October 3, 2023 • 31 mins
Open-source innovation is the future of AI. In this episode of Smart Talks with IBM, Malcolm Gladwell and Tim Harford discuss the open-source AI community with Jeff Boudier, head of product and growth at Hugging Face. They chat about the history and future of open-source AI, its critical importance to AI progress, the IBM watsonx partnership with Hugging Face, and how businesses can leverage open-source AI for their specific needs.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:04):
Welcome to Tech Stuff, a production from iHeartRadio. Today, we
are witnessed to one of those rare moments in history,
the rise of an innovative technology with the potential to
radically transform business and society forever. That technology, of course,
(00:24):
is artificial intelligence, and it's the central focus for this
new season of Smart Talks with IBM. Join hosts from
your favorite Pushkin podcasts as they talk with industry experts
and leaders to explore how businesses can integrate AI into
their workflows and help drive real change in this new
era of AI, and of course, host Malcolm Gladwell will
(00:46):
be there to guide you through the season and throw
in his two cents as well. Look out for new
episodes of Smart Talks with IBM every other week on
the iHeartRadio app, Apple Podcasts, wherever you get your podcasts,
and learn more at IBM dot com slash smart Talks.
Speaker 2 (01:04):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
we're continuing our conversation with new creators visionaries who are
creatively applying technology in business to drive change, but with
a focus on the transformative power of artificial intelligence and
(01:28):
what it means to leverage AI as a game changing
multiplier for your business. Our guest today is Jeff Boutier,
head of Product and Growth at hugging Face, the leading
open source and open science artificial intelligence platform. An engineer
by background, he has a self professed obsession with the
(01:49):
business of technology. Recently, IBM and hugging Face announced a
collaboration bringing together hugging faces repositories of open source AI
models with IBM's Watson X platform. It's a move that
gives businesses even more access to AI while staying true
to IBM's long standing philosophy of supporting open source technology.
(02:14):
With open source, businesses can build better AI models that
suit their specific needs using their own proprietary data while
browsing a ready catalog of pre trained models. In today's episode,
you'll hear why open source is so crucial to the
advancement of AI, how IBM's Watson X interacts with open
(02:36):
source AI, and Jeff's thoughts on why this singular omnipotent
AI model is a myth. Jeff spoke with Tim Harford,
host of the Pushkin podcast Cautionary Tales, a longtime columnist
at the Financial Times, where he writes the Undercover Economist.
Tim is also a BBC broadcaster with his show More
(02:57):
or Less. Okay, let's get to the interview.
Speaker 3 (03:08):
I am a Jeff Boudier and I'm a product director
at hugging.
Speaker 4 (03:12):
Face, So I'm immediately intrigue. Hugging Face. Is this a
reference to the Alien movie or something else?
Speaker 3 (03:20):
It is not, and it may be not obvious to
a listener, but hugging Face is the name of that
cute emoji, you know, the one that's smiling with his
two hands extended like that to give you a big hug.
That's hugging Face. So basically we name the company after
an emoji.
Speaker 4 (03:40):
And it is I saw your website and it is
a very friendly emoji. So that's that's nice. So tell
us a little bit about hugging Face and about what
you do that.
Speaker 3 (03:48):
Of course, hugging Face is the leading open platform for
AI builders, and it's the place that's all the AI
researchers use to share their work, their new AI models
and collaborate around them. It's the place where the data
(04:09):
scientists go and find those pre train models and access
them and use them and work with them, and increasingly
it's the place where developers are coming to turn all
of these AI models and data sets into their own applications,
their own features.
Speaker 4 (04:30):
So it's like the I don't know, the Facebook group
or the Reddit or the Twitter for people who are
interested in particularly generative language AI, or all kinds of
artificial intelligence.
Speaker 3 (04:42):
All kinds of AI really, and of course generative AIS
this new wave that has caught the world by storm.
But on Hiking Face you can find any kind of model,
the new sort of transformers models to do anything from
translation or if you wanted to transcribe what I'm saying
(05:04):
into text, then you would use a transformer model. If
you wanted to then take that text and make a summary,
that would be another transformer model. If you wanted to
create a nice little thumbnail for this podcast by typeing
a sentence, that would be another type of model. So
all these models you can find. There's actually three hundred
(05:26):
thousands that are free and publicly accessible. You can find
them on our website at Hikingphase dot co and use
them using our open source libraries.
Speaker 4 (05:38):
And so this is this is fascinating. So there are
three hundred thousand models. Now when you say model, I'm
thinking in my head, oh, it's kind of like a
computer program. There were three hundred thousand computer programs. Is
that roughly right or it not?
Speaker 3 (05:52):
Really, it's a general idea. A model is a giant
set of numbers that are working together to sift through
some inputs that you're going to give it. So think
of it of a big black box filled with numbers,
(06:14):
and you give it as an input, maybe some text,
maybe a prompt, so you're asking, you're giving an instruction
to the model, or maybe you give it an image
as an input, and then it will sift through that
information thanks to all of these numbers, which we call
(06:35):
in the field parameters, and it will produce an output.
So when I told you, hey, we can transcribe this
conversation into text, the input would have been the conversation
in an audio file, and then the output would have
been the text of the transcription. If you want to
create a thumbnail for this podcast episode, then the input
(06:57):
would be what we call the prompt, which is really
a text description like a Frenchman in San Francisco talking
about machine learning, and the output would be completely original image.
