Shownotes Transcript
Hello, hello. Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio, and IBM. I'm Malcolm Gladwell.
This season, we're diving back into the world of artificial intelligence, but with a focus on the powerful concept of open, its possibilities, implications, and misconceptions. We'll look at openness from a variety of angles and explore how the concept is already reshaping industry's ways of doing business and our very notion of what's possible.
On today's episode, I sat down with Mo Duffy, software engineering manager at Red Hat, who works on InstructLab, a project co-developed by Red Hat and IBM. Mo shared with me how this new initiative is revolutionizing AI training, making it not only more accessible, but also more inclusive.
This project, unique in the industry, allows developers to submit incremental contributions to one base AI model, creating a continuous loop of development, much like normal open-source software. By leveraging community contributions and IBM's cutting-edge Granite models, Mo and the team of IBMers and Red Hatters are paving the way for a future where AI development is a communal endeavor.
Her insights into open source software extend beyond technical proficiency to the profound impact of collaborative effort. At the heart of Mo's work is a belief in democratizing technology, ensuring that AI becomes a tool accessible to all. So let's explore how Mo, Red Hat and IBM are empowering individuals and businesses alike to reshape the future of technology through collaboration and innovation.
Mo, thank you for joining me today. Thank you so much for having me. You have just about the most Irish name ever. I do. Very proud. You weren't born in Ireland. No. My grandparents. Oh, your grandparents. Oh, I see. Where did you grow up? New York? Queens? Oh, you're... Oh, I see.
So tell me a little bit about how you got to Red Hat. What was your path? When I was in high school, I was a chatty girl, teenage girl on the phone. We had one phone line. My older brother was studying at the local state college, computer science, and he had to telnet in to compile his homework. One phone line, and I'm on it all the time. He got very frustrated, and he needed a compiler to do his homework. So he bought Red Hat Linux from CompUSA, brought it home.
And that was on the family computer. So I learned Linux and I started playing around with it. I really liked it because you could customize everything, like the entire user interface. You could actually modify the code of the programs you were using to do what you wanted. And for me, it was really cool because especially when you're a kid and like people tell you this is the way things are and you just have to deal with it. It's nice to be like, I'm going to make things the way I want. Modify the code. You were playing. Yeah. Yeah.
It was amazing. And it was just such a time and like before it was cool, I was doing it. And what I saw in that as sort of the potential, like number one of like a community of people working together and like...
the internet existed. It was slow. It involved modems. But there were people that you could talk to who would give you tips and you'd share information. And this collaborative building something together is really something special, right? I could file a complaint to whatever large software company made whatever software I was into. Or I could go to an open source software community and be like, hey, guys, I think we should do this. I'm like, yeah, okay, I'll help. I'll pitch in. So you don't feel powerless. You feel like you can have an impact. And that was really exciting to me.
However, open source software has a reputation for not having the best user interface, not the best user experience. So I ended up studying computer science and electronic media dual major, and then I did human computer interaction as my master's. And my thought was, wouldn't it be nice if this free software accessible to anybody, if it was easier to use, some more people could use it and take advantage of it. And so
So long story short, I ended up going to Red Hat saying, hey, I want to learn how you guys work. Let me embed in your team. Draft out of my graduate program. And I'm like, I want to do this for a living. This is cooler. So I thought this is the way to go. And I've been there ever since. They haven't been able to get rid of me. To backtrack just a little bit, you were talking about the sense of community that surrounds this way of thinking about software. Talk a little bit more about
what that community is like, the benefits of that community, why it appeals to you. Sure. Well, you know, part of the reason I actually ended up going to the graduate school track, suddenly you're a peer of your professors and you're working side by side with them. At some point they retire and you're the next generation. So it's sharing information, building on the work of others in sort of this cycle that extends past the human lifespan.
And in the same way, like the open source model is very similar, but you're actually, you're building something. And it's something in me, I'm just really attracted. Like, I don't like talking about stuff. I like doing stuff with open source software. The software doesn't cost anything. The code is out there, generally uses open standards for the file formats. I can open up files that I created in open source tools as a high school student today. Yeah.
