Hey everyone, it's Robert and Joe here. Today we've got something a little different to share with you. It's a new season of the Smart Talks with IBM podcast series.

Hey everyone, it's Robert and Joe here. Today we've got something a little different to share with you. It's a new season of the Smart Talks with IBM podcast series.

This season, on smart Talks, Malcolm Gladwell and team are diving into the transformative world of artificial intelligence with a fresh perspective on the concept of open What does open really mean in the context of AI. It can mean open source code or open data, but it also encompasses fostering an ecosystem of ideas, ensuring diverse perspectives are heard, and enabling new levels of transparency.

This season, on smart Talks, Malcolm Gladwell and team are diving into the transformative world of artificial intelligence with a fresh perspective on the concept of open What does open really mean in the context of AI. It can mean open source code or open data, but it also encompasses fostering an ecosystem of ideas, ensuring diverse perspectives are heard, and enabling new levels of transparency.

Join hosts from your favorite pushkin podcasts as they explore how openness and AI is reshaping industries, driving innovation, and redefining what's possible. You'll hear from industry experts and leaders about the implication and possibilities of open AI, and of course, Malcolm Gladwell will be there to guide you through the season with his unique insights.

Join hosts from your favorite pushkin podcasts as they explore how openness and AI is reshaping industries, driving innovation, and redefining what's possible. You'll hear from industry experts and leaders about the implication and possibilities of open AI, and of course, Malcolm Gladwell will be there to guide you through the season with his unique insights.

Look out for new episodes of Smart Talks every other week on the iHeartRadio app, Apple Podcasts, or wherever you get your podcast and learn more at IBM dot com, Slash smart Talks.

Look out for new episodes of Smart Talks every other week on the iHeartRadio app, Apple Podcasts, or wherever you get your podcast and learn more at IBM dot com, Slash smart Talks.

Pushkin.

Pushkin.

Hello, Hello, Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. 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 industries, ways of doing business and our very notion of what's possible. In today's episode, Jacob Goldstein sat down with maryam Ashuri, the Director of Product Management and a Head of Product for IBM's Watson x dot AI, where she spearheads the product strategy and delivery of IBM's watsonex foundation models. She is a technologist with more than fifteen years of experience developing data driven technologies. The conversation focused on how enterprises can use technology to build and deliver greater transparency in AI. With Granite. Mariam explained how Grantite can be utilized to improve efficiency across various domains. She discussed how these models are being used in real world business applications, particularly in areas like customer care, where AI can help enable quick, accurate responses based on internal company data. Mariam provided a fascinating look into how enterprises have moved from mere experimentation with generative AI to actual production, navigating challenges such as increased latency, cost, and energy consumption. She highlighted how the emerging trend of smaller models customized with proprietary data can potentially deliver high performance at a fraction of the cost, marking a significant shift in how enterprises leverage AI. Whether you're an AI enthusiast, we're a business leader looking to harness the power of artificial intelligence, this episode is packed with valuable insights and forward thinking strategies.

Hello, Hello, Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glabo. 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 industries, ways of doing business and our very notion of what's possible. In today's episode, Jacob Goldstein sat down with maryam Ashuri, the Director of Product Management and a Head of Product for IBM's Watson x dot AI, where she spearheads the product strategy and delivery of IBM's watsonex foundation models. She is a technologist with more than fifteen years of experience developing data driven technologies. The conversation focused on how enterprises can use technology to build and deliver greater transparency in AI. With Granite. Mariam explained how Grantite can be utilized to improve efficiency across various domains. She discussed how these models are being used in real world business applications, particularly in areas like customer care, where AI can help enable quick, accurate responses based on internal company data. Mariam provided a fascinating look into how enterprises have moved from mere experimentation with generative AI to actual production, navigating challenges such as increased latency, cost, and energy consumption. She highlighted how the emerging trend of smaller models customized with proprietary data can potentially deliver high performance at a fraction of the cost, marking a significant shift in how enterprises leverage AI. Whether you're an AI enthusiast, we're a business leader looking to harness the power of artificial intelligence, this episode is packed with valuable insights and forward thinking strategies.

Let's just start with your background. How did you come to work at IBM.

Let's just start with your background. How did you come to work at IBM.

I join IBM right after I graduated. I have an AI background, and throughout the years, I've held many roles in design, engineering, development, research, mostly focused on AI application development and design. In my current job, I'm the product owner for What's the Next DAYI, which is the IBM platform for enterprise AI. What excites me about this job, I would say, is the technology advancements over the last eighteen months in the market. We've been witnessing how GENERATIVELI has been changing the market. The way that I see that is JENNYI has been perhaps one of the largest paradigm shifts when we think about productivity. The same way that Internet and personal computers impacted the productivity of workforce, now we are witnessing another wave of all those opportunities that it can unlock for especially enterprise AI when it comes to enhancing the productivity of the workforce and releasing some time that can potentially be put into creating more value work for enterprise. So that's the major part that I picked this team to have an impact on the market and the community, but also of course using the skills that I gain through all these years through IBM to help to establish IBM as the market leader for enterprise AI.

