Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. This season
Speaker 1: on smart Talks with IBM, Malcolm Gladwell and team are
Speaker 1: diving into the transformative world of artificial intelligence with a
Speaker 1: fresh perspective on the concept of open What does open
Speaker 1: really mean in the context of AI. It can mean
Speaker 1: open source code or open data, but it also encompasses
Speaker 1: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
Speaker 1: and enabling new levels of transparency. Join hosts from your
Speaker 1: favorite Pushkin podcasts as they explore how openness and AI
Speaker 1: is reshaping industries, driving innovation, and redefining what's possible. You'll
Speaker 1: hear from industry experts and leaders about the implications and
Speaker 1: possibilities of open AI, and of course, Malcolm Gladwell will
Speaker 1: be there to guide you through the season with his
Speaker 1: unique insights. Look out for new episodes of Smart Talks
Speaker 1: every other week on the iHeartRadio app, Apple Podcasts, or
Speaker 1: wherever you get your podcasts, and learn more at IBM
Speaker 1: dot com, slash smart Talks.
Speaker 2: Pushkin Hello, Hello, Welcome to Smart Talks with IBM, a
Speaker 2: podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glawell.
Speaker 2: This season, we're diving back into the world of artificial intelligence,
Speaker 2: but with a focus on the powerful concept of open
Speaker 2: its possibilities, implications, and misconceptions. We'll look at openness from
Speaker 2: a variety of angles and explore how the concept is
Speaker 2: already reshaping industries, ways of doing business and our very
Speaker 2: notion of what's possible. In today's episode, Jacob Goldstein sat
Speaker 2: down with Mariam Ashuri, the Director of Product Management and
Speaker 2: a Head of product for IBM's Watson x dot AI,
Speaker 2: where she spearheads the product strategy and delivery of IBM's
Speaker 2: watsonx foundation models. She is a technologist with more than
Speaker 2: fifteen years of experience developing data driven technologies. The conversation
Speaker 2: focused on how enterprises can use technology to build and
Speaker 2: deliver greater transparency in AI. With Granite, Mariam explained how
Speaker 2: Grantite can be utilized to improve efficiency across various domains.
Speaker 2: She discussed how these models are being used in real
Speaker 2: world business applications, particularly in areas like customer care, where
Speaker 2: AI can help enable quick, accurate responses based on internal
Speaker 2: company data. Mariam provided a fascinating look into how enterprises
Speaker 2: have moved from mere experimentation with generative AI to actual production,
Speaker 2: navigating challenges such as increased latency, cost and energy consumption.
Speaker 2: She highlighted how the emerging trend of smaller models customized
Speaker 2: with proprietary data can potentially deliver high performance at a
Speaker 2: fraction of the cost, marking a significant shift in how
Speaker 2: enterprises leverage AI. Whether you're an AI enthusiast or a
Speaker 2: business leader looking to harness the power of artificial intelligence,
Speaker 2: this episode is packed with valuable insights and forward thinking strategies.
Speaker 3: Let's just start with your background. How did you come
Speaker 3: to work at IBM.
Speaker 4: I join IBM right after I graduated. I have an
Speaker 4: AI background, and throughout the years, I've held many roles
Speaker 4: in design, engineering, development research, mostly focused on AI application
Speaker 4: development and design. In my current job, I'm the product
Speaker 4: owner for What's the Next Day I, which is the
Speaker 4: IBM platform for enterprise AI. What excites me about this job,
Speaker 4: I would say, is the technology advancements over the last
Speaker 4: eighteen months in the market. We've been witnessing how generative
Speaker 4: ALI has been changing the market. The way that I
Speaker 4: see that is JENNAI has been perhaps one of the
Speaker 4: largest paradigm shifts. When we think about productivity the same
Speaker 4: way that Internet and personal computers impacted the productivity of workforce.
Speaker 4: Now we are witnessing another wave of all those opportunities
Speaker 4: that it can unlock for especially enterprise AI when it
Speaker 4: comes to enhancing the productivity of the workforce and releasing
Speaker 4: some time that can potentially be put into creating more
Speaker 4: value work for enterprise. So that's the major part that
Speaker 4: I picked this team to have an impact on the
Speaker 4: market and the community, but also of course using the
Speaker 4: skills that I gain through all these years through IBM
Speaker 4: to help to establish IBM as the market leader for
Speaker 4: enterprise AI.