So that's how I think about what an AI model is,
and I think what we're starting to realize is that
(07:20):
this is becoming the new way of building technology in
the world. It has been for the field of dealing
understanding generating text for quite some time, but now it's
sort of moving across every field of technology. We have
models to create images, as I say, but also to
(07:41):
generate new proteins to make predictions on numerical data. So
every kind of field of machine learning is now using
this new type of models. But what's interesting is that
if you're, say a product manager at a company, and
you say, hey, I want to build a feature that
(08:03):
does this. A few years ago, the approach would have
been to ask a software developer to write a thousand
lines of code in order to build a prototype. And
the new way of doing things today is to go
look for an off the shelf pre train model that
does a pretty good job at solving exactly that problem,
(08:27):
so you can create a prototype of that feature fast.
So it's a new approach of building tech.
Speaker 4 (08:33):
I'm not a programmer, but I'm aware that there was
this idea of open source code, and now we have
open source models. So what does it mean for something
to be open source.
Speaker 3 (08:43):
Open source AI actually means a lot of different specific things.
It's the open source implementation of the model. So if
you use the Hugging Phase transformers library to use a model,
you're using an open source code library to use that model.
Speaker 4 (09:03):
Just to end up on the transformers. These are these
kind of ways of turning a picture of a dog
into a text output that says, hey, this is a
picture of a dog, or this is a French text
and with the transformers helping you turn it into English text,
or it's doing all of these things that you've been describing.
That's the transformer is the kind of the engine at
(09:23):
the heart of that.
Speaker 3 (09:25):
Yes, exactly. And we call them transformers because they correspond
to this new way of building machine learning models that
was introduced by Google actually with a very important paper
called Attention is All You Need and that was published
in twenty seventeen by researchers out of Google Deep Mind.
Speaker 4 (09:47):
Well that's just six years so new.
Speaker 3 (09:51):
It is very new, and ever since the piece of
innovation of like new model architectures has real really accelerated.
But it really started from this inflection point that came
from this paper and its implementation in what is now
called Transformer models, the transformer that has conquered every area
(10:16):
of machine learning since.
Speaker 4 (10:18):
Okay, so say turned up. So you've got this library
of Transformer models and that open source, and that means
that means what anyone can use them for free, or
that anybody can implement them for free. What does it mean?
Speaker 3 (10:32):
So again, there's lots that go into it, but the
most important thing is for the model itself to be
available so that a data scientists or an engineer can
download them and use them. And also there are a
lot of considerations about how you make them accessible, and
(10:54):
a very important one is whether or not you give
access to the training data, all the information that went
into training that model and teaching it to do what
it's trained to do.
Speaker 4 (11:09):
So I might have fed millions of words into a
into a language transformer, or I might have fed millions
of photographs into a into a picture transformer.
Speaker 3 (11:18):
Yeah, yes, and now it's trillions and that and the
accessibility of that training data is very very important.
Speaker 4 (11:27):
What's the relationship between the hugging face libraries and GitHub, which,
if I understand GitHub correctly, it's this the repository of
open source code lots and lots of lines of code
and routines and programs that are shared and updated and tracked,
(11:47):
and they're all available on GitHub, which sounds similar to
what you're doing with hugging face for AI. So what
what what is the interaction or the relationship there?
Speaker 3 (11:56):
Yeah, I think you nailed it on the head there.
So hugging phase is to AI what GitHub is to code, right,
It's this central platform where AI builders can go find
and collaborate around AI artifacts, which are models and data sets.
So it's quite different than software, but we play this
(12:18):
central role in the community to share and collaborate and
access all of those artifacts for AI, like GitHub offers
for code.
Speaker 4 (12:30):
And that community must be incredibly important. I mean, the
open source is nothing if you don't have a community
of people working on it. So how have you been
able to foster and nurture that community.
Speaker 3 (12:42):
Well, I think it goes to the origins of the
transformer model and hugging and face role into that. So
when the first sort of open model came out, it
was called Bird and it came out of Google. The
only way you could would access it was to use
(13:02):
a tool called TensorFlow. But it happened that most of
the AI community was using a different tool called PyTorch,
and something that Hugging Face did is to make that
new model Bert accessible to all PyTorch user and they
(13:25):
did it in open source. It was a project called
Bert's pre Trained PyTorch or bird pitworch pre trained.
Speaker 4 (13:33):
So this is like being able to play my Zelda
game on an Xbox or a PlayStation, right or am
I not really understanding what's going on?
Speaker 3 (13:41):
No, That's exactly what it is. And the thing is
everybody was using the game Boy and so it became
a very popular and from there the community sort of
gathered to make all the other models that were then
published by AI researchers available with that library, which was
quickly renamed from bird bretrain Bytorch into Transformers to welcome
(14:07):
like all of these different new models, and today that's
open source library. Transformers is what all AI builders are
using when they want to access those models, see how
they work, and build upon them.
Speaker 4 (14:23):
What's striking about this field is that it's changing so fast,
it's improving so quickly. So how do open source models
keep up with that? How do they get iterated and improved?
Speaker 3 (14:35):
Actually? It's not so much that open source is keeping
up with it. It's actually open source that is driving
that is driving this piece of change. And that's because
with open source and open research data, scientists researchers can
build upon each other's work, they can reproduce each other's work,
(14:55):
they can access each other's work using our open source library,
et cetera. So in a sense, it's not really that
open source AI is a new idea. It's rather the opposite.