Because they were using open formats and that software still exists. I can still compile the code and it's an active community project. Like these things can outlast any single company in the same way that the academic community has been going on for so many years and hopefully will continue.
continue moving on. So it was sort of like not just the community around it, but just the knowledge sharing and also bringing up the next generation as well. Like all of that stuff really appealed to me. And also at the center of it, the fact that we could democratize it by following this open source process and feel like we have some control. We're not at the mercy of some faceless corporation making changes and we have no impact like that really appealed to me too. For those of us who are not software aficionados,
Take a step backwards and give me a kind of description of terms.
what's the opposite of open source? Proprietary? Proprietary is what we say. So specifically and practically, the difference would be what between something that was open source and something that was proprietary? Sure. So there's a lot of difference. So with open source software, you get these rights when you're given the software. You get the right to be able to share it. And depending on the different licenses that are considered open source have different little things that you have to be aware of. With proprietary code, it's
100% copyright the company. Even a lot of times when you sign your employment contract for a software company and you write code for them, you don't own it. You sign over your rights to the company. So if you leave the company, the code doesn't go with you. It stays in the ownership of that company. So then when like one company buys out another and kills a product, that code's gone. It's gone.
For a business, why would a business want to have open source code as opposed to proprietary? Well, for the same reasons. Say you're a business. You've invested all this money into this software platform.
right? And you've upskilled your employees on it, and it's a core part of your business. And then a few years later, that company goes out of business or something happens, or even something less drastic. You really need this feature. But for the company that makes the software, it's not in their best interest. It's not worth the investment. They're not going to do it. How do you get that feature? You either have to completely migrate to another solution. And this is something that's core at your business. That's going to be a big deal to migrate.
But if it's open source...
you could either hire a team of experts, you could hire software engineers who are able to go do this for you. Go in the upstream software community, implement the feature that you want, and it'll be rolled into the next version of that company's software. So even if that company didn't want to implement the feature, if they did it open source, they would inherit that feature from the upstream community is what we call it. So you have some control over the situation if it's open source. You have an opportunity to actually process
affect change in the product and you could then pick it up or pay somebody else to pick it up or another company could form and pick it up and keep it going. So there's more possibilities if it's open source. It's more like it's like an insurance policy almost. So innovation from the standpoint of the customer, innovation is a lot easier when you're working in an open source environment. Absolutely. Yeah.
So now at Red Hat, you're working with something called InstructLab. Tell us a little bit about what that is. So the thing that really excites me about getting to work on this project is AI has been this scary thing for me because it's one of those things like in order to be able to pre-train a model,
you have to have unobtainium GPUs. You have to have rich resources. It takes months, it takes expertise. There's a small handful of companies that can build a model from pre-trained to something usable. And it kind of feels like those early days when I was kind of delving in software. In the same way, I think if more people could contribute to AI models...
then it wouldn't be just influenced by whichever company had the resources to build it. And there's been a lot of emphasis on pre-training models, so taking massive terabytes, data sets, throwing them through masses of GPUs over months of time, spending hundreds of millions of dollars to build a base model. But what Instruct Lab does is say, okay, you have a base model.
We're going to fine tune it on the other end. It takes less compute resources. The way we've built in Struck Lab, you can play around with the technology and learn it on an off-the-shelf laptop that you can actually buy. So in this way, we're enabling a much broader set of people to play with AI, to contribute it, to modify it. And I'll tell you one story from Red Hat. Suchi, who is our chief diversity officer, very
Very interested in inclusive language and open source software. Doesn't have any experience with AI.
We have a community model that we have an upstream project around for people to contribute knowledge and skills to the model. She's like, I want to teach the model how to use inclusive language, like replace this word with this word or this word with this word. I'm like, oh, that's so cool. So she paired up with Nicholas, who is a technical guy at Red Hat, and they built and submitted a skill to the model that you can just tell the model, can you please take this document and translate this language to more inclusive language and it will do it.
And they submitted it to the community. They were so proud. It was like, that's the kind of thing that like, you know, maybe a company would be incentivized to do that. But if you have some tooling that's open source and something that anybody could access, then those communities could actually get together and build that knowledge into AI models. Just so I understand, what you guys have is the structure for an AI system.
