Jeremy Howard: fast.ai Deep Learning Courses and Research
Guests: Jeremy Howard
Full transcript · Read the full conversation in order, with its original context.
Contents12 chapters
§01Transcript · 00:00–09:18§02Transcript · 09:23–19:05§03Transcript · 19:07–29:14§04Transcript · 29:17–39:15§05Transcript · 39:19–48:17§06Transcript · 48:22–56:56§07Transcript · 56:58–1:05:02§08Transcript · 1:05:05–1:13:52§09Transcript · 1:13:53–1:23:18§10Transcript · 1:23:21–1:32:05§11Transcript · 1:32:06–1:40:48§12Transcript · 1:40:49–1:44:03§01Transcript · 00:00–09:18
The following is a conversation with Jeremy Howard.
He's the founder of Fast AI, a research institute
dedicated to making deep learning more accessible.
He's also a distinguished research scientist
at the University of San Francisco,
a former president of Kegel,
as well as a top-ranking competitor there.
And in general, he's a successful entrepreneur,
educator, researcher, and an inspiring personality
in the AI community.
When someone asks me, how do I get started with deep learning?
Fast AI is one of the top places I point them to.
It's free, it's easy to get started,
it's insightful and accessible.
And if I may say so, it has very little BS.
They can sometimes dilute the value of educational content
on popular topics like deep learning.
Fast AI has a focus on practical application of deep learning
and hands-on exploration of the cutting edge
that is incredibly both accessible to beginners
and useful to experts.
This is the Artificial Intelligence Podcast.
If you enjoy it, subscribe on YouTube,
give it five stars on iTunes, support it on Patreon,
or simply connect with me on Twitter,
@LexFriedman, spelled F-R-I-D-M-A-N.
And now, here's my conversation with Jeremy Howard.
What's the first program you ever written?
- First program I wrote that I remember
would be at high school.
I did an assignment where I decided to try to find out
if there were some better musical scales
than the normal 12-tone, 12-interval scale.
So I wrote a program on my Commodore 64 in BASIC
that searched through other scale sizes
to see if it could find one where
there were more accurate harmonies.
- Like mid-tone?
- Like you want an actual exactly three to two ratio,
whereas with a 12-interval scale,
it's not exactly three to two, for example.
So that's well-tempered, as they say in the--
- In BASIC on a Commodore 64.
- Yeah.
- Where was the interest in music from?
Or is it just--
- I did music all my life, so I played saxophone
and clarinet and piano and guitar and drums and whatever, so.
- How does that thread go through your life?
Where's music today?
- It's not where I wish it was.
For various reasons, couldn't really keep it going,
particularly 'cause I had a lot of problems with RSI,
with my fingers, and so I had to kind of like
cut back anything that used hands and fingers.
I hope one day I'll be able to get back to it health-wise.
- So there's a love for music underlying it all?
- For sure, yeah.
- What's your favorite instrument?
- Saxophone.
- Sax.
- It's a baritone saxophone.
Well, probably bass saxophone, but they're awkward.
- Well, I always love it when music
is coupled with programming.
There's something about a brain that utilizes those
that emerges with creative ideas.
So you've used and studied quite a few programming languages.
Can you give an overview of what you've used?
What are the pros and cons of each?
- Well, my favorite programming environment,
almost certainly, was Microsoft Access
back in the earliest days.
So that was Visual Basic for Applications,
which is not a good programming language,
but the programming environment was fantastic.
It's like the ability to create user interfaces
and tie data and actions to them and create reports
and all that, I've never seen anything as good.
There's things nowadays like Airtable,
which are like small subsets of that,
which people love for good reason,
but unfortunately nobody's ever achieved anything like that.
- What is that?
If you could pause on that for a second.
- Oh, Access?
- Access is a database.
- It was a database program that Microsoft produced,
part of Office, and it kind of withered, you know,
but basically it lets you in a totally graphical way
create tables and relationships and queries
and tie them to forms and set up, you know,
event handlers and calculations.
And it was a very complete, powerful system
designed for not massive scalable things,
but for like useful little applications that I loved.
- So what's the connection between Excel and Access?
- So very close.
So Access kind of was the relational database equivalent,
if you like.
So people still do a lot of that stuff
that should be in Access in Excel,
because they know it.
Excel's great as well.
So, but it's just not as rich a programming model
as VBA combined with a relational database.
And so I've always loved relational databases,
but today programming on top of a relational database
is just a lot more of a headache.
You know, you generally either need to kind of,
you know, you need something that connects,
that runs some kind of database server,
unless you use SQLite, which has its own issues.
Then you kind of often,
if you want to get a nice programming model,
you'll need to like create an, add an ORM on top.
And then, I don't know,
there's all these pieces to tie together,
and it's just a lot more awkward than it should be.
There are people that are trying to make it easier.
So in particular, I think of F#, you know, Don Syme,
who him and his team have done a great job
of making something like a database appear
in the type system.
So you actually get like tab completion for fields
and tables and stuff like that.
Anyway, so that was kind of, anyway,
so like that whole VBA office thing,
I guess was a starting point, which I still miss.
And I got into standard Visual Basic, which-
- That's interesting just to pause on that for a second.
It's interesting that you're connecting programming languages
to the ease of management of data.
- Yeah.
- So in your use of programming languages,
you always had a love and a connection with data.
- I've always been interested in doing useful things
for myself and for others,
which generally means getting some data
and doing something with it and putting it out there again.
So that's been my interest throughout.
So I also did a lot of stuff with AppleScript
back in the early days.
So it's kind of nice being able to get the computer
and computers to talk to each other
and to do things for you.
And then I think that one,
the programming language I most loved
then would have been Delphi, which was Object Pascal,
created by Anders Helsberg,
who previously did Turbo Pascal
and then went on to create .NET
and then went on to create TypeScript.
Delphi was amazing 'cause it was like a compiled,
fast language that was as easy to use as Visual Basic.
- Delphi, what is it similar to in more modern languages?
- Visual Basic.
- Visual Basic.
- Yeah, but a compiled fast version.
So I'm not sure there's anything quite like it anymore.
If you took like C# or Java
and got rid of the virtual machine
and replaced it with something,
you could compile a small type binary.
I feel like it's where Swift could get to
with the new Swift UI
and the cross-platform development going on.
Like that's one of my dreams
is that we'll hopefully get back to where Delphi was.
There is actually a free Pascal project nowadays
called Lazarus,
which is also attempting to kind of recreate Delphi.
So they're making good progress.
- So, okay, Delphi,
that's one of your favorite programming languages.
- Or at least programming environments.
Again, I'd say Pascal's not a nice language.
If you wanted to know specifically
about what languages I like,
I would definitely pick J
as being an amazingly wonderful language.
- What's J?
- J, are you aware of APL?
- I am not.
- Okay, so. - Except from doing
a little research on the work you've done.
- Okay, so not at all surprising
you're not familiar with it
'cause it's not well known,
but it's actually one of the main
families of programming languages
going back to the late '50s, early '60s.
So there was a couple of major directions.
One was the kind of Lambda calculus,
Alonzo Church direction,
which I guess kind of Lisp and Scheme and whatever,
which has a history going back
to the early days of computing.
The second was the kind of imperative slash OO,
algo, similar, going under C, C++, so forth.
§02Transcript · 09:23–19:05
There was a third,
which are called array-oriented languages,
which started with a paper by a guy called Ken Iverson,
which was actually a math theory paper,
not a programming paper.
It was called "Notation as a Tool for Thought."
And it was the development of a new type of math notation.
And the idea is that this math notation
was much more flexible, expressive,
and also well-defined than traditional math notation,
which is none of those things.
Math notation is awful.
And so he actually turned that into a programming language.
'Cause this was the early '50s,
or the, sorry, late '50s, all the names were available.
So he called his language a programming language, or APL.
- APL, wow.
- So APL is a implementation of notation
as a tool for thought, by which he means math notation.
And Ken and his son went on to do many things,
but eventually they actually produced
a new language that was built
on top of all the learnings of APL,
and that was called J.
And J is the most expressive, composable language of,
beautifully designed language I've ever seen.
- Does it have object-oriented components?
Does it have that kind of thing, or is it more like--
- Not really, it's an array-oriented language.
It's a new, it's the third path.
- Are you saying array?
- Array-oriented, yeah.
- What does it mean to be array-oriented?
- So array-oriented means that you generally
don't use any loops, but the whole thing is done
with kind of a extreme version of broadcasting,
if you're familiar with that NumPy/Python concept.
So you do a lot with one line of code.
It looks a lot like math notation.
- So it's basically--
- Highly compact.
And the idea is that you can kind of,
because you can do so much with one line of code,
a single screen of code is very unlikely to,
you very rarely need more than that to express your program.
And so you can kind of keep it all in your head,
and you can kind of clearly communicate it.
It's interesting, APL created two main branches,
K and J.
J is this kind of like open-source niche community
of crazy enthusiasts like me.
And then the other path, K, is fascinating.
It's an astonishingly expensive programming language,
which many of the world's most
ludicrously rich hedge funds use.
So the entire K machine is so small,
it sits inside level three cache on your CPU,
and it easily wins every benchmark I've ever seen
in terms of data processing speed.
But you don't come across it very much,
it's like $100,000 per CPU to run it.
But it's like this path of programming languages
is just so much, I don't know,
so much more powerful in every way
than the ones that almost anybody uses every day.
- So it's all about computation.
It's really focusing on computation.
- It's pretty heavily focused on computation.