There's been a blip of time in which closed source
AI seemed to be the dominant way, but it's really
(15:16):
a blip. In fact, you know, none of the incredible
advances that we're marvel about today would be possible without
open source. We're standing upon the shoulders of fifty years
of research and open source software. So I think that
that's really important. If it wasn't for that, we'll probably
be fifty years away from having these amazing experiences like
(15:40):
JGBT or stable diffusion, et cetera. So it's really open
source that is fueling this pace of change, all these
new models, all these new capabilities. To give you an example,
so Meta released the Lama large language model just a
few months ago, and ever since, there's been this Cambrian
(16:03):
explosion of variations and improvements upon the original models, and
today there are over a thousands of them that we
host and track and evaluate. So yeah, open source is
really the gas and the engine for that.
Speaker 2 (16:21):
Jeff just made it clear that it is open source,
not closed that sets the pace for AI innovation. If
that's true, then forward thinking businesses shouldn't shy from leveraging
open source AI to solve their own proprietary challenges. But
how businesses can face serious obstacles when trying to adopt
(16:43):
open source technologies, like complying with government regulation or making
sure their customers data stays protected. In the next part
of their conversation, Jeff and Tim discuss how IBM's collaboration
with hugging Face empowers businesses to tap into the open
source AI community and how the watsonex platform can enable
(17:04):
them to customize those AI models to their needs.
Speaker 4 (17:09):
Just want to ask about the partnership between hugging Face
and an IBM. How did that come about?
Speaker 3 (17:16):
Well, it came through a conversation, a conversation between our CEO,
Clement de Lange and Bill Higgins IBM, who's really really
close to all the amazing research work and open source
work that's happening at IBM, and that conversation sort of
(17:39):
sparked the evidence that we needed to do something together.
We share a lot of values in terms of the
importance of open source, which is fundamental to us, with
the importance of doing things in an ethics first way
to enable the commune to incorporate ethical considerations in how
(18:04):
they're building AI. And we sort of have a different
audience to start with, which is all the AI builders
use hiking phase today to access all the models we
talked about, to use them using our open source and
build with them. And IBM has this incredible history of
(18:27):
working with enterprise companies and enabling them to make use
of that technology in a way that's compliant with everything
that an enterprise requires, and so being able to marry
these two things together is an amazing opportunity. And now
we can enable the largest corporations that have sort of
(18:49):
complex requirements in order to deploy machine learning systems and
give them an easy experience to take advantage of all
the latest and great is that AA has to offer
through our platform.
Speaker 4 (19:04):
Let's talk about this idea of a single model or
a variety of models, because what I've been hearing you say.
You've been saying, oh, there are lots of models, there
are hundreds of thousands of models available on hugging Face.
But you've also said there's this single thing, the transformer,
and they're all transformers. So if they're all basically the
(19:26):
same thing, why can't you just build one super clever
model that can do everything.
Speaker 3 (19:34):
That's a really interesting idea and very much a new idea.
The reason we have over a million repositories three hundred
thousand free and accessible models on a hiking Face platform
is that models are typically trained to do one thing,
and they're typically trained to do one thing with specific
(19:55):
types of data. And what became new and evidence in
the research that came out over the last couple of
years is that if you train a big enough model
with enough data, then those models start to have sort
of general capabilities. You can ask them to do different things.
(20:19):
You can even train them to respond to instructions. So
with the same model, you can say, hey, summarize this paragraph,
translate this into English, start a conversation in French, and
pivot to German. And so these are general sort of
language capabilities. And I think when CHGBT came online and
(20:42):
the world sort of discovered these new capabilities, there was,
at least for a short period, this sort of idea,
this sort of myth that the endgame of all this
is maybe one or a handful of models there are
so much better than anything else than exists, that they
(21:03):
can do anything that we can ask them to do,
and that's the only model that we will need. And I,
for one, think it is a myth. I don't think
it is practical for a variety of reasons. Say you're
writing an email and you have like this great suggestion
(21:23):
of text to sort of complete your sentence, Well, that's AI.
That's a large language model, that's a transformer model that
does that. So there are a ton of existing use
cases like this, and these use cases are powered by
specific models that have been trained to do one thing
well and to do it fast. If you wanted to
(21:44):
apply these sort of all knowing, powerful oracle type of model,
you would not be able to serve millions of customers
through a search engine. You will not be able to
complete people's sentences because the amount of money that you
would need, the number of computers that you would need
(22:07):
to run such of service just exceeds what is available
on the planet. So one reason for which it's not
a practical scenario is that it's just very expensive to
run those very very large models.
Speaker 4 (22:27):
What I'm hearing is it's like, look, if you want
to screw in a screw you need a screwdriver. You
don't want an entire tool shed full of tools if
the task is to screw in a screwdriver, and sure
you could bring the toolshed that are all the tools.
There's a screwdriver there, but it's not necessary. It's incredibly expensive,
it's incredibly cumbersome, and that cost exists even though maybe
(22:52):
is the user who's just typing in a into a
prompt box. The user may not see it, but it's
still very real.