And in other cases, individual companies own and train their own AI systems. It takes an enormous amount of resources. They hoover up all kinds of information, train it according to their own hidden set of rules, and then a customer might
use that for some price. What you're saying is, in the same way that we democratized the writing of software before, let's democratize the training of an AI system. So anyone can contribute here and teach the model the things that they're interested in teaching the model. I'm guessing, correct me. On the one hand, this model, at least in the beginning, is going to have a lot fewer resources available to it. But on the other hand, it's going to have a much more diverse set of
That's right. And the other thing is that IBM basically, as part of this project, has something called the Granite Model Family, and they've donated some granite models. So these are the ones that take the months and terabytes of data and all the GPUs to train. So IBM has created one of those.
And they have listed out and linked to the data sets that they used. And they talk about the relative proportions they used when pre-training. So it's not just a black box. You know where the data came from, which is a pretty open position to take. That is what we recommend as the base. So you use the InstructLab tuning. You take this base granite model that IBM has provided. And you use the InstructLab tooling that Red Hat works on. And you use that to fine tune the model to make it
whatever you want. I want to go back for a moment to the partnership between IBM and Red Hat here with them providing the granite model to your Instruct Lab. Is this the first time Red Hat and IBM have collaborated like this?
I think it's something that's been going on. Like another product within the Red Hat family would be OpenShift AI, where they collaborate a lot with IBM research team. Like BLM is one of the components of that product, that there's a nice kind of exchange and collaboration between the two companies. How large is the potential community of people who might contribute to InstructLab?
It could be thousands of people. I mean, we'll see. It's early days. This is early technology that was invented at IBM Research that they partnered with us at Red Hat to kind of build the software around it. There's still more to go. Like right now, we have a team in the community that's actually trying to build a web interface to make it easier for anybody to contribute. So we have a lot of those sort of user experience opportunities.
for the contributor to the model stuff to work out that we're still actively building on. But like my vision for it even is I like going back to that academic model of learning from what others and building upon it over time. It would be very good for us to sort of go out and try to collaborate with academics of all fields. Like, hey, you know, the model doesn't know about your field. We
would you like to put something into the model about your field so it knows about it? Or even, you know, talk to the model. It got it wrong. Let's correct it. Can we lean on your expertise to correct it and make sure it gets it right? And sort of use that community model as a way for everybody to collaborate. Because before InstructLab,
My understanding is if you wanted to take a model that's open source licensed and play with it, you could do that. You could take a model kind of off the shelf from Hugging Face and fine tune it yourself. But it's a bit of a dead end because you made your contributions, but there's no way for other people to collaborate with you. So the way that we've built this is based on how the technology works.
everybody can contribute to it. This is something that it can keep growing and growing and growing over time. Yeah, yeah. What's the level of expertise necessary to be a contributor?
You don't need to be a data scientist and you don't need to have exotic hardware. Honestly, if you don't even have laptop hardware that meets the spec for doing InstructLabs laptop version, you can submit it to the community and then we'll actually build it for you. We have bots and stuff that do that. And we're hoping over time to make that more accessible, first by having a user interface and then maybe later on having a web service. Yeah. So give me an example of how a business might make use of InstructLabs.
One of the things that businesses are doing with AI right now is using hosted API services. They're quite expensive, but they're finding value, but it's hard given the amount of money they're spending. And one of the things that's a little scary about it too is like you have very sensitive internal documents and you have employees maybe not understanding what they're actually doing. Because, you know, how would you if you're not technical enough? When you're asking said employees,
public web service AI model information about copy pasting internal company documents. It's going across the internet into another company's hands and that company probably shouldn't have access to that. So what both Red Hat and IBM in this space are looking at, like the InstructLab model is very modest. It's 7 billion parameter model, very small. It's very cheap to serve inference on a 7 billion parameter model.
It's competing with trillion parameter models that are hosted. You take this small model that is cheap to run inference on. You train it with your own company's proprietary data inside the walls of your company on your own hardware. You can do all sorts of actual data analysis on your most sensitive data and have the confidence that it's not left the premises.