I join IBM right after I graduated. I have an AI background, and throughout the years, I've held many roles in design, engineering, development, research, mostly focused on AI application development and design. In my current job, I'm the product owner for What's the Next DAYI, which is the IBM platform for enterprise AI. What excites me about this job, I would say, is the technology advancements over the last eighteen months in the market. We've been witnessing how GENERATIVELI has been changing the market. The way that I see that is JENNYI has been perhaps one of the largest paradigm shifts when we think about productivity. The same way that Internet and personal computers impacted the productivity of workforce, now we are witnessing another wave of all those opportunities that it can unlock for especially enterprise AI when it comes to enhancing the productivity of the workforce and releasing some time that can potentially be put into creating more value work for enterprise. So that's the major part that I picked this team to have an impact on the market and the community, but also of course using the skills that I gain through all these years through IBM to help to establish IBM as the market leader for enterprise AI.

So you talked about JENAI as this sort of generational, transformational technological force, and I'm curious just in terms of how it's going to come into the world, Like, how do you see market adoption of GENAI sort of evolving from here?

So you talked about JENAI as this sort of generational, transformational technological force, and I'm curious just in terms of how it's going to come into the world, Like, how do you see market adoption of GENAI sort of evolving from here?

Well, last year was the year of excitement about generative AI. Most of the companies were experimenting and exploring with GENI. We see that energy shifted towards how to best monetize that technology. Almost half of the market has moved from investigation to pilots. Ten percent has moved to production. When you're exploring with this technology, you're looking for a valve factor, You're looking for an AHA moment. That's why very large general purpose models shine. But as companies move toward production and scale, they soon realized the past success is not that straightforward. For example, they're larger the model, the larger computer resources it requires. That translates to increased latency that's your response time. That translates to increased cost. That translates to increase carbon food print, and energy consumption. So think about that. At the scale of enterprise in production, some of them can be a showstopper.

Well, last year was the year of excitement about generative AI. Most of the companies were experimenting and exploring with GENI. We see that energy shifted towards how to best monetize that technology. Almost half of the market has moved from investigation to pilots. Ten percent has moved to production. When you're exploring with this technology, you're looking for a valve factor, You're looking for an AHA moment. That's why very large general purpose models shine. But as companies move toward production and scale, they soon realized the past success is not that straightforward. For example, they're larger the model, the larger computer resources it requires. That translates to increased latency that's your response time. That translates to increased cost. That translates to increase carbon food print, and energy consumption. So think about that. At the scale of enterprise in production, some of them can be a showstopper.

Because of this.

Because of this.

Reason, what actually c is emerging in the market is instead of focusing on very large general purpose models, coming back to very small, trustworthy models that they can customize on their own proprietary data that's the data about their customers, that the data about their specific domains to create something differentiated that is much smaller and delivers the performance that they want on a target use case for a fraction of the cost.

Reason, what actually c is emerging in the market is instead of focusing on very large general purpose models, coming back to very small, trustworthy models that they can customize on their own proprietary data that's the data about their customers, that the data about their specific domains to create something differentiated that is much smaller and delivers the performance that they want on a target use case for a fraction of the cost.

Uh huh. So let's talk a little bit more specifically about what you're working on. Talk about Granite. First of all, tell me what is Granite.

Uh huh. So let's talk a little bit more specifically about what you're working on. Talk about Granite. First of all, tell me what is Granite.

Granite is our industrial leading family of models, flagship IBM models. These are the models that we train from scratch. When offered to our platform, we offer indemnification and we stand behind them today. It comes in four flavors, language, code, time series, and geospecial models. Granite Language series is covering English, Spanish, German, Portuguese and Japanese. We have a combination of commercial and open source language models on Granite. For example, we recently released the Granite seven B language model, small powerful English model. On the code front, our models are state of the art models ranging from three billion to thirty four billion parameters. These are very powerful models that performs or outperforms in some cases the popular open source models in their weight class. So very powerful models.

Granite is our industrial leading family of models, flagship IBM models. These are the models that we train from scratch. When offered to our platform, we offer indemnification and we stand behind them today. It comes in four flavors, language, code, time series, and geospecial models. Granite Language series is covering English, Spanish, German, Portuguese and Japanese. We have a combination of commercial and open source language models on Granite. For example, we recently released the Granite seven B language model, small powerful English model. On the code front, our models are state of the art models ranging from three billion to thirty four billion parameters. These are very powerful models that performs or outperforms in some cases the popular open source models in their weight class. So very powerful models.

So I get the idea a big picture about these models, but it would be helpful to just get a sense specifically of what they're doing, Like, can you give me any specific examples of how these models are being used in businesses in the real world right now?

