.280 --> 42:51.200
 And then they start thinking,

42:51.200 --> 42:52.640
 well, why am I labeling everything?

42:52.640 --> 42:54.840
 I'm only, the machine's only making mistakes

42:54.840 --> 42:56.040
 on those two classes.

42:56.040 --> 42:56.880
 They're the hard ones.

42:56.880 --> 42:58.840
 Maybe I'll just start labeling those two classes

42:58.840 --> 43:00.360
 and then you start thinking,

43:00.360 --> 43:01.560
 well, why did I do that manually?

43:01.560 --> 43:03.000
 Why can't I just get the system to tell me

43:03.000 --> 43:04.760
 which things are going to be harder steps?

43:04.760 --> 43:06.200
 It's an obvious thing to do.

43:06.200 --> 43:11.400
 But yeah, it's just like transfer learning.

43:11.400 --> 43:14.120
 It's understudied and the academic world

43:14.120 --> 43:17.440
 just has no reason to care about practical results.

43:17.440 --> 43:18.360
 The funny thing is, like,

43:18.360 --> 43:19.920
 I've only really ever written one paper.

43:19.920 --> 43:21.520
 I hate writing papers.

43:21.520 --> 43:22.760
 And I didn't even write it.

43:22.760 --> 43:25.480
 It was my colleague, Sebastian Ruder, who actually wrote it.

43:25.480 --> 43:28.040
 I just did the research for it.

43:28.040 --> 43:31.640
 But it was basically introducing successful transfer learning

43:31.640 --> 43:34.200
 to NLP for the first time.

43:34.200 --> 43:37.000
 And the algorithm is called ULMfit.

43:37.000 --> 43:42.320
 And I actually wrote it for the course,

43:42.320 --> 43:43.720
 for the first day of course.

43:43.720 --> 43:45.360
 I wanted to teach people NLP.

43:45.360 --> 43:47.520
 And I thought I only want to teach people practical stuff.

43:47.520 --> 43:50.560
 And I think the only practical stuff is transfer learning.

43:50.560 --> 43:53.360
 And I couldn't find any examples of transfer learning in NLP.

43:53.360 --> 43:54.560
 So I just did it.

43:54.560 --> 43:57.320
 And I was shocked to find that as soon as I did it,

43:57.320 --> 44:01.080
 which, you know, the basic prototype took a couple of days,

44:01.080 --> 44:02.520
 smashed the state of the art

44:02.520 --> 44:04.760
 on one of the most important data sets in a field

44:04.760 --> 44:06.720
 that I knew nothing about.

44:06.720 --> 44:10.400
 And I just thought, well, this is ridiculous.

44:10.400 --> 44:13.800
 And so I spoke to Sebastian about it.

44:13.800 --> 44:17.680
 And he kindly offered to write it up the results.

44:17.680 --> 44:21.360
 And so it ended up being published in ACL,

44:21.360 --> 44:25.560
 which is the top computational linguistics conference.

44:25.560 --> 44:28.880
 So like, people do actually care once you do it.

44:28.880 --> 44:34.160
 But I guess it's difficult for maybe junior researchers.

44:34.160 --> 44:37.720
 I don't care whether I get citations or papers or whatever.

44:37.720 --> 44:39.640
 There's nothing in my life that makes that important,

44:39.640 --> 44:41.240
 which is why I've never actually

44:41.240 --> 44:43.040
 bothered to write a paper myself.

44:43.040 --> 44:44.400
 But for people who do, I guess they

44:44.400 --> 44:50.960
 have to pick the kind of safe option, which is like,

44:50.960 --> 44:52.720
 yeah, make a slight improvement on something

44:52.720 --> 44:55.160
 that everybody's already working on.

44:55.160 --> 44:59.040
 Yeah, nobody does anything interesting or succeeds

44:59.040 --> 45:01.240
 in life with the safe option.

45:01.240 --> 45:02.960
 Well, I mean, the nice thing is nowadays,

45:02.960 --> 45:05.320
 everybody is now working on NLP transfer learning.

45:05.320 --> 45:12.240
 Because since that time, we've had GPT and GPT2 and BERT.

45:12.240 --> 45:15.400
 So yeah, once you show that something's possible,

45:15.400 --> 45:17.680
 everybody jumps in, I guess.

45:17.680 --> 45:19.320
 I hope to be a part of it.

45:19.320 --> 45:21.600
 I hope to see more innovation and active learning

45:21.600 --> 45:22.160
 in the same way.

45:22.160 --> 45:24.560
 I think transfer learning and active learning

45:24.560 --> 45:27.360
 are a fascinating public open work.

45:27.360 --> 45:30.160
 I actually helped start a startup called Platform AI, which

45:30.160 --> 45:31.760
 is really all about active learning.

45:31.760 --> 45:34.200
 And yeah, it's been interesting trying

45:34.200 --> 45:36.920
 to kind of see what research is out there

45:36.920 --> 45:37.800
 and make the most of it.

45:37.800 --> 45:39.200
 And there's basically none.

45:39.200 --> 45:41.040
 So we've had to do all our own research.

45:41.040 --> 45:44.240
 Once again, and just as you described,

45:44.240 --> 45:47.640
 can you tell the story of the Stanford competition,

45:47.640 --> 45:51.520
 Dawn Bench, and fast AI's achievement on it?

45:51.520 --> 45:51.960
 Sure.

45:51.960 --> 45:55.560
 So something which I really enjoy is that I basically

45:55.560 --> 45:59.000
 teach two courses a year, the practical deep learning

45:59.000 --> 46:02.120
 for coders, which is kind of the introductory course,

46:02.120 --> 46:04.280
 and then cutting edge deep learning for coders, which

46:04.280 --> 46:08.080
 is the kind of research level course.

46:08.080 --> 46:14.320
 And while I teach those courses, I basically

46:14.320 --> 46:18.440
 have a big office at the University of San Francisco.

46:18.440 --> 46:19.800
 It'd be enough for like 30 people.

46:19.800 --> 46:22.960
 And I invite any student who wants to come and hang out

46:22.960 --> 46:25.320
 with me while I build the course.

46:25.320 --> 46:26.640
 And so generally, it's full.

46:26.640 --> 46:30.880
 And so we have 20 or 30 people in a big office

46:30.880 --> 46:33.880
 with nothing to do but study deep learning.

46:33.880 --> 46:35.880
 So it was during one of these times

46:35.880 --> 46:38.640
 that somebody in the group said, oh, there's

46:38.640 --> 46:41.480
 a thing called Dawn Bench that looks interesting.

46:41.480 --> 46:42.800
 And I say, what the hell is that?

46:42.800 --> 46:44.120
 I'm going to set out some competition

46:44.120 --> 46:46.440
 to see how quickly you can train a model.

46:46.440 --> 46:50.080
 It seems kind of not exactly relevant to what we're doing,

46:50.080 --> 46:51.440
 but it sounds like the kind of thing

46:51.440 --> 46:52.480
 which you might be interested in.

46:52.480 --> 46:53.960
 And I checked it out and I was like, oh, crap.

46:53.960 --> 46:55.840
 There's only 10 days till it's over.

46:55.840 --> 46:58.120
 It's pretty much too late.

46:58.120 --> 47:01.000
 And we're kind of busy trying to teach this course.

47:01.000 --> 47:05.640
 But we're like, oh, it would make an interesting case study

47:05.640 --> 47:08.200
 for the course like it's all the stuff we're already doing.

47:08.200 --> 47:11.120
 Why don't we just put together our current best practices

47:11.120 --> 47:12.480
 and ideas.

47:12.480 --> 47:16.880
 So me and I guess about four students just decided

47:16.880 --> 47:17.560
 to give it a go.

47:17.560 --> 47:19.880
 And we focused on this small one called

47:19.880 --> 47:24.640
 SciFar 10, which is little 32 by 32 pixel images.

47:24.640 --> 47:26.160
 Can you say what Dawn Bench is?

47:26.160 --> 47:29.560
 Yeah, so it's a competition to train a model as fast as possible.

47:29.560 --> 47:31.000
 It was run by Stanford.

47:31.000 --> 47:32.480
 And as cheap as possible, too.

47:32.480 --> 47:34.320
 That's also another one for as cheap as possible.

47:34.320 --> 47:38.160
 And there's a couple of categories, ImageNet and SciFar 10.

47:38.160 --> 47:42.080
 So ImageNet's this big 1.3 million image thing

47:42.080 --> 47:45.400
 that took a couple of days to train.

47:45.400 --> 47:51.240
 I remember a friend of mine, Pete Warden, who's now at Google.

47:51.240 --> 47:53.760
 I remember he told me how he trained ImageNet a few years

47:53.760 --> 47:59.440
 ago when he basically had this little granny flat out

47:59.440 --> 48:01.920
 the back that he turned into was ImageNet training center.

48:01.920 --> 48:04.240
 And after a year of work, he figured out

48:04.240 --> 48:07.040
 how to train it in 10 days or something.

48:07.040 --> 48:08.480
 It's like that was a big job.

48:08.480 --> 48:10.640
 Whereas SciFar 10, at that time, you

48:10.640 --> 48:13.040
 could train in a few hours.

48:13.040 --> 48:14.520
 It's much smaller and easier.

48:14.520 --> 48:18.120
 So we thought we'd try SciFar 10.

48:18.120 --> 48:23.800
 And yeah, I've really never done that before.

48:23.800 --> 48:27.920
 Like, things like using more than one GPU at a time

48:27.920 --> 48:29.800
 was something I tried to avoid.

48:29.800 --> 48:32.160
 Because to me, it's very against the whole idea

48:32.160 --> 48:35.080
 of accessibility, is she better do things with one GPU?

48:35.080 --> 48:36.480
 I mean, have you asked in the past

48:36.480 --> 48:39.680
 before, after having accomplished something,

48:39.680 --> 48:42.520
 how do I do this faster, much faster?

48:42.520 --> 48:43.240
 Oh, always.

48:43.240 --> 48:44.680
 But it's always, for me, it's always,

48:44.680 --> 48:47.640
 how do I make it much faster on a single GPU

48:47.640 --> 48:50.400
 that a normal person could afford in their day to day life?

48:50.400 --> 48:54.760
 It's not, how could I do it faster by having a huge data

48:54.760 --> 48:55.280
 center?

48:55.280 --> 48:57.240
 Because to me, it's all about, like,

48:57.240 --> 48:59.560
 as many people should be able to use something as possible

48:59.560 --> 49:04.160
 without fussing around with infrastructure.

49:04.160 --> 49:06.080
 So anyway, so in this case, it's like, well,

49:06.080 --> 49:10.240
 we can use 8GPUs just by renting a AWS machine.

49:10.240 --> 49:11.920
 So we thought we'd try that.

49:11.920 --> 49:16.560
 And yeah, basically, using the stuff we were already doing,

49:16.560 --> 49:20.360
 we were able to get the speed.

49:20.360 --> 49:25.360
 Within a few days, we had the speed down to a very small

49:25.360 --> 49:26.040
 number of minutes.

49:26.040 --> 49:28.800
 I can't remember exactly how many minutes it was,

49:28.800 --> 49:31.440
 but it might have been like 10 minutes or something.

49:31.440 --> 49:34.200
 And so yeah, we found ourselves at the top of the leaderboard

49:34.200 --> 49:38.720
 easily for both time and money, which really shocked me.

49:38.720 --> 49:40.160
 Because the other people competing in this

49:40.160 --> 49:41.880
 were like Google and Intel and stuff,

49:41.880 --> 49:45.360
 where I know a lot more about this stuff than I think we do.

49:45.360 --> 49:46.800
 So then we emboldened.

49:46.800 --> 49:50.640
 We thought, let's try the ImageNet one too.

49:50.640 --> 49:53.280
 I mean, it seemed way out of our league.

49:53.280 --> 49:57.120
 But our goal was to get under 12 hours.

49:57.120 --> 49:59.280
 And we did, which was really exciting.

49:59.280 --> 50:01.440
 And we didn't put anything up on the leaderboard,

50:01.440 --> 50:03.080
 but we were down to like 10 hours.

50:03.080 --> 50:10.000
 But then Google put in like five hours or something,

50:10.000 --> 50:13.360
 and we're just like, oh, we're so screwed.

50:13.360 --> 50:16.880
 But we kind of thought, well, keep trying.

50:16.880 --> 50:17.880
 If Google can do it in five hours.

50:17.880 --> 50:20.760
 I mean, Google did it on five hours on like a TPU pod

50:20.760 --> 50:24.280
 or something, like a lot of hardware.

50:24.280 --> 50:26.360
 But we kind of like had a bunch of ideas to try.

50:26.360 --> 50:28.920
 Like a really simple thing was, why

50:28.920 --> 50:30.480
 are we using these big images?

50:30.480 --> 50:36.280
 They're like 224, 256 by 256 pixels.

50:36.280 --> 50:37.640
 Why don't we try smaller ones?

50:37.640 --> 50:41.360
 And just to elaborate, there's a constraint on the accuracy

50:41.360 --> 50:43.080
 that your train model is supposed to achieve.