In that use case, you're not actually training the model for everyone. You're just taking it and doing some private stuff on it. Exactly. Which doesn't leave the building. But that's separate from an interaction where you're doing something that
contributes overall. Right. And that's something maybe that I should be more clear about is there's sort of two tracks here. And this is very Red Hat classic. You have your upstream community track and you have your business product track. So the upstream community track is just enabling anybody to contribute to a model in a collaborative way and play with it. The downstream product business oriented track is now take that tech that we've honed and developed in the open community and
and apply it to your business knowledge and skills. This community-driven approach marks a pivotal shift towards more accessible AI solutions. The contrast between externally hosted AI services and the open model enhanced by InstructLab underscores the potential for broader adoption of AI in diverse business contexts.
She envisions a future in which technological innovation is more tailored to individual business needs, guided by principles of openness and security. Let's do an imaginary case study. Sure. I'm a law firm. I'm in entertainment law. I have 100 clients who are big stars. They all have incredibly complicated contracts. I feed a thousand of my company's contracts from the last 10 years.
into the model. And then every time I have a new contract, I ask the model, "Am I missing something? Can you go back and look through all our old contracts and show me a contract that is missing key components or exposes us to some liability?"
In that case, the model would know my law firm contracts really, really well. It's as if they've been working at my law firm. They're not distracted by other people's particular styles or a bunch of contracts from the utility industry or they know entertainment law contracts. Exactly. Yeah. And you can train it in your own image, your style of doing things. It's something that...
Yeah.
Give me a sort of a real-world case study where you think the business use case would be really powerful. What's a business that really could see an advantage to using InstructLab in this way? The demo that I've given a couple times at different events used an imaginary insurance company. So you say you have this company, you have to recommend repairs for various types of claims.
You've been doing this for years. You know if, you know, the windshield's broken and you've gotten this type of accident and it's this model car. These are the kinds of things you want to look at. So...
You could talk to any insurance agent in the field and be like, "Oh, you know, it's a Tesla. You might want to look at the battery or something." They'll have some latent knowledge just so you can take that and train it into a model. Honestly, I think these kind of new technologies are better when they're less visible. So say you have the claims agents in the field and they have this tool and they're kind of entering the claim data, they're on the scene at the car.
And it might say, oh, look, I see this is a Ford Fiesta. These are things you want to look at for this type of accident. As you're entering the data, it could be going through the knowledge you had loaded into the model and be making these suggestions based on your company's background. And, hey, you know, let's not make the same mistake twice. Let's make new mistakes and let's learn from the stuff we already did. So that's one example. But there's so many different industries and ways that this could help and it could make those agents in the field more efficient. Yeah.
Have you had anyone talk to you about using InstructLab in a way that surprised you? I mean, some people have done funky things, but sort of playing with the skill stuff, that's where I see a lot of creativity. The difference between knowledge and skills is that knowledge is pretty understandable, right? Like, oh, historical insurance claims or, you know, legal contracts.
Skills are a little different. So whenever somebody submits a skill, sometimes it tends to be really creative because it's not something that's super intuitive. Somebody submitted a skill. I don't know how well it worked, but it was like making ASCII art, like draw me a, I don't know, draw me a dog and it'll do like an ASCII art dog. I mean, it's stuff that you can do programmatically. One that was actually very, very helpful was, you know, take this table of data and convert it to this format. Like, Ooh, that's nice. That actually saves me time.
How far away are we from the day when I, Malcolm Gladwell, technology ignoramus, can go home and easily interact with InstructLab? Maybe a few months. A few months? I thought you were going to say a few years. No, I think it could be a few months.
Wow. I hope. Hey, it's power open source innovation. Yeah. Oh, that's really interesting. Yeah. I'm always taken by surprise. I'm still thinking in 20th century terms about how long things take. You're in the 22nd century as far as I can tell. Honestly, the Instruct Lab core invention was invented in a hotel room at an AI conference in December with an amazing group of IBM research guys. December of 2023. Wait.
