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+WEBVTT
+
+00:00.000 --> 00:03.120
+ The following is a conversation with Jeremy Howard.
+
+00:03.120 --> 00:07.080
+ He's the founder of Fast AI, a research institute dedicated
+
+00:07.080 --> 00:09.760
+ to making deep learning more accessible.
+
+00:09.760 --> 00:12.560
+ He's also a distinguished research scientist
+
+00:12.560 --> 00:14.600
+ at the University of San Francisco,
+
+00:14.600 --> 00:17.600
+ a former president of Kegel, as well as a top breaking
+
+00:17.600 --> 00:18.800
+ competitor there.
+
+00:18.800 --> 00:21.680
+ And in general, he's a successful entrepreneur,
+
+00:21.680 --> 00:25.240
+ educator, researcher, and an inspiring personality
+
+00:25.240 --> 00:27.000
+ in the AI community.
+
+00:27.000 --> 00:28.680
+ When someone asked me, how do I get
+
+00:28.680 --> 00:30.240
+ started with deep learning?
+
+00:30.240 --> 00:33.360
+ Fast AI is one of the top places I point them to.
+
+00:33.360 --> 00:34.120
+ It's free.
+
+00:34.120 --> 00:35.520
+ It's easy to get started.
+
+00:35.520 --> 00:37.600
+ It's insightful and accessible.
+
+00:37.600 --> 00:40.960
+ And if I may say so, it has very little BS.
+
+00:40.960 --> 00:44.160
+ It can sometimes dilute the value of educational content
+
+00:44.160 --> 00:46.720
+ on popular topics like deep learning.
+
+00:46.720 --> 00:49.440
+ Fast AI has a focus on practical application
+
+00:49.440 --> 00:51.600
+ of deep learning and hands on exploration
+
+00:51.600 --> 00:53.880
+ of the cutting edge that is incredibly
+
+00:53.880 --> 00:57.960
+ both accessible to beginners and useful to experts.
+
+00:57.960 --> 01:01.360
+ This is the Artificial Intelligence Podcast.
+
+01:01.360 --> 01:03.760
+ If you enjoy it, subscribe on YouTube,
+
+01:03.760 --> 01:06.920
+ give it five stars on iTunes, support it on Patreon,
+
+01:06.920 --> 01:09.040
+ or simply connect with me on Twitter.
+
+01:09.040 --> 01:13.280
+ Alex Friedman, spelled F R I D M A N.
+
+01:13.280 --> 01:18.560
+ And now, here's my conversation with Jeremy Howard.
+
+01:18.560 --> 01:21.680
+ What's the first program you ever written?
+
+01:21.680 --> 01:24.800
+ First program I wrote that I remember
+
+01:24.800 --> 01:29.200
+ would be at high school.
+
+01:29.200 --> 01:31.240
+ I did an assignment where I decided
+
+01:31.240 --> 01:36.240
+ to try to find out if there were some better musical scales
+
+01:36.240 --> 01:40.640
+ than the normal 12 tone, 12 interval scale.
+
+01:40.640 --> 01:43.680
+ So I wrote a program on my Commodore 64 in BASIC
+
+01:43.680 --> 01:46.080
+ that searched through other scale sizes
+
+01:46.080 --> 01:48.440
+ to see if it could find one where there
+
+01:48.440 --> 01:51.880
+ were more accurate harmonies.
+
+01:51.880 --> 01:53.040
+ Like mid tone?
+
+01:53.040 --> 01:56.520
+ Like you want an actual exactly 3 to 2 ratio,
+
+01:56.520 --> 01:59.400
+ where else with a 12 interval scale,
+
+01:59.400 --> 02:01.480
+ it's not exactly 3 to 2, for example.
+
+02:01.480 --> 02:05.080
+ So that's well tempered, as they say.
+
+02:05.080 --> 02:07.680
+ And BASIC on a Commodore 64.
+
+02:07.680 --> 02:09.440
+ Where was the interest in music from?
+
+02:09.440 --> 02:10.480
+ Or is it just technical?