Speaker 3 (23:00):
That's right. And then another one is performance. So taking
the screwdriver example, so and by the way, like we're
not quite there at this moment where we have this
all knowing, powerful oracle that is still sort of a
sci fi scenario, but we have screw drivers, but we
also have the leatherman, right, the multitol Swiss army knife.
(23:21):
And that's sort of the moment that we are in today.
But now if I'm trying to open up my computer,
turns out that it requires a specific kind of screw
like these tiny little tork screws, and having a torqu
screwdriver will get me much further than trying to use
my leather man, where maybe I'll get the knife blade
(23:43):
and it will mess up the screw and maybe eventually
I'll get to what I need. But my point is
that if you take a very specifically trained model for
a particular problem, it will work much better. It will
give you better results than a very very generalistic, big
model that can do a lot of things. And so
(24:05):
for things like search engines or things like translation, for
things that are very specific, companies are much better off
using smaller, more efficient models that produce better results.
Speaker 4 (24:19):
That's really interesting. And presumably then being able to know
which model to use, or being able to know who
to ask which model to use, becomes a very important capability.
Speaker 3 (24:31):
Yes, and that's what we're trying to make easy through
our platform.
Speaker 4 (24:37):
So tell me about how this works with IBM's what's
an X platform? How do you see hugging faces customers
benefiting from that?
Speaker 3 (24:47):
The end goal is to make it really easy for
what's an X customers to make use of all the
great models and libraries that we talked about, all the
the three hundred thousand models are today on hugging face
and to do this we need to really collaborate deeply
(25:08):
with the IBM teams that build the What's and X
platform so that our libraries, our open source our models
are well integrated into the platform. If you are a
single user, if you are a data science student and
you want to use a model, is we make it
super easy, right. We have our open source library. You
(25:29):
can download the model on your computer and run with
it then. But in enterprises there is a vast complexity
of infrastructure and rules around what people can do and
how the data can be accessed, and all this complexity
is sort of solved by the Watson X platform.
Speaker 4 (25:53):
This season of the Smart Talks podcast features what we're
calling new creators. Do you see yourself as being a
creative person?
Speaker 3 (26:02):
Ah, I think it's a requirement for the job. I mean,
we're in such a new and rapidly evolving industry that
we have to be creative in order to invent the
business models the use cases of tomorrow. My role within
the company is really to create the business around all
(26:24):
the great work of our science and open source and
product team, and by and large, the business model of
AI within the whole ecosystem is still something that companies
are trying to figure out. So creativity is really important
to really have the conversation with companies, understand what they're
(26:47):
trying to do, and then build the right kind of solution.
So that's like where creativity comes into play.
Speaker 4 (26:54):
And one of the things that you've you've been talking
about is just this growing number of models, this growing
number of capabilities, this growing number of use cases enormously
exciting but also I think completely bewildering for most people
(27:16):
who are trying to navigate their way through this maze
of possibilities that is growing faster than they can even
learn about it. So how are you helping people navigate
and make choices in that environment? And how does the
partnership with IBM help with that?
Speaker 3 (27:35):
Well? As I said, our vision is that AI machine
learning is becoming the default way of creating technology and
that means like every product, app, service that you're going
to be using is going to be using AI to
do whatever it is better faster, And I guess there
(27:57):
are two competing visions of doing world coming from that.
There is this vision of the oracle, all powerful model
that can do everything, and our vision is different. Our
vision is that every single company will be able to
(28:17):
create their own models that they own, that they can use,
that they control, and that's the vision that we're trying
to bring to life through our open source tools that
make this work easy. Through our platform where you can
find all those pre train models are shared by the community.
(28:39):
So we really want to empower companies to build their
own stuff, not to outsource all the intelligence to a
third party. And the What's on next platform from IBM
gives those tools to enterprise companies, So that's you can
use the open source models hiking Face offers, then you
(29:02):
can improve them with your own data without sharing that
data to a third party, and then you could do
all of this work in compliance with whatever governance requirements
that you have for your company, maybe your finance services
company and you have a specific set of rules, maybe
(29:25):
your healthcare company and you have very strong privacy requirements
for patients data. Maybe your tech company, and you have
your customers, your users personal information, so you need to
be able to do this work respecting all of that.
Speaker 4 (29:44):
Jeff Bridier, thank you very much.
Speaker 3 (29:46):
Thanks so much to it's fun.
Speaker 2 (29:50):
To create the AI models of the future. We're going
to need open source. That means as a place for
business in the open source community to harness the game
changing potential of AI innovation. Like Jeff said, businesses face
unique challenges they need to solve at scale without proper
support systems. Tapping into open source AI at enterprise level
(30:13):
is daunting finding the right size model for the job,
fine tuning its purpose, all while addressing governance requirements around
data privacy and ethics. So for businesses, IBM's collaboration with
hugging Face is a market progress because it signifies that
business can tap into open source AI while preserving enterprise
(30:36):
level integrity. Businesses should embrace the open source community and
the AI future, much like hugging Face and its emoji
namesake suggests. I'm Malcolm Gladwell. This is a paid advertisement
from IBM. Smart Talks with IBM is produced by Matt Romano,
David jaw Nisha Nkat and Royston Deserve with Jacob Goldstein
(31:00):
by Lydia gene Kott. Our engineers are Jason Gambrel, Sarah
Bruger and Ben Tolliday. Theme song by Gramoscope. Special thanks
to Carlei Migliori, Andy Kelly, Kathy Callahan, and the eight
Bar and IBM teams, as well as the Pushkin marketing team.