Back up. You have to tell the story. This group of guys we've been working with, they were at this conference together. And it's a really funny story because, you know, it's hard to get access to GPUs. And like even, you know, you're at IBM and it's hard to get access because everybody wants access. They did it over Christmas break because nobody was using the cluster at the time. And they ran all of these experiments. And I'm like, oh, this is really cool. And their idea was we can do a stripped down AI model experiment.
And was the idea, even back then, combine it with granite? What was the core of the original idea? The original idea, it's sort of multi, there's like multiple aspects to it. So like one of the aspects, it actually came on later, but it starts at the beginning of the workflow, is you're using a taxonomy. Mm-hmm.
to organize how you're fine tuning the model. So the old approach, they call it the blender approach. You just take a bunch of data of roughly the type of data that you'd like and you kind of throw it in and then see what comes out. Don't like it? Okay, throw in more, try again, see what comes out.
They had used this taxonomy technique. So you actually build like a taxonomy of like categories and subfolders of like, this is the knowledge and skills that we want to train into the model. And that way you're sort of systematic about what you're adding. And you can also identify gaps pretty easily. Oh, I don't have a category for that. Let me add that. So that's like one of the parts of the invention here. Point number one is.
Let's be intentional and deliberate in how we build and train this thing. Yeah. And then the next component would be,
Okay, so it's actually quite expensive. Part of the expense of like tuning models and just training models in general is coming up with the data. And what they wanted to do is have a technique where you could have just a little bit of data and expand it with something they're calling synthetic data generation. And this is where it's sort of like you have this student and teacher workflow. So you have your taxonomy here.
The taxonomy has sort of the knowledge, like a business's knowledge documents their insurance claims. And it has these quizzes that you write, and that's to teach the model. So I'm writing a quiz based, just like you do in school. You read the chapter on the American Revolution, and then you answer a 10-question quiz, where you're giving the model quiz. You need at least five questions and answers, and the answers need to be taken from the context of the document.
And then you run it through a process called synthetic data generation. And it looks at the documents or look at the history chapter. It'll look at the questions and answers. And then it'll look to that original document and come up with more questions and answers based on the format of the questions and answers you made.
So you can take five questions of answers, amplify them into 100 questions and answers, 200 questions and answers. And it's a second model that is making the questions and answers. So it's synthetic data generation using an AI model to make the questions. We use an open source model to do that. So
So that's the second part. And then the third part is we have a multi-phase tuning technique to actually take the synthetic data and then basically bake it into the model. So sort of that's the approach. A general philosophy of the approach is using granite because we know where the data came from. Another approach is the fact that we're using small models that are cheap to run inference on. They're small enough that you can tune them on laptop hardware. You don't need all the fancy expensive GPU mania. You're good.
So sort of like a whole system. It's like not any one component, but it's sort of the approach they took was somewhat novel. And they were very excited when they saw the experimental results. There was a meeting between Red Hat and IBM. It was actually an IBM research meeting that Red Hatters were invited to. And I think the Red Hatters involved sort of saw the potential. Whoa, we can make models open source. Finally, rather than them just being these endless dead forks,
We could make it so people could contribute back and collaborate around it. So that's when Red Hat became interested in it. And we sort of worked together. And the research engineers from IBM Research who came up with the technique. And then my team, the software engineers who know how to take research code and productize it into actually runnable, supportable software kind of got together. We've been working.
hanging out in the Boston office at Red Hat and building it out. April 18th was when we went open source and we made all of our repositories with all of the code public. And right now we're working towards a product release or a supported product. How long did it take you to be convinced of the value of this idea? I mean, so people get together in this hotel room. They're running these experiments over Christmas. Are you aware of the experiments as they're running them?
No, I didn't find out until February. You found out in February. So they come to you in February and they say, Mo, can you recreate that conversation? Well...
Our CEO, Matt Hicks, and then Jeremy Eder, who's one of our distinguished engineers, and Steve Watt, who's a VP, were present, I think, at that meeting. So they kind of brought it back to us and said, listen, we've invited these IBM research folks to come visit in Boston, you know, work with them. Like, see, does this have any merit? Could we build something from it? And so they gave us some presentations. We were very excited. When they came to us, it only had support for Mac laptops.