+
+02:10.480 --> 02:14.640
+ I did music all my life, so I played saxophone and clarinet
+
+02:14.640 --> 02:18.120
+ and piano and guitar and drums and whatever.
+
+02:18.120 --> 02:22.200
+ How does that thread go through your life?
+
+02:22.200 --> 02:24.160
+ Where's music today?
+
+02:24.160 --> 02:28.320
+ It's not where I wish it was.
+
+02:28.320 --> 02:30.200
+ For various reasons, couldn't really keep it going,
+
+02:30.200 --> 02:32.560
+ particularly because I had a lot of problems with RSI,
+
+02:32.560 --> 02:33.480
+ with my fingers.
+
+02:33.480 --> 02:37.360
+ And so I had to cut back anything that used hands
+
+02:37.360 --> 02:39.360
+ and fingers.
+
+02:39.360 --> 02:43.920
+ I hope one day I'll be able to get back to it health wise.
+
+02:43.920 --> 02:46.240
+ So there's a love for music underlying it all.
+
+02:46.240 --> 02:47.840
+ Sure, yeah.
+
+02:47.840 --> 02:49.480
+ What's your favorite instrument?
+
+02:49.480 --> 02:50.360
+ Saxophone.
+
+02:50.360 --> 02:51.000
+ Sax.
+
+02:51.000 --> 02:52.840
+ Baritone saxophone.
+
+02:52.840 --> 02:57.440
+ Well, probably bass saxophone, but they're awkward.
+
+02:57.440 --> 03:00.120
+ Well, I always love it when music is
+
+03:00.120 --> 03:01.760
+ coupled with programming.
+
+03:01.760 --> 03:03.800
+ There's something about a brain that
+
+03:03.800 --> 03:07.520
+ utilizes those that emerges with creative ideas.
+
+03:07.520 --> 03:11.200
+ So you've used and studied quite a few programming languages.
+
+03:11.200 --> 03:15.120
+ Can you give an overview of what you've used?
+
+03:15.120 --> 03:17.920
+ What are the pros and cons of each?
+
+03:17.920 --> 03:21.960
+ Well, my favorite programming environment almost certainly
+
+03:21.960 --> 03:26.520
+ was Microsoft Access back in the earliest days.
+
+03:26.520 --> 03:29.080
+ So that was a special basic for applications, which
+
+03:29.080 --> 03:30.720
+ is not a good programming language,
+
+03:30.720 --> 03:33.080
+ but the programming environment is fantastic.
+
+03:33.080 --> 03:40.120
+ It's like the ability to create user interfaces and tied data
+
+03:40.120 --> 03:43.720
+ and actions to them and create reports and all that.
+
+03:43.720 --> 03:46.800
+ As I've never seen anything as good.
+
+03:46.800 --> 03:48.920
+ So things nowadays like Airtable, which
+
+03:48.920 --> 03:56.200
+ are like small subsets of that, which people love for good reason.
+
+03:56.200 --> 04:01.160
+ But unfortunately, nobody's ever achieved anything like that.
+
+04:01.160 --> 04:03.320
+ What is that, if you could pause on that for a second?
+
+04:03.320 --> 04:03.840
+ Oh, Access.
+
+04:03.840 --> 04:04.340
+ Access.
+
+04:04.340 --> 04:06.320
+ Is it a fundamental database?
+
+04:06.320 --> 04:09.600
+ It was a database program that Microsoft produced,
+
+04:09.600 --> 04:13.440
+ part of Office, and it kind of withered.
+
+04:13.440 --> 04:16.320
+ But basically, it lets you in a totally graphical way
+
+04:16.320 --> 04:18.480
+ create tables and relationships and queries
+
+04:18.480 --> 04:24.720
+ and tie them to forms and set up event handlers and calculations.
+
+04:24.720 --> 04:28.680
+ And it was a very complete, powerful system designed
+
+04:28.680 --> 04:35.000
+ for not massive scalable things, but for useful little applications
+
+04:35.000 --> 04:36.400
+ that I loved.
+
+04:36.400 --> 04:40.240
+ So what's the connection between Excel and Access?
+
+04:40.240 --> 04:42.160
+ So very close.