Smart Talks with IBM is a production of Pushkin Industries
(31:20):
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts.
Welcome to Tech Stuff, a production from iHeartRadio. Today, we
are witnessed to one of those rare moments in history,
the rise of an innovative technology with the potential to
radically transform business and society forever. That technology, of course,
(00:24):
is artificial intelligence, and it's the central focus for this
new season of Smart Talks with IBM. Join hosts from
your favorite Pushkin podcasts as they talk with industry experts
and leaders to explore how businesses can integrate AI into
their workflows and help drive real change in this new
era of AI, and of course, host Malcolm Gladwell will
(00:46):
be there to guide you through the season and throw
in his two cents as well. Look out for new
episodes of Smart Talks with IBM every other week on
the iHeartRadio app, Apple Podcasts, wherever you get your podcasts,
and learn more at IBM dot com slash smart Talks.
Speaker 2 (01:04):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
we're continuing our conversation with new creators visionaries who are
creatively applying technology in business to drive change, but with
a focus on the transformative power of artificial intelligence and
(01:28):
what it means to leverage AI as a game changing
multiplier for your business. Our guest today is Jeff Boutier,
head of Product and Growth at hugging Face, the leading
open source and open science artificial intelligence platform. An engineer
by background, he has a self professed obsession with the
(01:49):
business of technology. Recently, IBM and hugging Face announced a
collaboration bringing together hugging faces repositories of open source AI
models with IBM's Watson X platform. It's a move that
gives businesses even more access to AI while staying true
to IBM's long standing philosophy of supporting open source technology.
(02:14):
With open source, businesses can build better AI models that
suit their specific needs using their own proprietary data while
browsing a ready catalog of pre trained models. In today's episode,
you'll hear why open source is so crucial to the
advancement of AI, how IBM's Watson X interacts with open
(02:36):
source AI, and Jeff's thoughts on why this singular omnipotent
AI model is a myth. Jeff spoke with Tim Harford,
host of the Pushkin podcast Cautionary Tales, a longtime columnist
at the Financial Times, where he writes the Undercover Economist.
Tim is also a BBC broadcaster with his show More
(02:57):
or Less. Okay, let's get to the interview.
Speaker 3 (03:08):
I am a Jeff Boudier and I'm a product director
at hugging.
Speaker 4 (03:12):
Face, So I'm immediately intrigue. Hugging Face. Is this a
reference to the Alien movie or something else?
Speaker 3 (03:20):
It is not, and it may be not obvious to
a listener, but hugging Face is the name of that
cute emoji, you know, the one that's smiling with his
two hands extended like that to give you a big hug.
That's hugging Face. So basically we name the company after
an emoji.
Speaker 4 (03:40):
And it is I saw your website and it is
a very friendly emoji. So that's that's nice. So tell
us a little bit about hugging Face and about what
you do that.
Speaker 3 (03:48):
Of course, hugging Face is the leading open platform for
AI builders, and it's the place that's all the AI
researchers use to share their work, their new AI models
and collaborate around them. It's the place where the data
(04:09):
scientists go and find those pre train models and access
them and use them and work with them, and increasingly
it's the place where developers are coming to turn all
of these AI models and data sets into their own applications,
their own features.
Speaker 4 (04:30):
So it's like the I don't know, the Facebook group
or the Reddit or the Twitter for people who are
interested in particularly generative language AI, or all kinds of
artificial intelligence.
Speaker 3 (04:42):
All kinds of AI really, and of course generative AIS
this new wave that has caught the world by storm.
But on Hiking Face you can find any kind of model,
the new sort of transformers models to do anything from
translation or if you wanted to transcribe what I'm saying
(05:04):
into text, then you would use a transformer model. If
you wanted to then take that text and make a summary,
that would be another transformer model. If you wanted to
create a nice little thumbnail for this podcast by typeing
a sentence, that would be another type of model. So
all these models you can find. There's actually three hundred
(05:26):
thousands that are free and publicly accessible. You can find
them on our website at Hikingphase dot co and use
them using our open source libraries.
Speaker 4 (05:38):
And so this is this is fascinating. So there are
three hundred thousand models. Now when you say model, I'm
thinking in my head, oh, it's kind of like a
computer program. There were three hundred thousand computer programs. Is
that roughly right or it not?
Speaker 3 (05:52):
Really, it's a general idea. A model is a giant
set of numbers that are working together to sift through
some inputs that you're going to give it. So think
of it of a big black box filled with numbers,
(06:14):
and you give it as an input, maybe some text,
maybe a prompt, so you're asking, you're giving an instruction
to the model, or maybe you give it an image
as an input, and then it will sift through that
information thanks to all of these numbers, which we call
(06:35):
in the field parameters, and it will produce an output.
So when I told you, hey, we can transcribe this
conversation into text, the input would have been the conversation
in an audio file, and then the output would have
been the text of the transcription. If you want to
create a thumbnail for this podcast episode, then the input
(06:57):
would be what we call the prompt, which is really
a text description like a Frenchman in San Francisco talking
about machine learning, and the output would be completely original image.
