Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little different to share with you. It's a
Speaker 1: new season of the Smart Talks with IBM podcast series.
Speaker 2: This season, on smart Talks, Malcolm Gladwell and team are
Speaker 2: diving into the transformative world of artificial intelligence with a
Speaker 2: fresh perspective on the concept of open What does open
Speaker 2: really mean in the context of AI. It can mean
Speaker 2: open source code or open data, but it also encompasses
Speaker 2: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
Speaker 2: and enabling new levels of transparency.
Speaker 1: Join hosts from your favorite pushkin podcasts as they explore
Speaker 1: how openness and AI is reshaping industries, driving innovation, and
Speaker 1: redefining what's possible. You'll hear from industry experts and leaders
Speaker 1: about the implication and possibilities of open AI, and of course,
Speaker 1: Malcolm Gladwell will be there to guide you through the
Speaker 1: season with his unique insights.
Speaker 2: Look out for new episodes of Smart Talks every other
Speaker 2: week on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 2: get your podcast and learn more at IBM dot com,
Speaker 2: Slash smart Talks.
Speaker 3: Pushkin.
Speaker 4: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 4: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. This season,
Speaker 4: we're diving back into the world of artificial intelligence, but
Speaker 4: with a focus on the powerful concept of open its possibilities, implications,
Speaker 4: and misconceptions. We'll look at openness from a variety of
Speaker 4: angles and explore how the concept is already reshaping industries,
Speaker 4: ways of doing business and our very notion of what's possible.
Speaker 4: In today's episode, Jacob Goldstein sat down with maryam Ashuri,
Speaker 4: the Director of Product Management and a Head of Product
Speaker 4: for IBM's Watson x dot AI, where she spearheads the
Speaker 4: product strategy and delivery of IBM's watsonex foundation models. She
Speaker 4: is a technologist with more than fifteen years of experience
Speaker 4: developing data driven technologies. The conversation focused on how enterprises
Speaker 4: can use technology to build and deliver greater transparency in AI.
Speaker 4: With Granite. Mariam explained how Grantite can be utilized to
Speaker 4: improve efficiency across various domains. She discussed how these models
Speaker 4: are being used in real world business applications, particularly in
Speaker 4: areas like customer care, where AI can help enable quick,
Speaker 4: accurate responses based on internal company data. Mariam provided a
Speaker 4: fascinating look into how enterprises have moved from mere experimentation
Speaker 4: with generative AI to actual production, navigating challenges such as
Speaker 4: increased latency, cost, and energy consumption. She highlighted how the
Speaker 4: emerging trend of smaller models customized with proprietary data can
Speaker 4: potentially deliver high performance at a fraction of the cost,
Speaker 4: marking a significant shift in how enterprises leverage AI. Whether
Speaker 4: you're an AI enthusiast, we're a business leader looking to
Speaker 4: harness the power of artificial intelligence, this episode is packed
Speaker 4: 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 5: I join IBM right after I graduated. I have an
Speaker 5: AI background, and throughout the years, I've held many roles
Speaker 5: in design, engineering, development, research, mostly focused on AI application
Speaker 5: development and design. In my current job, I'm the product
Speaker 5: owner for What's the Next DAYI, which is the IBM
Speaker 5: platform for enterprise AI. What excites me about this job,
Speaker 5: I would say, is the technology advancements over the last
Speaker 5: eighteen months in the market. We've been witnessing how GENERATIVELI
Speaker 5: has been changing the market. The way that I see
Speaker 5: that is JENNYI has been perhaps one of the largest
Speaker 5: paradigm shifts when we think about productivity. The same way
Speaker 5: that Internet and personal computers impacted the productivity of workforce,
Speaker 5: now we are witnessing another wave of all those opportunities
Speaker 5: that it can unlock for especially enterprise AI when it
Speaker 5: comes to enhancing the productivity of the workforce and releasing
Speaker 5: some time that can potentially be put into creating more
Speaker 5: value work for enterprise. So that's the major part that
Speaker 5: I picked this team to have an impact on the
Speaker 5: market and the community, but also of course using the
Speaker 5: skills that I gain through all these years through IBM
Speaker 5: to help to establish IBM as the market leader for
Speaker 5: 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 5: Well, last year was the year of excitement about generative AI.
Speaker 5: Most of the companies were experimenting and exploring with GENI.