50:43.080 --> 50:45.760
 Yeah, you've got to achieve 93%.

50:45.760 --> 50:47.640
 I think it was for ImageNet.

50:47.640 --> 50:49.160
 Exactly.

50:49.160 --> 50:50.240
 Which is very tough.

50:50.240 --> 50:51.240
 So you have to repeat that.

50:51.240 --> 50:52.120
 Yeah, 93%.

50:52.120 --> 50:54.680
 Like they picked a good threshold.

50:54.680 --> 50:58.920
 It was a little bit higher than what the most commonly used

50:58.920 --> 51:03.320
 ResNet 50 model could achieve at that time.

51:03.320 --> 51:08.080
 So yeah, so it's quite a difficult problem to solve.

51:08.080 --> 51:09.920
 But yeah, we realized if we actually just

51:09.920 --> 51:16.200
 use 64 by 64 images, it trained a pretty good model.

51:16.200 --> 51:17.960
 And then we could take that same model

51:17.960 --> 51:19.560
 and just give it a couple of epochs

51:19.560 --> 51:21.880
 to learn 224 by 224 images.

51:21.880 --> 51:24.440
 And it was basically already trained.

51:24.440 --> 51:25.480
 It makes a lot of sense.

51:25.480 --> 51:27.200
 Like if you teach somebody, like here's

51:27.200 --> 51:30.240
 what a dog looks like, and you show them low res versions,

51:30.240 --> 51:33.640
 and then you say, here's a really clear picture of a dog.

51:33.640 --> 51:36.000
 They already know what a dog looks like.

51:36.000 --> 51:39.920
 So that, like, just we jumped to the front,

51:39.920 --> 51:46.400
 and we ended up winning parts of that competition.

51:46.400 --> 51:49.680
 We actually ended up doing a distributed version

51:49.680 --> 51:51.960
 over multiple machines a couple of months later

51:51.960 --> 51:53.560
 and ended up at the top of the leaderboard.

51:53.560 --> 51:55.440
 We had 18 minutes.

51:55.440 --> 51:56.280
 ImageNet.

51:56.280 --> 52:00.560
 Yeah, and people have just kept on blasting through again

52:00.560 --> 52:02.320
 and again since then.

52:02.320 --> 52:06.760
 So what's your view on multi GPU or multiple machine

52:06.760 --> 52:11.960
 training in general as a way to speed code up?

52:11.960 --> 52:13.680
 I think it's largely a waste of time.

52:13.680 --> 52:15.880
 Both multi GPU on a single machine and?

52:15.880 --> 52:17.640
 Yeah, particularly multi machines,

52:17.640 --> 52:18.880
 because it's just clunky.

52:21.840 --> 52:25.320
 Multi GPUs is less clunky than it used to be.

52:25.320 --> 52:28.520
 But to me, anything that slows down your iteration speed

52:28.520 --> 52:31.800
 is a waste of time.

52:31.800 --> 52:36.960
 So you could maybe do your very last perfecting of the model

52:36.960 --> 52:38.960
 on multi GPUs if you need to.

52:38.960 --> 52:44.560
 But so for example, I think doing stuff on ImageNet

52:44.560 --> 52:46.000
 is generally a waste of time.

52:46.000 --> 52:48.240
 Why test things on 1.3 million images?

52:48.240 --> 52:51.040
 Most of us don't use 1.3 million images.

52:51.040 --> 52:54.360
 And we've also done research that shows that doing things

52:54.360 --> 52:56.840
 on a smaller subset of images gives you

52:56.840 --> 52:59.280
 the same relative answers anyway.

52:59.280 --> 53:02.120
 So from a research point of view, why waste that time?

53:02.120 --> 53:06.200
 So actually, I released a couple of new data sets recently.

53:06.200 --> 53:08.880
 One is called ImageNet.

53:08.880 --> 53:12.920
 The French ImageNet, which is a small subset of ImageNet,

53:12.920 --> 53:15.200
 which is designed to be easy to classify.

53:15.200 --> 53:17.320
 What's how do you spell ImageNet?

53:17.320 --> 53:19.200
 It's got an extra T and E at the end,

53:19.200 --> 53:20.520
 because it's very French.

53:20.520 --> 53:21.640
 Image, OK.

53:21.640 --> 53:24.720
 And then another one called ImageWolf,

53:24.720 --> 53:29.840
 which is a subset of ImageNet that only contains dog breeds.

53:29.840 --> 53:31.120
 But that's a hard one, right?

53:31.120 --> 53:32.000
 That's a hard one.

53:32.000 --> 53:34.360
 And I've discovered that if you just look at these two

53:34.360 --> 53:39.120
 subsets, you can train things on a single GPU in 10 minutes.

53:39.120 --> 53:42.040
 And the results you get are directly transferrable

53:42.040 --> 53:44.320
 to ImageNet nearly all the time.

53:44.320 --> 53:46.600
 And so now I'm starting to see some researchers start

53:46.600 --> 53:48.960
 to use these smaller data sets.

53:48.960 --> 53:51.120
 I so deeply love the way you think,

53:51.120 --> 53:57.000
 because I think you might have written a blog post saying

53:57.000 --> 54:00.200
 that going with these big data sets

54:00.200 --> 54:03.920
 is encouraging people to not think creatively.

54:03.920 --> 54:04.560
 Absolutely.

54:04.560 --> 54:08.320
 So year two, it sort of constrains you

54:08.320 --> 54:09.840
 to train on large resources.

54:09.840 --> 54:11.280
 And because you have these resources,

54:11.280 --> 54:14.040
 you think more research will be better.

54:14.040 --> 54:17.760
 And then you start to like somehow you kill the creativity.

54:17.760 --> 54:18.000
 Yeah.

54:18.000 --> 54:20.760
 And even worse than that, Lex, I keep hearing from people

54:20.760 --> 54:23.480
 who say, I decided not to get into deep learning

54:23.480 --> 54:26.080
 because I don't believe it's accessible to people

54:26.080 --> 54:28.560
 outside of Google to do useful work.

54:28.560 --> 54:31.640
 So like I see a lot of people make an explicit decision

54:31.640 --> 54:36.000
 to not learn this incredibly valuable tool

54:36.000 --> 54:39.840
 because they've drunk the Google Kool Aid, which is that only

54:39.840 --> 54:42.440
 Google's big enough and smart enough to do it.

54:42.440 --> 54:45.400
 And I just find that so disappointing and it's so wrong.

54:45.400 --> 54:49.200
 And I think all of the major breakthroughs in AI

54:49.200 --> 54:53.280
 in the next 20 years will be doable on a single GPU.

54:53.280 --> 54:57.120
 Like I would say, my sense is all the big sort of.

54:57.120 --> 54:58.200
 Well, let's put it this way.

54:58.200 --> 55:00.200
 None of the big breakthroughs of the last 20 years

55:00.200 --> 55:01.720
 have required multiple GPUs.

55:01.720 --> 55:05.920
 So like batch norm, value, dropout,

55:05.920 --> 55:08.080
 to demonstrate that there's something to them.

55:08.080 --> 55:11.840
 Every one of them, none of them has required multiple GPUs.

55:11.840 --> 55:15.800
 GANs, the original GANs, didn't require multiple GPUs.

55:15.800 --> 55:18.040
 Well, and we've actually recently shown

55:18.040 --> 55:19.680
 that you don't even need GANs.

55:19.680 --> 55:23.360
 So we've developed GAN level outcomes

55:23.360 --> 55:24.720
 without needing GANs.

55:24.720 --> 55:26.880
 And we can now do it with, again,

55:26.880 --> 55:29.680
 by using transfer learning, we can do it in a couple of hours

55:29.680 --> 55:30.520
 on a single GPU.

55:30.520 --> 55:31.600
 So you're using a generator model

55:31.600 --> 55:32.960
 without the adversarial part?

55:32.960 --> 55:33.440
 Yeah.

55:33.440 --> 55:35.880
 So we've found loss functions that

55:35.880 --> 55:38.680
 work super well without the adversarial part.

55:38.680 --> 55:41.840
 And then one of our students, a guy called Jason Antich,

55:41.840 --> 55:44.640
 has created a system called Dealtify,

55:44.640 --> 55:47.280
 which uses this technique to colorize

55:47.280 --> 55:48.840
 old black and white movies.

55:48.840 --> 55:51.480
 You can do it on a single GPU, colorize a whole movie

55:51.480 --> 55:52.920
 in a couple of hours.

55:52.920 --> 55:56.080
 And one of the things that Jason and I did together

55:56.080 --> 56:00.480
 was we figured out how to add a little bit of GAN

56:00.480 --> 56:03.000
 at the very end, which it turns out for colorization,

56:03.000 --> 56:06.000
 makes it just a bit brighter and nicer.

56:06.000 --> 56:07.920
 And then Jason did masses of experiments

56:07.920 --> 56:10.000
 to figure out exactly how much to do.

56:10.000 --> 56:12.840
 But it's still all done on his home machine,

56:12.840 --> 56:15.400
 on a single GPU in his lounge room.

56:15.400 --> 56:19.200
 And if you think about colorizing Hollywood movies,

56:19.200 --> 56:21.720
 that sounds like something a huge studio would have to do.

56:21.720 --> 56:25.280
 But he has the world's best results on this.

56:25.280 --> 56:27.040
 There's this problem of microphones.

56:27.040 --> 56:28.640
 We're just talking to microphones now.

56:28.640 --> 56:29.140
 Yeah.

56:29.140 --> 56:32.520
 It's such a pain in the ass to have these microphones

56:32.520 --> 56:34.440
 to get good quality audio.

56:34.440 --> 56:36.720
 And I tried to see if it's possible to plop down

56:36.720 --> 56:39.960
 a bunch of cheap sensors and reconstruct higher quality

56:39.960 --> 56:41.840
 audio from multiple sources.

56:41.840 --> 56:45.440
 Because right now, I haven't seen work from, OK,

56:45.440 --> 56:48.760
 we can save inexpensive mics, automatically combining

56:48.760 --> 56:52.280
 audio from multiple sources to improve the combined audio.

56:52.280 --> 56:53.200
 People haven't done that.

56:53.200 --> 56:55.080
 And that feels like a learning problem.

56:55.080 --> 56:56.800
 So hopefully somebody can.

56:56.800 --> 56:58.760
 Well, I mean, it's evidently doable.

56:58.760 --> 57:01.000
 And it should have been done by now.

57:01.000 --> 57:03.640
 I felt the same way about computational photography

57:03.640 --> 57:04.480
 four years ago.

57:04.480 --> 57:05.240
 That's right.

57:05.240 --> 57:08.240
 Why are we investing in big lenses when

57:08.240 --> 57:13.160
 three cheap lenses plus actually a little bit of intentional

57:13.160 --> 57:16.640
 movement, so like take a few frames,

57:16.640 --> 57:19.840
 gives you enough information to get excellent subpixel

57:19.840 --> 57:22.440
 resolution, which particularly with deep learning,

57:22.440 --> 57:25.840
 you would know exactly what you meant to be looking at.

57:25.840 --> 57:28.200
 We can totally do the same thing with audio.

57:28.200 --> 57:30.720
 I think the madness that it hasn't been done yet.

57:30.720 --> 57:33.320
 Has there been progress on photography companies?

57:33.320 --> 57:33.820
 Yeah.

57:33.820 --> 57:36.720
 Photography is basically a standard now.

57:36.720 --> 57:41.120
 So the Google Pixel Nightlight, I

57:41.120 --> 57:43.240
 don't know if you've ever tried it, but it's astonishing.

57:43.240 --> 57:45.440
 You take a picture and almost pitch black

57:45.440 --> 57:49.120
 and you get back a very high quality image.

57:49.120 --> 57:51.440
 And it's not because of the lens.

57:51.440 --> 57:55.280
 Same stuff with like adding the bokeh to the background

57:55.280 --> 57:55.800
 blurring.

57:55.800 --> 57:57.200
 It's done computationally.

57:57.200 --> 57:58.520
 Just the pics over here.

57:58.520 --> 57:59.020
 Yeah.

57:59.020 --> 58:05.000
 Basically, everybody now is doing most of the fanciest stuff

58:05.000 --> 58:07.120
 on their phones with computational photography

58:07.120 --> 58:10.640
 and also increasingly, people are putting more than one lens

58:10.640 --> 58:11.840
 on the back of the camera.

58:11.840 --> 58:14.360
 So the same will happen for audio, for sure.

58:14.360 --> 58:16.520
 And there's applications in the audio side.

58:16.520 --> 58:19.360
 If you look at an Alexa type device,

58:19.360 --> 58:21.840
 most people I've seen, especially I worked at Google

58:21.840 --> 58:26.000
 before, when you look at noise background removal,

58:26.000 --> 58:29.480
 you don't think of multiple sources of audio.

58:29.480 --> 58:31.920
 You don't play with that as much as I would hope people would.

58:31.920 --> 58:33.640
 But I mean, you can still do it even with one.

58:33.640 --> 58:36.120
 Like, again, it's not much work's been done in this area.