So I get the idea a big picture about these models, but it would be helpful to just get a sense specifically of what they're doing, Like, can you give me any specific examples of how these models are being used in businesses in the real world right now?

Well, the top use cases for generative AI are really content generation, summarization, information extraction. Perhaps the most popular use case that we are seeing in enterprise is content grounded question and answering. So using these models as a base to connect them to a body of information let's say, their policies, their documents that is internal to the enterprise, and get the model to provide answers based on that question. One example of that is for customer agents customer care, when a customer is asking a question. Previously, the agent that responds to the customer had to answer the question and if they don't know the answer escalated to the product. Especially is keeping people on hold on the line to go figure out the answer for that and then come back. You can think of the time it takes to resolve an issue. But now we llms, we have an opportunity to automatically retrieve the information based on the internal documents of the company, formulate an answer, show it to the human agent, and then if they verify with the sources of varies coming from, they can just translate it directly to the customer.

Well, the top use cases for generative AI are really content generation, summarization, information extraction. Perhaps the most popular use case that we are seeing in enterprise is content grounded question and answering. So using these models as a base to connect them to a body of information let's say, their policies, their documents that is internal to the enterprise, and get the model to provide answers based on that question. One example of that is for customer agents customer care, when a customer is asking a question. Previously, the agent that responds to the customer had to answer the question and if they don't know the answer escalated to the product. Especially is keeping people on hold on the line to go figure out the answer for that and then come back. You can think of the time it takes to resolve an issue. But now we llms, we have an opportunity to automatically retrieve the information based on the internal documents of the company, formulate an answer, show it to the human agent, and then if they verify with the sources of varies coming from, they can just translate it directly to the customer.

This is a.

This is a.

Very simple example of how it's impacting the customer care.

Very simple example of how it's impacting the customer care.

So one big theme of this season is this idea of open and one of the things that's interesting to me about the work you're doing is you are using not only granted this model IBM developed, but you're also using third party models right from other places. So tell me about that work and how that is sort of fitting into your kind of real world typically enterprise Jenai work.

So one big theme of this season is this idea of open and one of the things that's interesting to me about the work you're doing is you are using not only granted this model IBM developed, but you're also using third party models right from other places. So tell me about that work and how that is sort of fitting into your kind of real world typically enterprise Jenai work.

When it comes to a model strategy, our strategy is really focused on two pillars, multimodel and multi deployment. It means that we don't believe one single model rules all the use cases. And I think at this point the market has also realized the enterprise markets in average today are using five to ten different models for different use cases.

When it comes to a model strategy, our strategy is really focused on two pillars, multimodel and multi deployment. It means that we don't believe one single model rules all the use cases. And I think at this point the market has also realized the enterprise markets in average today are using five to ten different models for different use cases.

Oh interesting.

Oh interesting.

So in our portfolio, if you look into what's on Extra DAYI today, we are offering a large sets of high performing, state of the art models coming from open source commercial models that we are bringing through our partners and also IBM developed models. In addition to all of these, we also have an option for bring your own model from outside the platform. Let's say you have a custom model that you made it yourself, you can bring it to the platform and really helping the customers to navigate through aid range of models and pick the right model for their target use case. Throughout that we've been heavily working with our partners, and you know, this is the market that is evolving rapidly. We've been at the forefront of a spit to delivery. One example that I like to highlight is recently Metal released Lama four or five billion, such a powerful model. On the same day that it was released to the market, we made it available in our platform to our customers the same day. And not only we delivered it on the same day. We are offering competitive pricing but also for flexibility in where to deploy. So we are giving an option to enterprise to deploy these models on the platform of dage choice, either multi cloud it can be gcpaws as youre IBM cloud, or on premises. The same for mistrall Ai. Mistrall Ai recently released the model misroll launch too on the same day we delivered that through the platform. That's an example of a commercial model. Lama as open source, but MS large two is a commercial model that we made available through the platform.

So in our portfolio, if you look into what's on Extra DAYI today, we are offering a large sets of high performing, state of the art models coming from open source commercial models that we are bringing through our partners and also IBM developed models. In addition to all of these, we also have an option for bring your own model from outside the platform. Let's say you have a custom model that you made it yourself, you can bring it to the platform and really helping the customers to navigate through aid range of models and pick the right model for their target use case. Throughout that we've been heavily working with our partners, and you know, this is the market that is evolving rapidly. We've been at the forefront of a spit to delivery. One example that I like to highlight is recently Metal released Lama four or five billion, such a powerful model. On the same day that it was released to the market, we made it available in our platform to our customers the same day. And not only we delivered it on the same day. We are offering competitive pricing but also for flexibility in where to deploy. So we are giving an option to enterprise to deploy these models on the platform of dage choice, either multi cloud it can be gcpaws as youre IBM cloud, or on premises. The same for mistrall Ai. Mistrall Ai recently released the model misroll launch too on the same day we delivered that through the platform. That's an example of a commercial model. Lama as open source, but MS large two is a commercial model that we made available through the platform.