Speaker 5: We see that energy shifted towards how to best monetize
Speaker 5: that technology. Almost half of the market has moved from
Speaker 5: investigation to pilots. Ten percent has moved to production. When
Speaker 5: you're exploring with this technology, you're looking for a valve factor,
Speaker 5: You're looking for an AHA moment. That's why very large
Speaker 5: general purpose models shine. But as companies move toward production
Speaker 5: and scale, they soon realized the past success is not
Speaker 5: that straightforward. For example, they're larger the model, the larger
Speaker 5: computer resources it requires. That translates to increased latency that's
Speaker 5: your response time. That translates to increased cost. That translates
Speaker 5: to increase carbon food print, and energy consumption. So think
Speaker 5: about that. At the scale of enterprise in production, some
Speaker 5: of them can be a showstopper.
Speaker 6: Because of this.
Speaker 5: Reason, what actually c is emerging in the market is
Speaker 5: instead of focusing on very large general purpose models, coming
Speaker 5: back to very small, trustworthy models that they can customize
Speaker 5: on their own proprietary data that's the data about their customers,
Speaker 5: that the data about their specific domains to create something
Speaker 5: differentiated that is much smaller and delivers the performance that
Speaker 5: they want on a target use case for a fraction
Speaker 5: of the cost.
Speaker 3: Uh huh. So let's talk a little bit more specifically
Speaker 3: about what you're working on. Talk about Granite. First of all,
Speaker 3: tell me what is Granite.
Speaker 5: Granite is our industrial leading family of models, flagship IBM models.
Speaker 5: These are the models that we train from scratch. When
Speaker 5: offered to our platform, we offer indemnification and we stand
Speaker 5: behind them today. It comes in four flavors, language, code,
Speaker 5: time series, and geospecial models. Granite Language series is covering English, Spanish, German,
Speaker 5: Portuguese and Japanese. We have a combination of commercial and
Speaker 5: open source language models on Granite. For example, we recently
Speaker 5: released the Granite seven B language model, small powerful English model.
Speaker 5: On the code front, our models are state of the
Speaker 5: art models ranging from three billion to thirty four billion parameters.
Speaker 5: These are very powerful models that performs or outperforms in
Speaker 5: some cases the popular open source models in their weight class.
Speaker 5: 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 5: Well, the top use cases for generative AI are really
Speaker 5: content generation, summarization, information extraction. Perhaps the most popular use
Speaker 5: case that we are seeing in enterprise is content grounded
Speaker 5: question and answering. So using these models as a base
Speaker 5: to connect them to a body of information let's say,
Speaker 5: their policies, their documents that is internal to the enterprise,
Speaker 5: and get the model to provide answers based on that question.
Speaker 5: One example of that is for customer agents customer care,
Speaker 5: when a customer is asking a question. Previously, the agent
Speaker 5: that responds to the customer had to answer the question
Speaker 5: and if they don't know the answer escalated to the product.
Speaker 5: Especially is keeping people on hold on the line to
Speaker 5: go figure out the answer for that and then come back.
Speaker 5: You can think of the time it takes to resolve
Speaker 5: an issue. But now we llms, we have an opportunity
Speaker 5: to automatically retrieve the information based on the internal documents
Speaker 5: of the company, formulate an answer, show it to the
Speaker 5: human agent, and then if they verify with the sources
Speaker 5: of varies coming from, they can just translate it directly
Speaker 5: to the customer.
Speaker 6: This is a.
Speaker 5: Very simple example of 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 granted this model IBM developed, but you're also
Speaker 3: using third party models right from other places. So tell
Speaker 3: me about that work and how that is sort of
Speaker 3: fitting into your kind of real world typically enterprise Jenai work.
Speaker 5: When it comes to a model strategy, our strategy is
Speaker 5: really focused on two pillars, multimodel and multi deployment. It
Speaker 5: means that we don't believe one single model rules all
Speaker 5: the use cases. And I think at this point the
Speaker 5: market has also realized the enterprise markets in average today
Speaker 5: are using five to ten different models for different use cases.
Speaker 3: Oh interesting.