58:36.120 --> 58:38.440
 So we're actually going to be releasing an audio library

58:38.440 --> 58:41.040
 soon, which hopefully will encourage development of this

58:41.040 --> 58:43.200
 because it's so underused.

58:43.200 --> 58:46.480
 The basic approach we used for our super resolution,

58:46.480 --> 58:49.960
 in which Jason uses for dealdify of generating

58:49.960 --> 58:51.920
 high quality images, the exact same approach

58:51.920 --> 58:53.480
 would work for audio.

58:53.480 --> 58:57.160
 No one's done it yet, but it would be a couple of months work.

58:57.160 --> 59:01.600
 OK, also learning rate in terms of dawn bench.

59:01.600 --> 59:04.280
 There's some magic on learning rate that you played around

59:04.280 --> 59:04.760
 with.

59:04.760 --> 59:05.800
 It's kind of interesting.

59:05.800 --> 59:08.120
 Yeah, so this is all work that came from a guy called Leslie

59:08.120 --> 59:09.360
 Smith.

59:09.360 --> 59:12.760
 Leslie's a researcher who, like us,

59:12.760 --> 59:17.720
 cares a lot about just the practicalities of training

59:17.720 --> 59:20.000
 neural networks quickly and accurately,

59:20.000 --> 59:22.120
 which you would think is what everybody should care about,

59:22.120 --> 59:25.000
 but almost nobody does.

59:25.000 --> 59:28.120
 And he discovered something very interesting,

59:28.120 --> 59:30.000
 which he calls super convergence, which

59:30.000 --> 59:32.360
 is there are certain networks that with certain settings

59:32.360 --> 59:34.320
 of high parameters could suddenly

59:34.320 --> 59:37.440
 be trained 10 times faster by using

59:37.440 --> 59:39.480
 a 10 times higher learning rate.

59:39.480 --> 59:44.680
 Now, no one published that paper

59:44.680 --> 59:49.520
 because it's not an area of active research

59:49.520 --> 59:50.440
 in the academic world.

59:50.440 --> 59:52.840
 No academics recognize this is important.

59:52.840 --> 59:56.080
 And also, deep learning in academia

59:56.080 --> 1:00:00.040
 is not considered a experimental science.

1:00:00.040 --> 1:00:02.440
 So unlike in physics, where you could say,

1:00:02.440 --> 1:00:05.360
 I just saw a subatomic particle do something

1:00:05.360 --> 1:00:07.240
 which the theory doesn't explain,

1:00:07.240 --> 1:00:10.440
 you could publish that without an explanation.

1:00:10.440 --> 1:00:12.120
 And then in the next 60 years, people

1:00:12.120 --> 1:00:14.120
 can try to work out how to explain it.

1:00:14.120 --> 1:00:16.200
 We don't allow this in the deep learning world.

1:00:16.200 --> 1:00:20.720
 So it's literally impossible for Leslie to publish a paper that

1:00:20.720 --> 1:00:23.560
 says, I've just seen something amazing happen.

1:00:23.560 --> 1:00:25.680
 This thing trained 10 times faster than it should have.

1:00:25.680 --> 1:00:27.080
 I don't know why.

1:00:27.080 --> 1:00:28.600
 And so the reviewers were like, well,

1:00:28.600 --> 1:00:30.280
 you can't publish that because you don't know why.

1:00:30.280 --> 1:00:31.000
 So anyway.

1:00:31.000 --> 1:00:32.680
 That's important to pause on because there's

1:00:32.680 --> 1:00:36.160
 so many discoveries that would need to start like that.

1:00:36.160 --> 1:00:39.280
 Every other scientific field I know of works of that way.

1:00:39.280 --> 1:00:42.520
 I don't know why ours is uniquely

1:00:42.520 --> 1:00:46.480
 disinterested in publishing unexplained

1:00:46.480 --> 1:00:47.680
 experimental results.

1:00:47.680 --> 1:00:48.680
 But there it is.

1:00:48.680 --> 1:00:51.200
 So it wasn't published.

1:00:51.200 --> 1:00:55.080
 Having said that, I read a lot more

1:00:55.080 --> 1:00:56.840
 unpublished papers and published papers

1:00:56.840 --> 1:01:00.080
 because that's where you find the interesting insights.

1:01:00.080 --> 1:01:02.680
 So I absolutely read this paper.

1:01:02.680 --> 1:01:08.120
 And I was just like, this is astonishingly mind blowing

1:01:08.120 --> 1:01:09.760
 and weird and awesome.

1:01:09.760 --> 1:01:12.400
 And why isn't everybody only talking about this?

1:01:12.400 --> 1:01:15.520
 Because if you can train these things 10 times faster,

1:01:15.520 --> 1:01:18.480
 they also generalize better because you're doing less epochs,

1:01:18.480 --> 1:01:20.080
 which means you look at the data less,

1:01:20.080 --> 1:01:22.400
 you get better accuracy.

1:01:22.400 --> 1:01:24.640
 So I've been kind of studying that ever since.

1:01:24.640 --> 1:01:28.520
 And eventually Leslie kind of figured out

1:01:28.520 --> 1:01:30.160
 a lot of how to get this done.

1:01:30.160 --> 1:01:32.280
 And we added minor tweaks.

1:01:32.280 --> 1:01:34.840
 And a big part of the trick is starting

1:01:34.840 --> 1:01:37.920
 at a very low learning rate, very gradually increasing it.

1:01:37.920 --> 1:01:39.800
 So as you're training your model,

1:01:39.800 --> 1:01:42.120
 you take very small steps at the start.

1:01:42.120 --> 1:01:44.080
 And you gradually make them bigger and bigger

1:01:44.080 --> 1:01:46.440
 until eventually you're taking much bigger steps

1:01:46.440 --> 1:01:49.400
 than anybody thought was possible.

1:01:49.400 --> 1:01:52.280
 There's a few other little tricks to make it work.

1:01:52.280 --> 1:01:55.240
 Basically, we can reliably get super convergence.

1:01:55.240 --> 1:01:56.640
 And so for the dorm bench thing,

1:01:56.640 --> 1:01:59.320
 we were using just much higher learning rates

1:01:59.320 --> 1:02:02.200
 than people expected to work.

1:02:02.200 --> 1:02:03.880
 What do you think the future of,

1:02:03.880 --> 1:02:05.200
 I mean, it makes so much sense for that

1:02:05.200 --> 1:02:08.640
 to be a critical hyperparameter learning rate that you vary.

1:02:08.640 --> 1:02:13.480
 What do you think the future of learning rate magic looks like?

1:02:13.480 --> 1:02:14.960
 Well, there's been a lot of great work

1:02:14.960 --> 1:02:17.400
 in the last 12 months in this area.

1:02:17.400 --> 1:02:20.800
 And people are increasingly realizing that we just

1:02:20.800 --> 1:02:23.120
 have no idea really how optimizers work.

1:02:23.120 --> 1:02:25.840
 And the combination of weight decay,

1:02:25.840 --> 1:02:27.480
 which is how we regularize optimizers,

1:02:27.480 --> 1:02:30.120
 and the learning rate, and then other things

1:02:30.120 --> 1:02:32.760
 like the epsilon we use in the atom optimizer,

1:02:32.760 --> 1:02:36.560
 they all work together in weird ways.

1:02:36.560 --> 1:02:38.560
 And different parts of the model,

1:02:38.560 --> 1:02:40.480
 this is another thing we've done a lot of work on,

1:02:40.480 --> 1:02:43.480
 is research into how different parts of the model

1:02:43.480 --> 1:02:46.600
 should be trained at different rates in different ways.

1:02:46.600 --> 1:02:49.040
 So we do something we call discriminative learning rates,

1:02:49.040 --> 1:02:51.040
 which is really important, particularly for transfer

1:02:51.040 --> 1:02:53.200
 learning.

1:02:53.200 --> 1:02:54.880
 So really, I think in the last 12 months,

1:02:54.880 --> 1:02:57.360
 a lot of people have realized that all this stuff is important.

1:02:57.360 --> 1:03:00.000
 There's been a lot of great work coming out.

1:03:00.000 --> 1:03:02.880
 And we're starting to see algorithms

1:03:02.880 --> 1:03:06.880
 appear which have very, very few dials, if any,

1:03:06.880 --> 1:03:07.920
 that you have to touch.

1:03:07.920 --> 1:03:09.240
 So I think what's going to happen

1:03:09.240 --> 1:03:10.840
 is the idea of a learning rate, well,

1:03:10.840 --> 1:03:14.360
 it almost already has disappeared in the latest research.

1:03:14.360 --> 1:03:18.240
 And instead, it's just like, we know enough

1:03:18.240 --> 1:03:22.440
 about how to interpret the gradients

1:03:22.440 --> 1:03:23.840
 and the change of gradients we see

1:03:23.840 --> 1:03:25.440
 to know how to set every parameter of our way.

1:03:25.440 --> 1:03:26.440
 There you can automate it.

1:03:26.440 --> 1:03:31.720
 So you see the future of deep learning, where really,

1:03:31.720 --> 1:03:34.600
 where is the input of a human expert needed?

1:03:34.600 --> 1:03:36.520
 Well, hopefully, the input of a human expert

1:03:36.520 --> 1:03:39.680
 will be almost entirely unneeded from the deep learning

1:03:39.680 --> 1:03:40.560
 point of view.

1:03:40.560 --> 1:03:43.480
 So again, Google's approach to this

1:03:43.480 --> 1:03:46.000
 is to try and use thousands of times more compute

1:03:46.000 --> 1:03:49.400
 to run lots and lots of models at the same time

1:03:49.400 --> 1:03:51.040
 and hope that one of them is good.

1:03:51.040 --> 1:03:51.960
 A lot of malkana stuff.

1:03:51.960 --> 1:03:56.800
 Yeah, a lot of malkana stuff, which I think is insane.

1:03:56.800 --> 1:04:01.720
 When you better understand the mechanics of how models learn,

1:04:01.720 --> 1:04:03.800
 you don't have to try 1,000 different models

1:04:03.800 --> 1:04:05.680
 to find which one happens to work the best.

1:04:05.680 --> 1:04:08.240
 You can just jump straight to the best one, which

1:04:08.240 --> 1:04:12.720
 means that it's more accessible in terms of compute, cheaper,

1:04:12.720 --> 1:04:14.920
 and also with less hyperparameters to set.

1:04:14.920 --> 1:04:16.800
 That means you don't need deep learning experts

1:04:16.800 --> 1:04:19.360
 to train your deep learning model for you,

1:04:19.360 --> 1:04:22.480
 which means that domain experts can do more of the work, which

1:04:22.480 --> 1:04:24.960
 means that now you can focus the human time

1:04:24.960 --> 1:04:28.320
 on the kind of interpretation, the data gathering,

1:04:28.320 --> 1:04:31.360
 identifying model errors, and stuff like that.

1:04:31.360 --> 1:04:32.840
 Yeah, the data side.

1:04:32.840 --> 1:04:34.720
 How often do you work with data these days

1:04:34.720 --> 1:04:38.680
 in terms of the cleaning, Darwin looked

1:04:38.680 --> 1:04:43.120
 at different species while traveling about,

1:04:43.120 --> 1:04:45.040
 do you look at data?

1:04:45.040 --> 1:04:49.400
 Have you, in your roots in Kaggle, just look at data?

1:04:49.400 --> 1:04:51.320
 Yeah, I mean, it's a key part of our course.

1:04:51.320 --> 1:04:53.480
 It's like before we train a model in the course,

1:04:53.480 --> 1:04:55.160
 we see how to look at the data.

1:04:55.160 --> 1:04:57.920
 And then the first thing we do after we train our first model,

1:04:57.920 --> 1:05:00.520
 which we fine tune an ImageNet model for five minutes.

1:05:00.520 --> 1:05:02.240
 And then the thing we immediately do after that

1:05:02.240 --> 1:05:05.760
 is we learn how to analyze the results of the model

1:05:05.760 --> 1:05:08.920
 by looking at examples of misclassified images,

1:05:08.920 --> 1:05:10.880
 and looking at a classification matrix,

1:05:10.880 --> 1:05:15.080
 and then doing research on Google

1:05:15.080 --> 1:05:18.000
 to learn about the kinds of things that it's misclassifying.

1:05:18.000 --> 1:05:19.520
 So to me, one of the really cool things

1:05:19.520 --> 1:05:21.840
 about machine learning models in general

1:05:21.840 --> 1:05:24.480
 is that when you interpret them, they

1:05:24.480 --> 1:05:27.360
 tell you about things like what are the most important features,

1:05:27.360 --> 1:05:29.400
 which groups you're misclassifying,

1:05:29.400 --> 1:05:32.440
 and they help you become a domain expert more quickly,

1:05:32.440 --> 1:05:34.880
 because you can focus your time on the bits

1:05:34.880 --> 1:05:38.680
 that the model is telling you is important.

1:05:38.680 --> 1:05:40.760
 So it lets you deal with things like data leakage,

1:05:40.760 --> 1:05:43.080
 for example, if it says, oh, the main feature I'm looking at

1:05:43.080 --> 1:05:45.240
 is customer ID.

1:05:45.240 --> 1:05:47.640
 And you're like, oh, customer ID should be predictive.