Great, So I want to talk about enterprise grade foundation models. Just to get into it briefly, what's a foundation model.

Great, So I want to talk about enterprise grade foundation models. Just to get into it briefly, what's a foundation model.

People associate foundation models with a large language model, but large language models are really a subset of foundation models. Large language models are focused on language, but foundation models can be code generators, can be focused on time series model we talked about, they can be images, it can be jew special models. So foundation model, as the term suggests that your foundations to create a series of subsequent models that can be customized for a downstream use case. And that's why they are calling them foundation models. Lm ME is a good example of that as a subset for language that you can further customize on your space.

People associate foundation models with a large language model, but large language models are really a subset of foundation models. Large language models are focused on language, but foundation models can be code generators, can be focused on time series model we talked about, they can be images, it can be jew special models. So foundation model, as the term suggests that your foundations to create a series of subsequent models that can be customized for a downstream use case. And that's why they are calling them foundation models. Lm ME is a good example of that as a subset for language that you can further customize on your space.

Data to get the model to do other works.

Data to get the model to do other works.

So the core of these foundation models, they are basically trained on an ab third amount of data data sets that most of the institutions today are sourcing them from the internet. So you can imagine what can potentially go to those models and then it comes to the enterprise and they start using it. So for us also, when we started looking into in particular, it was triggered by customers asking us to provide client protections on these models, and we started thinking about, let's look into how the models are trained and if you are comfortable of fering client protections on the models that are available in the market.

So the core of these foundation models, they are basically trained on an ab third amount of data data sets that most of the institutions today are sourcing them from the internet. So you can imagine what can potentially go to those models and then it comes to the enterprise and they start using it. So for us also, when we started looking into in particular, it was triggered by customers asking us to provide client protections on these models, and we started thinking about, let's look into how the models are trained and if you are comfortable of fering client protections on the models that are available in the market.

And guess what, for a.

And guess what, for a.

Majority of these models there is absolutely no visibility into what data vent into those models, not much transparency into how the model trains, and the responsibility lies on you as the customers we start using those models.

Majority of these models there is absolutely no visibility into what data vent into those models, not much transparency into how the model trains, and the responsibility lies on you as the customers we start using those models.

So just to be that is presenting like potential risk, real potential risk to a company that is using these models, it is.

So just to be that is presenting like potential risk, real potential risk to a company that is using these models, it is.

It is a potential risk in particular for the customers in highly regulated industries. So what we did for Granite was when we started training these models from scratch, Basically we went to the corpus of data that was available to us. So, for example, the very first version of Granite was exposed to twenty percent of its data from finance and legal because we have a lot of financial institutions as our clients. We worked directly with our IBM research to identify detectors for harmful information like haytyp use and profanity detectors.

It is a potential risk in particular for the customers in highly regulated industries. So what we did for Granite was when we started training these models from scratch, Basically we went to the corpus of data that was available to us. So, for example, the very first version of Granite was exposed to twenty percent of its data from finance and legal because we have a lot of financial institutions as our clients. We worked directly with our IBM research to identify detectors for harmful information like haytyp use and profanity detectors.

Okay, so we're talking about Granted, we're talking about this set of models IBM has developed. Let's talk about using Granite on Watson X compared to downloading open source models, Like how do those differ?

Okay, so we're talking about Granted, we're talking about this set of models IBM has developed. Let's talk about using Granite on Watson X compared to downloading open source models, Like how do those differ?

By using Granite and what's on ex you get two things. The first one is the client protection and thementification that we talked about. You get that if the model is consumed through our platform.

By using Granite and what's on ex you get two things. The first one is the client protection and thementification that we talked about. You get that if the model is consumed through our platform.

And the second.

And the second.

One is really the ecosystem of platform capabilities that we are offering to help you create value on top of those data. So for example, bringing your data to customize granted for your own specific use case. But also one thing that I like to highlight in particular is the AI governance. So when you get one of these pre train models, you put it in front of your own users. Through the input and instructions that the user provides for the model, they can notdge the model to potentially create undesired behavior and change the behavior of the model. And because of this is extremely important to automatically document the lineage of who touched the model at one point, so if something happens, you can trace it back and see where it's coming from. And that's what's an extra governance is offering automatically documenting the lineage. When you use the granite within the platform, you get all of those you can have the end to end governance, you can have access to all these scalable deployment opportunities that is available for you, like to allow you deploy them on the platform of your choice that we talked about, either multiple cloud or on prem and it also helps you to have access to avoid range of model customizations, approaches, prompt tuning, fine tuning, retrival augmented generations agents. There is a series of them available to use an apply to your model.