Speaker 3: So you talked about jenai as this sort of generational,
Speaker 3: transformational technological force, and I'm curious just in terms of
Speaker 3: how it's going to come into the world, Like, how
Speaker 3: do you see market adoption of genai sort of evolving
Speaker 3: from here?
Speaker 4: Well, last year was the year of excitement about generative AI.
Speaker 4: Most of the companies were experimenting and exploring with GENI.
Speaker 4: We see that energy shifted towards how to best monetize
Speaker 4: that technology. Almost half of the market has moved from
Speaker 4: investigation to pilots, ten percent has moved to production. When
Speaker 4: you're exploring with this technology, you're looking for a valve factor,
Speaker 4: You're looking for an AHA moment. That's why very large
Speaker 4: general purpose models shine. But as companies move toward production
Speaker 4: and scale, they soon realize the past success is not
Speaker 4: that straightforward. For example, they're larger the model, the larger
Speaker 4: computer resources it requires. That translates to increased latency that's
Speaker 4: your response time. That translates to increased cost. That translates
Speaker 4: to increase carbon food print, and energy consumption. So think
Speaker 4: about that. At the scale of enterprise in production, some
Speaker 4: of them can be a showstopper. Because of this reason,
Speaker 4: what actually c is emerging in the market is instead
Speaker 4: of focusing on very large general purpose models, coming back
Speaker 4: to very small, trustworthy models that they can customize on
Speaker 4: their own proprietary data. That's the data about their customers,
Speaker 4: that the data about their specific domains to create something
Speaker 4: differentiated that is much smaller and delivers the performance that
Speaker 4: they want on a target use is for a fraction
Speaker 4: of the cost.
Speaker 3: Uh huh. So let's talk a little bit more specifically
Speaker 3: about what you're working on. Let's talk about granite. First
Speaker 3: of all, tell me what is granite.
Speaker 4: Granite is our industrial leading family of models, flagship IBM models.
Speaker 4: These are the models that we train from scratch. When
Speaker 4: offered to our platform, we offer indemnification and we stand
Speaker 4: behind them today. It comes in four flavors, language, code,
Speaker 4: time series and geospecial models. Granite language series is covering English, Spanish, German,
Speaker 4: Portuguese and Japanese. We have a combination of commercial and
Speaker 4: open source language models on Granite. For example, we recently
Speaker 4: released the Granite seven B language model small powerful English model.
Speaker 4: On the code front, our models are state of the
Speaker 4: art models ranging from from three billion to thirty four
Speaker 4: billion parameters. These are very powerful models that performs or
Speaker 4: outperforms in some cases the popular open source models in
Speaker 4: their weight class. So very powerful models.
Speaker 3: So I get the idea a big picture about these models,
Speaker 3: but it would be helpful to just get a sense
Speaker 3: specifically of what they're doing, Like, can you give me
Speaker 3: any specific examples of how these models are being used
Speaker 3: in businesses in the real world right now?
Speaker 4: Well, the top use cases for generative AI are really
Speaker 4: content generation, summarization, information extraction. Perhaps the most popular use
Speaker 4: case that we are seeing in enterprise is content grounded
Speaker 4: question and answering, So using these models as a base
Speaker 4: to connect them to a body of information let's say
Speaker 4: their policies, their documents that is internal to the enterprise,
Speaker 4: and get the model to provide answers based on that questions.
Speaker 4: One example of that is for customer agents customer care,
Speaker 4: when a customer is asking a question. Previously, the agent
Speaker 4: that responds to the customer had to answer the question
Speaker 4: and if they don't know the answer escalated to the product,
Speaker 4: especially keeping people on hold on the line to go
Speaker 4: figure out the answer for that and then come back.
Speaker 4: You can think of the time it takes to resolve
Speaker 4: an issue. But now with LLMS, we have an opportunity
Speaker 4: to automatically retrieve the information based on the internal documents
Speaker 4: of the company, formulate an answer, show it to the
Speaker 4: human agent, and then if they verify with the sources
Speaker 4: of varies coming from, they can just translate it directly
Speaker 4: to the customer. This is a very simple example of
Speaker 4: how it's impacting the customer care.
