September 24, 2024 • 31 mins
As the scale of artificial intelligence continues to evolve, open technology like many of IBM’s Granite models are helping enhance transparency in AI and improve efficiency across businesses. In this episode of Smart Talks with IBM, Jacob Goldstein sat down with Maryam Ashoori, the Director of Product Management and Head of Product for IBM’s watsonx.ai, where she spearheads the product strategy and delivery of IBM’s watsonx Foundation Models. Together, they explored the shift from large general-purpose AI models to smaller, customizable models tailored to specific needs.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
Visit us at https://ibm.com/smarttalks
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:03):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glapwell. 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
(00:25):
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 Mariam Ashuri,
the Director of Product Management and Head of Product for
IBM's Watson x dot AI, where she spearheads the product
(00:46):
strategy and delivery of IBM's Watson x foundation models. She
is a technologist with more than fifteen years of experience
developing data driven technologies. The conversation folks on how enterprises
can use technology to build and deliver greater transparency in
AI With Granite. Meriam explained how Grantite can be utilized
(01:11):
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. Meriam provided a
(01:31):
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
(01:52):
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.
Speaker 2 (02:18):
Let's just start with your background. How did you come
to work at IBM.
Speaker 3 (02:22):
I joined 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
(02:45):
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 generative
II has been changing the market. The way that I
see that is JENAI has been perhaps one of the
largest paradigm shifts when we think about productivity. The same
(03:07):
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
(03:29):
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
(03:50):
enterprise AI.
Speaker 2 (03:51):
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?
Speaker 3 (04:09):
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,
(04:32):
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
(04:53):
your response time. That translates to increased cost. That translates
to increase carbon, food print, energy consumption. So think about that.
At the scale of enterprise in production, some of them
can be a showstopper. Because of this reason, what actually
c is emerging in the market is instead of focusing
(05:15):
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
(05:38):
a target use case for a fraction of the costs.
Speaker 2 (05:41):
Uh huh. So let's talk a little bit more specifically
about what you're working on. Let's talk about Granite. First
of all, tell me what is Granite.
Speaker 3 (05:51):
Granite is our industrial leading family of models, flagship IBM models.
These are the models that we 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,
(06:12):
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.
(06:34):
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 right class.
So very powerful models.
Speaker 2 (06:53):
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. And you give me any
specific examples of how these models are being used in
businesses in the real world right now.
Speaker 3 (07:08):
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,
(07:29):
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
(07:51):
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 them 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
(08:14):
human agent, and then if they verify with the sources
of aries coming from, they can just translate it directly
to the customer. This is a very simple example of
how it's impacting the customer care.
Speaker 2 (08:27):
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
(08:48):
fitting into your kind of real world typically enterprise GENAI work.
Speaker 3 (08:53):
When it comes to model strategy, our strategy is really
focused on two pillars, multimodel and multidiplom 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,
(09:15):
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
(09:36):
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 avoid 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
(09:57):
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
(10:17):
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 dat choice, either multi
cloud it can be gcpaws as youre IBM cloud, or
on premises. The same for mistrall Ai. Mistrall Ai recently
(10:41):
released the model misroll large two on the same day
we delivered that through the platform. That's an example of
a commercial model Lama but open source, but large two
is a commercial model that we made available through the platform.
Speaker 2 (10:56):
Great. So I want to talk about enterprise grade foundation models,
just to get into it briefly, what's a foundation model.
Speaker 3 (11:06):
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 jewispecial models. So foundation model, as the term suggests,
(11:29):
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 is a
good example of that as a subset for language that
you can further customize on your specific data to get
the model to do other works. So the core of
(11:52):
these foundation models, they are basically trained on an ab
third amount of data r data sets that 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,
(12:15):
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 we
are comfortable of fering client protections on the models that
are available in the market. And guess what, for a
majority of these models. There is absolutely no visibility into
(12:36):
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.
Speaker 2 (12:46):
So just to be clear, that is presenting like potential risk,
real potential risk to a company that is using these models.
Speaker 3 (12:54):
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 persons of its data
(13:16):
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 haytype
use and profanity detectors.
Speaker 2 (13:32):
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?
Speaker 3 (13:44):
By using Granite and Whatson X, you get two things.
The first one is the client protection and in themification
that we talked about. You get that if the model
is consumed through our platform. And the second one is
really the equos of platform capabilities that we are offering
to help you create value on top of those data,
(14:06):
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,
(14:28):
they can nodge 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
(14:52):
the lineage. When you use the grantite within the platform,
you get all of those you can have the end
to end governance, you can and 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
(15:15):
avoid range of model customizations approaches, promptuning, fine tuning, retrieval,
augmented generations agents. There is a series of them available
to use an apply to your model.