One is really the ecosystem of platform capabilities that we are offering to help you create value on top of those data. So for example, bringing your data to customize granted for your own specific use case. But also one thing that I like to highlight in particular is the AI governance. So when you get one of these pre train models, you put it in front of your own users. Through the input and instructions that the user provides for the model, they can notdge the model to potentially create undesired behavior and change the behavior of the model. And because of this is extremely important to automatically document the lineage of who touched the model at one point, so if something happens, you can trace it back and see where it's coming from. And that's what's an extra governance is offering automatically documenting the lineage. When you use the granite within the platform, you get all of those you can have the end to end governance, you can have access to all these scalable deployment opportunities that is available for you, like to allow you deploy them on the platform of your choice that we talked about, either multiple cloud or on prem and it also helps you to have access to avoid range of model customizations, approaches, prompt tuning, fine tuning, retrival augmented generations agents. There is a series of them available to use an apply to your model.

This distinction between large language models and foundation models is eye opening. Mariam emphasized that foundation models can be tailored to specific tasks, but with that versatility comes a significant challenge the lack of transparency and how these models are trained. This composed a real especially in highly regulated industries like finance. Essentially, by using Granite and watsonex together, enterprises get powerful and customizable tools.

This distinction between large language models and foundation models is eye opening. Mariam emphasized that foundation models can be tailored to specific tasks, but with that versatility comes a significant challenge the lack of transparency and how these models are trained. This composed a real especially in highly regulated industries like finance. Essentially, by using Granite and watsonex together, enterprises get powerful and customizable tools.

So let's talk about the future a little bit. What do you think are some of the big developments were likely to see in the realm of AI models?

So let's talk about the future a little bit. What do you think are some of the big developments were likely to see in the realm of AI models?

Very good question.

Very good question.

I feel like the generative AI of the past was powered by large language models. The generative AI of the future is going to reason, plan, act and reflect.

I feel like the generative AI of the past was powered by large language models. The generative AI of the future is going to reason, plan, act and reflect.

Huh, and so I mean in the context of Granite in particular, like, what are we likely to see both you know, in the near term and in the sort of medium to long term.

Huh, and so I mean in the context of Granite in particular, like, what are we likely to see both you know, in the near term and in the sort of medium to long term.

There are multiple elements to implement an agentic workflow that I just mentioned. One element of that is the LLM itself to be able to do the planning and reasoning and acting and doing something that we call tool calling. So basically, a series of tools are available to the model. You ask the model to call those and.

There are multiple elements to implement an agentic workflow that I just mentioned. One element of that is the LLM itself to be able to do the planning and reasoning and acting and doing something that we call tool calling. So basically, a series of tools are available to the model. You ask the model to call those and.

Make a call.

Make a call.

For example, we can say, hey, Granted, what is the weather like where Jacob lives. It's connect to web search API, look up your location. Then it's going to connect to weather API, calculate the weather and come back and formulate an answer and respond to that. So during this process, it first has to plan the task of how to answer that question, look into what are the tools that are available to it, and call them, and that's an ability of the model to do that. What we did with Granted was we expanded the Granite capabilities to be able to do function calling. So for example, today we have an open source granted to an eb function calling that is available on hugging face to try on and you can grab the model and the model has capability to do the tool callings. I'm anticipating that in the near future the planning and reasoning and acting and reflecting capabilities of the large language models are going to continue to evolve.

For example, we can say, hey, Granted, what is the weather like where Jacob lives. It's connect to web search API, look up your location. Then it's going to connect to weather API, calculate the weather and come back and formulate an answer and respond to that. So during this process, it first has to plan the task of how to answer that question, look into what are the tools that are available to it, and call them, and that's an ability of the model to do that. What we did with Granted was we expanded the Granite capabilities to be able to do function calling. So for example, today we have an open source granted to an eb function calling that is available on hugging face to try on and you can grab the model and the model has capability to do the tool callings. I'm anticipating that in the near future the planning and reasoning and acting and reflecting capabilities of the large language models are going to continue to evolve.

So thinking now from the point of view of buyers and users of AIS, really people who are listening from that perspective, as people are evaluating AI tools and solutions, what is the most important thing they should be thinking about? How do you think about kind of that process?

So thinking now from the point of view of buyers and users of AIS, really people who are listening from that perspective, as people are evaluating AI tools and solutions, what is the most important thing they should be thinking about? How do you think about kind of that process?

I think they should always start with the area at which they think it would benefit from AI, and then within that area, look into what data they have available to potentially fit into those AI service architects do they have access to quality data? And the second question that they have to ask themselves is do I have a trusted partner that can supply what I need to be able to implement AI. That can be a collection of the foundation models that you're going to need, that can be a collection of the platform capabilities that the trusted partner can offer you to implement such a thing. The third thing is go and evaluate the regulations. Does regulation allow you to apploy AI to the specific area that you are investigating and you're targeting for AI? And the last part, but not least, is back to the principles of design, thinking, what is the problem in that area? I'm solving with AI, and if AI is even appropriate, because we want to make sure that you use AI not just because it's a cool, hot toy in the market, but you are convinced that it can significantly enhance the user experience of your customers in that area. And once you have an answer to those all these four questions, then maybe you have a good candidates to start applying AI.