1:05:47.640 --> 1:05:52.280
 And then you can talk to the people that manage customer IDs,

1:05:52.280 --> 1:05:56.840
 and they'll tell you, oh, yes, as soon as a customer's application

1:05:56.840 --> 1:05:59.480
 is accepted, we add a one on the end of their customer ID

1:05:59.480 --> 1:06:01.200
 or something.

1:06:01.200 --> 1:06:04.360
 So yeah, looking at data, particularly

1:06:04.360 --> 1:06:06.600
 from the lens of which parts of the data the model says

1:06:06.600 --> 1:06:09.400
 is important, is super important.

1:06:09.400 --> 1:06:11.480
 Yeah, and using kind of using the model

1:06:11.480 --> 1:06:14.240
 to almost debug the data to learn more about the data.

1:06:14.240 --> 1:06:16.800
 Exactly.

1:06:16.800 --> 1:06:18.600
 What are the different cloud options

1:06:18.600 --> 1:06:20.160
 for training your networks?

1:06:20.160 --> 1:06:22.000
 Last question related to Don Bench.

1:06:22.000 --> 1:06:24.240
 Well, it's part of a lot of the work you do,

1:06:24.240 --> 1:06:27.280
 but from a perspective of performance,

1:06:27.280 --> 1:06:29.480
 I think you've written this in a blog post.

1:06:29.480 --> 1:06:32.720
 There's AWS, there's a TPU from Google.

1:06:32.720 --> 1:06:33.440
 What's your sense?

1:06:33.440 --> 1:06:34.520
 What the future holds?

1:06:34.520 --> 1:06:37.360
 What would you recommend now in terms of training in the cloud?

1:06:37.360 --> 1:06:40.520
 So from a hardware point of view,

1:06:40.520 --> 1:06:45.520
 Google's TPUs and the best Nvidia GPUs are similar.

1:06:45.520 --> 1:06:47.880
 And maybe the TPUs are like 30% faster,

1:06:47.880 --> 1:06:51.160
 but they're also much harder to program.

1:06:51.160 --> 1:06:54.720
 There isn't a clear leader in terms of hardware right now,

1:06:54.720 --> 1:06:57.840
 although much more importantly, the Nvidia's GPUs

1:06:57.840 --> 1:06:59.560
 are much more programmable.

1:06:59.560 --> 1:07:01.280
 They've got much more written problems.

1:07:01.280 --> 1:07:03.480
 That's the clear leader for me and where

1:07:03.480 --> 1:07:08.640
 I would spend my time as a researcher and practitioner.

1:07:08.640 --> 1:07:12.280
 But then in terms of the platform,

1:07:12.280 --> 1:07:15.680
 I mean, we're super lucky now with stuff like Google,

1:07:15.680 --> 1:07:21.520
 GCP, Google Cloud, and AWS that you can access a GPU

1:07:21.520 --> 1:07:25.440
 pretty quickly and easily.

1:07:25.440 --> 1:07:28.280
 But I mean, for AWS, it's still too hard.

1:07:28.280 --> 1:07:33.760
 You have to find an AMI and get the instance running

1:07:33.760 --> 1:07:37.080
 and then install the software you want and blah, blah, blah.

1:07:37.080 --> 1:07:40.400
 GCP is currently the best way to get

1:07:40.400 --> 1:07:42.320
 started on a full server environment

1:07:42.320 --> 1:07:46.120
 because they have a fantastic fast AI in PyTorch,

1:07:46.120 --> 1:07:51.120
 ready to go instance, which has all the courses preinstalled.

1:07:51.120 --> 1:07:53.040
 It has Jupyter Notebook prerunning.

1:07:53.040 --> 1:07:57.080
 Jupyter Notebook is this wonderful interactive computing

1:07:57.080 --> 1:07:59.440
 system, which everybody basically

1:07:59.440 --> 1:08:02.920
 should be using for any kind of data driven research.

1:08:02.920 --> 1:08:05.880
 But then even better than that, there

1:08:05.880 --> 1:08:09.560
 are platforms like Salamander, which we own,

1:08:09.560 --> 1:08:13.600
 and Paperspace, where literally you click a single button

1:08:13.600 --> 1:08:17.240
 and it pops up and you put a notebook straight away

1:08:17.240 --> 1:08:22.240
 without any kind of installation or anything.

1:08:22.240 --> 1:08:25.800
 And all the course notebooks are all preinstalled.

1:08:25.800 --> 1:08:28.560
 So for me, this is one of the things

1:08:28.560 --> 1:08:34.160
 we spent a lot of time curating and working on.

1:08:34.160 --> 1:08:35.960
 Because when we first started our courses,

1:08:35.960 --> 1:08:39.560
 the biggest problem was people dropped out of lesson one

1:08:39.560 --> 1:08:42.680
 because they couldn't get an AWS instance running.

1:08:42.680 --> 1:08:44.880
 So things are so much better now.

1:08:44.880 --> 1:08:47.760
 And we actually have, if you go to course.fast.ai,

1:08:47.760 --> 1:08:49.040
 the first thing it says is, here's

1:08:49.040 --> 1:08:50.480
 how to get started with your GPU.

1:08:50.480 --> 1:08:52.120
 And it's like, you just click on the link

1:08:52.120 --> 1:08:55.120
 and you click start and you're going.

1:08:55.120 --> 1:08:56.240
 You will go GCP.

1:08:56.240 --> 1:08:58.760
 I have to confess, I've never used the Google GCP.

1:08:58.760 --> 1:09:01.600
 Yeah, GCP gives you $300 of compute for free,

1:09:01.600 --> 1:09:04.920
 which is really nice.

1:09:04.920 --> 1:09:10.960
 But as I say, Salamander and Paperspace are even easier still.

1:09:10.960 --> 1:09:15.120
 So from the perspective of deep learning frameworks,

1:09:15.120 --> 1:09:18.400
 you work with Fast.ai, if you think of it as framework,

1:09:18.400 --> 1:09:22.960
 and PyTorch and TensorFlow, what are the strengths

1:09:22.960 --> 1:09:25.840
 of each platform in your perspective?

1:09:25.840 --> 1:09:29.240
 So in terms of what we've done our research on and taught

1:09:29.240 --> 1:09:34.400
 in our course, we started with Theano and Keras.

1:09:34.400 --> 1:09:38.120
 And then we switched to TensorFlow and Keras.

1:09:38.120 --> 1:09:40.400
 And then we switched to PyTorch.

1:09:40.400 --> 1:09:43.360
 And then we switched to PyTorch and Fast.ai.

1:09:43.360 --> 1:09:47.560
 And that kind of reflects a growth and development

1:09:47.560 --> 1:09:52.560
 of the ecosystem of deep learning libraries.

1:09:52.560 --> 1:09:57.040
 Theano and TensorFlow were great,

1:09:57.040 --> 1:10:01.360
 but were much harder to teach and to do research and development

1:10:01.360 --> 1:10:04.560
 on because they define what's called a computational graph

1:10:04.560 --> 1:10:06.680
 up front, a static graph, where you basically

1:10:06.680 --> 1:10:08.360
 have to say, here are all the things

1:10:08.360 --> 1:10:12.040
 that I'm going to eventually do in my model.

1:10:12.040 --> 1:10:15.080
 And then later on, you say, OK, do those things with this data.

1:10:15.080 --> 1:10:17.160
 And you can't debug them.

1:10:17.160 --> 1:10:18.560
 You can't do them step by step.

1:10:18.560 --> 1:10:20.160
 You can't program them interactively

1:10:20.160 --> 1:10:22.280
 in a Jupyter notebook and so forth.

1:10:22.280 --> 1:10:24.320
 PyTorch was not the first, but PyTorch

1:10:24.320 --> 1:10:27.400
 was certainly the strongest entrant to come along

1:10:27.400 --> 1:10:28.720
 and say, let's not do it that way.

1:10:28.720 --> 1:10:31.320
 Let's just use normal Python.

1:10:31.320 --> 1:10:32.880
 And everything you know about in Python

1:10:32.880 --> 1:10:34.000
 is just going to work.

1:10:34.000 --> 1:10:37.880
 And we'll figure out how to make that run on the GPU

1:10:37.880 --> 1:10:40.800
 as and when necessary.

1:10:40.800 --> 1:10:45.120
 That turned out to be a huge leap in terms

1:10:45.120 --> 1:10:46.800
 of what we could do with our research

1:10:46.800 --> 1:10:49.720
 and what we could do with our teaching.

1:10:49.720 --> 1:10:51.160
 Because it wasn't limiting.

1:10:51.160 --> 1:10:52.760
 Yeah, I mean, it was critical for us

1:10:52.760 --> 1:10:55.960
 for something like Dawnbench to be able to rapidly try things.

1:10:55.960 --> 1:10:58.560
 It's just so much harder to be a researcher and practitioner

1:10:58.560 --> 1:11:00.520
 when you have to do everything upfront

1:11:00.520 --> 1:11:03.400
 and you can't inspect it.

1:11:03.400 --> 1:11:07.360
 Problem with PyTorch is it's not at all

1:11:07.360 --> 1:11:09.360
 accessible to newcomers because you

1:11:09.360 --> 1:11:11.600
 have to write your own training loop

1:11:11.600 --> 1:11:15.680
 and manage the gradients and all this stuff.

1:11:15.680 --> 1:11:17.920
 And it's also not great for researchers

1:11:17.920 --> 1:11:20.680
 because you're spending your time dealing with all this boiler

1:11:20.680 --> 1:11:23.920
 plate and overhead rather than thinking about your algorithm.

1:11:23.920 --> 1:11:27.760
 So we ended up writing this very multi layered API

1:11:27.760 --> 1:11:31.040
 that at the top level, you can train a state of the art neural

1:11:31.040 --> 1:11:33.640
 network in three lines of code.

1:11:33.640 --> 1:11:35.920
 And which talks to an API, which talks to an API,

1:11:35.920 --> 1:11:38.880
 which talks to an API, which you can dive into at any level

1:11:38.880 --> 1:11:45.400
 and get progressively closer to the machine levels of control.

1:11:45.400 --> 1:11:47.480
 And this is the fast AI library.

1:11:47.480 --> 1:11:51.920
 That's been critical for us and for our students

1:11:51.920 --> 1:11:54.200
 and for lots of people that have won big learning

1:11:54.200 --> 1:11:58.560
 competitions with it and written academic papers with it.

1:11:58.560 --> 1:12:00.680
 It's made a big difference.

1:12:00.680 --> 1:12:03.960
 We're still limited though by Python.

1:12:03.960 --> 1:12:05.920
 And particularly this problem with things

1:12:05.920 --> 1:12:10.640
 like our current neural nets say where you just can't change

1:12:10.640 --> 1:12:13.320
 things unless you accept it going so slowly

1:12:13.320 --> 1:12:15.680
 that it's impractical.

1:12:15.680 --> 1:12:18.320
 So in the latest incarnation of the course

1:12:18.320 --> 1:12:20.880
 and with some of the research we're now starting to do,

1:12:20.880 --> 1:12:24.480
 we're starting to do some stuff in Swift.

1:12:24.480 --> 1:12:28.920
 I think we're three years away from that being

1:12:28.920 --> 1:12:31.080
 super practical, but I'm in no hurry.

1:12:31.080 --> 1:12:35.480
 I'm very happy to invest the time to get there.

1:12:35.480 --> 1:12:38.000
 But with that, we actually already

1:12:38.000 --> 1:12:41.840
 have a nascent version of the fast AI library for vision

1:12:41.840 --> 1:12:44.720
 running on Swift and TensorFlow.

1:12:44.720 --> 1:12:48.040
 Because Python for TensorFlow is not going to cut it.

1:12:48.040 --> 1:12:49.920
 It's just a disaster.

1:12:49.920 --> 1:12:54.440
 What they did was they tried to replicate the bits

1:12:54.440 --> 1:12:56.640
 that people were saying they like about PyTorch,

1:12:56.640 --> 1:12:59.160
 this kind of interactive computation.

1:12:59.160 --> 1:13:02.760
 But they didn't actually change their foundational runtime

1:13:02.760 --> 1:13:03.920
 components.

1:13:03.920 --> 1:13:06.640
 So they kind of added this like syntax, sugar,

1:13:06.640 --> 1:13:08.560
 they call TF Eager, TensorFlow Eager, which

1:13:08.560 --> 1:13:10.880
 makes it look a lot like PyTorch.

1:13:10.880 --> 1:13:16.400
 But it's 10 times slower than PyTorch to actually do a step.

1:13:16.400 --> 1:13:19.080
 So because they didn't invest the time

1:13:19.080 --> 1:13:22.080
 in retooling the foundations because their code base

1:13:22.080 --> 1:13:23.520
 is so horribly complex.

1:13:23.520 --> 1:13:25.280
 Yeah, I think it's probably very difficult

1:13:25.280 --> 1:13:26.440
 to do that kind of rejoining.

1:13:26.440 --> 1:13:28.680
 Yeah, well, particularly the way TensorFlow was written,

1:13:28.680 --> 1:13:31.480
 it was written by a lot of people very quickly

1:13:31.480 --> 1:13:33.320
 in a very disorganized way.