+
+04:42.160 --> 04:47.680
+ So Access was the relational database equivalent,
+
+04:47.680 --> 04:48.360
+ if you like.
+
+04:48.360 --> 04:51.080
+ So people still do a lot of that stuff
+
+04:51.080 --> 04:54.120
+ that should be in Access in Excel because they know it.
+
+04:54.120 --> 04:56.680
+ Excel's great as well.
+
+04:56.680 --> 05:01.760
+ But it's just not as rich a programming model as VBA
+
+05:01.760 --> 05:04.680
+ combined with a relational database.
+
+05:04.680 --> 05:07.320
+ And so I've always loved relational databases.
+
+05:07.320 --> 05:11.080
+ But today, programming on top of relational databases
+
+05:11.080 --> 05:13.840
+ is just a lot more of a headache.
+
+05:13.840 --> 05:16.680
+ You generally either need to kind of,
+
+05:16.680 --> 05:19.040
+ you need something that connects, that runs some kind
+
+05:19.040 --> 05:21.560
+ of database server, unless you use SQLite, which
+
+05:21.560 --> 05:25.000
+ has its own issues.
+
+05:25.000 --> 05:26.320
+ Then you kind of often, if you want
+
+05:26.320 --> 05:27.760
+ to get a nice programming model, you
+
+05:27.760 --> 05:30.440
+ need to create an ORM on top.
+
+05:30.440 --> 05:34.360
+ And then, I don't know, there's all these pieces tied together.
+
+05:34.360 --> 05:37.000
+ And it's just a lot more awkward than it should be.
+
+05:37.000 --> 05:39.200
+ There are people that are trying to make it easier,
+
+05:39.200 --> 05:44.480
+ so in particular, I think of Fsharp, Don Syme, who him
+
+05:44.480 --> 05:49.320
+ and his team have done a great job of making something
+
+05:49.320 --> 05:51.640
+ like a database appear in the type system,
+
+05:51.640 --> 05:54.960
+ so you actually get tab completion for fields and tables
+
+05:54.960 --> 05:57.840
+ and stuff like that.
+
+05:57.840 --> 05:59.280
+ Anyway, so that was kind of, anyway,
+
+05:59.280 --> 06:01.880
+ so that whole VBA Office thing, I guess,
+
+06:01.880 --> 06:04.560
+ was a starting point, which is your miss.
+
+06:04.560 --> 06:07.800
+ And I got into Standard Visual Basic, which
+
+06:07.800 --> 06:09.840
+ that's interesting, just to pause on that for a second.
+
+06:09.840 --> 06:12.600
+ And it's interesting that you're connecting programming
+
+06:12.600 --> 06:18.200
+ languages to the ease of management of data.
+
+06:18.200 --> 06:20.600
+ So in your use of programming languages,
+
+06:20.600 --> 06:24.880
+ you always had a love and a connection with data.
+
+06:24.880 --> 06:28.640
+ I've always been interested in doing useful things for myself
+
+06:28.640 --> 06:31.880
+ and for others, which generally means getting some data
+
+06:31.880 --> 06:34.600
+ and doing something with it and putting it out there again.
+
+06:34.600 --> 06:38.400
+ So that's been my interest throughout.
+
+06:38.400 --> 06:41.560
+ So I also did a lot of stuff with Apple script
+
+06:41.560 --> 06:43.880
+ back in the early days.
+
+06:43.880 --> 06:47.960
+ So it's kind of nice being able to get the computer
+
+06:47.960 --> 06:52.960
+ and computers to talk to each other and to do things for you.
+
+06:52.960 --> 06:56.600
+ And then I think that one night, the programming language
+
+06:56.600 --> 06:59.960
+ I most loved then would have been Delphi, which
+
+06:59.960 --> 07:05.960
+ was Object Pascal created by Anders Halsberg, who previously
+
+07:05.960 --> 07:08.840
+ did Turbo Pascal and then went on to create.net
+
+07:08.840 --> 07:11.080
+ and then went on to create TypeScript.