I think they should always start with the area at which they think it would benefit from AI, and then within that area, look into what data they have available to potentially fit into those AI service architects do they have access to quality data? And the second question that they have to ask themselves is do I have a trusted partner that can supply what I need to be able to implement AI. That can be a collection of the foundation models that you're going to need, that can be a collection of the platform capabilities that the trusted partner can offer you to implement such a thing. The third thing is go and evaluate the regulations. Does regulation allow you to apploy AI to the specific area that you are investigating and you're targeting for AI? And the last part, but not least, is back to the principles of design, thinking, what is the problem in that area? I'm solving with AI, and if AI is even appropriate, because we want to make sure that you use AI not just because it's a cool, hot toy in the market, but you are convinced that it can significantly enhance the user experience of your customers in that area. And once you have an answer to those all these four questions, then maybe you have a good candidates to start applying AI.

What about from the side of project managers who are trying to just keep up with how fast things are changing, how fast innovation is happening, Like, what advice would you give those people?

What about from the side of project managers who are trying to just keep up with how fast things are changing, how fast innovation is happening, Like, what advice would you give those people?

My advice would be focused on agility. This is a market that is evolving rapidly and the winners of the market would be those that are able to take advantage of the best the market can offer at any point of time. So in order to do that, they need to be open to experimentation, continuous learning, and to rapidly adopting the new ideas.

My advice would be focused on agility. This is a market that is evolving rapidly and the winners of the market would be those that are able to take advantage of the best the market can offer at any point of time. So in order to do that, they need to be open to experimentation, continuous learning, and to rapidly adopting the new ideas.

And when you think about the future and GENAI, is there a particular, say problem that you are most excited to solve.

And when you think about the future and GENAI, is there a particular, say problem that you are most excited to solve.

I think that would be productivity. If you look into the stats that are out there, there are surveys that confirm that sixty to seventy persons of the time of our employees can be potentially enhanced to the productivity gains of generative I For example, I personally myself use my product for content generation a lot, so the time that it frees up can be potentially put into generating a higher value work. And because of that, I'm super excited with all the opportunities that it represents for enterprises to go and dedicate the time of the employees to higher value items.

I think that would be productivity. If you look into the stats that are out there, there are surveys that confirm that sixty to seventy persons of the time of our employees can be potentially enhanced to the productivity gains of generative I For example, I personally myself use my product for content generation a lot, so the time that it frees up can be potentially put into generating a higher value work. And because of that, I'm super excited with all the opportunities that it represents for enterprises to go and dedicate the time of the employees to higher value items.

Great. Okay, a couple of Granite specific questions. So what are like the key things you want the world to know about Granite.

Great. Okay, a couple of Granite specific questions. So what are like the key things you want the world to know about Granite.

Granite is open, trusted, and targeted. Two ways to think about openness. One open as open weights it's available for public to download, and the second one is open as in there is less restrictions on how the customers can legally use these models for a range of use cases. We have released Grantite open source models on their Apache license that is enabling a large range of use cases. The second one was trusted. We talked about that like it's rooted in the trustworthy governance process that we established thereund how we are training these models and the responsibility that we take for these models, and the third one is targeted, targeted for enterprise. We talked about like exposing Granted to enterprise data or the domain specific Granted some of them like Cobalt Java Translation that is targeting to solve the specific enterprise needs. And that's granite, so open, trusted, and targeted.

Granite is open, trusted, and targeted. Two ways to think about openness. One open as open weights it's available for public to download, and the second one is open as in there is less restrictions on how the customers can legally use these models for a range of use cases. We have released Grantite open source models on their Apache license that is enabling a large range of use cases. The second one was trusted. We talked about that like it's rooted in the trustworthy governance process that we established thereund how we are training these models and the responsibility that we take for these models, and the third one is targeted, targeted for enterprise. We talked about like exposing Granted to enterprise data or the domain specific Granted some of them like Cobalt Java Translation that is targeting to solve the specific enterprise needs. And that's granite, so open, trusted, and targeted.

So there are a lot of models out in the world all of a sudden, right, it's a crowded market. Where does granted fit in that universe? What is the market for granted?

So there are a lot of models out in the world all of a sudden, right, it's a crowded market. Where does granted fit in that universe? What is the market for granted?

We talked about the enterprise market shifting away from very large general purpose models to target a smaller models, and Granted is a small model that enterprise can pick up and customize on their proprietary data to create something that is differentiated for a target use case. So Granted is well suited as a small, domain specific business, ready tailored for business and trained on enterprise data to solve enterprise questions.