1:13:33.320 --> 1:13:36.000
 So when you actually look in the code, as I do often,

1:13:36.000 --> 1:13:38.840
 I'm always just like, oh, god, what were they thinking?

1:13:38.840 --> 1:13:41.480
 It's just, it's pretty awful.

1:13:41.480 --> 1:13:47.080
 So I'm really extremely negative about the potential future

1:13:47.080 --> 1:13:52.120
 for Python TensorFlow that Swift for TensorFlow

1:13:52.120 --> 1:13:53.760
 can be a different beast altogether.

1:13:53.760 --> 1:13:57.560
 It can be like, it can basically be a layer on top of MLIR

1:13:57.560 --> 1:14:02.640
 that takes advantage of all the great compiler stuff

1:14:02.640 --> 1:14:04.760
 that Swift builds on with LLVM.

1:14:04.760 --> 1:14:07.040
 And yeah, it could be absolutely.

1:14:07.040 --> 1:14:10.320
 I think it will be absolutely fantastic.

1:14:10.320 --> 1:14:11.920
 Well, you're inspiring me to try.

1:14:11.920 --> 1:14:17.640
 Evan truly felt the pain of TensorFlow 2.0 Python.

1:14:17.640 --> 1:14:19.040
 It's fine by me.

1:14:19.040 --> 1:14:21.080
 But of course.

1:14:21.080 --> 1:14:23.240
 Yeah, I mean, it does the job if you're using

1:14:23.240 --> 1:14:27.720
 predefined things that somebody's already written.

1:14:27.720 --> 1:14:29.920
 But if you actually compare, like I've

1:14:29.920 --> 1:14:33.680
 had to do a lot of stuff with TensorFlow recently,

1:14:33.680 --> 1:14:35.480
 you actually compare like, I want

1:14:35.480 --> 1:14:37.360
 to write something from scratch.

1:14:37.360 --> 1:14:39.040
 And you're like, I just keep finding it's like, oh,

1:14:39.040 --> 1:14:41.560
 it's running 10 times slower than PyTorch.

1:14:41.560 --> 1:14:43.800
 So is the biggest cost.

1:14:43.800 --> 1:14:47.360
 Let's throw running time out the window.

1:14:47.360 --> 1:14:49.640
 How long it takes you to program?

1:14:49.640 --> 1:14:51.000
 That's not too different now.

1:14:51.000 --> 1:14:54.080
 Thanks to TensorFlow Eager, that's not too different.

1:14:54.080 --> 1:14:58.640
 But because so many things take so long to run,

1:14:58.640 --> 1:15:00.320
 you wouldn't run it at 10 times slower.

1:15:00.320 --> 1:15:03.000
 Like, you just go like, oh, this is taking too long.

1:15:03.000 --> 1:15:04.240
 And also, there's a lot of things

1:15:04.240 --> 1:15:05.840
 which are just less programmable,

1:15:05.840 --> 1:15:09.000
 like tf.data, which is the way data processing works

1:15:09.000 --> 1:15:11.400
 in TensorFlow, is just this big mess.

1:15:11.400 --> 1:15:13.160
 It's incredibly inefficient.

1:15:13.160 --> 1:15:14.800
 And they kind of had to write it that way

1:15:14.800 --> 1:15:19.160
 because of the TPU problems I described earlier.

1:15:19.160 --> 1:15:24.680
 So I just feel like they've got this huge technical debt,

1:15:24.680 --> 1:15:27.960
 which they're not going to solve without starting from scratch.

1:15:27.960 --> 1:15:29.440
 So here's an interesting question then.

1:15:29.440 --> 1:15:34.720
 If there's a new student starting today,

1:15:34.720 --> 1:15:37.480
 what would you recommend they use?

1:15:37.480 --> 1:15:39.160
 Well, I mean, we obviously recommend

1:15:39.160 --> 1:15:42.760
 FastAI and PyTorch because we teach new students.

1:15:42.760 --> 1:15:43.960
 And that's what we teach with.

1:15:43.960 --> 1:15:46.080
 So we would very strongly recommend that

1:15:46.080 --> 1:15:50.280
 because it will let you get on top of the concepts much

1:15:50.280 --> 1:15:51.960
 more quickly.

1:15:51.960 --> 1:15:53.160
 So then you'll become an action.

1:15:53.160 --> 1:15:56.400
 And you'll also learn the actual state of the art techniques.

1:15:56.400 --> 1:15:59.240
 So you actually get world class results.

1:15:59.240 --> 1:16:03.000
 Honestly, it doesn't much matter what library

1:16:03.000 --> 1:16:09.240
 you learn because switching from Shaina to MXNet to TensorFlow

1:16:09.240 --> 1:16:12.000
 to PyTorch is going to be a couple of days work

1:16:12.000 --> 1:16:15.280
 if you long as you understand the foundation as well.

1:16:15.280 --> 1:16:21.600
 But you think we'll Swift creep in there as a thing

1:16:21.600 --> 1:16:22.960
 that people start using?

1:16:22.960 --> 1:16:26.400
 Not for a few years, particularly because Swift

1:16:26.400 --> 1:16:33.440
 has no data science community, libraries, schooling.

1:16:33.440 --> 1:16:39.080
 And the Swift community has a total lack of appreciation

1:16:39.080 --> 1:16:41.040
 and understanding of numeric computing.

1:16:41.040 --> 1:16:43.640
 So they keep on making stupid decisions.

1:16:43.640 --> 1:16:47.480
 For years, they've just done dumb things around performance

1:16:47.480 --> 1:16:50.280
 and prioritization.

1:16:50.280 --> 1:16:56.360
 That's clearly changing now because the developer of Chris

1:16:56.360 --> 1:16:59.960
 Lattner is working at Google on Swift for TensorFlow.

1:16:59.960 --> 1:17:03.200
 So that's a priority.

1:17:03.200 --> 1:17:05.000
 It'll be interesting to see what happens with Apple

1:17:05.000 --> 1:17:10.000
 because Apple hasn't shown any sign of caring

1:17:10.000 --> 1:17:12.960
 about numeric programming in Swift.

1:17:12.960 --> 1:17:16.600
 So hopefully they'll get off their arse

1:17:16.600 --> 1:17:18.840
 and start appreciating this because currently all

1:17:18.840 --> 1:17:24.240
 of their low level libraries are not written in Swift.

1:17:24.240 --> 1:17:27.640
 They're not particularly Swifty at all, stuff like Core ML.

1:17:27.640 --> 1:17:30.840
 They're really pretty rubbish.

1:17:30.840 --> 1:17:32.760
 So yeah, so there's a long way to go.

1:17:32.760 --> 1:17:35.360
 But at least one nice thing is that Swift for TensorFlow

1:17:35.360 --> 1:17:40.000
 can actually directly use Python code and Python libraries.

1:17:40.000 --> 1:17:44.240
 Literally, the entire lesson one notebook of fast AI

1:17:44.240 --> 1:17:47.800
 runs in Swift right now in Python mode.

1:17:47.800 --> 1:17:50.800
 So that's a nice intermediate thing.

1:17:50.800 --> 1:17:56.800
 How long does it take if you look at the two fast AI courses,

1:17:56.800 --> 1:18:00.360
 how long does it take to get from 0.0 to completing

1:18:00.360 --> 1:18:02.360
 both courses?

1:18:02.360 --> 1:18:04.800
 It varies a lot.

1:18:04.800 --> 1:18:12.360
 Somewhere between two months and two years, generally.

1:18:12.360 --> 1:18:15.360
 So for two months, how many hours a day on average?

1:18:15.360 --> 1:18:20.360
 So like somebody who is a very competent coder

1:18:20.360 --> 1:18:27.360
 can can do 70 hours per course and pick up.

1:18:27.360 --> 1:18:28.360
 70, 70.

1:18:28.360 --> 1:18:29.360
 That's it?

1:18:29.360 --> 1:18:30.360
 OK.

1:18:30.360 --> 1:18:36.360
 But a lot of people I know take a year off to study fast AI

1:18:36.360 --> 1:18:39.360
 full time and say at the end of the year,

1:18:39.360 --> 1:18:42.360
 they feel pretty competent.

1:18:42.360 --> 1:18:45.360
 Because generally, there's a lot of other things you do.

1:18:45.360 --> 1:18:48.360
 Generally, they'll be entering Kaggle competitions.

1:18:48.360 --> 1:18:51.360
 They might be reading Ian Goodfellow's book.

1:18:51.360 --> 1:18:54.360
 They might be doing a bunch of stuff.

1:18:54.360 --> 1:18:57.360
 And often, particularly if they are a domain expert,

1:18:57.360 --> 1:19:01.360
 their coding skills might be a little on the pedestrian side.

1:19:01.360 --> 1:19:04.360
 So part of it's just like doing a lot more writing.

1:19:04.360 --> 1:19:07.360
 What do you find is the bottleneck for people usually,

1:19:07.360 --> 1:19:11.360
 except getting started and setting stuff up?

1:19:11.360 --> 1:19:13.360
 I would say coding.

1:19:13.360 --> 1:19:17.360
 The people who are strong coders pick it up the best.

1:19:17.360 --> 1:19:21.360
 Although another bottleneck is people who have a lot of

1:19:21.360 --> 1:19:27.360
 experience of classic statistics can really struggle

1:19:27.360 --> 1:19:30.360
 because the intuition is so the opposite of what they're used to.

1:19:30.360 --> 1:19:33.360
 They're very used to trying to reduce the number of parameters

1:19:33.360 --> 1:19:38.360
 in their model and looking at individual coefficients

1:19:38.360 --> 1:19:39.360
 and stuff like that.

1:19:39.360 --> 1:19:42.360
 So I find people who have a lot of coding background

1:19:42.360 --> 1:19:45.360
 and know nothing about statistics are generally

1:19:45.360 --> 1:19:48.360
 going to be the best stuff.

1:19:48.360 --> 1:19:51.360
 So you taught several courses on deep learning

1:19:51.360 --> 1:19:54.360
 and as Feynman says, the best way to understand something

1:19:54.360 --> 1:19:55.360
 is to teach it.

1:19:55.360 --> 1:19:58.360
 What have you learned about deep learning from teaching it?

1:19:58.360 --> 1:20:00.360
 A lot.

1:20:00.360 --> 1:20:03.360
 It's a key reason for me to teach the courses.

1:20:03.360 --> 1:20:06.360
 Obviously, it's going to be necessary to achieve our goal

1:20:06.360 --> 1:20:09.360
 of getting domain experts to be familiar with deep learning,

1:20:09.360 --> 1:20:12.360
 but it was also necessary for me to achieve my goal

1:20:12.360 --> 1:20:16.360
 of being really familiar with deep learning.

1:20:16.360 --> 1:20:24.360
 I mean, to see so many domain experts from so many different

1:20:24.360 --> 1:20:28.360
 backgrounds, it's definitely, I wouldn't say taught me,

1:20:28.360 --> 1:20:31.360
 but convinced me something that I liked to believe was true,

1:20:31.360 --> 1:20:34.360
 which was anyone can do it.

1:20:34.360 --> 1:20:37.360
 So there's a lot of kind of snobbishness out there about

1:20:37.360 --> 1:20:39.360
 only certain people can learn to code,

1:20:39.360 --> 1:20:42.360
 only certain people are going to be smart enough to do AI.

1:20:42.360 --> 1:20:44.360
 That's definitely bullshit.

1:20:44.360 --> 1:20:48.360
 I've seen so many people from so many different backgrounds

1:20:48.360 --> 1:20:52.360
 get state of the art results in their domain areas now.

1:20:52.360 --> 1:20:56.360
 It's definitely taught me that the key differentiator

1:20:56.360 --> 1:21:00.360
 between people that succeed and people that fail is tenacity.

1:21:00.360 --> 1:21:03.360
 That seems to be basically the only thing that matters.

1:21:03.360 --> 1:21:07.360
 A lot of people give up.

1:21:07.360 --> 1:21:13.360
 But if the ones who don't give up pretty much everybody succeeds,

1:21:13.360 --> 1:21:17.360
 even if at first I'm just kind of thinking,

1:21:17.360 --> 1:21:20.360
 wow, they really aren't quite getting it yet, are they?

1:21:20.360 --> 1:21:24.360
 But eventually people get it and they succeed.

1:21:24.360 --> 1:21:27.360
 So I think that's been, I think they're both things I liked

1:21:27.360 --> 1:21:29.360
 to believe was true, but I don't feel like I really had

1:21:29.360 --> 1:21:31.360
 strong evidence for them to be true,

1:21:31.360 --> 1:21:34.360
 but now I can see I've seen it again and again.

1:21:34.360 --> 1:21:39.360
 So what advice do you have for someone

1:21:39.360 --> 1:21:42.360
 who wants to get started in deep learning?

1:21:42.360 --> 1:21:44.360
 Train lots of models.

1:21:44.360 --> 1:21:47.360
 That's how you learn it.

1:21:47.360 --> 1:21:51.360
 So I think, it's not just me.