Speaker 3: So one big theme of this season is this idea
Speaker 3: of open And one of the things that's interesting to
Speaker 3: me about the work you're doing is you are using
Speaker 3: not only ran at this model IBM developed, but you're
Speaker 3: also using third party models right from other places. So
Speaker 3: tell me about that work and how that is sort
Speaker 3: of fitting into your kind of real world typically enterprise
Speaker 3: GENAI work.
Speaker 4: When it comes to model strategy, our strategy is really
Speaker 4: focused on two pillars, multimodel and multi deployment. It means
Speaker 4: that we don't believe one single model rules all the
Speaker 4: use cases. And I think at this point the market
Speaker 4: has also realized the enterprise markets in average today are
Speaker 4: using five to ten different models for different use cases.
Speaker 3: Oh interesting.
Speaker 4: So in our portfolio, if you look into what's on
Speaker 4: extradai today, we are offering a large sets of high performing,
Speaker 4: state of the art models coming from open source commercial
Speaker 4: models that we are bringing through our partners and also
Speaker 4: IBM developed models. In addition to all of these, we
Speaker 4: also have an option for bring your own model from
Speaker 4: outside the platform. Let's say you have a custom model
Speaker 4: that you made it yourself, you can bring it to
Speaker 4: the platform and really helping the customers to navigate through
Speaker 4: avoid range of models and pick the right model for
Speaker 4: their target use case. Throughout that, we've been heavily working
Speaker 4: with our partners and you know, this is the market
Speaker 4: that is evolving rapidly. We've been at the forefront of
Speaker 4: a spit to delivery. One example that I like to
Speaker 4: highlight is recently Metal released Lama four or five billion,
Speaker 4: such a powerful model. On the same day that it
Speaker 4: was released to the market, we made it available in
Speaker 4: our platform to our customers the same day, and not
Speaker 4: only we delivered it on the same day. We are
Speaker 4: offering competitive pricing but also flexibility in where to deploy.
Speaker 4: So we are giving an option to enterprise to deploy
Speaker 4: these models on the platform of dage choice, either multi
Speaker 4: cloud it can be gcpaws as youre IBM cloud, or
Speaker 4: on premises the same for mistrall Ai MISTROLEI recently released
Speaker 4: the model Misrol large two on the same day we
Speaker 4: delivered that through the platform. That's an example of a
Speaker 4: commercial model. Lama as open source, but large two is
Speaker 4: a commercial model that we made available through the platform Great.
Speaker 3: So I want to talk about enterprise grade foundation models,
Speaker 3: just to get into it briefly. What's a foundation model.
Speaker 4: People associate foundation models with a large language model, but
Speaker 4: large language models are really a subset of foundation models.
Speaker 4: Large language models are focused on language, but foundation models
Speaker 4: can be code generators, can be focused on time series
Speaker 4: model we talked about, they can be images, it can
Speaker 4: be jewy special models. So foundation model, as the term
Speaker 4: suggests that your foundations to create a series of subsequent
Speaker 4: models that can be customized for a downstream use case.
Speaker 4: And that's why they are calling that foundation models. LM
Speaker 4: is a good example of that as a subset for
Speaker 4: language that you can further customize on your specific data
Speaker 4: to get the model to do other works. So the
Speaker 4: core of these foundation models, they are basically trained on
Speaker 4: an ab third amount of data data sets that most
Speaker 4: of the institutions today are sourcing them from the internet,
Speaker 4: So you can imagine what can potentially go to those models,
Speaker 4: and then it comes to the enterprise and they start
Speaker 4: using it. So for us also, when we started looking
Speaker 4: into in particular, it was triggered by customers asking us
Speaker 4: to provide client protections on these models, and we started
Speaker 4: thinking about, let's look into how the models are trained
Speaker 4: and if you are comfortable of fering client protections on
Speaker 4: the models that are available in the market. And guess what,
Speaker 4: For a majority of these models, there is absolutely no
Speaker 4: visibility into what data vent into those models, not much
Speaker 4: transparent into how the model trains and the responsibility lies
Speaker 4: on you as the customers to start using those models.