We talked about the enterprise market shifting away from very large general purpose models to target a smaller models, and Granted is a small model that enterprise can pick up and customize on their proprietary data to create something that is differentiated for a target use case. So Granted is well suited as a small, domain specific business, ready tailored for business and trained on enterprise data to solve enterprise questions.

You mentioned small as one of the things that granted is why is that useful in some contexts for enterprise for businesses.

You mentioned small as one of the things that granted is why is that useful in some contexts for enterprise for businesses.

The larger the model, the larger computer resources it requires, it translates to increased latency that's your response time. It translates to increased cost and in translates to increased carbon footprint and energy consumption. So at this case of enterprise transactions, when you move to production and you want to scale, some of these challenges can be multiple times stronger. Like costs can add up, the energy consumption can be a serious thing, and the latency is depending on the application, can be a showstopper and blocker because for longer, larger models, more powerful models, it just takes the way longer time to process and calculate the output.

The larger the model, the larger computer resources it requires, it translates to increased latency that's your response time. It translates to increased cost and in translates to increased carbon footprint and energy consumption. So at this case of enterprise transactions, when you move to production and you want to scale, some of these challenges can be multiple times stronger. Like costs can add up, the energy consumption can be a serious thing, and the latency is depending on the application, can be a showstopper and blocker because for longer, larger models, more powerful models, it just takes the way longer time to process and calculate the output.

For you, we are going to finish up with a speed round and I want you to just answer with the first thing that comes to mind. Don't overthink this, Okay, complete this sentence. In five years, AI.

For you, we are going to finish up with a speed round and I want you to just answer with the first thing that comes to mind. Don't overthink this, Okay, complete this sentence. In five years, AI.

Will be invisible.

Will be invisible.

Ah, I like that. What do you mean by that?

Ah, I like that. What do you mean by that?

Today?

Today?

AI is everywhere. But if you ask my kids at home, they know AI. But if you say very like how do you use AI, they don't know the answer because it's so blended in their life that they don't feel like it's something that they are using. They are getting used to that. So when I think of next generation and the years to come, that generation is so used to AI being part of their life that they feel like it's just there. That's one, and the second one is the simplicity of interaction with AI that you don't feel like you're interacting with the system. It's just there, like you talk to AI. Everything is automated. So I would say the simplicity and being blended to solve the right problems is the part that I'm referring to as invisible. Like Internet is everywhere and it's invisible. But we used to dial in, like you remember the dialing zone to connect the Internet.

AI is everywhere. But if you ask my kids at home, they know AI. But if you say very like how do you use AI, they don't know the answer because it's so blended in their life that they don't feel like it's something that they are using. They are getting used to that. So when I think of next generation and the years to come, that generation is so used to AI being part of their life that they feel like it's just there. That's one, and the second one is the simplicity of interaction with AI that you don't feel like you're interacting with the system. It's just there, like you talk to AI. Everything is automated. So I would say the simplicity and being blended to solve the right problems is the part that I'm referring to as invisible. Like Internet is everywhere and it's invisible. But we used to dial in, like you remember the dialing zone to connect the Internet.

It's gone. The Internet is completely invisible today.

It's gone. The Internet is completely invisible today.

Right, Like we used to talk about logging on, right, and you don't log on anymore because you're always logged on.

Right, Like we used to talk about logging on, right, and you don't log on anymore because you're always logged on.

Yeah, always connected.

Yeah, always connected.

Yeah. What's the number one thing that people misunderstand about AI?

Yeah. What's the number one thing that people misunderstand about AI?

AI is anivitable but should not be feared.

AI is anivitable but should not be feared.

What advice would you give yourself ten years ago to better prepare you for today?

What advice would you give yourself ten years ago to better prepare you for today?

I would say, develop a broad range of skills. Even if you think they will not help you today, they may be valuable in the future.

I would say, develop a broad range of skills. Even if you think they will not help you today, they may be valuable in the future.

So on the consumer side, right now, we hear a lot about chatbots and image generators. But on the business side, what do you think is the next big business application?

So on the consumer side, right now, we hear a lot about chatbots and image generators. But on the business side, what do you think is the next big business application?

AI? Influencers generating content.

AI? Influencers generating content.

Huh how do you use AI in your day to day life today?

Huh how do you use AI in your day to day life today?

One simple example is LinkedIn posts. I love it to just go to my product. I'll give you an example, which is my favorite one. Lama three point one four or five b the post that I announced on LinkedIn on Hey, IBM is releasing the model on the same day it was generated by lamatory point one four or five billion. So using the same model to post the generate the announcement note very elegant.

One simple example is LinkedIn posts. I love it to just go to my product. I'll give you an example, which is my favorite one. Lama three point one four or five b the post that I announced on LinkedIn on Hey, IBM is releasing the model on the same day it was generated by lamatory point one four or five billion. So using the same model to post the generate the announcement note very elegant.

