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