Speaker 3: So just to be clear, that is presenting like potential risk,
Speaker 3: real potential risk to a company that is using these models.
Speaker 4: It is. It is a potential risk in particular for
Speaker 4: the customers in highly regulated industries. So what we did
Speaker 4: for Granite was when we started training these models from scratch,
Speaker 4: basically we went to the corpus of data that was
Speaker 4: available to us. So, for example, the very first version
Speaker 4: of Granite was exposed to twenty persons of its data
Speaker 4: from finance and legal because we have a lot of
Speaker 4: financial institutions as our clients. We worked directly with our
Speaker 4: IBM research to identify detectors for harmful information like haytyp
Speaker 4: use and profanity detectors.
Speaker 3: Okay, so we're talking about Granted, we're talking about this
Speaker 3: set of models IBM has developed. Let's talk about using
Speaker 3: Granite on WATSONEX compared to downloading open source models, Like,
Speaker 3: how do those differ?
Speaker 4: By using Granite and whats on X, you get two things.
Speaker 4: The first one is the client protection and in thementification
Speaker 4: that we talked about, you get that if the model
Speaker 4: is consumed through our platform and the second one is
Speaker 4: really the ecosystem of platform capabilities that we are offering
Speaker 4: to help you create value on top of those data.
Speaker 4: So for example, bringing your data to customize granted for
Speaker 4: your own specific use case. But also one thing that
Speaker 4: I like to highlight in particular is the AI governance.
Speaker 4: So when you get one of these pre train models,
Speaker 4: you put it in front of your own users. Through
Speaker 4: the input and instructions that the user provides for the model,
Speaker 4: they can notdge the model to potentially create undesired behavior
Speaker 4: and change the behavior of the model. And because of
Speaker 4: this is extremely important to automatically document the lead age
Speaker 4: of who touched the model at what point, so if
Speaker 4: something happens, you can trace it back and see where
Speaker 4: it's coming from. And that's what's an extra governance is
Speaker 4: offering automatically documenting the lineage. When you use the granite
Speaker 4: within the platform you get all of those, you can
Speaker 4: have the end to end governance, You can have access
Speaker 4: to all these scalable deployment opportunities that is available for you,
Speaker 4: like to allow you deploy them on the platform of
Speaker 4: your choice that we talked about, either multiple cloud or
Speaker 4: on prem and it also helps you to have access
Speaker 4: to avoid range of model customizations, approaches, prompt tuning, fine tuning,
Speaker 4: retrival augmented generations agents. There is a series of them
Speaker 4: available to use an apply to your model.
Speaker 2: This distinction between large language models and foundation models is
Speaker 2: eye opening. Mariam emphasized that foundation models can be tailored
Speaker 2: to specific tasks, but with that versatility comes a significant
Speaker 2: challenge the lack of transparency and how these models are trained.
Speaker 2: This composed a real risk, especially in highly regulated industries
Speaker 2: like finance. Essentially, by using Granite and watsonex together, enterprises
Speaker 2: get powerful and customizable tools.
Speaker 3: So let's talk about the future a little bit. What
Speaker 3: do you think are some of the big developments we're
Speaker 3: likely to see in the realm of AI models?
Speaker 4: Very good question. I feel like the generative AI of
Speaker 4: the past was powered by large language models. The generative
Speaker 4: AI of the future is going to reason, plan, act
Speaker 4: and reflect.
Speaker 3: Huh, and so I mean in the context of Granite
Speaker 3: in particular, like, what are we likely to see both
Speaker 3: you know, in the near term and in the sort
Speaker 3: of medium to long term.
Speaker 4: There are multiple elements to implement an agentic workflow that
Speaker 4: I just mentioned. One element of that is the LLM
Speaker 4: itself to be able to do the planning and reasoning
Speaker 4: and acting and doing something that we call tool calling.
Speaker 4: So basically, a series of tools are available to the model.
Speaker 4: You ask the model to call those and make a call.