Is there anything else I should ask you?

Is there anything else I should ask you?

Oh, we didn't talk about instruct lab. So when you grab a model, you start from the model, but you need to then customize it on your proprietary data to create value on top of that. So instruct lab is giving you a method based on open source contributions to collectively contribute to improve the base model. So if you're an enterprise, you can leverage your internal employees to collectively all contribute to improve the model. And I'll give you an example of why it matters. Like if you go to hugging Pace today and look for Lama, there are about fifty thousand different lama us coming up. And the reason is because there is no way to contribute to the base model. If you're a developer, you have to make a colon of the copy of the model and finding need for your own purpose. We figure the method that we call instruct lab to be able to collectively collect all that information and contribute to the base model and enhance.

Oh, we didn't talk about instruct lab. So when you grab a model, you start from the model, but you need to then customize it on your proprietary data to create value on top of that. So instruct lab is giving you a method based on open source contributions to collectively contribute to improve the base model. So if you're an enterprise, you can leverage your internal employees to collectively all contribute to improve the model. And I'll give you an example of why it matters. Like if you go to hugging Pace today and look for Lama, there are about fifty thousand different lama us coming up. And the reason is because there is no way to contribute to the base model. If you're a developer, you have to make a colon of the copy of the model and finding need for your own purpose. We figure the method that we call instruct lab to be able to collectively collect all that information and contribute to the base model and enhance.

So that's instruct lab.

So that's instruct lab.

I just wanted to highlight the value of being open because that's another topic that has been emerging in the market over the past eighteen months. In particular, I believe the future of AI is open, and we've been seeing how the open source markets has been changing, how the models are accessible to a wider audience, and good things typically happen when you make technology pieces accessible to a broader range of community to stress test that, and that's the direction that we've been adopting with granted, and I felt like that's really the adoption that the market kit is gonna emerge to moving forward.

I just wanted to highlight the value of being open because that's another topic that has been emerging in the market over the past eighteen months. In particular, I believe the future of AI is open, and we've been seeing how the open source markets has been changing, how the models are accessible to a wider audience, and good things typically happen when you make technology pieces accessible to a broader range of community to stress test that, and that's the direction that we've been adopting with granted, and I felt like that's really the adoption that the market kit is gonna emerge to moving forward.

Yeah, there's this interesting I think, maybe naively unintuitive, but it makes sense once you think about it, thing that open source things are safer. You might naively think, oh no, put it in a box so nobody can see it and that'll be safer, But like it turns out of the world. If you let everybody poke at it, the world will find the vulnerabilities for you and you can fix them. Right.

Yeah, there's this interesting I think, maybe naively unintuitive, but it makes sense once you think about it, thing that open source things are safer. You might naively think, oh no, put it in a box so nobody can see it and that'll be safer, But like it turns out of the world. If you let everybody poke at it, the world will find the vulnerabilities for you and you can fix them. Right.

That's exactly what's going to happen.

That's exactly what's going to happen.

Yeah, great, it was lovely to talk with you. Thank you so much for your time.

Yeah, great, it was lovely to talk with you. Thank you so much for your time.

The same here, thanks Jacob, and.

The same here, thanks Jacob, and.

That wraps up this episode. A huge thanks to Mariam and Jacob. Today's conversation open my eyes as to how open technology and AI are intersecting to create more transparent and efficient systems for enterprises. From the power of smaller, more targeted models like granted to the importance of trust and governance in AI, these developments are reshaping how businesses operate at their core. As we continue to unpack the complexities of artificial intelligence, it's clear that openness, whether in data, technology or collaboration, is not just a concept, but a driving force that can unlock new possibilities. Smart Talks with IBM is produced by Matt Romano, Joey fish Ground, Amy Gains McQuaid, and Jacob Goldstein. We're edited by Lydia Jane kott Or. Engineers are Sarah Brugerier and Ben Tolliday. Theme song by Gramoscope special thanks to the eight 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 Glauwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.

That wraps up this episode. A huge thanks to Mariam and Jacob. Today's conversation open my eyes as to how open technology and AI are intersecting to create more transparent and efficient systems for enterprises. From the power of smaller, more targeted models like granted to the importance of trust and governance in AI, these developments are reshaping how businesses operate at their core. As we continue to unpack the complexities of artificial intelligence, it's clear that openness, whether in data, technology or collaboration, is not just a concept, but a driving force that can unlock new possibilities. Smart Talks with IBM is produced by Matt Romano, Joey fish Ground, Amy Gains McQuaid, and Jacob Goldstein. We're edited by Lydia Jane kott Or. Engineers are Sarah Brugerier and Ben Tolliday. Theme song by Gramoscope special thanks to the eight 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 Glauwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.