+
+07:11.080 --> 07:16.720
+ Delphi was amazing because it was like a compiled, fast language
+
+07:16.720 --> 07:20.200
+ that was as easy to use as Visual Basic.
+
+07:20.200 --> 07:27.480
+ Delphi, what is it similar to in more modern languages?
+
+07:27.480 --> 07:28.840
+ Visual Basic.
+
+07:28.840 --> 07:29.680
+ Visual Basic.
+
+07:29.680 --> 07:32.320
+ Yeah, that a compiled, fast version.
+
+07:32.320 --> 07:37.080
+ So I'm not sure there's anything quite like it anymore.
+
+07:37.080 --> 07:42.520
+ If you took C Sharp or Java and got rid of the virtual machine
+
+07:42.520 --> 07:45.040
+ and replaced it with something, you could compile a small type
+
+07:45.040 --> 07:46.520
+ binary.
+
+07:46.520 --> 07:51.680
+ I feel like it's where Swift could get to with the new Swift
+
+07:51.680 --> 07:56.640
+ UI and the cross platform development going on.
+
+07:56.640 --> 08:01.600
+ That's one of my dreams is that we'll hopefully get back
+
+08:01.600 --> 08:02.840
+ to where Delphi was.
+
+08:02.840 --> 08:08.520
+ There is actually a free Pascal project nowadays
+
+08:08.520 --> 08:10.320
+ called Lazarus, which is also attempting
+
+08:10.320 --> 08:13.960
+ to recreate Delphi.
+
+08:13.960 --> 08:16.080
+ They're making good progress.
+
+08:16.080 --> 08:21.000
+ So OK, Delphi, that's one of your favorite programming languages?
+
+08:21.000 --> 08:22.360
+ Well, it's programming environments.
+
+08:22.360 --> 08:26.280
+ Again, say Pascal's not a nice language.
+
+08:26.280 --> 08:27.880
+ If you wanted to know specifically
+
+08:27.880 --> 08:30.360
+ about what languages I like, I would definitely
+
+08:30.360 --> 08:35.480
+ pick Jay as being an amazingly wonderful language.
+
+08:35.480 --> 08:37.000
+ What's Jay?
+
+08:37.000 --> 08:39.600
+ Jay, are you aware of APL?
+
+08:39.600 --> 08:43.520
+ I am not, except from doing a little research on the work
+
+08:43.520 --> 08:44.080
+ you've done.
+
+08:44.080 --> 08:47.280
+ OK, so not at all surprising you're not
+
+08:47.280 --> 08:49.040
+ familiar with it because it's not well known,
+
+08:49.040 --> 08:55.480
+ but it's actually one of the main families of programming
+
+08:55.480 --> 08:57.920
+ languages going back to the late 50s, early 60s.
+
+08:57.920 --> 09:01.720
+ So there was a couple of major directions.
+
+09:01.720 --> 09:04.440
+ One was the kind of lambda, calculus,
+
+09:04.440 --> 09:08.640
+ Alonzo church direction, which I guess kind of Lisbon scheme
+
+09:08.640 --> 09:12.040
+ and whatever, which has a history going back
+
+09:12.040 --> 09:13.440
+ to the early days of computing.
+
+09:13.440 --> 09:17.360
+ The second was the kind of imperative slash
+
+09:17.360 --> 09:23.240
+ OO, algo, similar going on to C, C++, so forth.
+
+09:23.240 --> 09:26.960
+ There was a third, which are called array oriented languages,
+
+09:26.960 --> 09:31.720
+ which started with a paper by a guy called Ken Iverson, which
+
+09:31.720 --> 09:37.480
+ was actually a math theory paper, not a programming paper.
+
+09:37.480 --> 09:41.520
+ It was called Notation as a Tool for Thought.
+
+09:41.520 --> 09:45.320
+ And it was the development of a new type of math notation.
+
+09:45.320 --> 09:48.560
+ And the idea is that this math notation was much more
+
+09:48.560 --> 09:54.480
+ flexible, expressive, and also well defined than traditional
+
+09:54.480 --> 09:56.440
+ math notation, which is none of those things.