Speaker 4: For example, we can say, hey, Granted, what is the
Speaker 4: weather like where Jacob lives. It's connect to web search API,
Speaker 4: look up your location. Then it's going to connect to
Speaker 4: weather API, calculate the weather and come back and formulate
Speaker 4: an answer and respond to that. So during this process,
Speaker 4: it first has to plan the task of how to
Speaker 4: answer that question, look into what are the tools that
Speaker 4: are available to it and call them, and that's an
Speaker 4: ability of the model to do that. What we did
Speaker 4: with Granted was we expanded the granite capabilities to be
Speaker 4: able to do function calling. So for example, today we
Speaker 4: have an open source granted to an eb function calling
Speaker 4: that is available on hugging face to try on and
Speaker 4: you can grab the model and the model has capability
Speaker 4: to do the tool callings. I'm anticipating that in the
Speaker 4: near future the planning and reasoning and acting and reflecting
Speaker 4: capabilities of the large language models are going to continue
Speaker 4: to evolve.
Speaker 3: So thinking now from the point of view of buyers
Speaker 3: and users of AIS really people who are listening from
Speaker 3: that perspective. As people are evaluating AI tools and solutions,
Speaker 3: what is the most important thing they should be thinking about?
Speaker 3: How do you think about kind of that process?
Speaker 4: I think they should always start with the area at
Speaker 4: which they think it would benefit from AI, and then
Speaker 4: within that area, look into what data they have available
Speaker 4: to potentially fit into those AI service architects do they
Speaker 4: have access to quality data? And the second question that
Speaker 4: they have to ask themselves is do I have a
Speaker 4: trusted partner that can supply what I need to be
Speaker 4: able to implement AI. That can be a collection of
Speaker 4: the foundation models that you're going to need, That can
Speaker 4: be a collection of the platform capabilities that the trusted
Speaker 4: partner can offer you to implement such a thing. The
Speaker 4: third thing is go and evaluate the regulations. Does regulation
Speaker 4: allow you to applyoy AI to the specific area that
Speaker 4: you are investigating and you're targeting for AI. And the
Speaker 4: last part, but not least, is back to the principles
Speaker 4: of design thinking what is the problem in that area
Speaker 4: I'm solving with AI? And if AI is even appropriate?
Speaker 4: Because we want to make sure that you use AI
Speaker 4: not just because it's a cool, hot toy in the market,
Speaker 4: but you are convinced that it can significantly enhance the
Speaker 4: user experience of your customers in that area. And once
Speaker 4: you have an answer to those all these four questions,
Speaker 4: then maybe you have a good candidates to start applying AIT.
Speaker 3: And what about from the side of project managers who
Speaker 3: are trying to just keep up with how fast things
Speaker 3: are changing, how fast innovation is happening, Like, what advice
Speaker 3: would you give those people?
Speaker 4: My advice would be focus on agility. This is a
Speaker 4: market that is evolving rapidly and the winners of the
Speaker 4: market would be those that are able to take advantage
Speaker 4: of the best the market can offer at any point
Speaker 4: of time. So in order to do that, they need
Speaker 4: to be open to experimentation, continuous learning, and to rapidly
Speaker 4: adopting the new ideas.
Speaker 3: And when you think about the future and GENAI, is
Speaker 3: there a particular, say problem that you are most excited
Speaker 3: to solve.
Speaker 4: I think that would be productive. If you look into
Speaker 4: the stats that are out there, there are surveys that
Speaker 4: confirm that sixty to seventy percents of the time of
Speaker 4: our employees can be potentially enhanced to the productivity gains
Speaker 4: of generative For example, I personally myself use my product
Speaker 4: for content generation a lot, so the time that it
Speaker 4: frees up can be potentially put into generating a higher
Speaker 4: value work. And because of that, I'm super excited with
Speaker 4: all the opportunities that it represents for enterprises to go
Speaker 4: and dedicate the time of the employees to higher value items.
Speaker 3: Great. Okay, a couple Granite specific questions. So what are
Speaker 3: like the key things you want the world to know about.