Speaker 5: So in our portfolio, if you look into what's on
Speaker 5: Extra DAYI today, we are offering a large sets of
Speaker 5: high performing, state of the art models coming from open
Speaker 5: source commercial models that we are bringing through our partners
Speaker 5: and also IBM developed models. In addition to all of these,
Speaker 5: we also have an option for bring your own model
Speaker 5: from outside the platform. Let's say you have a custom
Speaker 5: model that you made it yourself, you can bring it
Speaker 5: to the platform and really helping the customers to navigate
Speaker 5: through aid range of models and pick the right model
Speaker 5: for their target use case. Throughout that we've been heavily
Speaker 5: working with our partners, and you know, this is the
Speaker 5: market that is evolving rapidly. We've been at the forefront
Speaker 5: of a spit to delivery. One example that I like
Speaker 5: to highlight is recently Metal released Lama four or five billion,
Speaker 5: such a powerful model. On the same day that it
Speaker 5: was released to the market, we made it available in
Speaker 5: our platform to our customers the same day. And not
Speaker 5: only we delivered it on the same day. We are
Speaker 5: offering competitive pricing but also for flexibility in where to deploy.
Speaker 5: So we are giving an option to enterprise to deploy
Speaker 5: these models on the platform of dage choice, either multi
Speaker 5: cloud it can be gcpaws as youre IBM cloud, or
Speaker 5: on premises. The same for mistrall Ai. Mistrall Ai recently
Speaker 5: released the model misroll launch too on the same day
Speaker 5: we delivered that through the platform. That's an example of
Speaker 5: a commercial model. Lama as open source, but MS large
Speaker 5: two is a commercial model that we made available through
Speaker 5: the platform.
Speaker 3: Great, So I want to talk about enterprise grade foundation models.
Speaker 3: Just to get into it briefly, what's a foundation model.
Speaker 5: People associate foundation models with a large language model, but
Speaker 5: large language models are really a subset of foundation models.
Speaker 5: Large language models are focused on language, but foundation models
Speaker 5: can be code generators, can be focused on time series
Speaker 5: model we talked about, they can be images, it can
Speaker 5: be jew special models. So foundation model, as the term
Speaker 5: suggests that your foundations to create a series of subsequent
Speaker 5: models that can be customized for a downstream use case.
Speaker 5: And that's why they are calling them foundation models. Lm
Speaker 5: ME is a good example of that as a subset
Speaker 5: for language that you can further customize on your space.
Speaker 6: Data to get the model to do other works.
Speaker 5: So the core of these foundation models, they are basically
Speaker 5: trained on an ab third amount of data data sets
Speaker 5: that most of the institutions today are sourcing them from
Speaker 5: the internet. So you can imagine what can potentially go
Speaker 5: to those models and then it comes to the enterprise
Speaker 5: and they start using it. So for us also, when
Speaker 5: we started looking into in particular, it was triggered by
Speaker 5: customers asking us to provide client protections on these models,
Speaker 5: and we started thinking about, let's look into how the
Speaker 5: models are trained and if you are comfortable of fering
Speaker 5: client protections on the models that are available in the market.
Speaker 6: And guess what, for a.
Speaker 5: Majority of these models there is absolutely no visibility into
Speaker 5: what data vent into those models, not much transparency into
Speaker 5: how the model trains, and the responsibility lies on you
Speaker 5: as the customers we start using those models.
Speaker 3: So just to be that is presenting like potential risk,
Speaker 3: real potential risk to a company that is using these models,
Speaker 3: it is.
Speaker 5: It is a potential risk in particular for the customers
Speaker 5: in highly regulated industries. So what we did for Granite
Speaker 5: was when we started training these models from scratch, Basically
Speaker 5: we went to the corpus of data that was available
Speaker 5: to us. So, for example, the very first version of
Speaker 5: Granite was exposed to twenty percent of its data from
Speaker 5: finance and legal because we have a lot of financial
Speaker 5: institutions as our clients. We worked directly with our IBM
Speaker 5: research to identify detectors for harmful information like haytyp use
Speaker 5: 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 Watson X compared to downloading open source models,
Speaker 3: Like how do those differ?
Speaker 5: By using Granite and what's on ex you get two things.
Speaker 5: The first one is the client protection and thementification that
Speaker 5: we talked about. You get that if the model is
Speaker 5: consumed through our platform.
Speaker 6: And the second.
Speaker 5: One is really the ecosystem of platform capabilities that we
Speaker 5: are offering to help you create value on top of
Speaker 5: those data. So for example, bringing your data to customize
Speaker 5: granted for your own specific use case. But also one
Speaker 5: thing that I like to highlight in particular is the
Speaker 5: AI governance. So when you get one of these pre
Speaker 5: train models, you put it in front of your own users.