+
+09:56.440 --> 09:59.160
+ Math notation is awful.
+
+09:59.160 --> 10:02.840
+ And so he actually turned that into a programming language.
+
+10:02.840 --> 10:06.720
+ Because this was the late 50s, all the names were available.
+
+10:06.720 --> 10:10.520
+ So he called his programming language, or APL.
+
+10:10.520 --> 10:11.160
+ APL, what?
+
+10:11.160 --> 10:15.360
+ So APL is a implementation of notation
+
+10:15.360 --> 10:18.280
+ as a tool for thought, by which he means math notation.
+
+10:18.280 --> 10:22.880
+ And Ken and his son went on to do many things,
+
+10:22.880 --> 10:26.720
+ but eventually they actually produced a new language that
+
+10:26.720 --> 10:28.440
+ was built on top of all the learnings of APL.
+
+10:28.440 --> 10:32.800
+ And that was called J. And J is the most
+
+10:32.800 --> 10:41.040
+ expressive, composable, beautifully designed language
+
+10:41.040 --> 10:42.400
+ I've ever seen.
+
+10:42.400 --> 10:44.520
+ Does it have object oriented components?
+
+10:44.520 --> 10:45.520
+ Does it have that kind of thing?
+
+10:45.520 --> 10:46.240
+ Not really.
+
+10:46.240 --> 10:47.720
+ It's an array oriented language.
+
+10:47.720 --> 10:51.400
+ It's the third path.
+
+10:51.400 --> 10:52.760
+ Are you saying array?
+
+10:52.760 --> 10:53.720
+ Array oriented.
+
+10:53.720 --> 10:54.200
+ Yeah.
+
+10:54.200 --> 10:55.480
+ It needs to be array oriented.
+
+10:55.480 --> 10:57.480
+ So array oriented means that you generally
+
+10:57.480 --> 10:59.520
+ don't use any loops.
+
+10:59.520 --> 11:02.240
+ But the whole thing is done with kind
+
+11:02.240 --> 11:06.360
+ of an extreme version of broadcasting,
+
+11:06.360 --> 11:09.880
+ if you're familiar with that NumPy slash Python concept.
+
+11:09.880 --> 11:14.240
+ So you do a lot with one line of code.
+
+11:14.240 --> 11:17.520
+ It looks a lot like math.
+
+11:17.520 --> 11:20.280
+ Notation is basically highly compact.
+
+11:20.280 --> 11:22.800
+ And the idea is that you can kind of,
+
+11:22.800 --> 11:24.760
+ because you can do so much with one line of code,
+
+11:24.760 --> 11:27.720
+ a single screen of code is very unlikely to,
+
+11:27.720 --> 11:31.080
+ you very rarely need more than that to express your program.
+
+11:31.080 --> 11:33.240
+ And so you can kind of keep it all in your head.
+
+11:33.240 --> 11:36.000
+ And you can kind of clearly communicate it.
+
+11:36.000 --> 11:41.560
+ It's interesting that APL created two main branches, K and J.
+
+11:41.560 --> 11:47.920
+ J is this kind of like open source niche community of crazy
+
+11:47.920 --> 11:49.360
+ enthusiasts like me.
+
+11:49.360 --> 11:52.120
+ And then the other path, K, was fascinating.
+
+11:52.120 --> 11:56.600
+ It's an astonishingly expensive programming language,
+
+11:56.600 --> 12:01.920
+ which many of the world's most ludicrously rich hedge funds
+
+12:01.920 --> 12:02.840
+ use.
+
+12:02.840 --> 12:06.640
+ So the entire K machine is so small,
+
+12:06.640 --> 12:09.320
+ it sits inside level three cache on your CPU.
+
+12:09.320 --> 12:14.040
+ And it easily wins every benchmark I've ever seen
+
+12:14.040 --> 12:16.440
+ in terms of data processing speed.
+
+12:16.440 --> 12:17.840
+ But you don't come across it very much,
+
+12:17.840 --> 12:22.640
+ because it's like $100,000 per CPU to run it.
