Hugging Face and watsonx: why open source is the future of AI in business

Hugging Face and watsonx: why open source is the future of AI in business

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Hugging Face and watsonx: why open  source is the future of AI in business Malcolm Gladwell: 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 what it means to leverage AI as  a game- changing multiplier for YOUR business.  Our guest today is Jeff Boudier [BOO-dee-ay],  Head of Product and Growth at Hugging Face,   the leading open-source and open-  science artificial-intelligence   platform. Before getting into the world of  open-source AI, Jeff co-founded a company   called Stupeflix, a video-editing software which  was eventually acquired by GoPro. An engineer  

by background, he has a self-professed  obsession with the business of technology.  Recently, IBM and Hugging Face  announced a collaboration,   bringing together Hugging Face’s repositories  of open-source AI models with IBM’s watsonx   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. 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 pretrained models.  In today’s episode, you’ll hear why open  source is so crucial to the advancement of AI,   how IBM’s watsonx interacts with open-source  AI, and Jeff’s thoughts on why the 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 or Less.  OK, let’s get to the interview. Jeff Boudier: Hi, I'm Jeff Boudier,  

and I'm a Product Director at Hugging Face.  Tim Harford: So I'm immediately  intrigued. Hugging Face—is this a  reference to the Alien movie, or something else? Jeff Boudier: 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 named the company after an emoji. Tim Harford: Okay. And it is—I saw your website,   and it is a very friendly emoji. So that's  nice. So tell us a little bit about Hugging  

Face and, and about what you do there. Jeff Boudier: Of course. Well, Hugging   Face is the leading open platform for AI  builders, and it's the place that all of   the AI researchers use to share their work,  their new AI models, and collaborate around   them. It's the place where the data scientists  go and find those pretrained 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 datasets into their   own applications, their own features. Tim Harford: So like 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? Jeff Boudier: All kinds of AI, really. And   of course generative AI is this new wave  that has caught the world by storm. But  

on Hugging 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 into text, uh, 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 the, this podcast by typing a sentence,   that would be, uh, another type of model. So all  of these models you can find—there's actually,  

300,000 that are free and publicly accessible—you  can find them on our website at,   and use them, using our open-source libraries. Tim Harford: And so this is—this is fascinating.   So, so there are 300,000 models. Now, when  you say “model,” I'm thinking in my head,   “Oh, it's kind of like, um, like a computer  program.” There were 300,000 computer programs.   Is that—is that roughly right? Or it—not really? Jeff Boudier: It's a general idea. A model is a  

giant, set of numbers that are working together  to sift through some input that you're going to   give it. So think of it as a big black box  filled with numbers. And you give it as a,   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,   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 would be   what we call the “prompt,” which is really  a text description— like, uh, “French man in   San Francisco talking about machine learning.” And the output would be a 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 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 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 tech company, and you say,   “Hey, I want to build a feature that 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 a new way of doing things today is to go look  for an off-the-shelf, pretrained model that does a   pretty good job of solving exactly that problem.  So you can create a prototype of that feature   fast. So it's a new approach to building tech. Tim Harford: I'm not a programmer, but I'm aware  

that there's this idea of open-source code,  and now we have open-source models. So what   does it mean for something to be “open source”? Jeff Boudier: Yes, “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 Face transformers  library to use a model, you're using an   open-source code library to use that model. Tim Harford: Just to interrupt 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, the transformer is the—kind of   the engine at the, at the heart of that. Jeff Boudier: 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 it  was published in 2017 by researchers out   of Google DeepMind. Tim Harford: Wow,  

that's just six years. That's so new. Jeff Boudier: It is very new, and ever since,   the pace of innovation has really, 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 of machine learning since. Tim Harford: Okay. Sorry to interrupt. So,  so—you've got this library of transformer models,   and they're open source, and that means, that  means what? Anyone can use them for free? Or that,   or that anybody can implement  them for free? What does it mean?  Jeff Boudier: So again, there's, like, lots that  go into it, but the most important thing is for   the model itself to be available so that a  data scientist or an engineer can download   them and use them. And also there are a lot  of considerations about how you make them   accessible. And 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, what it's trained to do.  That's, uh— Tim Harford: 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. Yeah. Jeff Boudier: Yes. And the accessibility   of that training data is very, very important. TIM HARFORD: What's the relationship between the,  