Speaker 5: Through the input and instructions that the user provides for
Speaker 5: the model, they can notdge the model to potentially create
Speaker 5: undesired behavior and change the behavior of the model. And
Speaker 5: because of this is extremely important to automatically document the
Speaker 5: lineage of who touched the model at one point, so
Speaker 5: if something happens, you can trace it back and see
Speaker 5: where it's coming from. And that's what's an extra governance
Speaker 5: is offering automatically documenting the lineage. When you use the
Speaker 5: granite within the platform, you get all of those you
Speaker 5: can have the end to end governance, you can have
Speaker 5: access to all these scalable deployment opportunities that is available
Speaker 5: for you, like to allow you deploy them on the
Speaker 5: platform of your choice that we talked about, either multiple
Speaker 5: cloud or on prem and it also helps you to
Speaker 5: have access to avoid range of model customizations, approaches, prompt tuning,
Speaker 5: fine tuning, retrival augmented generations agents. There is a series
Speaker 5: of them available to use an apply to your model.
Speaker 4: This distinction between large language models and foundation models is
Speaker 4: eye opening. Mariam emphasized that foundation models can be tailored
Speaker 4: to specific tasks, but with that versatility comes a significant
Speaker 4: challenge the lack of transparency and how these models are trained.
Speaker 4: This composed a real especially in highly regulated industries like finance. Essentially,
Speaker 4: by using Granite and watsonex together, enterprises get powerful and
Speaker 4: 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 were
Speaker 3: likely to see in the realm of AI models?
Speaker 6: Very good question.
Speaker 5: I feel like the generative AI of the past was
Speaker 5: powered by large language models. The generative AI of the
Speaker 5: future is going to reason, plan, act 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 5: There are multiple elements to implement an agentic workflow that
Speaker 5: I just mentioned. One element of that is the LLM
Speaker 5: itself to be able to do the planning and reasoning
Speaker 5: and acting and doing something that we call tool calling.
Speaker 5: So basically, a series of tools are available to the model.
Speaker 5: You ask the model to call those and.
Speaker 6: Make a call.
Speaker 5: For example, we can say, hey, Granted, what is the
Speaker 5: weather like where Jacob lives. It's connect to web search API,
Speaker 5: look up your location. Then it's going to connect to
Speaker 5: weather API, calculate the weather and come back and formulate
Speaker 5: an answer and respond to that. So during this process,
Speaker 5: it first has to plan the task of how to
Speaker 5: answer that question, look into what are the tools that
Speaker 5: are available to it, and call them, and that's an
Speaker 5: ability of the model to do that. What we did
Speaker 5: with Granted was we expanded the Granite capabilities to be
Speaker 5: able to do function calling. So for example, today we
Speaker 5: have an open source granted to an eb function calling
Speaker 5: that is available on hugging face to try on and
Speaker 5: you can grab the model and the model has capability
Speaker 5: to do the tool callings. I'm anticipating that in the
Speaker 5: near future the planning and reasoning and acting and reflecting
Speaker 5: capabilities of the large language models are going to continue
Speaker 5: 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 5: I think they should always start with the area at
Speaker 5: which they think it would benefit from AI, and then
Speaker 5: within that area, look into what data they have available
Speaker 5: to potentially fit into those AI service architects do they
Speaker 5: have access to quality data? And the second question that
Speaker 5: they have to ask themselves is do I have a
Speaker 5: trusted partner that can supply what I need to be
Speaker 5: able to implement AI. That can be a collection of
Speaker 5: the foundation models that you're going to need, that can
Speaker 5: be a collection of the platform capabilities that the trusted
Speaker 5: partner can offer you to implement such a thing. The
Speaker 5: third thing is go and evaluate the regulations. Does regulation
Speaker 5: allow you to apploy AI to the specific area that
Speaker 5: you are investigating and you're targeting for AI? And the
Speaker 5: last part, but not least, is back to the principles
Speaker 5: of design, thinking, what is the problem in that area?
Speaker 5: I'm solving with AI, and if AI is even appropriate,
Speaker 5: because we want to make sure that you use AI
Speaker 5: not just because it's a cool, hot toy in the market,
Speaker 5: but you are convinced that it can significantly enhance the
Speaker 5: user experience of your customers in that area. And once
Speaker 5: you have an answer to those all these four questions,
Speaker 5: then maybe you have a good candidates to start applying AI.