I mean, so much of programming
is data processing by definition.
So there's a lot of things you can do with it.
But yeah, there's not much work being done
on making like user interface toolkits or whatever.
I mean, there's some, but they're not great.
- At the same time, you've done a lot of stuff
with Perl and Python.
- Yeah.
- So where does that fit into the picture
of J and K and APL and--
- Well, it's just much more pragmatic.
Like in the end, you kind of have to end up
where the libraries are,
'cause to me, my focus is on productivity.
I just wanna get stuff done and solve problems.
So Perl was great.
I created an email company called Fastmail
and Perl was great 'cause back in the late '90s,
early 2000s, it just had a lot of stuff it could do.
I still had to write my own monitoring system
and my own web framework, my own whatever,
'cause like none of that stuff existed,
but it was a super flexible language to do that in.
- And you used Perl for Fastmail, you used it as a backend?
Like, so everything was written in Perl?
- Yeah, yeah, everything was Perl.
- Why do you think Perl hasn't succeeded
or hasn't dominated the market
where Python really takes over a lot of the same tasks?
- Well, I mean, Perl did dominate.
It was-- - Four times.
- Everything, everywhere, but then the guy
that ran Perl, Larry Wool,
kind of just didn't put the time in anymore.
And no project can be successful if there isn't,
you know, particularly one that started
with a strong leader that loses that strong leadership.
So then Python has kind of replaced it.
You know, Python is a lot less elegant language
in nearly every way, but it has the data science libraries
and a lot of them are pretty great.
So I kind of use it 'cause it's the best we have,
but it's definitely not good enough.
- But what do you think the future of programming looks like?
What do you hope the future of programming looks like
if we zoom in on the computational fields,
on data science, on machine learning?
- I hope Swift is successful because the goal of Swift,
the way Chris Latner describes it,
is to be infinitely hackable, and that's what I want.
I want something where me and the people I do research with
and my students can look at and change everything
from top to bottom.
There's nothing mysterious and magical and inaccessible.
Unfortunately with Python, it's the opposite of that
because Python's so slow, it's extremely unhackable.
You get to a point where it's like,
okay, from here on down, it's C.
So your debugger doesn't work in the same way,
your profiler doesn't work in the same way,
your build system doesn't work in the same way.
It's really not very hackable at all.
- What's the part you like to be hackable?
Is it for the objective of optimizing training
of neural networks, inference of neural networks?
Is it performance of the system
or is there some non-performance related just--
- It's everything.
I mean, in the end, I wanna be productive as a practitioner.
So that means that, so like at the moment,
our understanding of deep learning is incredibly primitive.
There's very little we understand.
Most things don't work very well,
even though it works better than anything else out there.
There's so many opportunities to make it better.
So you look at any domain area, like, I don't know,
speech recognition with deep learning
or natural language processing classification
with deep learning or whatever.
Every time I look at an area with deep learning,
I always see like, oh, it's terrible.
There's lots and lots of obviously stupid ways to do things
that need to be fixed.
So then I wanna be able to jump in there
and quickly experiment and make them better.
- You think the programming language has a role in that?
- Huge role, yeah.
So currently Python has a big gap
in terms of our ability to innovate,
particularly around recurrent neural networks
and natural language processing,
because it's so slow.
The actual loop where we actually loop through words,
we have to do that whole thing in CUDA C.
So we actually can't innovate with the kernel,
the heart of that most important algorithm.
And it's just a huge problem.
And this happens all over the place.
So we hit research limitations.
Another example, convolutional neural networks,
which are actually the most popular architecture
for lots of things, maybe most things in deep learning.
We almost certainly should be using
sparse convolutional neural networks,
but only like two people are,
because to do it, you have to rewrite
all of that CUDA C level stuff.
And yeah, just researchers and practitioners don't.
So like there's just big gaps
in like what people actually research on,
what people actually implement
because of the programming language problem.
- So you think it's just too difficult to write in CUDA C
that a programming, like a higher level programming language
like Swift should enable the easier,
fooling around creative stuff with RNNs
or with sparse convolutional neural networks?
- Kind of.
- Who's at fault?
Who's at charge of making it easy
for a researcher to play around?
- I mean, no one's at fault.
It's just nobody's got around to it yet.
Or it's just, it's hard, right?
And I mean, part of the fault
is that we ignored that whole APL kind of direction,
almost nearly everybody did for 60 years, 50 years.
But recently people have been starting
to reinvent pieces of that
and kind of create some interesting new directions
in the compiler technology.
§03Transcript · 19:07–29:14
So the place where that's particularly happening right now
is something called MLIR,
which is something that again,
Chris Latner, the Swift guy is leading.
And yeah, 'cause it's actually not gonna be Swift
on its own that solves this problem
because the problem is that currently writing
a acceptably fast GPU program
is too complicated regardless of what language you use.
And that's just because if you have to deal with the fact
that I've got 10,000 threads
and I have to synchronize between them all
and I have to put my thing into grid blocks
and think about warps and all this stuff,
it's just so much boilerplate that to do that well,
you have to be a specialist at that
and it's gonna be a year's work to optimize
that algorithm in that way.
But with things like tensor comprehensions
and tile and MLIR and TVM,
there's all these various projects
which are all about saying,
let's let people create like domain specific languages
for tensor computations.
These are the kinds of things we do generally on the GPU
for deep learning and then have a compiler
which can optimize that tensor computation.
A lot of this work is actually sitting on top
of a project called Halide,
which is a mind blowing project
where they came up with such a domain specific language.
In fact, two, one domain specific language for expressing
this is what my tensor computation is.
And another domain specific language for expressing
this is the kind of the way I want you to structure
the compilation of that and like do it block by block
and do these bits in parallel.
And they were able to show how you can compress
the amount of code by 10X compared to optimized GPU code
and get the same performance.
So that's like, so these are the things
that kind of sitting on top of that kind of research
and MLIR is pulling a lot of those best practices together.
And now we're starting to see work done on making
all of that directly accessible through Swift
so that I could use Swift to kind of write
those domain specific languages.
And hopefully we'll get then Swift CUDA kernels
written in a very expressive and concise way
that looks a bit like J in APL
and then Swift layers on top of that
and then a Swift UI on top of that.
And, you know, that'll be so nice
if we can get to that point.
- Now, does it all eventually boil down
to CUDA and NVIDIA GPUs?
- Unfortunately at the moment it does,
but one of the nice things about MLIR
if AMD ever gets their act together,
which they probably won't,
is that they or others could write MLIR backends
for other GPUs or other tensor computation devices
of which today there are increasing number
like Graph Core or Vertex AI or whatever.
So yeah, being able to target lots of backends
would be another benefit of this.
And the market really needs competition
'cause at the moment NVIDIA is massively overcharging
for their kind of enterprise class cards
because there is no serious competition
'cause nobody else is doing the software properly.
- In the cloud there is some competition, right?
But-
- Not really, other than TPUs perhaps.
But TPUs are almost unprogrammable at the moment.
- So you can't, the TPUs has the same problem that you can't-
- It's even worse.
So TPUs, Google actually made an explicit decision
to make them almost entirely unprogrammable
because they felt that there was too much IP in there.
And if they gave people direct access to program them,
people would learn their secrets.
So you can't actually directly program the memory in a TPU.
You can't even directly create code that runs on
and that you look at on the machine that has the GPU.
It all goes through a virtual machine.
So all you can really do is this kind of cookie cutter thing
of like plug-in high-level stuff together,
which is just super tedious and annoying
and totally unnecessary.
- So what was the, tell me if you could,
the origin story of fast AI?
- Fast AI?
- The origin story of fast AI.
What is the motivation, its mission, its dream?
- So I guess the founding story is heavily tied
to my previous startup, which is a company called Analytic,
which was the first company to focus on deep learning
for medicine.
And I created that because I saw there was a huge
opportunity to, there's about a 10X shortage
of the number of doctors in the world,
in the developing world that we need.
Expected it would take about 300 years
to train enough doctors to meet that gap.
But I guess that maybe if we used deep learning
for some of the analytics, we could maybe make it
so you don't need as highly trained doctors.
- For diagnosis?
- For diagnosis and treatment planning.
- Where's the biggest benefit, just before we get
to fast AI, where's the biggest benefit of AI in medicine
that you see today?
- Not much happening today in terms of like stuff
that's actually out there, it's very early,
but in terms of the opportunity, it's to take markets
like India and China and Indonesia,
which have big populations, Africa,
small numbers of doctors, and provide diagnostic,
particularly treatment planning and triage kind of on device
so that if you do a test for malaria or tuberculosis
or whatever, you immediately get something
that even a healthcare worker that's had a month
of training can get a very high quality assessment
of whether the patient might be at risk and tell,
okay, we'll send them off to a hospital.
So for example, in Africa, outside of South Africa,
there's only five pediatric radiologists
for the entire continent, so most countries don't have any.
So if your kid is sick and they need something diagnosed
through medical imaging, the person, even if you're able
to get medical imaging done, the person that looks at it
will be a nurse at best, but actually in India, for example,
and China, almost no x-rays are read by anybody,
by any trained professional because they don't have enough.
So if instead we had a algorithm that could take
the most likely high risk 5% and say, triage basically,
say, okay, someone needs to look at this,
it would massively change the kind of way that
what's possible with medicine in the developing world.
And remember, increasingly, they have money.
They're the developing world, they're not the poor world,
they're the developing world, so they have the money,
so they're building the hospitals,
they're getting the diagnostic equipment,
but there's no way for a very long time
will they be able to have the expertise.