1:21:51.360 --> 1:21:53.360
 I think our course is very good,

1:21:53.360 --> 1:21:55.360
 but also lots of people independently have said it's very good.

1:21:55.360 --> 1:21:58.360
 It recently won the CogEx Award for AI courses,

1:21:58.360 --> 1:22:00.360
 it's being the best in the world.

1:22:00.360 --> 1:22:02.360
 I'd say come to our course, course.fast.ai.

1:22:02.360 --> 1:22:05.360
 And the thing I keep on harping on in my lessons is

1:22:05.360 --> 1:22:08.360
 train models, print out the inputs to the models,

1:22:08.360 --> 1:22:10.360
 print out to the outputs to the models,

1:22:10.360 --> 1:22:14.360
 like study, you know, change the inputs a bit,

1:22:14.360 --> 1:22:16.360
 look at how the outputs vary,

1:22:16.360 --> 1:22:22.360
 just run lots of experiments to get an intuitive understanding

1:22:22.360 --> 1:22:24.360
 of what's going on.

1:22:24.360 --> 1:22:28.360
 To get hooked, do you think, you mentioned training,

1:22:28.360 --> 1:22:32.360
 do you think just running the models inference?

1:22:32.360 --> 1:22:35.360
 If we talk about getting started.

1:22:35.360 --> 1:22:37.360
 No, you've got to fine tune the models.

1:22:37.360 --> 1:22:39.360
 So that's the critical thing,

1:22:39.360 --> 1:22:43.360
 because at that point, you now have a model that's in your domain area.

1:22:43.360 --> 1:22:46.360
 So there's no point running somebody else's model,

1:22:46.360 --> 1:22:48.360
 because it's not your model.

1:22:48.360 --> 1:22:50.360
 So it only takes five minutes to fine tune a model

1:22:50.360 --> 1:22:52.360
 for the data you care about.

1:22:52.360 --> 1:22:54.360
 And in lesson two of the course,

1:22:54.360 --> 1:22:56.360
 we teach you how to create your own dataset from scratch

1:22:56.360 --> 1:22:58.360
 by scripting Google image search.

1:22:58.360 --> 1:23:02.360
 And we show you how to actually create a web application running online.

1:23:02.360 --> 1:23:05.360
 So I create one in the course that differentiates

1:23:05.360 --> 1:23:08.360
 between a teddy bear, a grizzly bear, and a brown bear.

1:23:08.360 --> 1:23:10.360
 And it does it with basically 100% accuracy.

1:23:10.360 --> 1:23:13.360
 It took me about four minutes to scrape the images

1:23:13.360 --> 1:23:15.360
 from Google search in the script.

1:23:15.360 --> 1:23:18.360
 There's a little graphical widgets we have in the notebook

1:23:18.360 --> 1:23:21.360
 that help you clean up the dataset.

1:23:21.360 --> 1:23:24.360
 There's other widgets that help you study the results

1:23:24.360 --> 1:23:26.360
 and see where the errors are happening.

1:23:26.360 --> 1:23:29.360
 And so now we've got over a thousand replies

1:23:29.360 --> 1:23:32.360
 in our Share Your Work Here thread of students saying,

1:23:32.360 --> 1:23:34.360
 here's the thing I built.

1:23:34.360 --> 1:23:36.360
 And so there's people who, like,

1:23:36.360 --> 1:23:38.360
 and a lot of them are state of the art.

1:23:38.360 --> 1:23:40.360
 Like somebody said, oh, I tried looking at Dev and Gary characters

1:23:40.360 --> 1:23:42.360
 and I couldn't believe it.

1:23:42.360 --> 1:23:44.360
 The thing that came out was more accurate

1:23:44.360 --> 1:23:46.360
 than the best academic paper after lesson one.

1:23:46.360 --> 1:23:48.360
 And then there's others which are just more kind of fun,

1:23:48.360 --> 1:23:53.360
 like somebody who's doing Trinidad and Tobago hummingbirds.

1:23:53.360 --> 1:23:55.360
 So that's kind of their national bird.

1:23:55.360 --> 1:23:57.360
 And Susie's got something that can now classify Trinidad

1:23:57.360 --> 1:23:59.360
 and Tobago hummingbirds.

1:23:59.360 --> 1:24:02.360
 So yeah, train models, fine tune models with your dataset

1:24:02.360 --> 1:24:05.360
 and then study their inputs and outputs.

1:24:05.360 --> 1:24:07.360
 How much is Fast AI courses?

1:24:07.360 --> 1:24:09.360
 Free.

1:24:09.360 --> 1:24:11.360
 Everything we do is free.

1:24:11.360 --> 1:24:13.360
 We have no revenue sources of any kind.

1:24:13.360 --> 1:24:15.360
 It's just a service to the community.

1:24:15.360 --> 1:24:17.360
 You're a saint.

1:24:17.360 --> 1:24:20.360
 Okay, once a person understands the basics,

1:24:20.360 --> 1:24:22.360
 trains a bunch of models,

1:24:22.360 --> 1:24:25.360
 if we look at the scale of years,

1:24:25.360 --> 1:24:27.360
 what advice do you have for someone wanting

1:24:27.360 --> 1:24:30.360
 to eventually become an expert?

1:24:30.360 --> 1:24:32.360
 Train lots of models.

1:24:32.360 --> 1:24:35.360
 Specifically, train lots of models in your domain area.

1:24:35.360 --> 1:24:37.360
 So an expert, what, right?

1:24:37.360 --> 1:24:40.360
 We don't need more expert, like,

1:24:40.360 --> 1:24:45.360
 create slightly evolutionary research in areas

1:24:45.360 --> 1:24:47.360
 that everybody's studying.

1:24:47.360 --> 1:24:50.360
 We need experts at using deep learning

1:24:50.360 --> 1:24:52.360
 to diagnose malaria.

1:24:52.360 --> 1:24:55.360
 Well, we need experts at using deep learning

1:24:55.360 --> 1:25:00.360
 to analyze language to study media bias.

1:25:00.360 --> 1:25:08.360
 So we need experts in analyzing fisheries

1:25:08.360 --> 1:25:11.360
 to identify problem areas and the ocean.

1:25:11.360 --> 1:25:13.360
 That's what we need.

1:25:13.360 --> 1:25:17.360
 So become the expert in your passion area.

1:25:17.360 --> 1:25:21.360
 And this is a tool which you can use for just about anything,

1:25:21.360 --> 1:25:24.360
 and you'll be able to do that thing better than other people,

1:25:24.360 --> 1:25:26.360
 particularly by combining it with your passion

1:25:26.360 --> 1:25:27.360
 and domain expertise.

1:25:27.360 --> 1:25:28.360
 So that's really interesting.

1:25:28.360 --> 1:25:30.360
 Even if you do want to innovate on transfer learning

1:25:30.360 --> 1:25:32.360
 or active learning,

1:25:32.360 --> 1:25:34.360
 your thought is, I mean,

1:25:34.360 --> 1:25:38.360
 what I certainly share is you also need to find

1:25:38.360 --> 1:25:41.360
 a domain or data set that you actually really care for.

1:25:41.360 --> 1:25:42.360
 Right.

1:25:42.360 --> 1:25:45.360
 If you're not working on a real problem that you understand,

1:25:45.360 --> 1:25:47.360
 how do you know if you're doing it any good?

1:25:47.360 --> 1:25:49.360
 How do you know if your results are good?

1:25:49.360 --> 1:25:51.360
 How do you know if you're getting bad results?

1:25:51.360 --> 1:25:52.360
 Why are you getting bad results?

1:25:52.360 --> 1:25:54.360
 Is it a problem with the data?

1:25:54.360 --> 1:25:57.360
 How do you know you're doing anything useful?

1:25:57.360 --> 1:26:00.360
 Yeah, to me, the only really interesting research is,

1:26:00.360 --> 1:26:03.360
 not the only, but the vast majority of interesting research

1:26:03.360 --> 1:26:06.360
 is try and solve an actual problem and solve it really well.

1:26:06.360 --> 1:26:10.360
 So both understanding sufficient tools on the deep learning side

1:26:10.360 --> 1:26:14.360
 and becoming a domain expert in a particular domain

1:26:14.360 --> 1:26:18.360
 are really things within reach for anybody.

1:26:18.360 --> 1:26:19.360
 Yeah.

1:26:19.360 --> 1:26:23.360
 To me, I would compare it to studying self driving cars,

1:26:23.360 --> 1:26:26.360
 having never looked at a car or been in a car

1:26:26.360 --> 1:26:29.360
 or turned a car on, which is like the way it is

1:26:29.360 --> 1:26:30.360
 for a lot of people.

1:26:30.360 --> 1:26:33.360
 They'll study some academic data set

1:26:33.360 --> 1:26:36.360
 where they literally have no idea about that.

1:26:36.360 --> 1:26:37.360
 By the way, I'm not sure how familiar

1:26:37.360 --> 1:26:39.360
 you are with autonomous vehicles,

1:26:39.360 --> 1:26:42.360
 but that is literally, you describe a large percentage

1:26:42.360 --> 1:26:45.360
 of robotics folks working in self driving cars,

1:26:45.360 --> 1:26:48.360
 as they actually haven't considered driving.

1:26:48.360 --> 1:26:50.360
 They haven't actually looked at what driving looks like.

1:26:50.360 --> 1:26:51.360
 They haven't driven.

1:26:51.360 --> 1:26:52.360
 And it applies.

1:26:52.360 --> 1:26:54.360
 It's a problem because you know when you've actually driven,

1:26:54.360 --> 1:26:57.360
 these are the things that happened to me when I was driving.

1:26:57.360 --> 1:26:59.360
 There's nothing that beats the real world examples

1:26:59.360 --> 1:27:02.360
 or just experiencing them.

1:27:02.360 --> 1:27:04.360
 You've created many successful startups.

1:27:04.360 --> 1:27:08.360
 What does it take to create a successful startup?

1:27:08.360 --> 1:27:12.360
 Same thing as becoming successful deep learning practitioner,

1:27:12.360 --> 1:27:14.360
 which is not giving up.

1:27:14.360 --> 1:27:22.360
 So you can run out of money or run out of time

1:27:22.360 --> 1:27:24.360
 or run out of something, you know,

1:27:24.360 --> 1:27:27.360
 but if you keep costs super low

1:27:27.360 --> 1:27:29.360
 and try and save up some money beforehand

1:27:29.360 --> 1:27:34.360
 so you can afford to have some time,

1:27:34.360 --> 1:27:37.360
 then just sticking with it is one important thing.

1:27:37.360 --> 1:27:42.360
 Doing something you understand and care about is important.

1:27:42.360 --> 1:27:44.360
 By something, I don't mean...

1:27:44.360 --> 1:27:46.360
 The biggest problem I see with deep learning people

1:27:46.360 --> 1:27:49.360
 is they do a PhD in deep learning

1:27:49.360 --> 1:27:52.360
 and then they try and commercialize their PhD.

1:27:52.360 --> 1:27:53.360
 It does a waste of time

1:27:53.360 --> 1:27:55.360
 because that doesn't solve an actual problem.

1:27:55.360 --> 1:27:57.360
 You picked your PhD topic

1:27:57.360 --> 1:28:00.360
 because it was an interesting kind of engineering

1:28:00.360 --> 1:28:02.360
 or math or research exercise.

1:28:02.360 --> 1:28:06.360
 But yeah, if you've actually spent time as a recruiter

1:28:06.360 --> 1:28:10.360
 and you know that most of your time was spent sifting through resumes

1:28:10.360 --> 1:28:12.360
 and you know that most of the time

1:28:12.360 --> 1:28:14.360
 you're just looking for certain kinds of things

1:28:14.360 --> 1:28:19.360
 and you can try doing that with a model for a few minutes

1:28:19.360 --> 1:28:21.360
 and see whether that's something which a model

1:28:21.360 --> 1:28:23.360
 seems to be able to do as well as you could,

1:28:23.360 --> 1:28:27.360
 then you're on the right track to creating a startup.

1:28:27.360 --> 1:28:30.360
 And then I think just being...

1:28:30.360 --> 1:28:34.360
 Just be pragmatic and...

1:28:34.360 --> 1:28:36.360
 try and stay away from venture capital money

1:28:36.360 --> 1:28:38.360
 as long as possible, preferably forever.

1:28:38.360 --> 1:28:42.360
 So yeah, on that point, do you...

1:28:42.360 --> 1:28:43.360
 venture capital...

1:28:43.360 --> 1:28:46.360
 So were you able to successfully run startups

1:28:46.360 --> 1:28:48.360
 with self funded for quite a while?

1:28:48.360 --> 1:28:50.360
 Yeah, so my first two were self funded

1:28:50.360 --> 1:28:52.360
 and that was the right way to do it.

1:28:52.360 --> 1:28:53.360
 Is that scary?

1:28:53.360 --> 1:28:55.360
 No.

1:28:55.360 --> 1:28:57.360
 VCs startups are much more scary

1:28:57.360 --> 1:29:00.360
 because you have these people on your back

1:29:00.360 --> 1:29:01.360
 who do this all the time

1:29:01.360 --> 1:29:03.360
 and who have done it for years

1:29:03.360 --> 1:29:05.360
 telling you grow, grow, grow, grow.