Of course, you know, Red Hat, we're Linux people. So we're like, all right, we've got to fix that. So a bunch of the junior engineers around the office kind of came in and they're like, okay, we're going to build Linux support. And they had it within like a couple of days. It was crazy because this was just meant to be, hey guys, you know what? These are invited guests visiting our office. See what happens. And we ended up doing like,
Weeks of hack fests and late night pizzas in the conference room and like playing around with it and learning. And it was it was very fun. It's pretty cool. Anyone else do anything like this? Is not my understanding that anybody else is doing it yet. Maybe others will try. A lot of the focus has been on that pre-training phase. Mm hmm.
But for us, again, that fine tuning, it's more accessible because you don't need all the exotic hardware. It doesn't take months. You can do it on a laptop. You can do a smoke test version of it in
in less than an hour. What does the word smoke test mean? Smoke test means you're not doing a full fine tuning on the model. It's a different tuning process. It's like kind of lower quality, so it'll run on lower grade hardware. So you can kind of see, hmm, did it move the model or not? But it's not going to give you like the full picture. You need higher end hardware to actually do the full thing. So that's what the product will enable you to do once it's launched is you're going to need the GPUs, but when you have them, we'll help you make the best usage of them. Yeah, yeah.
And there's a little detail I want to go back to. Sure. In order to run the tests on this idea way back when, they needed time on the GPUs. So this would be the in-house IBM, and they were quiet at Christmas. So how much time would you need on the GPUs to kind of get proof of concept? Well, what happens is, and it's sort of like a lot of trial and error, right? And there's a lot about this stuff that like,
You come up with a hypothesis, you test it out, did it work or not? Okay. It's just like, you know, in the lab, you know, Bunsen burners and beakers and whatever. So it really depends, but it can be hours. It can be days. It really depends on what they're trying to do. And then sometimes they can cut the time down, you know, with the number of GPUs you have. So like I have a cluster of eight GPUs. Okay. It might take a day, but then if I can get 32, I can pipeline it and make it go faster and get it down to a few hours. So it really depends, you know, but it's like,
Everybody's home for the holidays. It's a lovely playground to kind of get that stuff going fast. Let's jump forward one year. Tell me the status of this project. Tell me who's using it. Tell me how big is it. Give me your optimistic but plausible prediction about what InstructLab looks like a year from now. A year from now, I would like to see kind of a vibrant community around InstructLab.
not just building knowledge and skills into a model, but coming up with better techniques and innovation around how we do it. So I'd like to see like the contributor experience as we grow more and more contributors to be refined. So like a year from now, Malcolm Gladwell could come impart some of his wisdom into the model and it wouldn't be difficult. It wouldn't be a big lift. I would love to see the user interface tooling for doing that to be more sophisticated. I would love to see more people
taking this and even using it, maybe you're not sharing it with the community, but you're using it for some private usage. Like I'll give you an example. I'm in contact with a fellow who is doing AI research and he's working with doctors. They're GPs in an area of Canada where there's not enough GPs for the number of patients.
So, you know, anything you can do to save doctors time to get to the next patient. It's like one of the things that he has been doing experiments with is can we use an open source licensed model that the doctor can run on their laptop so they don't have to worry about all of the different privacy rules like it's private on the laptop right there.
take his live transcription of his conversation with the patient and then convert it automatically to a SOAP format that can be entered in the database. Typically, this will take a doctor 15 to 20 minutes of paperwork. Yeah.
Why not save him the paperwork and at least have the model take a stab? Does the model then hold on to that information and he interacts with the model again when... Well, that's the thing. Not within Struck Lab. Maybe that could be a future development. It doesn't... Once you're doing inference, it's not ingesting what you're saying to it back in. It's only the fine-tuning phase. So the idea would be the doctor could maybe load in past patient data as knowledge. And then when he's trying to diagnose, maybe... You know what I'm saying? Like...
But the main idea is somebody might have some private usage. I would love to see...
more usage of this tool to enable people who otherwise never would have had access to this type of technology who never like, you know, a small country GP doctors, it doesn't have GPUs. They're not going to hire some company to custom build them a model, but maybe on the weekend, if he's a techie guy, he can play with it. This is interesting, Mo. I mean, the more you talk, the more I'm realizing that the simplicity of this model is the killer app here.