- Shortage of expertise, okay, and that's where
the deep learning systems can step in
and magnify the expertise they do have, essentially.
- Yeah.
- So you do see, just to linger it a little bit longer,
the interaction, do you still see the human experts
still at the core of these systems?
- Yeah, absolutely.
- Or is there something in medicine that could be automated
almost completely?
- I don't see the point of even thinking about that,
because we have such a shortage of people,
why would we want to find a way not to use them?
Like, we have people, so the idea of,
even from an economic point of view,
if you can make them 10x more productive,
getting rid of the person doesn't impact
your unit economics at all, and it totally ignores the fact
that there are things people do better than machines.
So it's just, to me, that's not a useful way
of framing the problem.
- I guess, just to clarify, I guess I meant
there may be some problems where you can avoid
even going to the expert ever, sort of maybe preventative
care or some basic stuff, allowing the expert to focus
on the things that are really that, you know.
- Well, that's what the triage would do, right?
So the triage would say, okay, this 99% triage,
sure, there's nothing here.
So, you know, that can be done on device,
and they can just say, okay, go home.
So the experts are being used to look at the stuff
which has some chance it's worth looking at,
which most things is not, you know, it's fine.
- Why do you think we haven't quite made progress
on that yet, in terms of the scale of how much AI
is applied in the method?
- There's a lot of reasons.
I mean, one is it's pretty new.
I only started in Lytic in like 2014, and before that,
like, it's hard to express to what degree
the medical world was not aware of the opportunities here.
So I went to RSNA, which is the world's largest
radiology conference, and I told everybody I could,
you know, like, I'm doing this thing with deep learning,
please come and check it out.
And no one had any idea what I was talking about,
and no one had any interest in it.
So like, we've come from absolute zero, which is hard,
and then the whole regulatory framework, education system,
everything is just set up to think of doctoring
in a very different way.
So today, there is a small number of people
§04Transcript · 29:17–39:15
who are deep learning practitioners and doctors
at the same time, and we're starting to see
the first ones come out of their PhD programs,
so Zach Kahan over in Boston, Cambridge,
has a number of students now who are data science experts,
deep learning experts, and actual medical doctors.
Quite a few doctors have completed our fast AI course now
and are publishing papers and creating journal reading
groups in the American Council of Radiology,
and like, it's just starting to happen.
But it's gonna be a long process.
The regulators have to learn how to regulate this,
they have to build, you know, guidelines,
and then the lawyers at hospitals have to develop
a new way of understanding that sometimes it makes sense
for data to be, you know, looked at in raw form
in large quantities in order to create
world-changing results.
- Yeah, so regulation around data, all that,
it sounds, well, it's probably the hardest problem,
but sounds reminiscent of autonomous vehicles as well.
Many of the same regulatory challenges,
many of the same data challenges.
- Yeah, I mean, funnily enough,
the problem is less the regulation
and more the interpretation of that regulation
by lawyers in hospitals.
So HIPAA is actually, was designed to,
the P in HIPAA is not standing,
does not stand for privacy, it stands for portability.
It's actually meant to be a way that data can be used.
And it was created with lots of gray areas
because the idea is that would be more practical
and it would help people to use this legislation
to actually share data in a more thoughtful way.
Unfortunately, it's done the opposite
because when a lawyer sees a gray area,
they say, oh, if we don't know, we won't get sued,
then we can't do it.
So HIPAA is not exactly the problem.
The problem is more that there's,
hospital lawyers are not incented to make bold decisions
about data portability.
- Or even to embrace technology that saves lives.
They more wanna not get in trouble
for embracing that technology.
- Also, it is also, saves lives in a very abstract way,
which is like, oh, we've been able to release
these 100,000 anonymized records.
I can't point at the specific person
whose life that saved.
I can say like, oh, we ended up with this paper,
which found this result, which diagnosed a thousand
more people than we would have otherwise,
but it's like, which ones were helped?
It's very abstract.
- Yeah, and on the counter side of that,
you may be able to point to a life that was taken
because of something that was--
- Yeah, or a person whose privacy was violated.
It's like, oh, this specific person,
you know, was de-identified.
- So-- - Identified.
- Just a fascinating topic.
We're jumping around.
We'll get back to fast AI, but on the question of privacy,
data is the fuel for so much innovation in deep learning.
What's your sense on privacy,
whether we're talking about Twitter, Facebook, YouTube,
just the technologies like in the medical field
that rely on people's data in order to create impact.
How do we get that right, respecting people's privacy
and yet creating technology that is learned from data?
- One of my areas of focus is on doing more with less data,
which, so most vendors, unfortunately,
are strongly incented to find ways
to require more data and more computation.
So Google and IBM being the most obvious--
- IBM.
- Yeah, so Watson. - Watson.
- So Google and IBM both strongly push the idea
that you have to be, you know,
that they have more data and more computation
and more intelligent people than anybody else.
And so you have to trust them to do things
'cause nobody else can do it.
And Google's very upfront about this.
Like Jeff Dean has gone out there and given talks
and said, "Our goal is to require
"a thousand times more computation, but less people."
Our goal is to use the people that you have better
and the data you have better
and the computation you have better.
So one of the things that we've discovered is,
or at least highlighted, is that you very, very,
very often don't need much data at all.
And so the data you already have in your organization
will be enough to get state-of-the-art results.
So like my starting point would be to kind of say
around privacy is a lot of people are looking for ways
to share data and aggregate data,
but I think often that's unnecessary.
They assume that they need more data than they do
'cause they're not familiar with the basics
of transfer learning, which is this critical technique
for needing orders of magnitude less data.
- Is your sense, one reason you might wanna collect data
from everyone is like in the recommender system context,
where your individual, Jeremy Howard's individual data
is the most useful for providing a product
that's impactful for you.
So for giving you advertisements,
for recommending to you movies,
for doing medical diagnosis.
Is your sense we can build with a small amount of data,
general models that will have a huge impact for most people
that we don't need to have data from each individual?
- On the whole, I'd say yes.
I mean, there are things like,
you know, recommender systems have this cold start problem
where, you know, Jeremy is a new customer.
We haven't seen him before.
So we can't recommend him things based on what else
he's bought and liked with us.
And there's various workarounds to that.
Like in a lot of music programs,
we'll start out by saying,
which of these artists do you like?
Which of these albums do you like?
Which of these songs do you like?
Netflix used to do that.
Nowadays, they tend not to.
People kind of don't like that
'cause they think, oh, we don't wanna bother the user.
So you could work around that
by having some kind of data sharing
where you get my marketing record from Axiom or whatever
and try to guestion that.
To me, the benefit to me and to society
of saving me five minutes on answering some questions
versus the negative externalities
of the privacy issue doesn't add up.
So I think like a lot of the time,
the places where people are invading our privacy
in order to provide convenience
is really about just trying to make them more money
and they move these negative externalities
to places that they don't have to pay for them.
So when you actually see regulations appear
that actually cause the companies
that create these negative externalities
to have to pay for it themselves,
they say, well, we can't do it anymore.
So the cost is actually too high.
But for something like medicine,
yeah, I mean, the hospital has my medical imaging,
my pathology studies, my medical records.
And also I own my medical data.
So I help a startup called DocAI.
One of the things DocAI does is that it has an app
you can connect to Sutter Health and LabCorp and Walgreens
and download your medical data to your phone
and then upload it again at your discretion
to share it as you wish.
So with that kind of approach,
we can share our medical information
with the people we want to.
- Yeah, so control.
I mean, really being able to control
who you share it with and so on.
So that has a beautiful, interesting tangent,
but to return back to the origin story of Fast.ai.
All right, so before I started Fast.ai,
I spent a year researching
where are the biggest opportunities for deep learning?
'Cause I knew from my time at Kaggle in particular
that deep learning had kind of hit this threshold point
where it was rapidly becoming the state-of-the-art approach
in every area that looked at it.
And I'd been working with neural nets for over 20 years.
I knew that from a theoretical point of view,
once it hit that point, it would do that
in kind of just about every domain.
And so I kind of spent a year researching
what are the domains that's gonna have
the biggest low-hanging fruit in the shortest time period.
I picked medicine, but there were so many I could have picked
and so there was a kind of level of frustration for me
of like, okay, I'm really glad we've opened up
the medical deep learning world
and today it's huge, as you know,
but we can't do, I can't do everything.
I don't even know, like in medicine,
it took me a really long time to even get a sense
of like what kind of problems do medical practitioners solve?
What kind of data do they have?
Who has that data?
So I kind of felt like I need to approach this differently
if I wanna maximize the positive impact of deep learning.
Rather than me picking an area
§05Transcript · 39:19–48:17
and trying to become good at it and building something,
I should let people who are already domain experts
in those areas and who already have the data
do it themselves.
So that was the reason for Fast.ai
is to basically try and figure out
how to get deep learning into the hands of people
who could benefit from it and help them to do so
in as quick and easy and effective a way as possible.
- Got it, so sort of empower the domain experts.
- Yeah, and like partly it's 'cause like,
unlike most people in this field,
my background is very applied and industrial.
Like my first job was at McKinsey and Company.
I spent 10 years in management consulting.
I spend a lot of time with domain experts,
so I kind of respect them and appreciate them
and I know that's where the value generation in society is.
And so I also know how most of them can't code
and most of them don't have the time to invest,
you know, three years in a graduate degree or whatever.
So it's like, how do I upskill those domain experts?