1:29:05.360 --> 1:29:07.360
 And they don't care if you fail.

1:29:07.360 --> 1:29:09.360
 They only care if you don't grow fast enough.

1:29:09.360 --> 1:29:10.360
 So that's scary.

1:29:10.360 --> 1:29:13.360
 We're else doing the ones myself

1:29:13.360 --> 1:29:17.360
 with partners who were friends.

1:29:17.360 --> 1:29:20.360
 It's nice because we just went along

1:29:20.360 --> 1:29:22.360
 at a pace that made sense

1:29:22.360 --> 1:29:24.360
 and we were able to build it to something

1:29:24.360 --> 1:29:27.360
 which was big enough that we never had to work again

1:29:27.360 --> 1:29:29.360
 but was not big enough that any VC

1:29:29.360 --> 1:29:31.360
 would think it was impressive

1:29:31.360 --> 1:29:35.360
 and that was enough for us to be excited.

1:29:35.360 --> 1:29:38.360
 So I thought that's a much better way

1:29:38.360 --> 1:29:40.360
 to do things for most people.

1:29:40.360 --> 1:29:42.360
 And generally speaking now for yourself

1:29:42.360 --> 1:29:44.360
 but how do you make money during that process?

1:29:44.360 --> 1:29:47.360
 Do you cut into savings?

1:29:47.360 --> 1:29:49.360
 So yeah, so I started Fast Mail

1:29:49.360 --> 1:29:51.360
 and Optimal Decisions at the same time

1:29:51.360 --> 1:29:54.360
 in 1999 with two different friends.

1:29:54.360 --> 1:29:59.360
 And for Fast Mail,

1:29:59.360 --> 1:30:03.360
 I guess I spent $70 a month on the server.

1:30:03.360 --> 1:30:06.360
 And when the server ran out of space

1:30:06.360 --> 1:30:09.360
 I put a payments button on the front page

1:30:09.360 --> 1:30:11.360
 and said if you want more than 10 meg of space

1:30:11.360 --> 1:30:15.360
 you have to pay $10 a year.

1:30:15.360 --> 1:30:18.360
 So run low like I keep your cost down.

1:30:18.360 --> 1:30:19.360
 Yeah, so I kept my cost down

1:30:19.360 --> 1:30:22.360
 and once I needed to spend more money

1:30:22.360 --> 1:30:25.360
 I asked people to spend the money for me

1:30:25.360 --> 1:30:29.360
 and that was that basically from then on.

1:30:29.360 --> 1:30:34.360
 We were making money and I was profitable from then.

1:30:34.360 --> 1:30:37.360
 For Optimal Decisions it was a bit harder

1:30:37.360 --> 1:30:40.360
 because we were trying to sell something

1:30:40.360 --> 1:30:42.360
 that was more like a $1 million sale

1:30:42.360 --> 1:30:46.360
 but what we did was we would sell scoping projects

1:30:46.360 --> 1:30:50.360
 so kind of like prototypy projects

1:30:50.360 --> 1:30:51.360
 but rather than doing it for free

1:30:51.360 --> 1:30:54.360
 we would sell them $50,000 to $100,000.

1:30:54.360 --> 1:30:57.360
 So again we were covering our costs

1:30:57.360 --> 1:30:58.360
 and also making the client feel like

1:30:58.360 --> 1:31:00.360
 we were doing something valuable.

1:31:00.360 --> 1:31:06.360
 So in both cases we were profitable from six months in.

1:31:06.360 --> 1:31:08.360
 Nevertheless it's scary.

1:31:08.360 --> 1:31:10.360
 I mean, yeah, sure.

1:31:10.360 --> 1:31:13.360
 I mean it's scary before you jump in

1:31:13.360 --> 1:31:18.360
 and I guess I was comparing it to the scaredyness of VC.

1:31:18.360 --> 1:31:20.360
 I felt like with VC stuff it was more scary.

1:31:20.360 --> 1:31:24.360
 Much more in somebody else's hands.

1:31:24.360 --> 1:31:26.360
 Will they fund you or not?

1:31:26.360 --> 1:31:28.360
 What do they think of what you're doing?

1:31:28.360 --> 1:31:30.360
 I also found it very difficult with VC's back startups

1:31:30.360 --> 1:31:33.360
 to actually do the thing which I thought was important

1:31:33.360 --> 1:31:35.360
 for the company rather than doing the thing

1:31:35.360 --> 1:31:38.360
 which I thought would make the VC happy.

1:31:38.360 --> 1:31:40.360
 Now, VC's always tell you not to do the thing

1:31:40.360 --> 1:31:41.360
 that makes them happy

1:31:41.360 --> 1:31:43.360
 but then if you don't do the thing that makes them happy

1:31:43.360 --> 1:31:45.360
 they get sad.

1:31:45.360 --> 1:31:48.360
 And do you think optimizing for the whatever they call it

1:31:48.360 --> 1:31:52.360
 the exit is a good thing to optimize for?

1:31:52.360 --> 1:31:54.360
 I mean it can be but not at the VC level

1:31:54.360 --> 1:31:59.360
 because the VC exit needs to be, you know, a thousand X.

1:31:59.360 --> 1:32:02.360
 So where else the lifestyle exit

1:32:02.360 --> 1:32:04.360
 if you can sell something for $10 million

1:32:04.360 --> 1:32:06.360
 then you've made it, right?

1:32:06.360 --> 1:32:08.360
 So it depends.

1:32:08.360 --> 1:32:10.360
 If you want to build something that's going to,

1:32:10.360 --> 1:32:13.360
 you're kind of happy to do forever then fine.

1:32:13.360 --> 1:32:16.360
 If you want to build something you want to sell

1:32:16.360 --> 1:32:18.360
 then three years time that's fine too.

1:32:18.360 --> 1:32:21.360
 I mean they're both perfectly good outcomes.

1:32:21.360 --> 1:32:24.360
 So you're learning Swift now?

1:32:24.360 --> 1:32:26.360
 In a way, I mean you already.

1:32:26.360 --> 1:32:31.360
 And I read that you use at least in some cases

1:32:31.360 --> 1:32:34.360
 space repetition as a mechanism for learning new things.

1:32:34.360 --> 1:32:38.360
 I use Anki quite a lot myself.

1:32:38.360 --> 1:32:41.360
 I actually don't never talk to anybody about it.

1:32:41.360 --> 1:32:44.360
 Don't know how many people do it

1:32:44.360 --> 1:32:46.360
 and it works incredibly well for me.

1:32:46.360 --> 1:32:48.360
 Can you talk to your experience?

1:32:48.360 --> 1:32:52.360
 Like how did you, what do you, first of all, okay,

1:32:52.360 --> 1:32:53.360
 let's back it up.

1:32:53.360 --> 1:32:55.360
 What is space repetition?

1:32:55.360 --> 1:33:00.360
 So space repetition is an idea created

1:33:00.360 --> 1:33:03.360
 by a psychologist named Ebbinghaus,

1:33:03.360 --> 1:33:06.360
 I don't know, must be a couple hundred years ago

1:33:06.360 --> 1:33:08.360
 or something 150 years ago.

1:33:08.360 --> 1:33:11.360
 He did something which sounds pretty damn tedious.

1:33:11.360 --> 1:33:16.360
 He found random sequences of letters on cards

1:33:16.360 --> 1:33:21.360
 and tested how well he would remember those random sequences

1:33:21.360 --> 1:33:23.360
 a day later, a week later, whatever.

1:33:23.360 --> 1:33:26.360
 He discovered that there was this kind of a curve

1:33:26.360 --> 1:33:29.360
 where his probability of remembering one of them

1:33:29.360 --> 1:33:31.360
 would be dramatically smaller the next day

1:33:31.360 --> 1:33:32.360
 and then a little bit smaller the next day

1:33:32.360 --> 1:33:34.360
 and a little bit smaller the next day.

1:33:34.360 --> 1:33:37.360
 What he discovered is that if he revised those cards

1:33:37.360 --> 1:33:42.360
 a day, the probabilities would decrease at a smaller rate

1:33:42.360 --> 1:33:44.360
 and then if he revised them again a week later,

1:33:44.360 --> 1:33:46.360
 they would decrease at a smaller rate again.

1:33:46.360 --> 1:33:51.360
 And so he basically figured out a roughly optimal equation

1:33:51.360 --> 1:33:56.360
 for when you should revise something you want to remember.

1:33:56.360 --> 1:34:00.360
 So space repetition learning is using this simple algorithm,

1:34:00.360 --> 1:34:03.360
 just something like revise something after a day

1:34:03.360 --> 1:34:06.360
 and then three days and then a week and then three weeks

1:34:06.360 --> 1:34:07.360
 and so forth.

1:34:07.360 --> 1:34:10.360
 And so if you use a program like Anki, as you know,

1:34:10.360 --> 1:34:12.360
 it will just do that for you.

1:34:12.360 --> 1:34:14.360
 And it will say, did you remember this?

1:34:14.360 --> 1:34:18.360
 And if you say no, it will reschedule it back to be

1:34:18.360 --> 1:34:22.360
 appear again like 10 times faster than it otherwise would have.

1:34:22.360 --> 1:34:27.360
 It's a kind of a way of being guaranteed to learn something

1:34:27.360 --> 1:34:30.360
 because by definition, if you're not learning it,

1:34:30.360 --> 1:34:33.360
 it will be rescheduled to be revised more quickly.

1:34:33.360 --> 1:34:37.360
 Unfortunately though, it doesn't let you fool yourself.

1:34:37.360 --> 1:34:42.360
 If you're not learning something, you know your revisions

1:34:42.360 --> 1:34:44.360
 will just get more and more.

1:34:44.360 --> 1:34:48.360
 So you have to find ways to learn things productively

1:34:48.360 --> 1:34:50.360
 and effectively treat your brain well.

1:34:50.360 --> 1:34:57.360
 So using mnemonics and stories and context and stuff like that.

1:34:57.360 --> 1:34:59.360
 So yeah, it's a super great technique.

1:34:59.360 --> 1:35:01.360
 It's like learning how to learn is something

1:35:01.360 --> 1:35:05.360
 which everybody should learn before they actually learn anything.

1:35:05.360 --> 1:35:07.360
 But almost nobody does.

1:35:07.360 --> 1:35:10.360
 Yes, so what have you, so it certainly works well

1:35:10.360 --> 1:35:14.360
 for learning new languages, for, I mean, for learning,

1:35:14.360 --> 1:35:16.360
 like small projects almost.

1:35:16.360 --> 1:35:19.360
 But do you, you know, I started using it for,

1:35:19.360 --> 1:35:22.360
 I forget who wrote a blog post about this inspired me.

1:35:22.360 --> 1:35:25.360
 It might have been you, I'm not sure.

1:35:25.360 --> 1:35:28.360
 I started when I read papers.

1:35:28.360 --> 1:35:31.360
 I'll, concepts and ideas, I'll put them.

1:35:31.360 --> 1:35:32.360
 Was it Michael Nielsen?

1:35:32.360 --> 1:35:33.360
 It was Michael Nielsen.

1:35:33.360 --> 1:35:34.360
 Yeah, it was Michael Nielsen.

1:35:34.360 --> 1:35:36.360
 Michael started doing this recently

1:35:36.360 --> 1:35:39.360
 and has been writing about it.

1:35:39.360 --> 1:35:44.360
 I, so the kind of today's ebbing house is a guy called Peter Wozniak

1:35:44.360 --> 1:35:47.360
 who developed a system called Super Memo.

1:35:47.360 --> 1:35:51.360
 And he's been basically trying to become like

1:35:51.360 --> 1:35:55.360
 the world's greatest renaissance man over the last few decades.

1:35:55.360 --> 1:36:00.360
 He's basically lived his life with space repetition learning

1:36:00.360 --> 1:36:03.360
 for everything.

1:36:03.360 --> 1:36:07.360
 I, and sort of like Michael's only very recently got into this,

1:36:07.360 --> 1:36:09.360
 but he started really getting excited about doing it

1:36:09.360 --> 1:36:10.360
 for a lot of different things.

1:36:10.360 --> 1:36:14.360
 For me personally, I actually don't use it

1:36:14.360 --> 1:36:16.360
 for anything except Chinese.

1:36:16.360 --> 1:36:21.360
 And the reason for that is that Chinese is specifically a thing.

1:36:21.360 --> 1:36:26.360
 I made a conscious decision that I want to continue to remember

1:36:26.360 --> 1:36:29.360
 even if I don't get much of a chance to exercise it

1:36:29.360 --> 1:36:33.360
 because like I'm not often in China, so I don't.

1:36:33.360 --> 1:36:37.360
 Or else something like programming languages or papers,

1:36:37.360 --> 1:36:39.360
 they have a very different approach,

1:36:39.360 --> 1:36:42.360
 which is I try not to learn anything from them,

1:36:42.360 --> 1:36:46.360
 but instead I try to identify the important concepts

1:36:46.360 --> 1:36:48.360
 and like actually ingest them.