the Hugging Face libraries and, uh, 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, that are shared and updated  and tracked, and they're all available on,   on GitHub. Which sounds similar to what you're  doing with Hugging Face for AI. So what,   what is the interaction or the relationship there? JEFF BOUDIER Yeah, I think you nailed it on the   head. So Hugging Face 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 datasets. So it's  

quite—it's different than software, but we play  the central, the central role in the community to   share and collaborate and access all of those  artifacts for AI like GitHub offers for code.  Tim Harford: 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— Jeff Boudier: Well, I think it goes to the  origins of the transformer model, and, and   Hugging Face rolled into that. So when the first,  sort of, open model came out, it was called BERT,  

and it came out of Google. The only way you could  access it was to use 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 users. And they did it  in open source. It was a project called “BERT's   Pretrained PyTorch” or “BERT PyTorch Pretrained.” Tim Harford: So this is like being able to play my   Zelda game on, on an Xbox or a PlayStation, right?  Or am I not really understanding what's going on?  Jeff Boudier: No, that's exactly what it is. And  the thing is, everybody was using the Game Boy,   and so it became very popular. And from there,  the community sort of gathered to make all of  

the other models that were then published by  AI researchers available through that library,   which was quickly renamed from “BERT’s Pretrained  PyTorch” into “transformers” to welcome, 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. Tim Harford: 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? Jeff Boudier: 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   pace 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. They can  

access each other's work using our  open-source libraries, 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  seems to be the dominant way, but it's really a   blip. In fact, you know, none of the incredible  advances that we're marveling about today would be possible without open source. We're  standing upon the shoulders of 50 years  

of research in open-source software. So  I think that that's really important;   if it wasn't for that, we'd probably be 50  years away from having this—amazing experiences   like ChatGPT or Stable Diffusion, et cetera. So it's really open source that is fueling this   pace of change—all, all these new models, all  these new capabilities. To give you an example:   so Meta released, uh, LLaMA large language  model just a few months ago. And ever since,   there's been this Cambrian explosion of  variations and improvements upon the original   models. And today there are over a thousand  of them that we host and track and evaluate.  So, yeah, open source is really  the gas and the engine for that.

Malcolm Gladwell: 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 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 watsonx platform can enable them  to customize those AI models to their needs.  Tim Harford: I just wanted to ask  about the partnership between Hugging   Face and IBM. How did that come about? Jeff Boudier: Well, it came through a   conversation. A conversation between our CEO,  Clément Delangue, 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 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 a, an ethics-first way, to enable the community  to incorporate ethical considerations in   how they're building AI. And we sort of have  a different audience to start with, which is   all the AI builders use Hugging Face 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 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, complex requirements in order   to deploy machine-learning systems and  give them an easy experience—to take   advantage of all the latest and greatest  that AI has to offer through our platform.  Tim Harford: Let's talk about this idea of a, 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 same thing,   why can't you just build one super-clever  model that can do everything?  Jeff Boudier: That's a really interesting idea,  and very much a new idea. And the reason we have  