I think that would be a super powerful thing,
you know, biggest societal impact I could have.
So yeah, that was the thinking.
- So, so much of Fast.ai students and researchers
and the things you teach are pragmatically minded,
practically minded, figuring out ways
how to solve real problems and fast.
So from your experience, what's the difference
between theory and practice of deep learning?
- Well, most of the research in the deep mining world
is a total waste of time.
- Right, that's what I was getting at.
- Yeah, it's a problem in science in general.
Scientists need to be published,
which means they need to work on things
that their peers are extremely familiar with
and can recognize and advance in that area.
So that means that they all need to work on the same thing.
And so it really, and the thing they work on,
there's nothing to encourage them to work on things
that are practically useful.
So you get just a whole lot of research,
which is minor advances in stuff
that's been very highly studied
and has no significant practical impact.
Whereas the things that really make a difference,
like I mentioned transfer learning,
like if we can do better at transfer learning,
then it's this like world-changing thing
where suddenly like lots more people
can do world-class work with less resources and less data.
But almost nobody works on that.
Or another example, active learning,
which is the study of like,
how do we get more out of the human beings in the loop?
- That's my favorite topic.
- Yeah, so active learning is great,
but it's almost nobody working on it
because it's just not a trendy thing right now.
- You know what, somebody started to interrupt.
He was saying that nobody is publishing on active learning,
but there's people inside companies,
anybody who actually has to solve a problem,
they're going to innovate on active learning.
- Yeah, everybody kind of reinvents active learning
when they actually have to work in practice
because they start labeling things and they think,
gosh, this is taking a long time and it's very expensive.
And then they start thinking,
well, why am I labeling everything?
I'm only, the machine's only making mistakes
on those two classes, they're the hard ones.
Maybe I'll just start labeling those two classes.
And then you start thinking,
well, why did I do that manually?
Why can't I just get the system to tell me
which things are gonna be hardest?
It's an obvious thing to do,
but yeah, it's just like transfer learning,
it's understudied and the academic world
just has no reason to care about practical results.
The funny thing is, I've only really ever written one paper.
I hate writing papers and I didn't even write it.
It was my colleague, Sebastian Ruder, who actually wrote it.
I just did the research for it,
but it was basically introducing transfer learning,
successful transfer learning to NLP for the first time.
The algorithm is called ULMfit.
And I actually wrote it for the course,
for the first AI course.
I wanted to teach people NLP
and I thought I only wanna teach people practical stuff.
And I think the only practical stuff is transfer learning.
And I couldn't find any examples of transfer learning in NLP,
so I just did it.
And I was shocked to find that as soon as I did it,
which the basic prototype took a couple of days,
smashed the state of the art
on one of the most important data sets
in a field that I knew nothing about.
And I just thought, well, this is ridiculous.
And so I spoke to Sebastian about it
and he kindly offered to write it up, the results.
And so it ended up being published in ACL,
which is the top computational linguistics conference.
So like people do actually care once you do it,
but I guess it's difficult for maybe like junior researchers
or like, I don't care whether I get citations
or papers or whatever.
There's nothing in my life that makes that important,
which is why I've never actually bothered
to write a paper myself.
But for people who do,
I guess they have to pick the kind of safe option,
which is like, yeah, make a slight improvement
on something that everybody's already working on.
- Yeah, nobody does anything interesting
or succeeds in life with the safe option.
- Although, I mean, the nice thing is nowadays,
everybody is now working on NLP transfer learning
because since that time we've had GPT and GPT-2 and BERT
and it's like, it's so, yeah,
once you show that something's possible,
everybody jumps in, I guess.
- I hope to be a part of,
and I hope to see more innovation
in active learning in the same way.
I think transfer learning and active learning
are fascinating public open work.
- I actually helped start a startup called Platform AI,
which is really all about active learning.
And yeah, it's been interesting trying to kind of
see what research is out there and make the most of it.
And there's basically none.
So we've had to do all our own research.
- Once again, and just as you described.
Can you tell the story of the Stanford competition,
DawnBench and Fast.ai's achievement on it?
- Sure, so something which I really enjoy
is that I basically teach two courses a year,
the practical deep learning for coders,
which is kind of the introductory course
and then cutting edge deep learning for coders,
which is the kind of research level course.
And while I teach those courses,
I basically have a big office
at the University of San Francisco,
it'd be enough for like 30 people.
And I invite anybody, any student who wants to come
and hang out with me while I build the course.
And so generally it's full.
And so we have 20 or 30 people in a big office
with nothing to do, but study deep learning.
So it was during one of these times
that somebody in the group said,
"Oh, there's a thing called DawnBench,
it looks interesting."
And I was like, "What the hell is that?"
And they set out some competition
to see how quickly you can train a model.
Seems kind of not exactly relevant to what we're doing,
but it sounds like the kind of thing
which you might be interested in.
I checked it out and I was like,
"Oh crap, there's only 10 days till it's over.
It's pretty much too late."
And we're kind of busy trying to teach this course.
But we're like, "Oh, it would make an interesting
case study for the course.
Like it's all the stuff we're already doing.
Why don't we just put together
our current best practices and ideas?"
So me and I guess about four students
just decided to give it a go.
And we focused on this small one called Cypher 10,
which is little 32 by 32 pixel images.
- Can you say what DawnBench is?
- Yeah, so it's a competition
to train a model as fast as possible.
It was run by Stanford.
- And as cheap as possible too.
- That's also another one for as cheap as possible.
And there's a couple of categories, ImageNet and Cypher 10.
So ImageNet is this big 1.3 million image thing
that took a couple of days to train.
Remember a friend of mine, Pete Warden,
who's now at Google.
I remember he told me how he trained ImageNet
a few years ago, and he basically like had this
little granny flat out the back
that he turned into his ImageNet training center.
And he figured, you know, after like a year of work,
he figured out how to train it in like 10 days or something.
It's like, that was a big job.
Well, Cypher 10 at that time, you could train in a few hours.
You know, it was much smaller and easier.
So we thought we'd try Cypher 10.
And yeah, I'd really never done that before.
§06Transcript · 48:22–56:56
Like I'd never really, like things like using more
than one GPU at a time was something I tried to avoid.
'Cause to me, it's like very against the whole idea
of accessibility is you should be able to do things
with one GPU.
- I mean, have you asked in the past before,
after having accomplished something,
how do I do this faster, much faster?
- Oh, always, but it's always, for me, it's always,
how do I make it much faster on a single GPU
that a normal person could afford in their day-to-day life?
It's not, how could I do it faster by, you know,
having a huge data center?
'Cause to me, it's all about like,
as many people should be able to use something as possible
without fussing around with infrastructure.
So anyways, in this case, it's like, well,
we can use eight GPUs just by renting a AWS machine.
So we thought we'd try that.
And yeah, basically using the stuff we were already doing,
we were able to get, you know, the speed,
you know, within a few days, we had the speed down to,
I don't know, a very small number of minutes.
I can't remember exactly how many minutes it was,
but it might've been like 10 minutes or something.
And so, yeah, we found ourselves at the top
of the leaderboard easily for both time and money,
which really shocked me
'cause the other people competing in this
were like Google and Intel and stuff
were like, know a lot more about this stuff
than I think we do.
So then we were emboldened.
We thought, let's try the ImageNet one too.
I mean, it seemed way out of our league,
but our goal was to get under 12 hours.
And we did, which was really exciting.
And, but we didn't put anything up on the leaderboard,
but we were down to like 10 hours,
but then Google put in some,
like five hours or something,
we're just like, oh, we're so screwed.
But we kind of thought we'll keep trying,
if Google can do it in five,
I mean, Google did on five hours on some,
on like a TPU pod or something,
like a lot of hardware.
But we kind of like had a bunch of ideas to try,
like a really simple thing was,
why are we using these big images?
They're like 224 or 256 by 256 pixels.
Why don't we try smaller ones?
- And just to elaborate,
there's a constraint on the accuracy
that your train model is supposed to achieve.
- Yeah, you gotta achieve 93%,
I think it was for ImageNet, exactly.
- Which is very tough, so you have to-
- Yeah, 93%, like they picked a good threshold.
It was a little bit higher
than what the most commonly used ResNet-50 model
could achieve at that time.
So yeah, so it's quite a difficult problem to solve.
But yeah, we realized if we actually
just use 64 by 64 images,
it trained a pretty good model.
And then we could take that same model
and just give it a couple of epochs
to learn 224 by 224 images.
And it was basically already trained,
which makes a lot of sense.
Like if you teach somebody,
like here's what a dog looks like
and you show them low res versions,
and then you say, here's a really clear picture of a dog,
they already know what a dog looks like.
So that like, just, we jumped to the front
and we ended up winning parts of that competition.
We actually ended up doing a distributed version
over multiple machines a couple of months later
and ended up at the top of the leaderboard.
We had 18 minutes.
- (laughs) ImageNet.
- Yeah, and it was,
and people have just kept on blasting through
again and again since then, so.
- So what's your view on multi GPU
or multiple machine training in general
as a way to speed code up?
- I think it's largely a waste of time.
- Both multi GPU on a single machine and?
- Yeah, particularly multi machines
'cause it's just clunky.
Multi GPUs is less clunky than it used to be.
But to me, anything that slows down your iteration speed
is a waste of time.
So you could maybe do your very last,
you know, perfecting of the model on multi GPUs
if you need to.
But, so for example,
I think doing stuff on ImageNet is generally a waste of time.