Once you know you can run it on a laptop, you have democratized use in a way that's inconceivable with some of these other much more complex. But that's interesting because one would have thought intuitively that at the beginning that the winner was going to be the one with the biggest, most complex device.
And you're saying actually, no, there's a whole series of uses where being lean and... Focused. Focused is actually, you know, it enables a whole class of uses. Maybe another way of saying this is...
Who wouldn't be a potential Instruct Lab customer? We don't know yet. It's so new. Like, we haven't really had enough people experimenting and playing with it and finding out all the things yet. But that's the thing that's so exciting about it. It's like, I can't wait to see what people do. Is this the most exciting thing you've worked on in your career? I think so. I think so. Yeah. Well, we are reaching the end of our time. But before we finish, we're going to do a little speed round. Sure. All right.
Complete the following sentence. In five years, AI will... Be boring. It will be integrated. It'll just work. And there will be no now with AI thing. It'll just be normal. What's the number one thing that people misunderstand about AI? It's just matrix algebra. It's just numbers. It's not sentient. It's not coming to take us over. It's just numbers. You're on this side of... You're on the... Team humanity. Yeah, you're on team humanity. Good. Good.
What advice would you give yourself 10 years ago to better prepare for today? Learn Python for real. It's a programming language that is extensively used in the community. I've always dabbled in it, but I wish I had taken it more seriously. Yeah. Did you say you had a daughter? I have three daughters. You have three daughters? I have two. You're, if you got three, you're on your own. Are you making them study Python?
I am actually trying to do that. We're using a micro:bit microcontroller tool to do like a custom video game controller. They prefer Scratch because it's a visual programming language, but it has a Python interface too and I'm like pushing them towards Python. Chat box and image generators are the biggest things in consumer AI right now. What do you think is the next big business application?
private models, small models, fine-tuned on your company's data for you to use exclusively.
Are you using AI in your own personal life these days? Honestly, I think a lot of us are using it and we don't even realize it. Yeah. I mean, I'm an aficionado of foreign languages. There's translation programs that are built using machine learning underneath. One of the things I've been dabbling with lately is using text summarizations because I tend to be very loquacious in my note-taking and that is not so useful for other people who would just like a paragraph. So that's something I've been experimenting with myself just to help my everyday work. Yeah.
We hear many definitions of open related to technology. What's your definition of open and how does it help you innovate? My definition of open is basically sharing.
And being vulnerable, like not just sharing in a have a cookie way, but in a, you know what, I don't actually know how this works. Could you help me? And being open to being wrong, being open to somebody helping you and making that collaboration work. So it's not just about like the artifact you're opening. It's your approach, like how you do things being open. Yeah, yeah.
Well, I think that wraps us up. How can listeners follow your work and learn more about Granite and InstructLab? Sure. You can visit our project webpage at instructlab.ai, or you can visit our GitHub at github.com slash instructlab. We have lots of instructions on how to get involved in InstructLab. Wonderful. Thank you so much. Thank you, Malcolm. A big thank you to Mo for the engaging discussion on the groundbreaking possibilities of InstructLab. Thank you.
We've explored how this platform has the potential to revolutionize industries from insurance to entertainment law by using an open source community approach that makes it easier for more people from all backgrounds to fine tune models for specific purposes, ultimately making AI more accessible and impactful than ever.
Looking ahead, the future of AI isn't just about technological efficiency. It's about enhancing our everyday experiences in ways that were never possible before. Like streamlining work for doctors to improve the patient experience or assisting insurance agents to improve the claims experience. InstructLab is paving the way for more open, accessible AI future, one that's built on collaboration and humanity.
Smart Talks with IBM is produced by Matt Romano, Joey Fishground, and Jacob Goldstein. We're edited by Lydia Jean Cott. Our engineers are Sarah Bruguere and Ben Tolliday. Theme song by Gramascope.
Special thanks to the 8 Bar 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.
I'm Malcolm Gladwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions. ♪