1:36:48.360 --> 1:36:53.360
 So like really understand that concept deeply

1:36:53.360 --> 1:36:54.360
 and study it carefully.

1:36:54.360 --> 1:36:56.360
 Well, decide if it really is important.

1:36:56.360 --> 1:37:00.360
 If it is like incorporate it into our library,

1:37:00.360 --> 1:37:03.360
 you know, incorporate it into how I do things

1:37:03.360 --> 1:37:06.360
 or decide it's not worth it.

1:37:06.360 --> 1:37:12.360
 So I find I then remember the things that I care about

1:37:12.360 --> 1:37:15.360
 because I'm using it all the time.

1:37:15.360 --> 1:37:19.360
 So for the last 25 years,

1:37:19.360 --> 1:37:23.360
 I've committed to spending at least half of every day

1:37:23.360 --> 1:37:25.360
 learning or practicing something new,

1:37:25.360 --> 1:37:28.360
 which is all my colleagues have always hated

1:37:28.360 --> 1:37:30.360
 because it always looks like I'm not working on

1:37:30.360 --> 1:37:31.360
 what I'm meant to be working on,

1:37:31.360 --> 1:37:34.360
 but that always means I do everything faster

1:37:34.360 --> 1:37:36.360
 because I've been practicing a lot of stuff.

1:37:36.360 --> 1:37:39.360
 So I kind of give myself a lot of opportunity

1:37:39.360 --> 1:37:41.360
 to practice new things.

1:37:41.360 --> 1:37:47.360
 And so I find now I don't often kind of find myself

1:37:47.360 --> 1:37:50.360
 wishing I could remember something

1:37:50.360 --> 1:37:51.360
 because if it's something that's useful,

1:37:51.360 --> 1:37:53.360
 then I've been using it a lot.

1:37:53.360 --> 1:37:55.360
 It's easy enough to look it up on Google.

1:37:55.360 --> 1:37:59.360
 But speaking Chinese, you can't look it up on Google.

1:37:59.360 --> 1:38:01.360
 Do you have advice for people learning new things?

1:38:01.360 --> 1:38:04.360
 What have you learned as a process?

1:38:04.360 --> 1:38:07.360
 I mean, it all starts just making the hours

1:38:07.360 --> 1:38:08.360
 and the day available.

1:38:08.360 --> 1:38:10.360
 Yeah, you've got to stick with it,

1:38:10.360 --> 1:38:12.360
 which is, again, the number one thing

1:38:12.360 --> 1:38:14.360
 that 99% of people don't do.

1:38:14.360 --> 1:38:16.360
 So the people I started learning Chinese with,

1:38:16.360 --> 1:38:18.360
 none of them were still doing it 12 months later.

1:38:18.360 --> 1:38:20.360
 I'm still doing it 10 years later.

1:38:20.360 --> 1:38:22.360
 I tried to stay in touch with them,

1:38:22.360 --> 1:38:24.360
 but they just, no one did it.

1:38:24.360 --> 1:38:26.360
 For something like Chinese,

1:38:26.360 --> 1:38:28.360
 like study how human learning works.

1:38:28.360 --> 1:38:31.360
 So every one of my Chinese flashcards

1:38:31.360 --> 1:38:33.360
 is associated with a story,

1:38:33.360 --> 1:38:36.360
 and that story is specifically designed to be memorable.

1:38:36.360 --> 1:38:38.360
 And we find things memorable,

1:38:38.360 --> 1:38:41.360
 funny or disgusting or sexy

1:38:41.360 --> 1:38:44.360
 or related to people that we know or care about.

1:38:44.360 --> 1:38:47.360
 So I try to make sure all the stories that are in my head

1:38:47.360 --> 1:38:50.360
 have those characteristics.

1:38:50.360 --> 1:38:52.360
 Yeah, so you have to, you know,

1:38:52.360 --> 1:38:55.360
 you won't remember things well if they don't have some context.

1:38:55.360 --> 1:38:57.360
 And yeah, you won't remember them well

1:38:57.360 --> 1:39:00.360
 if you don't regularly practice them,

1:39:00.360 --> 1:39:02.360
 whether it be just part of your day to day life

1:39:02.360 --> 1:39:05.360
 for the Chinese and me flashcards.

1:39:05.360 --> 1:39:09.360
 I mean, the other thing is, let yourself fail sometimes.

1:39:09.360 --> 1:39:11.360
 So like, I've had various medical problems

1:39:11.360 --> 1:39:13.360
 over the last few years,

1:39:13.360 --> 1:39:16.360
 and basically my flashcards just stopped

1:39:16.360 --> 1:39:18.360
 for about three years.

1:39:18.360 --> 1:39:21.360
 And then there've been other times I've stopped

1:39:21.360 --> 1:39:24.360
 for a few months, and it's so hard because you get back to it,

1:39:24.360 --> 1:39:27.360
 and it's like, you have 18,000 cards due.

1:39:27.360 --> 1:39:30.360
 It's like, and so you just have to go,

1:39:30.360 --> 1:39:33.360
 all right, well, I can either stop and give up everything

1:39:33.360 --> 1:39:36.360
 or just decide to do this every day for the next two years

1:39:36.360 --> 1:39:38.360
 until I get back to it.

1:39:38.360 --> 1:39:41.360
 The amazing thing has been that even after three years,

1:39:41.360 --> 1:39:45.360
 I, you know, the Chinese were still in there.

1:39:45.360 --> 1:39:47.360
 Like, it was so much faster to relearn

1:39:47.360 --> 1:39:49.360
 than it was to mine the first time.

1:39:49.360 --> 1:39:51.360
 Yeah, absolutely.

1:39:51.360 --> 1:39:52.360
 It's in there.

1:39:52.360 --> 1:39:55.360
 I have the same with guitar, with music and so on.

1:39:55.360 --> 1:39:58.360
 It's sad because work sometimes takes away

1:39:58.360 --> 1:40:00.360
 and then you won't play for a year.

1:40:00.360 --> 1:40:03.360
 But really, if you then just get back to it every day,

1:40:03.360 --> 1:40:05.360
 you're right there again.

1:40:05.360 --> 1:40:08.360
 What do you think is the next big breakthrough

1:40:08.360 --> 1:40:09.360
 in artificial intelligence?

1:40:09.360 --> 1:40:12.360
 What are your hopes in deep learning or beyond

1:40:12.360 --> 1:40:14.360
 that people should be working on,

1:40:14.360 --> 1:40:16.360
 or you hope there'll be breakthroughs?

1:40:16.360 --> 1:40:18.360
 I don't think it's possible to predict.

1:40:18.360 --> 1:40:20.360
 I think what we already have

1:40:20.360 --> 1:40:23.360
 is an incredibly powerful platform

1:40:23.360 --> 1:40:26.360
 to solve lots of societally important problems

1:40:26.360 --> 1:40:28.360
 that are currently unsolved.

1:40:28.360 --> 1:40:30.360
 I just hope that people will, lots of people

1:40:30.360 --> 1:40:33.360
 will learn this toolkit and try to use it.

1:40:33.360 --> 1:40:36.360
 I don't think we need a lot of new technological breakthroughs

1:40:36.360 --> 1:40:39.360
 to do a lot of great work right now.

1:40:39.360 --> 1:40:42.360
 And when do you think we're going to create

1:40:42.360 --> 1:40:44.360
 a human level intelligence system?

1:40:44.360 --> 1:40:45.360
 Do you think?

1:40:45.360 --> 1:40:46.360
 I don't know.

1:40:46.360 --> 1:40:47.360
 How hard is it?

1:40:47.360 --> 1:40:48.360
 How far away are we?

1:40:48.360 --> 1:40:49.360
 I don't know.

1:40:49.360 --> 1:40:50.360
 I have no way to know.

1:40:50.360 --> 1:40:51.360
 I don't know.

1:40:51.360 --> 1:40:53.360
 Like, I don't know why people make predictions about this

1:40:53.360 --> 1:40:57.360
 because there's no data and nothing to go on.

1:40:57.360 --> 1:40:59.360
 And it's just like,

1:40:59.360 --> 1:41:03.360
 there's so many societally important problems

1:41:03.360 --> 1:41:04.360
 to solve right now,

1:41:04.360 --> 1:41:08.360
 I just don't find it a really interesting question

1:41:08.360 --> 1:41:09.360
 to even answer.

1:41:09.360 --> 1:41:12.360
 So in terms of societally important problems,

1:41:12.360 --> 1:41:15.360
 what's the problem that is within reach?

1:41:15.360 --> 1:41:17.360
 Well, I mean, for example,

1:41:17.360 --> 1:41:19.360
 there are problems that AI creates, right?

1:41:19.360 --> 1:41:21.360
 So more specifically,

1:41:22.360 --> 1:41:26.360
 labor force displacement is going to be huge

1:41:26.360 --> 1:41:28.360
 and people keep making this

1:41:28.360 --> 1:41:31.360
 frivolous econometric argument of being like,

1:41:31.360 --> 1:41:33.360
 oh, there's been other things that aren't AI

1:41:33.360 --> 1:41:34.360
 that have come along before

1:41:34.360 --> 1:41:37.360
 and haven't created massive labor force displacement.

1:41:37.360 --> 1:41:39.360
 Therefore, AI won't.

1:41:39.360 --> 1:41:41.360
 So that's a serious concern for you?

1:41:41.360 --> 1:41:42.360
 Oh, yeah.

1:41:42.360 --> 1:41:43.360
 Andrew Yang is running on it.

1:41:43.360 --> 1:41:44.360
 Yeah.

1:41:44.360 --> 1:41:46.360
 It's desperately concerned.

1:41:46.360 --> 1:41:52.360
 And you see already that the changing workplace

1:41:52.360 --> 1:41:55.360
 has lived to a hollowing out of the middle class.

1:41:55.360 --> 1:41:58.360
 You're seeing that students coming out of school today

1:41:58.360 --> 1:42:03.360
 have a less rosy financial future ahead of them

1:42:03.360 --> 1:42:04.360
 than the parents did,

1:42:04.360 --> 1:42:06.360
 which has never happened in recent,

1:42:06.360 --> 1:42:08.360
 in the last 300 years.

1:42:08.360 --> 1:42:11.360
 We've always had progress before.

1:42:11.360 --> 1:42:16.360
 And you see this turning into anxiety and despair

1:42:16.360 --> 1:42:19.360
 and even violence.

1:42:19.360 --> 1:42:21.360
 So I very much worry about that.

1:42:21.360 --> 1:42:24.360
 You've written quite a bit about ethics, too.

1:42:24.360 --> 1:42:27.360
 I do think that every data scientist

1:42:27.360 --> 1:42:32.360
 working with deep learning needs to recognize

1:42:32.360 --> 1:42:34.360
 they have an incredibly high leverage tool

1:42:34.360 --> 1:42:36.360
 that they're using that can influence society

1:42:36.360 --> 1:42:37.360
 in lots of ways.

1:42:37.360 --> 1:42:38.360
 And if they're doing research,

1:42:38.360 --> 1:42:41.360
 that research is going to be used by people

1:42:41.360 --> 1:42:42.360
 doing this kind of work

1:42:42.360 --> 1:42:44.360
 and they have a responsibility

1:42:44.360 --> 1:42:46.360
 to consider the consequences

1:42:46.360 --> 1:42:49.360
 and to think about things like

1:42:49.360 --> 1:42:53.360
 how will humans be in the loop here?

1:42:53.360 --> 1:42:55.360
 How do we avoid runaway feedback loops?

1:42:55.360 --> 1:42:58.360
 How do we ensure an appeals process for humans

1:42:58.360 --> 1:43:00.360
 that are impacted by my algorithm?

1:43:00.360 --> 1:43:04.360
 How do I ensure that the constraints of my algorithm

1:43:04.360 --> 1:43:08.360
 are adequately explained to the people that end up using them?

1:43:08.360 --> 1:43:11.360
 There's all kinds of human issues,

1:43:11.360 --> 1:43:13.360
 which only data scientists

1:43:13.360 --> 1:43:17.360
 are actually in the right place to educate people about,

1:43:17.360 --> 1:43:21.360
 but data scientists tend to think of themselves as

1:43:21.360 --> 1:43:22.360
 just engineers

1:43:22.360 --> 1:43:24.360
 and that they don't need to be part of that process,

1:43:24.360 --> 1:43:26.360
 which is wrong.

1:43:26.360 --> 1:43:29.360
 Well, you're in the perfect position to educate them better,

1:43:29.360 --> 1:43:32.360
 to read literature, to read history,

1:43:32.360 --> 1:43:35.360
 to learn from history.

1:43:35.360 --> 1:43:38.360
 Well, Jeremy, thank you so much for everything you do

1:43:38.360 --> 1:43:40.360
 for inspiring a huge amount of people,

1:43:40.360 --> 1:43:42.360
 getting them into deep learning

1:43:42.360 --> 1:43:44.360
 and having the ripple effects,

1:43:44.360 --> 1:43:48.360
 the flap of a butterfly's wings that will probably change the world.

1:43:48.360 --> 1:44:17.360
 So thank you very much.