Why test things on 1.3 million images?
Most of us don't use 1.3 million images.
And we've also done research that shows that
doing things on a smaller subset of images
gives you the same relative answers anyway.
So from a research point of view, why waste that time?
So actually I released a couple of new datasets recently.
One is called ImageNet,
the French ImageNet, which is a small subset of ImageNet,
which is designed to be easy to classify.
- What's, how do you spell ImageNet?
- It's got an extra T and E at the end
'cause it's very French.
- Image, okay.
- Yeah, and then another one called ImageWolf,
which is a subset of ImageNet that only contains dog breeds.
- And that's a hard one, right?
- That's a hard one.
And I've discovered that if you just look
at these two subsets,
you can train things on a single GPU in 10 minutes
and the results you get directly transferable
to ImageNet nearly all the time.
And so now I'm starting to see some researchers
start to use these much smaller datasets.
- So deeply love the way you think
because I think you might've written a blog post saying
that sort of going to these big datasets
is encouraging people to not think creatively.
- Absolutely.
- So you're too, it sort of constrains you
to train on large resources.
And because you have these resources,
you think more resources will be better.
And then you start, so like somehow you kill the creativity.
- Yeah, and even worse than that, Lex,
I keep hearing from people who say,
"I decided not to get into deep learning
because I don't believe it's accessible
to people outside of Google to do useful work."
So like I see a lot of people make an explicit decision
to not learn this incredibly valuable tool
because they've drunk the Google Kool-Aid,
which is that only Google's big enough
and smart enough to do it.
And I just find that so disappointing and it's so wrong.
- And I think all of the major breakthroughs in AI
in the next 20 years will be doable on a single GPU.
Like I would say my sense is all the big sort of-
- Well, let's put it this way.
None of the big breakthroughs of the last 20 years
have required multiple GPUs.
So like batch norm, value, dropout.
- To demonstrate that there's something to that.
- Every one of them, none of them has required multiple GPUs.
- GANs, the original GANs didn't require multiple GPUs.
- Well, and we've actually recently shown
that you don't even need GANs.
So we've developed GAN level outcomes without needing GANs.
And we can now do it with, again,
by using transfer learning,
we can do it in a couple of hours on a single GPU.
- Just using a generated model,
like without the adversarial part?
- Yeah, so we've found loss functions
that work super well without the adversarial part.
And then one of our students, a guy called Jason Antich,
has created a system called DeOldify,
which uses this technique to colorize
old black and white movies.
You can do it on a single GPU,
colorize a whole movie in a couple of hours.
And one of the things that Jason and I did together
was we figured out how to add a little bit of GAN
at the very end, which it turns out for colorization
makes it just a bit brighter and nicer.
And then Jason did masses of experiments
to figure out exactly how much to do,
but it's still all done on his home machine
on a single GPU in his lounge room.
And like, if you think about like
colorizing Hollywood movies,
that sounds like something a huge studio would have to do,
but he has the world's best results on this.
- There's this problem of microphones.
We're just talking to microphones now.
It's such a pain in the ass to have these microphones
to get good quality audio.
And I tried to see if it's possible to plop down
a bunch of cheap sensors and reconstruct
higher quality audio from multiple sources.
'Cause right now I haven't seen work from,
okay, we can save inexpensive mics,
automatically combining audio from multiple sources
to improve the combined audio.
People haven't done that.
And that feels like a learning problem.
So hopefully somebody can.
- Well, I mean, it's eminently doable
§07Transcript · 56:58–1:05:02
and it should have been done by now.
I felt the same way about computational photography
four years ago.
Why are we investing in big lenses
when three cheap lenses,
plus actually a little bit of intentional movement?
So like take a few frames,
gives you enough information to get excellent sub-pixel
resolution, which particularly with deep learning,
you would know exactly what you're meant to be looking at.
We can totally do the same thing with audio.
I think it's madness that it hasn't been done yet.
- Is there been progress on the photography company?
- Yeah, photography is basically a standard now.
So the Google Pixel Night Light,
I don't know if you've ever tried it,
but it's astonishing.
You take a picture in almost pitch black
and you get back a very high quality image.
And it's not because of the lens.
Same stuff with like adding the bokeh
to the background blurring done computationally.
- This is the pixel right here.
- Yeah, basically everybody now is doing most
of the fanciest stuff on their phones
with computational photography.
And also increasingly people are putting more than one lens
on the back of the camera.
So the same will happen for audio for sure.
- And there's applications in the audio side.
If you look at an Alexa type device,
most people I've seen, especially I worked at Google before,
when you look at noise background removal,
you don't think of multiple sources of audio.
You don't play with that as much as I would hope people would.
- But I mean, you can still do it even with one.
Like again, it's not much work's been done in this area.
So we're actually gonna be releasing an audio library soon,
which hopefully will encourage development of this
'cause it's so underused.
The basic approach we used for our super resolution
in which Jason uses for DeOldify
of generating high quality images,
the exact same approach would work for audio.
No one's done it yet,
but it would be a couple of months work.
- Okay, also learning rate in terms of DawnBench.
There's some magic on learning rate
that you played around with.
That's kind of interesting.
- Yeah, so this is all work that came
from a guy called Leslie Smith.
Leslie's a researcher who like us cares a lot
about just the practicalities of training neural networks
quickly and accurately,
which you would think is what everybody should care about,
but almost nobody does.
And he discovered something very interesting,
which he calls super convergence,
which is there are certain networks
that with certain settings of high parameters
could suddenly be trained 10 times faster
by using a 10 times higher learning rate.
Now, no one published that paper
because it's not an area of kind of active research
in the academic world.
No academics recognize this is important.
And also deep learning in academia
is not considered a experimental science.
So unlike in physics where you could say like,
I just saw a subatomic particle do something
which the theory doesn't explain,
you could publish that without an explanation.
And then in the next 60 years,
people can try to work out how to explain it.
We don't allow this in the deep learning world.
So it's literally impossible for Leslie
to publish a paper that says,
I've just seen something amazing happen.
This thing trained 10 times faster than it should have.
I don't know why.
And so the reviewers were like,
well, you can't publish that 'cause you don't know why.
So anyway.
- That's important to pause on
because there's so many discoveries
that would need to start like that.
- Every other scientific field I know of works that way.
I don't know why ours is uniquely disinterested
in publishing unexplained experimental results,
but there it is.
So it wasn't published.
Having said that,
I read a lot more unpublished papers than published papers
'cause that's where you find the interesting insights.
So I absolutely read this paper.
And I was just like,
this is astonishingly mind-blowing and weird and awesome.
And like, why isn't everybody only talking about this?
Because like, if you can train these things 10 times faster,
they also generalize better
because you're doing less epochs,
which means you look at the data less,
you get better accuracy.
So I've been kind of studying that ever since.
And eventually Leslie kind of figured out
a lot of how to get this done.
And we added minor tweaks
and a big part of the trick
is starting at a very low learning rate,
very gradually increasing it.
So as you're training your model,
you would take very small steps at the start
and you gradually make them bigger and bigger
until eventually you're taking much bigger steps
than anybody thought was possible.
There's a few other little tricks to make it work,
but basically we can reliably get super convergence.
And so for the DawnBench thing,
we were using just much higher learning rates
than people expected to work.
- What do you think the future of,
I mean, it makes so much sense for that
to be a critical hyperparameter learning rate that you vary.
What do you think the future
of learning rate magic looks like?
- Well, there's been a lot of great work
in the last 12 months in this area.
And people are increasingly realizing that,
like we just have no idea really how optimizers work.
And the combination of weight decay,
which is how we regularize optimizers
and the learning rate,
and then other things like the epsilon we use
in the atom optimizer,
they all work together in weird ways.
And different parts of the model,
this is another thing we've done a lot of work on
is research into how different parts of the model
should be trained at different rates in different ways.
So we do something we call discriminative learning rates,
which is really important,
particularly for transfer learning.
So really I think in the last 12 months,
a lot of people have realized
that all this stuff is important,
there's been a lot of great work coming out,
and we're starting to see algorithms appear,
which have very, very few dials,
if any, that you have to touch.
So I think what's gonna happen
is the idea of a learning rate,
it almost already has disappeared in the latest research.
And instead it's just like,
we know enough about how to interpret the gradients
and the change of gradients we see
to know how to set every parameter.
- That you can automate it.
So you see the future of deep learning,
where really, where's the input of a human expert needed?
- Well, hopefully the input of a human expert
will be almost entirely unneeded
from the deep learning point of view.
So again, like Google's approach to this
is to try and use thousands of times more compute
to run lots and lots of models at the same time
and hope that one of them is good.
- AutoML kind of?
- Yeah, AutoML kind of stuff, which I think is insane.
(laughing)
When you better understand the mechanics
of how models learn,
you don't have to try a thousand different models
to find which one happens to work the best.
You can just jump straight to the best one,
which means that it's more accessible
in terms of compute, cheaper,
and also with less hyperparameters to set,
it means you don't need deep learning experts
to train your deep learning model for you,
which means that domain experts can do more of the work,
which means that now you can focus the human time
on the kind of interpretation, the data gathering,
identifying model errors and stuff like that.
- Yeah, the data side.
How often do you work with data these days
in terms of the cleaning, looking at it?
Like Darwin looked at different species
while traveling about.
Do you look at data?
Have you in your roots in Kaggle?
- Always, yeah. - Just look at data?
- Yeah, I mean, it's a key part of our course
is like before we train a model in the course,
we see how to look at the data.