Speaker 4: Granted Granite is open, trusted, and targeted. Two ways to
Speaker 4: think about openness. One open as open weights it's a
Speaker 4: very lebe for public to download. And the second one
Speaker 4: is open as in there is less restrictions on how
Speaker 4: the customers can legally use these models for a range
Speaker 4: of use cases. We have released Grantite open source models
Speaker 4: on their Apache license that is enabling a large range
Speaker 4: of use cases. The second one was trusted. We talked
Speaker 4: about that like it's rooted in the trustworthy governance process
Speaker 4: that we established thereund how we are training these models
Speaker 4: and the responsibility that we take for these models. And
Speaker 4: the third one is targeted, targeted for enterprise. We talked
Speaker 4: about like exposing Granted to enterprise data or the domain
Speaker 4: specific Granted some of them like Cobalt Java Translation that
Speaker 4: is targeting to solve specific enterprise needs and that's granite,
Speaker 4: so open, trusted and targeted.
Speaker 3: So there are a lot of models out in the
Speaker 3: world all of a sudden, right, it's a crowded market.
Speaker 3: Where does it fit in that universe? What is the
Speaker 3: market for granted?
Speaker 4: We talked about the enterprise market shifting away from very
Speaker 4: large general purpose models to target a smaller models, and
Speaker 4: Granted is a small model that enterprise can pick up
Speaker 4: and customize on their proprietary data to create something that
Speaker 4: is differentiated for a target use case. So Granted is
Speaker 4: well suited as a small, domain specific business, ready tailored
Speaker 4: for business and trained on enterprise data to solve enterprise questions.
Speaker 3: You mentioned small as one of the things that granted
Speaker 3: is why is that useful in some contexts for enterprise
Speaker 3: for businesses.
Speaker 4: The larger the model, the larger computer resources it requires.
Speaker 4: It translates to increased latency that's your response time. It
Speaker 4: translates to increase costs, and in translates to increase carbon
Speaker 4: footprint and energy consumption. So at the scale of enterprise transactions.
Speaker 4: When you move to production and you want to scale,
Speaker 4: some of these challenges can be multiple times stronger, like
Speaker 4: costs can add up, the energy consumption can be a
Speaker 4: serious thing, and the latency is depending on the application,
Speaker 4: can be a showstopper and blockier because for longer, larger models,
Speaker 4: more powerful models, it just takes the way longer time
Speaker 4: to process and calculate the output.
Speaker 3: For you, we are going to finish up with a
Speaker 3: speed round and I want you to just answer with
Speaker 3: the first thing that comes to mind. Don't overthink this, Okay,
Speaker 3: complete this sentence. In five years, AI will be invisible. Ah,
Speaker 3: I like that. What do you mean by that?
Speaker 4: Today? AI is everywhere? But if you ask my kids
Speaker 4: at home, they know AI. But if you say very like,
Speaker 4: how do you use AI, they don't know the answer
Speaker 4: because it's so blended in their life that they don't
Speaker 4: feel like it's something that they are using. They are
Speaker 4: getting used to that. So when I think of next
Speaker 4: generation and the years to come, that generation is so
Speaker 4: used to AI being part of their life that they
Speaker 4: feel like it's just there. That's one and the second
Speaker 4: one is the simplicity of interaction. With AI that you
Speaker 4: don't feel like you're interacting with the system. It's just there,
Speaker 4: like you talk to AI. Everything is automated. So I
Speaker 4: would say the simplicity and being blended to solve the
Speaker 4: right problems is the part that I'm referring to as invisible.
Speaker 4: Like Internet is everywhere and it's invisible. But we used
Speaker 4: to dial in, like you remember the dialing zone to
Speaker 4: connect the Internet. It's gone. The Internet is completely invisible today.
Speaker 3: Right, Like we used to talk about logging on, right,
Speaker 3: and you don't log on anymore because you're always logged on.
Speaker 4: Yep, you're always connected.
Speaker 3: Yeah, what's the number one thing that people misunderstand about AI?
Speaker 4: AI is an ivitable but should not be feared.
Speaker 3: What advice would you give yourself ten years ago to
Speaker 3: better prepare you for today?
Speaker 4: I would say, develop a broad range of skills. Even
Speaker 4: if you think they will not help you today, they
Speaker 4: may be valuable in the future.
Speaker 3: So on the consumer side, right now, we hear a
Speaker 3: lot about chatbots and image generators. But on the business side,
Speaker 3: what do you think is the next big business application?