+
+12:22.640 --> 12:26.240
+ But it's like this path of programming languages
+
+12:26.240 --> 12:29.760
+ is just so much, I don't know, so much more powerful
+
+12:29.760 --> 12:33.840
+ in every way than the ones that almost anybody uses every day.
+
+12:33.840 --> 12:37.400
+ So it's all about computation.
+
+12:37.400 --> 12:38.360
+ It's really focusing on it.
+
+12:38.360 --> 12:40.640
+ Pretty heavily focused on computation.
+
+12:40.640 --> 12:44.320
+ I mean, so much of programming is data processing
+
+12:44.320 --> 12:45.640
+ by definition.
+
+12:45.640 --> 12:49.000
+ And so there's a lot of things you can do with it.
+
+12:49.000 --> 12:51.320
+ But yeah, there's not much work being
+
+12:51.320 --> 12:57.080
+ done on making user interface toolkills or whatever.
+
+12:57.080 --> 12:59.400
+ I mean, there's some, but they're not great.
+
+12:59.400 --> 13:03.160
+ At the same time, you've done a lot of stuff with Perl and Python.
+
+13:03.160 --> 13:08.320
+ So what does that fit into the picture of J and K and APL
+
+13:08.320 --> 13:08.880
+ and Python?
+
+13:08.880 --> 13:12.400
+ Well, it's just much more pragmatic.
+
+13:12.400 --> 13:13.960
+ In the end, you kind of have to end up
+
+13:13.960 --> 13:17.960
+ where the libraries are.
+
+13:17.960 --> 13:21.320
+ Because to me, my focus is on productivity.
+
+13:21.320 --> 13:23.800
+ I just want to get stuff done and solve problems.
+
+13:23.800 --> 13:27.360
+ So Perl was great.
+
+13:27.360 --> 13:29.760
+ I created an email company called Fastmail.
+
+13:29.760 --> 13:35.200
+ And Perl was great, because back in the late 90s, early 2000s,
+
+13:35.200 --> 13:38.160
+ it just had a lot of stuff it could do.
+
+13:38.160 --> 13:41.840
+ I still had to write my own monitoring system
+
+13:41.840 --> 13:43.840
+ and my own web framework and my own whatever,
+
+13:43.840 --> 13:45.760
+ because none of that stuff existed.
+
+13:45.760 --> 13:50.280
+ But it was a super flexible language to do that in.
+
+13:50.280 --> 13:52.720
+ And you used Perl for Fastmail.
+
+13:52.720 --> 13:54.520
+ You used it as a back end.
+
+13:54.520 --> 13:55.800
+ So everything was written in Perl?
+
+13:55.800 --> 13:56.520
+ Yeah.
+
+13:56.520 --> 13:58.720
+ Yeah, everything was Perl.
+
+13:58.720 --> 14:04.480
+ Why do you think Perl hasn't succeeded or hasn't dominated
+
+14:04.480 --> 14:07.120
+ the market where Python really takes over a lot of the
+
+14:07.120 --> 14:08.200
+ tests?
+
+14:08.200 --> 14:09.640
+ Well, I mean, Perl did dominate.
+
+14:09.640 --> 14:13.080
+ It was everything, everywhere.
+
+14:13.080 --> 14:19.920
+ But then the guy that ran Perl, Larry Wall,
+
+14:19.920 --> 14:22.280
+ just didn't put the time in anymore.
+
+14:22.280 --> 14:29.680
+ And no project can be successful if there isn't.
+
+14:29.680 --> 14:32.640
+ Particularly one that started with a strong leader that
+
+14:32.640 --> 14:35.040
+ loses that strong leadership.
+
+14:35.040 --> 14:38.040
+ So then Python has kind of replaced it.
+
+14:38.040 --> 14:45.040
+ Python is a lot less elegant language in nearly every way.
+
+14:45.040 --> 14:48.880
+ But it has the data science libraries.
+
+14:48.880 --> 14:51.240
+ And a lot of them are pretty great.
+
+14:51.240 --> 14:58.280
+ So I kind of use it because it's the best we have.
+
+14:58.280 --> 15:01.800
+ But it's definitely not good enough.