And then after, the first thing we do
after we train our first model,
which we fine tune an ImageNet model for five minutes.
And then the thing we immediately do after that
is we learn how to analyze the results of the model
§08Transcript · 1:05:05–1:13:52
by looking at examples of misclassified images
and looking at a classification matrix
and then doing like research on Google
to learn about the kinds of things that it's misclassifying.
So to me, one of the really cool things
about machine learning models in general
is that when you interpret them,
they tell you about things like,
what are the most important features?
Which groups you're misclassifying?
And they help you become a domain expert more quickly
because you can focus your time on the bits
that the model is telling you is important.
So it lets you deal with things like data leakage,
for example, if it says,
"Oh, the main feature I'm looking at is customer ID."
You know, when you're like,
"Oh, customer ID shouldn't be predictive."
And then you can talk to the people
that manage customer IDs and they'll tell you like,
"Oh yes, as soon as a customer's application is accepted,
we add a one on the end of their customer ID or something."
So yeah, model, looking at data,
particularly from the lens of which parts of the data
the model says is important is super important.
- Yeah, and using the model to almost debug the data
to learn more about the data.
- Exactly.
- What are the different cloud options
for training your networks?
Last question related to DawnBench.
Well, it's part of a lot of the work you do,
but from a perspective of performance,
I think you've written this in a blog post.
There's AWS, there's a TPU from Google.
What's your sense, what the future holds?
What would you recommend now in terms of-
- So from a hardware point of view,
Google's TPUs and the best Nvidia GPUs are similar.
I mean, maybe the TPUs are like 30% faster,
but they're also much harder to program.
There isn't a clear leader in terms of hardware right now,
although much more importantly,
the Nvidia GPUs are much more programmable.
They've got much more written for all of them.
So like that's the clear leader for me
and where I would spend my time
as a researcher and practitioner.
But then in terms of the platform,
I mean, we're super lucky now
with stuff like Google GCP, Google Cloud,
and AWS that you can access a GPU pretty quickly and easily.
But I mean, for AWS, it's still too hard.
Like you have to find an AMI and get the instance running
and then install the software you want and blah, blah, blah.
GCP is still, is currently the best way to get started
on a full server environment
because they have a fantastic fast AI
and PyTorch ready to go instance,
which has all the courses pre-installed.
It has Jupyter Notebook pre-running.
Jupyter Notebook is this wonderful
interactive computing system,
which everybody basically should be using
for any kind of data-driven research.
But then even better than that,
there are platforms like Salamander,
which we own and Paperspace,
where literally you click a single button
and it pops up a Jupyter Notebook straight away
without any kind of installation or anything.
And all the course notebooks are all pre-installed.
So like for me, this is one of the things
we spent a lot of time kind of curating and working on.
'Cause when we first started our courses,
the biggest problem was people dropped out of lesson one
'cause they couldn't get an AWS instance running.
So things are so much better now.
And like we actually have, if you go to course.fast.ai,
the first thing it says is,
"Here's how to get started with your GPU."
And there's like, you just click on the link
and you click start and you're going.
- You will go GCP.
I have to confess, I've never used the Google GCP.
- Yeah, GCP gives you $300 of compute for free,
which is really nice.
But as I say, Salamander and Paperspace
are even easier still.
- Okay.
So from the perspective of deep learning frameworks,
you work with Fast.ai, if you go to this framework,
and PyTorch and TensorFlow.
What are the strengths of each platform?
- Sure. - Your perspective.
- So in terms of what we've done our research on
and taught in our course,
we started with Theano and Keras.
And then we switched to TensorFlow and Keras.
And then we switched to PyTorch
and then we switched to PyTorch and Fast.ai.
And that kind of reflects a growth and development
of the ecosystem of deep learning libraries.
Theano and TensorFlow were great,
but were much harder to teach
and to do research and development on
because they define what's called a computational graph
up front, a static graph,
where you basically have to say,
here are all the things that I'm going to eventually do
in my model.
And then later on you say,
okay, do those things with this data.
And you can't like debug them,
you can't do them step-by-step,
you can't program them interactively
in a Jupyter notebook and so forth.
PyTorch was not the first,
but PyTorch was certainly the strongest entrant
to come along and say,
let's not do it that way,
let's just use normal Python.
And everything you know about in Python
is just gonna work.
And we'll figure out how to make that run on the GPU
as and when necessary.
That turned out to be a huge leap
in terms of what we could do with our research
and what we could do with our teaching.
- 'Cause it wasn't limiting.
- Yeah, I mean, it was critical for us
for something like DawnBench
to be able to rapidly try things.
It's just so much harder to be a researcher
and practitioner when you have to do everything up front
and you can't inspect it.
The problem with PyTorch is
it's not at all accessible to newcomers
because you have to write your own training loop
and manage the gradients and all this stuff.
And it's also not great for researchers
because you're spending your time
dealing with all this boilerplate and overhead
rather than thinking about your algorithm.
So we ended up writing this very multi-layered API
that at the top level,
you can train a state-of-the-art neural network
in three lines of code.
And which kind of talks to an API,
which talks to an API, which talks to an API,
which like you can dive into at any level
and get progressively closer to the machine
kind of levels of control.
And this is the Fast.ai library.
That's been critical for us and for our students
and for lots of people that have won
big learning competitions with it
and written academic papers with it.
It's made a big difference.
We're still limited though by Python
and particularly this problem with things like
recurrent neural nets say where you just can't change things
unless you accept it going so slowly that it's impractical.
So in the latest incarnation of the course
and with some of the research we're now starting to do,
we're starting to do some stuff in Swift.
I think we're three years away
from that being super practical,
but I'm in no hurry.
I'm very happy to invest the time to get there.
But with that, we actually already have a nascent version
of the Fast.ai library for vision
running on Swift for TensorFlow.
'Cause Python for TensorFlow is not gonna cut it.
It's just a disaster.
What they did was they tried to replicate
the bits that people were saying they like about PyTorch,
this kind of interactive computation,
but they didn't actually change
their foundational runtime components.
So they kind of added this like syntax sugar
they call TF eager, TensorFlow eager,
which makes it look a lot like PyTorch,
but it's 10 times slower than PyTorch to actually do a step.
So because they didn't invest the time
in like retooling the foundations
'cause their code base is so horribly complex.
- Yeah, I think it's probably very difficult
to do that kind of retooling.
- Yeah, well, particularly the way TensorFlow was written,
it was written by a lot of people very quickly
in a very disorganized way.
So like when you actually look in the code, as I do often,
I'm always just like, oh God, what were they thinking?
It's just, it's pretty awful.
So I'm really extremely negative
about the potential future for Python.
- TensorFlow, Python for TensorFlow.
- But Swift for TensorFlow
can be a different beast altogether.
§09Transcript · 1:13:53–1:23:18
It can be like, it can basically be a layer on top of MLIR
that takes advantage of all the great compiler stuff
that Swift builds on with LLVM.
And yeah, it could be,
I think it will be absolutely fantastic.
- Well, you're inspiring me to try.
I haven't truly felt the pain of TensorFlow 2.0 Python.
It's fine by me, but-
- Yeah, I mean, it does the job
if you're using like predefined things
that somebody's already written.
But if you actually compare, you know,
like I've had to do,
'cause I've been having to do a lot of stuff
with TensorFlow recently,
you actually compare like,
okay, I want to write something from scratch.
And you're like, I just keep finding it's like,
oh, it's running 10 times slower than PyTorch.
- So is the biggest cost,
let's throw running time out the window,
how long it takes you to program?
- That's not too different now.
Thanks to TensorFlow Eager, that's not too different.
But because so many things take so long to run,
you wouldn't run it at 10 times slower.
Like you just go like, oh, this is taking too long.
And also there's a lot of things
which are just less programmable,
like tf.data, which is the way data processing works
in TensorFlow is just this big mess.
It's incredibly inefficient.
And I kind of had to write it that way
because of the TPU problems I described earlier.
So I just, you know,
I just feel like they've got this huge technical debt,
which they're not going to solve
without starting from scratch.
- So here's an interesting question then.
If there's a new student starting today,
what would you recommend they use?
- Well, I mean, we obviously recommend Fast.ai and PyTorch
because we teach new students
and that's what we teach with.
So we would very strongly recommend that
because it will let you get on top of the concepts
much more quickly.
So then you'll become an actual,
and you'll also learn the actual state of the art techniques,
you know, so you actually get world-class results.
Honestly, it doesn't much matter what library you learn
because switching from Chainer to MXNet
to TensorFlow to PyTorch is gonna be a couple of days work
as long as you understand the foundation as well.
- But you think we'll swift creep in there
as a thing that people start using?
- Not for a few years,
particularly because like Swift has no data science community,
libraries, tooling. - So code bases are out there.
- And the Swift community has a total lack of appreciation
and understanding of numeric computing.
So like they keep on making stupid decisions,
you know, for years they've just done dumb things
around performance and prioritization.
That's clearly changing now
because the developer of Swift, Chris Latner,
is working at Google on Swift for TensorFlow.
So like that's a priority.
It'll be interesting to see what happens with Apple
because like Apple hasn't shown any sign of caring
about numeric programming in Swift.
So I mean, hopefully they'll get off their ass
and start appreciating this
'cause currently all of their low level libraries
are not written in Swift.
They're not particularly Swifty at all,
stuff like core ML, they're really pretty rubbish.