So that's how I think about what an AI model is,
and I think what we're starting to realize is that
(07:20):
this is becoming the new way of building technology in
the world. It has been for the field of dealing
understanding generating text for quite some time, but now it's
sort of moving across every field of technology. We have
models to create images, as I say, but also to
(07:41):
generate new proteins to make predictions on numerical data. So
every kind of field of machine learning is now using
this new type of models. But what's interesting is that
if you're, say a product manager at a company, and
you say, hey, I want to build a feature that
(08:03):
does this. A few years ago, the approach would have
been to ask a software developer to write a thousand
lines of code in order to build a prototype. And
the new way of doing things today is to go
look for an off the shelf pre train model that
does a pretty good job at solving exactly that problem,
(08:27):
so you can create a prototype of that feature fast.
So it's a new approach of building tech.
Speaker 4 (08:33):
I'm not a programmer, but I'm aware that there was
this idea of open source code, and now we have
open source models. So what does it mean for something
to be open source.
Speaker 3 (08:43):
Open source AI actually means a lot of different specific things.
It's the open source implementation of the model. So if
you use the Hugging Phase transformers library to use a model,
you're using an open source code library to use that model.
Speaker 4 (09:03):
Just to end up on the transformers. These are these
kind of ways of turning a picture of a dog
into a text output that says, hey, this is a
picture of a dog, or this is a French text
and with the transformers helping you turn it into English text,
or it's doing all of these things that you've been describing.
That's the transformer is the kind of the engine at
(09:23):
the heart of that.
Speaker 3 (09:25):
Yes, exactly. And we call them transformers because they correspond
to this new way of building machine learning models that
was introduced by Google actually with a very important paper
called Attention is All You Need and that was published
in twenty seventeen by researchers out of Google Deep Mind.
Speaker 4 (09:47):
Well that's just six years so new.
Speaker 3 (09:51):
It is very new, and ever since the piece of
innovation of like new model architectures has real really accelerated.
But it really started from this inflection point that came
from this paper and its implementation in what is now
called Transformer models, the transformer that has conquered every area
(10:16):
of machine learning since.
Speaker 4 (10:18):
Okay, so say turned up. So you've got this library
of Transformer models and that open source, and that means
that means what anyone can use them for free, or
that anybody can implement them for free. What does it mean?
Speaker 3 (10:32):
So again, there's lots that go into it, but the
most important thing is for the model itself to be
available so that a data scientists or an engineer can
download them and use them. And also there are a
lot of considerations about how you make them accessible, and
(10:54):
a very important one is whether or not you give
access to the training data, all the information that went
into training that model and teaching it to do what
it's trained to do.
Speaker 4 (11:09):
So I might have fed millions of words into a
into a language transformer, or I might have fed millions
of photographs into a into a picture transformer.
Speaker 3 (11:18):
Yeah, yes, and now it's trillions and that and the
accessibility of that training data is very very important.
Speaker 4 (11:27):
What's the relationship between the hugging face libraries and GitHub, which,
if I understand GitHub correctly, it's this the repository of
open source code lots and lots of lines of code
and routines and programs that are shared and updated and tracked,
(11:47):
and they're all available on GitHub, which sounds similar to
what you're doing with hugging face for AI. So what
what what is the interaction or the relationship there?
Speaker 3 (11:56):
Yeah, I think you nailed it on the head there.
So hugging phase is to AI what GitHub is to code, right,
It's this central platform where AI builders can go find
and collaborate around AI artifacts, which are models and data sets.
So it's quite different than software, but we play this
(12:18):
central role in the community to share and collaborate and
access all of those artifacts for AI, like GitHub offers
for code.
Speaker 4 (12:30):
And that community must be incredibly important. I mean, the
open source is nothing if you don't have a community
of people working on it. So how have you been
able to foster and nurture that community.
Speaker 3 (12:42):
Well, I think it goes to the origins of the
transformer model and hugging and face role into that. So
when the first sort of open model came out, it
was called Bird and it came out of Google. The
only way you could would access it was to use
(13:02):
a tool called TensorFlow. But it happened that most of
the AI community was using a different tool called PyTorch,
and something that Hugging Face did is to make that
new model Bert accessible to all PyTorch user and they
(13:25):
did it in open source. It was a project called
Bert's pre Trained PyTorch or bird pitworch pre trained.
Speaker 4 (13:33):
So this is like being able to play my Zelda
game on an Xbox or a PlayStation, right or am
I not really understanding what's going on?
Speaker 3 (13:41):
No, That's exactly what it is. And the thing is
everybody was using the game Boy and so it became
a very popular and from there the community sort of
gathered to make all the other models that were then
published by AI researchers available with that library, which was
quickly renamed from bird bretrain Bytorch into Transformers to welcome
(14:07):
like all of these different new models, and today that's
open source library. Transformers is what all AI builders are
using when they want to access those models, see how
they work, and build upon them.
Speaker 4 (14:23):
What's striking about this field is that it's changing so fast,
it's improving so quickly. So how do open source models
keep up with that? How do they get iterated and improved?
Speaker 3 (14:35):
Actually? It's not so much that open source is keeping
up with it. It's actually open source that is driving
that is driving this piece of change. And that's because
with open source and open research data, scientists researchers can
build upon each other's work, they can reproduce each other's work,
(14:55):
they can access each other's work using our open source library,
et cetera. So in a sense, it's not really that
open source AI is a new idea. It's rather the opposite.