+
+15:01.800 --> 15:04.040
+ What do you think the future of programming looks like?
+
+15:04.040 --> 15:06.880
+ What do you hope the future of programming looks like if we
+
+15:06.880 --> 15:10.200
+ zoom in on the computational fields on data science
+
+15:10.200 --> 15:11.800
+ and machine learning?
+
+15:11.800 --> 15:19.440
+ I hope Swift is successful because the goal of Swift,
+
+15:19.440 --> 15:21.000
+ the way Chris Latna describes it,
+
+15:21.000 --> 15:22.640
+ is to be infinitely hackable.
+
+15:22.640 --> 15:23.480
+ And that's what I want.
+
+15:23.480 --> 15:26.920
+ I want something where me and the people I do research with
+
+15:26.920 --> 15:30.360
+ and my students can look at and change everything
+
+15:30.360 --> 15:32.000
+ from top to bottom.
+
+15:32.000 --> 15:36.240
+ There's nothing mysterious and magical and inaccessible.
+
+15:36.240 --> 15:38.600
+ Unfortunately, with Python, it's the opposite of that
+
+15:38.600 --> 15:42.640
+ because Python is so slow, it's extremely unhackable.
+
+15:42.640 --> 15:44.840
+ You get to a point where it's like, OK, from here on down
+
+15:44.840 --> 15:47.320
+ at C. So your debugger doesn't work in the same way.
+
+15:47.320 --> 15:48.920
+ Your profiler doesn't work in the same way.
+
+15:48.920 --> 15:50.880
+ Your build system doesn't work in the same way.
+
+15:50.880 --> 15:53.760
+ It's really not very hackable at all.
+
+15:53.760 --> 15:55.600
+ What's the part you like to be hackable?
+
+15:55.600 --> 16:00.120
+ Is it for the objective of optimizing training
+
+16:00.120 --> 16:02.600
+ of neural networks, inference of neural networks?
+
+16:02.600 --> 16:04.360
+ Is it performance of the system?
+
+16:04.360 --> 16:08.440
+ Or is there some nonperformance related, just creative idea?
+
+16:08.440 --> 16:09.080
+ It's everything.
+
+16:09.080 --> 16:15.480
+ I mean, in the end, I want to be productive as a practitioner.
+
+16:15.480 --> 16:18.440
+ So at the moment, our understanding of deep learning
+
+16:18.440 --> 16:20.080
+ is incredibly primitive.
+
+16:20.080 --> 16:21.520
+ There's very little we understand.
+
+16:21.520 --> 16:24.200
+ Most things don't work very well, even though it works better
+
+16:24.200 --> 16:26.200
+ than anything else out there.
+
+16:26.200 --> 16:28.760
+ There's so many opportunities to make it better.
+
+16:28.760 --> 16:34.360
+ So you look at any domain area like speech recognition
+
+16:34.360 --> 16:37.720
+ with deep learning or natural language processing
+
+16:37.720 --> 16:39.440
+ classification with deep learning or whatever.
+
+16:39.440 --> 16:41.960
+ Every time I look at an area with deep learning,
+
+16:41.960 --> 16:44.480
+ I always see like, oh, it's terrible.
+
+16:44.480 --> 16:47.560
+ There's lots and lots of obviously stupid ways
+
+16:47.560 --> 16:50.000
+ to do things that need to be fixed.
+
+16:50.000 --> 16:53.320
+ So then I want to be able to jump in there and quickly
+
+16:53.320 --> 16:54.880
+ experiment and make them better.
+
+16:54.880 --> 16:59.320
+ Do you think the programming language has a role in that?
+
+16:59.320 --> 17:00.280
+ Huge role, yeah.
+
+17:00.280 --> 17:07.080
+ So currently, Python has a big gap in terms of our ability
+
+17:07.080 --> 17:11.880
+ to innovate particularly around recurrent neural networks
+
+17:11.880 --> 17:16.840
+ and natural language processing because it's so slow.
+
+17:16.840 --> 17:20.200
+ The actual loop where we actually loop through words,
+
+17:20.200 --> 17:23.760
+ 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.
+