Speaker 3: What about from the side of project managers who are
Speaker 3: trying to just keep up with how fast things are changing,
Speaker 3: how fast innovation is happening, Like, what advice would you
Speaker 3: give those people?
Speaker 5: My advice would be focused on agility. This is a
Speaker 5: market that is evolving rapidly and the winners of the
Speaker 5: market would be those that are able to take advantage
Speaker 5: of the best the market can offer at any point
Speaker 5: of time. So in order to do that, they need
Speaker 5: to be open to experimentation, continuous learning, and to rapidly
Speaker 5: 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 5: I think that would be productivity. If you look into
Speaker 5: the stats that are out there, there are surveys that
Speaker 5: confirm that sixty to seventy persons of the time of
Speaker 5: our employees can be potentially enhanced to the productivity gains
Speaker 5: of generative I For example, I personally myself use my
Speaker 5: product for content generation a lot, so the time that
Speaker 5: it frees up can be potentially put into generating a
Speaker 5: higher value work. And because of that, I'm super excited
Speaker 5: with all the opportunities that it represents for enterprises to
Speaker 5: go and dedicate the time of the employees to higher
Speaker 5: value items.
Speaker 3: Great. Okay, a couple of Granite specific questions. So what
Speaker 3: are like the key things you want the world to
Speaker 3: know about Granite.
Speaker 5: Granite is open, trusted, and targeted. Two ways to think
Speaker 5: about openness. One open as open weights it's available for
Speaker 5: public to download, and the second one is open as
Speaker 5: in there is less restrictions on how the customers can
Speaker 5: legally use these models for a range of use cases.
Speaker 5: We have released Grantite open source models on their Apache
Speaker 5: license that is enabling a large range of use cases.
Speaker 5: The second one was trusted. We talked about that like
Speaker 5: it's rooted in the trustworthy governance process that we established
Speaker 5: thereund how we are training these models and the responsibility
Speaker 5: that we take for these models, and the third one
Speaker 5: is targeted, targeted for enterprise. We talked about like exposing
Speaker 5: Granted to enterprise data or the domain specific Granted some
Speaker 5: of them like Cobalt Java Translation that is targeting to
Speaker 5: solve the specific enterprise needs. And that's granite, so open, trusted,
Speaker 5: 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 granted fit in that universe? What is the
Speaker 3: market for granted?
Speaker 5: We talked about the enterprise market shifting away from very
Speaker 5: large general purpose models to target a smaller models, and
Speaker 5: Granted is a small model that enterprise can pick up
Speaker 5: and customize on their proprietary data to create something that
Speaker 5: is differentiated for a target use case. So Granted is
Speaker 5: well suited as a small, domain specific business, ready tailored
Speaker 5: 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 5: The larger the model, the larger computer resources it requires,
Speaker 5: it translates to increased latency that's your response time. It
Speaker 5: translates to increased cost and in translates to increased carbon
Speaker 5: footprint and energy consumption. So at this case of enterprise transactions,
Speaker 5: when you move to production and you want to scale,
Speaker 5: some of these challenges can be multiple times stronger. Like
Speaker 5: costs can add up, the energy consumption can be a
Speaker 5: serious thing, and the latency is depending on the application,
Speaker 5: can be a showstopper and blocker because for longer, larger models,
Speaker 5: more powerful models, it just takes the way longer time
Speaker 5: 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.
Speaker 6: Will be invisible.
Speaker 3: Ah, I like that. What do you mean by that?
Speaker 6: Today?
Speaker 5: AI is everywhere. But if you ask my kids at home,
Speaker 5: they know AI. But if you say very like how
Speaker 5: do you use AI, they don't know the answer because
Speaker 5: it's so blended in their life that they don't feel
Speaker 5: like it's something that they are using. They are getting
Speaker 5: used to that. So when I think of next generation
Speaker 5: and the years to come, that generation is so used
Speaker 5: to AI being part of their life that they feel
Speaker 5: like it's just there. That's one, and the second one
Speaker 5: is the simplicity of interaction with AI that you don't
Speaker 5: feel like you're interacting with the system. It's just there,
Speaker 5: like you talk to AI. Everything is automated. So I
Speaker 5: would say the simplicity and being blended to solve the
Speaker 5: right problems is the part that I'm referring to as invisible.
Speaker 5: Like Internet is everywhere and it's invisible. But we used
Speaker 5: to dial in, like you remember the dialing zone to
Speaker 5: connect the Internet.
Speaker 6: 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 6: Yeah, always connected.