There's been a blip of time in which closed source
AI seemed to be the dominant way, but it's really
(15:16):
a blip. In fact, you know, none of the incredible
advances that we're marvel about today would be possible without
open source. We're standing upon the shoulders of fifty years
of research and open source software. So I think that
that's really important. If it wasn't for that, we'll probably
be fifty years away from having these amazing experiences like
(15:40):
JGBT or stable diffusion, et cetera. So it's really open
source that is fueling this pace of change, all these
new models, all these new capabilities. To give you an example,
so Meta released the Lama large language model just a
few months ago, and ever since, there's been this Cambrian
(16:03):
explosion of variations and improvements upon the original models, and
today there are over a thousands of them that we
host and track and evaluate. So yeah, open source is
really the gas and the engine for that.
Speaker 2 (16:21):
Jeff just made it clear that it is open source,
not closed that sets the pace for AI innovation. If
that's true, then forward thinking businesses shouldn't shy from leveraging
open source AI to solve their own proprietary challenges. But
how businesses can face serious obstacles when trying to adopt
(16:43):
open source technologies, like complying with government regulation or making
sure their customers data stays protected. In the next part
of their conversation, Jeff and Tim discuss how IBM's collaboration
with hugging Face empowers businesses to tap into the open
source AI community and how the watsonex platform can enable
(17:04):
them to customize those AI models to their needs.
Speaker 4 (17:09):
Just want to ask about the partnership between hugging Face
and an IBM. How did that come about?
Speaker 3 (17:16):
Well, it came through a conversation, a conversation between our CEO,
Clement de Lange and Bill Higgins IBM, who's really really
close to all the amazing research work and open source
work that's happening at IBM, and that conversation sort of
(17:39):
sparked the evidence that we needed to do something together.
We share a lot of values in terms of the
importance of open source, which is fundamental to us, with
the importance of doing things in an ethics first way
to enable the commune to incorporate ethical considerations in how
(18:04):
they're building AI. And we sort of have a different
audience to start with, which is all the AI builders
use hiking phase today to access all the models we
talked about, to use them using our open source and
build with them. And IBM has this incredible history of
(18:27):
working with enterprise companies and enabling them to make use
of that technology in a way that's compliant with everything
that an enterprise requires, and so being able to marry
these two things together is an amazing opportunity. And now
we can enable the largest corporations that have sort of
(18:49):
complex requirements in order to deploy machine learning systems and
give them an easy experience to take advantage of all
the latest and great is that AA has to offer
through our platform.
Speaker 4 (19:04):
Let's talk about this idea of a single model or
a variety of models, because what I've been hearing you say.
You've been saying, oh, there are lots of models, there
are hundreds of thousands of models available on hugging Face.
But you've also said there's this single thing, the transformer,
and they're all transformers. So if they're all basically the
(19:26):
same thing, why can't you just build one super clever
model that can do everything.
Speaker 3 (19:34):
That's a really interesting idea and very much a new idea.
The reason we have over a million repositories three hundred
thousand free and accessible models on a hiking Face platform
is that models are typically trained to do one thing,
and they're typically trained to do one thing with specific
(19:55):
types of data. And what became new and evidence in
the research that came out over the last couple of
years is that if you train a big enough model
with enough data, then those models start to have sort
of general capabilities. You can ask them to do different things.
(20:19):
You can even train them to respond to instructions. So
with the same model, you can say, hey, summarize this paragraph,
translate this into English, start a conversation in French, and
pivot to German. And so these are general sort of
language capabilities. And I think when CHGBT came online and
(20:42):
the world sort of discovered these new capabilities, there was,
at least for a short period, this sort of idea,
this sort of myth that the endgame of all this
is maybe one or a handful of models there are
so much better than anything else than exists, that they
(21:03):
can do anything that we can ask them to do,
and that's the only model that we will need. And I,
for one, think it is a myth. I don't think
it is practical for a variety of reasons. Say you're
writing an email and you have like this great suggestion
(21:23):
of text to sort of complete your sentence, Well, that's AI.
That's a large language model, that's a transformer model that
does that. So there are a ton of existing use
cases like this, and these use cases are powered by
specific models that have been trained to do one thing
well and to do it fast. If you wanted to
(21:44):
apply these sort of all knowing, powerful oracle type of model,
you would not be able to serve millions of customers
through a search engine. You will not be able to
complete people's sentences because the amount of money that you
would need, the number of computers that you would need
(22:07):
to run such of service just exceeds what is available
on the planet. So one reason for which it's not
a practical scenario is that it's just very expensive to
run those very very large models.
Speaker 4 (22:27):
What I'm hearing is it's like, look, if you want
to screw in a screw you need a screwdriver. You
don't want an entire tool shed full of tools if
the task is to screw in a screwdriver, and sure
you could bring the toolshed that are all the tools.
There's a screwdriver there, but it's not necessary. It's incredibly expensive,
it's incredibly cumbersome, and that cost exists even though maybe
(22:52):
is the user who's just typing in a into a
prompt box. The user may not see it, but it's
still very real.
Speaker 3 (23:00):
That's right. And then another one is performance. So taking
the screwdriver example, so and by the way, like we're
not quite there at this moment where we have this
all knowing, powerful oracle that is still sort of a
sci fi scenario, but we have screw drivers, but we
also have the leatherman, right, the multitol Swiss army knife.