So yeah, so there's a long way to go,
but at least one nice thing is that Swift for TensorFlow
can actually directly use Python code and Python libraries
in literally the entire lesson one notebook of fast AI
runs in Swift right now in Python mode.
So that's a nice intermediate thing.
- How long does it take,
if you look at the two fast AI courses,
how long does it take to get from point zero
to completing both courses?
- It varies a lot.
Somewhere between two months and two years generally.
- So for two months, how many hours a day?
- So like somebody who is a very competent coder
can do 70 hours per course and-
- 70, seven zero, that's it?
Okay.
- But a lot of people I know take a year off
to study fast AI full time and say at the end of the year,
they feel pretty competent.
'Cause generally there's a lot of other things you do.
Like generally they'll be entering Kaggle competitions.
They might be reading Ian Goodfellow's book.
They might, you know, they'll be doing a bunch of stuff.
And often, you know, particularly if they
are a domain expert, their coding skills
might be a little on the pedestrian side.
So part of it's just like doing a lot more writing.
- What do you find is the bottleneck for people usually,
except getting started and setting stuff up?
- I would say coding.
- Just-
- Yeah, I would say the best,
the people who are strong coders pick it up the best.
Although another bottleneck is people who have a lot
of experience of classic statistics can really struggle
because the intuition is so the opposite
of what they're used to.
They're very used to like trying to reduce the number
of parameters in their model and looking
at individual coefficients and stuff like that.
So I find people who have a lot of coding background
and know nothing about statistics are generally
gonna be the best off.
- So you taught several courses on deep learning
and as Feynman says,
"The best way to understand something is to teach it."
What have you learned about deep learning from teaching it?
- A lot.
It's a key reason for me to teach the courses.
I mean, obviously it's gonna be necessary
to achieve our goal of getting domain experts
to be familiar with deep learning,
but it was also necessary for me to achieve my goal
of being really familiar with deep learning.
I mean, to see so many domain experts
from so many different backgrounds,
it's definitely, I wouldn't say taught me,
but convinced me something that I liked to believe
was true, which was anyone can do it.
So there's a lot of kind of snobbishness out there
about only certain people can learn to code,
only certain people are gonna be smart enough to do AI.
That's definitely bullshit.
I've seen so many people
from so many different backgrounds get state-of-the-art
results in their domain areas now.
It's definitely taught me that the key differentiator
between people that succeed and people that fail
is tenacity.
That seems to be basically the only thing that matters.
The people, a lot of people give up.
And, but of the ones who don't give up,
pretty much everybody succeeds.
Even if at first I'm just kind of like thinking like,
wow, they really aren't quite getting it yet, are they?
But eventually people get it and they succeed.
So I think that's been,
I think they're both things I've liked to believe was true,
but I don't feel like I really had strong evidence
for them to be true,
but now I can say I've seen it again and again.
- So what advice do you have for someone
who wants to get started in deep learning?
- Train lots of models.
That's how you learn it.
So like, so I would, you know, I think, it's not just me.
I think our course is very good,
but also lots of people independently have said
it's very good.
It recently won the COGx award for AI courses
as being the best in the world.
I'd say come to our course, course.fast.ai.
And the thing I keep on hopping on in my lessons
is train models, print out the inputs to the models,
print out to the outputs to the models,
like study, you know, change the inputs a bit,
look at how the outputs vary,
just run lots of experiments to get a, you know,
an intuitive understanding of what's going on.
- To get hooked, do you think, you mentioned training,
do you think just running the models inference?
Like if we talk about getting started.
- No, you've got to fine tune the models.
So that's the critical thing,
'cause at that point you now have a model
that's in your domain area.
So there's no point running somebody else's model
'cause it's not your model.
Like, so it only takes five minutes to fine tune a model
for the data you care about.
And in lesson two of the course,
we teach you how to create your own dataset from scratch
by scripting Google image search.
So, and we show you how to actually create
a web application running online.
So I create one in the course that differentiates
between a teddy bear, a grizzly bear, and a brown bear.
And it does it with basically a hundred percent accuracy.
Took me about four minutes to scrape the images
from Google search in the script.
There's a little graphical widgets we have in the notebook
that help you clean up the dataset.
§10Transcript · 1:23:21–1:32:05
There's other widgets that help you study the results
to see where the errors are happening.
And so now we've got over a thousand replies
in our share your work here thread of students saying,
here's the thing I built.
And so there's people who like,
and a lot of them are state of the art.
Like somebody said, oh, I tried looking
at Devan Garey characters and I couldn't believe it.
The thing that came out was more accurate
than the best academic paper after lesson one.
And then there's others which are just more kind of fun.
Like somebody who's doing Trinidad and Tobago hummingbirds.
She said, that's kind of their national bird.
And she's got something that can now classify a Trinidad
and Tobago hummingbirds.
So yeah, train models, fine tune models with your dataset
and then study their inputs and outputs.
- How much is Fast.ai courses?
- Free.
Everything we do is free.
We have no revenue sources of any kind.
It's just a service to the community.
- You're a saint.
Okay.
Once a person understands the basics,
trains a bunch of models.
If we look at the scale of years,
what advice do you have for someone wanting
to eventually become an expert?
- Train lots of models.
(laughing)
Specifically train lots of models in your domain area.
So an expert what, right?
We don't need more expert,
like create slightly evolutionary research
in areas that everybody's studying.
We need experts at using deep learning
to diagnose malaria.
Or we need experts at using deep learning
to analyze language to study media bias.
So we need experts in analyzing fisheries
to identify problem areas in the ocean.
That's what we need.
So like become the expert in your passion area.
And this is a tool which you can use
for just about anything.
And you'll be able to do that thing better
than other people, particularly by combining it
with your passion and domain expertise.
- So that's really interesting.
Even if you do wanna innovate on transfer learning
or active learning, your thought is,
I mean, it's one I certainly share,
is you also need to find a domain or a dataset
that you actually really care for.
- Right.
If you're not working on a real problem that you understand,
how do you know if you're doing it any good?
How do you know if your results are good?
How do you know if you're getting bad results?
Why are you getting bad results?
Is it a problem with the data?
How do you know you're doing anything useful?
Yeah, to me, the only really interesting research
is not the only, but the vast majority
of interesting research is like try
and solve an actual problem and solve it really well.
- So both understanding sufficient tools
on the deep learning side and becoming a domain expert
in a particular domain are really things within reach
for anybody.
- Yeah, I mean, to me, I would compare it
to like studying self-driving cars,
having never looked at a car or been in a car
or turned a car on, which is like the way it is
for a lot of people.
They'll study some academic dataset
where they literally have no idea about that.
- By the way, I'm not sure how familiar
with autonomous vehicles, but that is literally,
you describe a large percentage of robotics folks
working in self-driving cars
is they actually haven't considered driving.
They haven't actually looked at what driving looks like.
They haven't driven.
- Right, and it's a problem because you know,
when you've actually driven, you know,
like these are the things that happened to me
when I was driving.
- There's nothing that beats the real world examples
of just experiencing them.
You've created many successful startups.
What does it take to create a successful startup?
- Same thing as becoming a successful
deep learning practitioner, which is not giving up.
So you can run out of money or run out of time
or run out of something, you know,
but if you keep costs super low
and try and save up some money beforehand
so you can afford to have some time,
then just sticking with it is one important thing.
Doing something you understand and care about is important.
By something, I don't mean,
the biggest problem I see with deep learning people
is they do a PhD in deep learning
and then they try and commercialize their PhD,
which is a waste of time
'cause that doesn't solve an actual problem.
You picked your PhD topic 'cause it was an interesting
kind of engineering or math or research exercise.
But yeah, if you've actually spent time as a recruiter
and you know that most of your time
was spent sifting through resumes
and you know that most of the time
you're just looking for certain kinds of things
and you can try doing that with a model for a few minutes
and see whether that's something which the model
seems to be able to do as well as you could,
then you're on the right track to creating a startup.
And then I think just, yeah, being,
just be pragmatic and try and stay away
from venture capital money as long as possible,
preferably forever.
- So yeah, on that point, do you,
venture capital, so did you,
were you able to successfully run startups
with self-funded for quite a while?
- Yeah, so my first two were self-funded
and that was the right way to do it.
- Is that scary?
- No, VC startups are much more scary
because you have these people on your back
who do this all the time and who have done it for years
telling you, "Grow, grow, grow, grow."
And they don't care if you fail,
they only care if you don't grow fast enough.
So that's scary, whereas doing the ones myself,
well, with partners who were friends,
it's nice 'cause we just went along at a pace
that made sense and we were able to build it to something
which was big enough that we never had to work again,
but it was not big enough that any VC
would think it was impressive.
And that was enough for us to be excited.
So I thought that's a much better way
to do things than most people.
- In generally speaking, not for yourself,
but how do you make money during that process?
Do you cut into savings?
- So yeah, so I started Fastmail and Optimal Decisions
at the same time in 1999 with two different friends.
And for Fastmail, I guess I spent $70 a month on the server.
And when the server ran out of space,
I put a payments button on the front page
and said, "If you want more than 10 megs of space,
you have to pay $10 a year."
And- - So run low,
like keep your cost down.
- Yeah, so I kept my cost down.
And once I needed to spend more money,
I asked people to spend the money for me.
And that was that basically from then on,
we were making money and I was profitable from then.
For Optimal Decisions, it was a bit harder
'cause we were trying to sell something
that was more like a $1 million sale.
But what we did was we would sell scoping projects.
So kind of like prototype-y projects,
but rather than doing it for free,
we would sell them for 50 to $100,000.
