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