Speaker 4: AI? Influencers generating content?
Speaker 3: Huh? How do you use AI in your day to
Speaker 3: day life today.
Speaker 4: One simple example is linked in posts. I love it
Speaker 4: to just go to my product. I'll give you an
Speaker 4: example which is my favorite one. Lama three point one
Speaker 4: four A five b the post that I announced on
Speaker 4: LinkedIn on hey, IBM is releasing the model on the
Speaker 4: same day it was generated by Lama three point one
Speaker 4: four five billion. So using the same model to post
Speaker 4: the generate the announcement note very elegant.
Speaker 3: Is there anything else I should ask you?
Speaker 4: Oh, we didn't talk about instruct lab. So when you
Speaker 4: grab a model, you start from the model, but you
Speaker 4: need to then customize it on your proprietary data to
Speaker 4: create value on top of that. So instruct lab is
Speaker 4: giving you a method based on open source contributions to
Speaker 4: collectively contribute to improve the base model. So if you're
Speaker 4: an enterprise, you can leverage your internal employees to collectively
Speaker 4: all contribute to improve the model. Give you an example
Speaker 4: of why it matters, Like if you go to hiking
Speaker 4: pace today and look for Lama, there are about fifty
Speaker 4: thousand different lamas coming up. And the reason is because
Speaker 4: there is no way to contribute to the base model.
Speaker 4: If you're a developer, you have to make a colon
Speaker 4: of the copy of the model and finding need for
Speaker 4: your own purpose. We figure the method that we call
Speaker 4: instruct lab to be able to collectively collect all that
Speaker 4: information and contribute to the base model and enhance. So
Speaker 4: that's instruct lab. I just wanted to highlight the value
Speaker 4: of being open because that's another topic that has been
Speaker 4: emerging in the market over the past eighteen months. In particular,
Speaker 4: I believe the future of AI is open and we've
Speaker 4: been seeing how the open source markets has been changing,
Speaker 4: how the models are accessible to a wider audience, and
Speaker 4: good things typically happen when you make technology pieces accessible
Speaker 4: to a broader range of community to stress test that,
Speaker 4: and that's the direction that we've been adopting with granted,
Speaker 4: and I feel like that's really the adoption that the
Speaker 4: market is going to emerge to moving forward.
Speaker 3: Yeah, there's this interesting I think maybe naively unintuitive, but
Speaker 3: it makes sense once you think about it, thing that
Speaker 3: open source things are safer. You might naively think, oh no,
Speaker 3: put it in a box so nobody can see it,
Speaker 3: and that'll be safer. But like it turns out, of
Speaker 3: the world, if you let everybody poke at it, the
Speaker 3: world will find the vulnerabilities for you and you can.
Speaker 4: Fix them right, That's exactly what's going to happen. Yeah.
Speaker 3: Great, it was lovely to talk with you. Thank you
Speaker 3: so much for your time.
Speaker 4: The same here, thanks Jacob, and.
Speaker 2: That wraps up this episode. A huge thanks to Marian
Speaker 2: and Jacob. Today's conversation open my eyes as to how
Speaker 2: open technology and AI are intersecting to create more transparent
Speaker 2: and efficient systems for enterprises. From the power of smaller,
Speaker 2: more targeted models like granted to the importance of trust
Speaker 2: and governance in AI, these developments are reshaping how businesses
Speaker 2: operate at their core. As we continue to unpack the
Speaker 2: complexities of artificial intelligence, it's clear that openness, whether in data,
Speaker 2: technology or collaboration, is not just a concept, but a
Speaker 2: driving force that can unlock new possibilities. Smart Talks with
Speaker 2: IBM is produced by Matt Romano, Joey fish Ground, Amy
Speaker 2: Gains McQuaid and Jacob Goldstein, who are edited by Lydia
Speaker 2: Jane kott Or. Engineers are Sarah Brugerer and Ben Tolliday.
Speaker 2: Theme song by Gramoscope. Special thanks to the eight Bar
Speaker 2: 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. I'm Malcolm Glauwell. This is a paid
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Speaker 2: necessarily represent IBM's positions, strategies, or opinions.