over a million repositories, 300,000 free  and accessible models, on the Hugging Face   platform is that models are typically trained  to do one thing—and they're typically trained   to do one thing with specific types of data. And what became new and evident in the research   that came out over the last couple years is that  if you train a big enough model with enough data,   then those models start to have a—sort  of general capabilities. You can ask   them to do different things. 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  then pivot to German.” And so these are general,   sort of, language capabilities. And I think when  ChatGPT came online, and 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 end game of all  this is maybe one or a handful of models are   so much better than anything else that exists,  that they can do anything that we can ask them to   do. And that's the only model that we will need.” And I, for one—I 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 of text to, sort of,  complete your sentence. Well, that's AI. That's,   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 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 would 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, to run such a service—it 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. Tim Harford: 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 toolshed full of tools if the  task is to screw in—a screwdriver. And sure,   you could bring the toolshed. There 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, as the  user who's just typing in a, into a prompt box,   the user may not see it, but it's still very real. Jeff Boudier: 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  screwdrivers, but we also have the Leatherman,   right? The, the multitool—a Swiss Army knife, and  that's, sort of the moment that we are in today. But now if I'm trying to open up my computer,  it turns out that it requires a specific kind   of a screw, like these tiny little Torx screws,  and having a Torx screwdriver will get me   much further than trying to use my Leatherman,  where maybe I'll get the knife blade and it'll,   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  for things like search engines, for things like   translation, for things that are very specific,  companies are much better off using smaller,   more-efficient models that produce better results. Tim Harford: 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. Jeff Boudier: Yes, and that's what we're   trying to make easy through our platform. Tim Harford: So tell me about how this works   with IBM's watsonx platform. How do you see  Hugging Faces’ customers benefiting from that?  Jeff Boudier: The end goal is to make it really  easy for watsonx customers to make use of all   the great models and libraries that we talked  about—all the, the, the 300,000 models that are   today on Hugging Face. And to do this, we need to  really collaborate deeply with the IBM teams that   build the watsonx platform, so that our libraries,  our open source, our models are well integrated   into the platform. If you're a single user, if  you are a data-science student, and you want to  

use a model, we make it super easy, right? We  have our open-source library. You 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 watsonx platform.  Tim Harford: This season of the  Smart Talks podcast features what   we're calling New Creators. Do you, do you  see yourself as being a creative person? Jeff Boudier: 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 of   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, really important—to  really have the conversation with companies,   understand what they're trying to do and then  build the right kind of solution. So that's,   like, where creativity comes into play. Tim Harford: And one of the things that you've,   you've been talking about is just this, 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 who are  trying to navigate their way through this, this   maze of possibilities that is, 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? Jeff Boudier: Hmm. Well, as I said,   like, 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 are two competing  visions of the 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 create their own models that they   own, uh, that they can use, that they control.  And that's the, the, 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 pretrained  models that are shared by the community.  So we really want to empower companies to  build their own stuff, not to outsource all   the intelligence to a third party, and the  watsonx platform from IBM gives those tools   to enterprise companies. So that's—you can use  the open-source models that Hugging Face offers.   Then you can improve them with your own data  without sharing that data to a third party.

And then you could do every—all of this work in  compliance with whatever governance requirements   you have for your company. Maybe you’re a finance  services company and you have a specific set of   rules. Maybe you’re a healthcare company and  you have very strong privacy requirements   for patients’ data. Maybe you’re a tech  company and you have your, your customers’,   your users’ personal information. So you need to  be able to do this work, respecting all of that. 

Tim Harford: Jeff Boudier. Thank you very much.  Jeff Boudier: Thanks so much, Tim. It was fun. Malcolm Gladwell: To create the AI models of  the future, we are going to need open source.   That means there’s 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 is daunting: finding the  right-sized 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 mark of progress because   it signifies that business can tap into open-  source AI while preserving enterprise-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 Zha [JAH], Nisha [Nih-sha] Venkat,   and Royston Beserve, with Jacob Goldstein. We’re  edited by Lidia Jean Kott. Our engineers are   Jason Gambrell, Sarah Bruguiere [Brew-Ghare (hard  G!)], and Ben Tolliday. Theme song by Gramoscope. Special thanks to Carly Migliori, Andy Kelly,  Kathy Callaghan [Calla-Han], and the EightBar and   IBM teams, as well as the Pushkin marketing team. Smart Talks with IBM is a production of Pushkin   Industries and Ruby Studio at iHeartMedia.  To find more Pushkin podcasts, listen on   the iHeartRadio app, Apple Podcasts,  or wherever you listen to podcasts.

2024-05-03 12:23

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