Speaker 3: Yeah. What's the number one thing that people misunderstand about AI?
Speaker 5: AI is anivitable 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 5: I would say, develop a broad range of skills. Even
Speaker 5: if you think they will not help you today, they
Speaker 5: 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 6: AI? Influencers generating content.
Speaker 3: Huh how do you use AI in your day to
Speaker 3: day life today?
Speaker 5: One simple example is LinkedIn posts. I love it to
Speaker 5: just go to my product. I'll give you an example,
Speaker 5: which is my favorite one. Lama three point one four
Speaker 5: or five b the post that I announced on LinkedIn
Speaker 5: on Hey, IBM is releasing the model on the same
Speaker 5: day it was generated by lamatory point one four or
Speaker 5: five billion. So using the same model to post the
Speaker 5: generate the announcement note very elegant.
Speaker 3: Is there anything else I should ask you?
Speaker 5: Oh, we didn't talk about instruct lab. So when you
Speaker 5: grab a model, you start from the model, but you
Speaker 5: need to then customize it on your proprietary data to
Speaker 5: create value on top of that. So instruct lab is
Speaker 5: giving you a method based on open source contributions to
Speaker 5: collectively contribute to improve the base model. So if you're
Speaker 5: an enterprise, you can leverage your internal employees to collectively
Speaker 5: all contribute to improve the model. And I'll give you
Speaker 5: an example of why it matters. Like if you go
Speaker 5: to hugging Pace today and look for Lama, there are
Speaker 5: about fifty thousand different lama us coming up. And the
Speaker 5: reason is because there is no way to contribute to
Speaker 5: the base model. If you're a developer, you have to
Speaker 5: make a colon of the copy of the model and
Speaker 5: finding need for your own purpose. We figure the method
Speaker 5: that we call instruct lab to be able to collectively
Speaker 5: collect all that information and contribute to the base model
Speaker 5: and enhance.
Speaker 6: So that's instruct lab.
Speaker 5: I just wanted to highlight the value of being open
Speaker 5: because that's another topic that has been emerging in the
Speaker 5: market over the past eighteen months. In particular, I believe
Speaker 5: the future of AI is open, and we've been seeing
Speaker 5: how the open source markets has been changing, how the
Speaker 5: models are accessible to a wider audience, and good things
Speaker 5: typically happen when you make technology pieces accessible to a
Speaker 5: broader range of community to stress test that, and that's
Speaker 5: the direction that we've been adopting with granted, and I
Speaker 5: felt like that's really the adoption that the market kit
Speaker 5: is gonna 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 3: fix them. Right.
Speaker 6: That's exactly what's going to happen.
Speaker 3: Yeah, great, it was lovely to talk with you. Thank
Speaker 3: you so much for your time.
Speaker 6: The same here, thanks Jacob, and.
Speaker 4: That wraps up this episode. A huge thanks to Mariam
Speaker 4: and Jacob. Today's conversation open my eyes as to how
Speaker 4: open technology and AI are intersecting to create more transparent
Speaker 4: and efficient systems for enterprises. From the power of smaller,
Speaker 4: more targeted models like granted to the importance of trust
Speaker 4: and governance in AI, these developments are reshaping how businesses
Speaker 4: operate at their core. As we continue to unpack the
Speaker 4: complexities of artificial intelligence, it's clear that openness, whether in data,
Speaker 4: technology or collaboration, is not just a concept, but a
Speaker 4: driving force that can unlock new possibilities. Smart Talks with
Speaker 4: IBM is produced by Matt Romano, Joey fish Ground, Amy
Speaker 4: Gains McQuaid, and Jacob Goldstein. We're edited by Lydia Jane
Speaker 4: kott Or. Engineers are Sarah Brugerier and Ben Tolliday. Theme
Speaker 4: song by Gramoscope special thanks to the eight Bar and
Speaker 4: IBM teams, as well as the Pushkin marketing team. Smart
Speaker 4: Talks with IBM is a production of Pushkin Industries and
Speaker 4: Ruby Studio at iHeartMedia. To find more Pushkin podcasts, listen
Speaker 4: on the iHeartRadio app, Apple Podcasts, or wherever you listen
Speaker 4: to podcasts. I'm Malcolm Glauwell. This is a paid advertisement
Speaker 4: from IBM. The conversations on this podcast don't necessarily represent
Speaker 4: IBM's positions, strategies, or opinions.