So again, we were covering our costs
and also making the client feel
like we were doing something valuable.
So in both cases, we were profitable
from six months in.
- Ah, nevertheless, it's scary.
- I mean, yeah, sure.
I mean, it's scary before you jump in.
And I guess I was comparing it to the scarediness of VC.
I felt like with VC stuff, it was more scary,
kind of much more in somebody else's hands,
will they fund you or not?
And what do they think of what you're doing?
I also found it very difficult with VC-backed startups
to actually do the thing which I thought was important
for the company rather than doing the thing
which I thought would make the VC happy.
Now, VCs always tell you not to do the thing
that makes them happy.
But then if you don't do the thing that makes them happy,
they get sad, so.
- And do you think optimizing for the,
whatever they call it, the exit,
is a good thing to optimize for?
- I mean, it can be, but not at the VC level,
'cause the VC exit needs to be, you know, a thousand X.
So, where else the lifestyle exit,
if you can sell something for $10 million,
you've made it, right?
§11Transcript · 1:32:06–1:40:48
So, I don't, it depends.
If you wanna build something that's gonna,
you're kind of happy to do forever, then fine.
If you wanna build something you wanna sell
in three years time, that's fine too.
I mean, they're both perfectly good outcomes.
- So, you're learning Swift now, in a way.
I mean, you already-- - Trying to.
- And I read that you use, at least in some cases,
spaced repetition as a mechanism for learning new things.
I use Anki quite a lot myself.
- Yeah, me too.
- I actually never talked to anybody about it.
Don't know how many people do it,
but it works incredibly well for me.
Can you talk through your experience?
Like, how did you, what do you,
first of all, okay, let's back it up.
What is spaced repetition?
- So, spaced repetition is an idea created
by a psychologist named Ebbinghaus.
I don't know, must be a couple of hundred years ago
or something, 150 years ago.
He did something which sounds pretty damn tedious.
He wrote down random sequences of letters on cards
and tested how well he would remember
those random sequences a day later,
a week later, whatever.
He discovered that there was this kind of a curve
where his probability of remembering one of them
would be dramatically smaller the next day
and then a little bit smaller the next day
and a little bit smaller the next day.
What he discovered is that if he revised those cards
after a day, the probabilities would decrease
at a smaller rate.
And then if he revised them again a week later,
they would decrease at a smaller rate again.
And so he basically figured out a roughly optimal equation
for when you should revise something you wanna remember.
So spaced repetition learning is using
this simple algorithm, just something like
revise something after a day and then three days
and then a week and then three weeks and so forth.
And so if you use a program like Anki, as you know,
it will just do that for you.
And it will say, did you remember this?
And if you say no, it will reschedule it back
to appear again like 10 times faster
than it otherwise would have.
It's a kind of a way of being guaranteed to learn something
because by definition, if you're not learning it,
it will be rescheduled to be revised more quickly.
Unfortunately though, it's also like,
it doesn't let you fool yourself.
If you're not learning something,
you know like your revisions will just get more and more.
So you have to find ways to learn things productively
and effectively like treat your brain well.
So using like mnemonics and stories
and context and stuff like that.
So yeah, it's a super great technique.
It's like learning how to learn is something which
everybody should learn before they actually learn anything,
but almost nobody does.
- So what have you, so it certainly works well
for learning new languages, for, I mean,
for learning like small projects almost,
but do you, you know, I started using it for,
I forget who wrote a blog post about this inspired me.
It might've been you, I'm not sure.
Is, I started when I read papers,
I'll concepts and ideas, I'll put them.
- Was it Michael Nielsen?
- It was Michael Nielsen.
- Yeah, so Michael started doing this recently
and has been writing about it.
I, so the kind of today's Ebbinghaus
is a guy called Peter Wozniak
who developed a system called SuperMemo.
And he's been basically trying to become like
the world's greatest Renaissance man
over the last few decades.
He's basically lived his life with space repetition,
learning for everything.
I, and sort of like,
Michael's only very recently got into this,
but he started really getting excited about doing it
for a lot of different things.
For me personally, I actually don't use it
for anything except Chinese.
And the reason for that is that Chinese
is specifically a thing I made a conscious decision
that I want to continue to remember,
even if I don't get much of a chance to exercise it,
'cause like I'm not often in China, so I don't.
Or else something like programming languages or papers,
I have a very different approach,
which is I try not to learn anything from them,
but instead I try to identify the important concepts
and like actually ingest them.
So like really understand that concept deeply
and study it carefully.
I will decide if it really is important,
if it is like incorporated into our library,
incorporated into how I do things
or decide it's not worth it.
So I find I then remember the things that I care about
because I'm using it all the time.
So for the last 25 years,
I've committed to spending at least half of every day
learning or practicing something new,
which is all my colleagues have always hated
because it always looks like I'm not working on
what I'm meant to be working on,
but it always means I do everything faster
because I've been practicing a lot of stuff.
So I kind of give myself a lot of opportunity
to practice new things.
And so I find now I don't,
yeah, I don't often kind of find myself
wishing I could remember something
'cause if it's something that's useful,
then I've been using it a lot.
It's easy enough to look it up on Google,
but speaking Chinese, you can't look it up on Google.
- Do you have advice for people learning new things?
So if you, what have you learned as a process?
I mean, it all starts with just making the hours
and the day available.
- Yeah, you gotta stick with it,
which is, again, the number one thing
that 99% of people don't do.
So the people I started learning Chinese with,
none of them were still doing it 12 months later.
I'm still doing it 10 years later.
I tried to stay in touch with them,
but they just, no one did it.
For something like Chinese,
like study how human learning works.
So every one of my Chinese flashcards
is associated with a story,
and that story is specifically designed to be memorable.
And we find things memorable,
which are like funny or disgusting or sexy
or related to people that we know or care about.
So I try to make sure all the stories that are in my head
have those characteristics.
Yeah, so you have to, you know,
you won't remember things well
if they don't have some context.
And yeah, you won't remember them well
if you don't regularly practice them,
whether it be just part of your day-to-day life
or the Chinese, I mean, flashcards.
I mean, the other thing is,
let yourself fail sometimes.
So like I've had various medical problems
over the last few years,
and basically my flashcards just stopped
for about three years.
And then there've been other times
I've stopped for a few months,
and it's so hard because you get back to it,
and it's like, you have 18,000 cards due.
It's like, and so you just have to go,
all right, well, I can either stop and give up everything
or just decide to do this every day
for the next two years until I get back to it.
The amazing thing has been that even after three years,
you know, the Chinese was still in there.
Like it was so much faster to relearn
than it was to learn the first time.
- Yeah, absolutely.
It's in there.
I have the same with guitar, with music and so on.
It's sad because the work sometimes takes away,
and then you won't play for a year.
But really, if you then just get back to it every day,
you're right there again.
What do you think is the next big breakthrough
in artificial intelligence?
What are your hopes in deep learning or beyond
that people should be working on,
or you hope there'll be breakthroughs?
- I don't think it's possible to predict.
I think what we already have
is an incredibly powerful platform
to solve lots of societally important problems
that are currently unsolved.
So I just hope that lots of people
will learn this toolkit and try to use it.
I don't think we need a lot of new technological breakthroughs
to do a lot of great work right now.
- And when do you think we're going to create
a human level intelligence system?
Do you think- - Don't know.
- How hard is it?
How far away are we?
- Don't know.
§12Transcript · 1:40:49–1:44:03
- Don't know. - I have no way to know.
I don't know.
I don't know why people make predictions about this
'cause there's no data and nothing to go on.
And it's just like,
there's so many societally important problems
to solve right now.
I just don't find it a really interesting question
to even answer.
- So in terms of societally important problems,
what's the problem that is within reach?
- Well, I mean, for example,
there are problems that AI creates, right?
So more specifically,
labor force displacement is going to be huge
and people keep making this frivolous econometric argument
of being like, oh, there's been other things that aren't AI
that have come along before
and haven't created massive labor force displacement,
therefore AI won't.
- So that's a serious concern for you?
- Oh, yeah. - Andrew Yang is running on it.
- Yeah, I'm desperately concerned.
And you see already that the changing workplace
has led to a hollowing out of the middle class.
You're seeing that students coming out of school today
have a less rosy financial future ahead of them
than their parents did,
which has never happened in the last few hundred years.
We've always had progress before.
And you see this turning into anxiety and despair
and even violence.
So I very much worry about that.
- You've written quite a bit about ethics too.
- I do think that every data scientist
working with deep learning needs to recognize
they have an incredibly high leverage tool
that they're using that can influence society
in lots of ways.
And if they're doing research,
that that research is gonna be used by people
doing this kind of work.
And they have a responsibility to consider the consequences
and to think about things like
how will humans be in the loop here?
How do we avoid runaway feedback loops?
How do we ensure an appeals process for humans
that are impacted by my algorithm?
How do I ensure that the constraints of my algorithm
are adequately explained to the people
that end up using them?
There's all kinds of human issues
which only data scientists are actually in the right place
to educate people about.
But data scientists tend to think of themselves
as just engineers and that they don't need
to be part of that process.
- For now.
- Yeah, which is wrong.
- Well, you're in a perfect position to educate them better,
to read literature, to read history, to learn from history.
Well, Jeremy, thank you so much for everything you do,
for inspiring a huge amount of people,
getting them into deep learning
and having the ripple effects,
the flap of a butterfly's wings
that will probably change the world.
So thank you very much.
- Cheers.
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