(23:21):
And that's sort of the moment that we are in today.
But now if I'm trying to open up my computer,
turns out that it requires a specific kind of screw
like these tiny little tork screws, and having a torqu
screwdriver will get me much further than trying to use
my leather man, where maybe I'll get the knife blade
(23:43):
and it will mess up the screw and maybe eventually
I'll get to what I need. But my point is
that if you take a very specifically trained model for
a particular problem, it will work much better. It will
give you better results than a very very generalistic, big
model that can do a lot of things. And so
(24:05):
for things like search engines or things like translation, for
things that are very specific, companies are much better off
using smaller, more efficient models that produce better results.
Speaker 4 (24:19):
That's really interesting. And presumably then being able to know
which model to use, or being able to know who
to ask which model to use, becomes a very important capability.
Speaker 3 (24:31):
Yes, and that's what we're trying to make easy through
our platform.
Speaker 4 (24:37):
So tell me about how this works with IBM's what's
an X platform? How do you see hugging faces customers
benefiting from that?
Speaker 3 (24:47):
The end goal is to make it really easy for
what's an X customers to make use of all the
great models and libraries that we talked about, all the
the three hundred thousand models are today on hugging face
and to do this we need to really collaborate deeply
(25:08):
with the IBM teams that build the What's and X
platform so that our libraries, our open source our models
are well integrated into the platform. If you are a
single user, if you are a data science student and
you want to use a model, is we make it
super easy, right. We have our open source library. You
(25:29):
can download the model on your computer and run with
it then. But in enterprises there is a vast complexity
of infrastructure and rules around what people can do and
how the data can be accessed, and all this complexity
is sort of solved by the Watson X platform.
Speaker 4 (25:53):
This season of the Smart Talks podcast features what we're
calling new creators. Do you see yourself as being a
creative person?
Speaker 3 (26:02):
Ah, I think it's a requirement for the job. I mean,
we're in such a new and rapidly evolving industry that
we have to be creative in order to invent the
business models the use cases of tomorrow. My role within
the company is really to create the business around all
(26:24):
the great work of our science and open source and
product team, and by and large, the business model of
AI within the whole ecosystem is still something that companies
are trying to figure out. So creativity is really important
to really have the conversation with companies, understand what they're
(26:47):
trying to do, and then build the right kind of solution.
So that's like where creativity comes into play.
Speaker 4 (26:54):
And one of the things that you've you've been talking
about is just this growing number of models, this growing
number of capabilities, this growing number of use cases enormously
exciting but also I think completely bewildering for most people
(27:16):
who are trying to navigate their way through this maze
of possibilities that is growing faster than they can even
learn about it. So how are you helping people navigate
and make choices in that environment? And how does the
partnership with IBM help with that?
Speaker 3 (27:35):
Well? As I said, our vision is that AI machine
learning is becoming the default way of creating technology and
that means like every product, app, service that you're going
to be using is going to be using AI to
do whatever it is better faster, And I guess there
(27:57):
are two competing visions of doing world coming from that.
There is this vision of the oracle, all powerful model
that can do everything, and our vision is different. Our
vision is that every single company will be able to
(28:17):
create their own models that they own, that they can use,
that they control, and that's the vision that we're trying
to bring to life through our open source tools that
make this work easy. Through our platform where you can
find all those pre train models are shared by the community.
(28:39):
So we really want to empower companies to build their
own stuff, not to outsource all the intelligence to a
third party. And the What's on next platform from IBM
gives those tools to enterprise companies, So that's you can
use the open source models hiking Face offers, then you
(29:02):
can improve them with your own data without sharing that
data to a third party, and then you could do
all of this work in compliance with whatever governance requirements
that you have for your company, maybe your finance services
company and you have a specific set of rules, maybe
(29:25):
your healthcare company and you have very strong privacy requirements
for patients data. Maybe your tech company, and you have
your customers, your users personal information, so you need to
be able to do this work respecting all of that.
Speaker 4 (29:44):
Jeff Bridier, thank you very much.
Speaker 3 (29:46):
Thanks so much to it's fun.
Speaker 2 (29:50):
To create the AI models of the future. We're going
to need open source. That means as a place for
business in the open source community to harness the game
changing potential of AI innovation. Like Jeff said, businesses face
unique challenges they need to solve at scale without proper
support systems. Tapping into open source AI at enterprise level
(30:13):
is daunting finding the right size model for the job,
fine tuning its purpose, all while addressing governance requirements around
data privacy and ethics. So for businesses, IBM's collaboration with
hugging Face is a market progress because it signifies that
business can tap into open source AI while preserving enterprise
(30:36):
level integrity. Businesses should embrace the open source community and
the AI future, much like hugging Face and its emoji
namesake suggests. I'm Malcolm Gladwell. This is a paid advertisement
from IBM. Smart Talks with IBM is produced by Matt Romano,
David jaw Nisha Nkat and Royston Deserve with Jacob Goldstein
(31:00):
by Lydia gene Kott. Our engineers are Jason Gambrel, Sarah
Bruger and Ben Tolliday. Theme song by Gramoscope. Special thanks
to Carlei Migliori, Andy Kelly, Kathy Callahan, and the eight
Bar and IBM teams, as well as the Pushkin marketing team.
Smart Talks with IBM is a production of Pushkin Industries
(31:20):
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts.
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