Speaker 1 (15:27):
This distinction between large language models and foundation models is
eye opening. Miriam 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 risk, especially in highly regulated industries
(15:50):
like finance. Essentially, by using Granite and watsonex together, enterprises
get powerful and customizable tools.
Speaker 2 (16:00):
So let's talk about the future a little bit. What
do you think are some of the big developments we're
likely to see in the realm of AI models?
Speaker 3 (16:07):
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.
Speaker 2 (16:23):
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.
Speaker 3 (16:34):
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.
(16:55):
You ask the model to call those and 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
(17:15):
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 callings. So for example, today we
(17:38):
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 calling. 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.
Speaker 2 (18:00):
So thinking now from the point of view of buyers
and users of AIS, really people who are listening kind
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?
Speaker 3 (18:21):
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
(18:42):
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 The thirting
is go and evaluate the regulations. Does regulation allow you
(19:07):
to applyoy 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
(19:27):
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 aiit.
Speaker 2 (19:47):
And 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?
Speaker 3 (20:00):
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
(20:26):
adopting the new ideas.
Speaker 2 (20:29):
And when you think about the future and GENAI, is
there a particular, say problem that you are most excited
to solve.
Speaker 3 (20:38):
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 percents 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
(21:01):
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 their employees to higher
value items.
Speaker 2 (21:20):
Great. Okay, a couple of Granite specific questions. So what
are like the key things you want the world to
know about Granite.
Speaker 3 (21:29):
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.
(21:52):
We have released Granite 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
(22:14):
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 specific enterprise needs. And that's Granite, so open, trusted
and targeted.
Speaker 2 (22:33):
So there are a lot of models out in the
world all of a sudden, right, it's a crowded market.
Where does Granite fit in that universe? What is the
market for granted?
Speaker 3 (22:43):
We talked about the enterprise market shifting away from very
large general purpose models to targeted, 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 alsuited
(23:07):
as a small domain specific business ready tailored for business
and trained on enterprise data to solve enterprise questions.
Speaker 2 (23:17):
You mentioned small as one of the things that granted
is why is that useful in some contexts for enterprise
for businesses.
Speaker 3 (23:27):
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 the scale of enterprise transactions,
(23:49):
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 assumption can be a
serious thing, and the latency is depending on the application,
can be a showstopper and blocker because for longer, larger models,
(24:11):
more powerful models, it just takes a way longer time
to process and calculate the output.
Speaker 2 (24:17):
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.
Speaker 3 (24:31):
Will be invisible.
Speaker 2 (24:33):
Ah, I like that. What do you mean by that?
Speaker 3 (24:36):
Today? AI is everywhere, But if you ask my kids
at home, they know AI. But if you say very a,
I 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
(24:57):
of next generation and the years to 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.
(25:19):
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. Internet is
completely invisible today.
Speaker 2 (25:40):
Right, Like we used to talk about logging on, right,
and you don't log on anymore because you're always logged on.
Speaker 3 (25:46):
Yep, you're always connected.
Speaker 2 (25:48):
Yeah. What's the number one thing that people misunderstand about AI?
Speaker 3 (25:53):
AI is an irritable but should not be feared.
Speaker 2 (25:59):
What advice would you give yourself ten years ago to
better prepare you for today?
Speaker 3 (26:05):
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.
Speaker 2 (26:14):
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.
Speaker 3 (26:26):
AI influencers generating content.
Speaker 2 (26:29):
Huh. How do you use AI in your day to
day life today?
Speaker 3 (26:33):
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
A five b the post that I announced on LinkedIn
on hey, IBM is releasing the model on the same
day it was generated by Lama three point one four
A five billion. So using the same model to post
(26:56):
the generate the announcement note very elegant.
Speaker 2 (27:00):
Is there anything else I should ask you?
Speaker 3 (27:02):
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
(27:23):
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 higging face today and look for Lama, there are
(27:45):
about fifty thousand different lamas 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 find
you need for your own purpose. We are the method
that we call instruct lab to be able to collectively
collect all that information and contribute to the base model
(28:08):
and enhance. 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
(28:31):
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 feel like that's really
the adoption that the market is going to emerge to
moving forward.
Speaker 2 (28:50):
Yeah, this interesting, I think, maybe naively unintuitive, but it
makes sense once you think about it. Thing that open
source things are safer. Evily 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.
Speaker 3 (29:10):
Right, That's exactly what's going to happen. Yeah.
Speaker 2 (29:13):
Great, it was lovely to talk with you. Thank you
so much for your time.
Speaker 3 (29:17):
The same here, thanks Jacob.
Speaker 1 (29:21):
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
(29:45):
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
(30:06):
IBM is produced by Matt Romano, Joey fish Ground, Amy
Gains McQuaid, and Jacob Goldstein, who are edited by Lydia
Jean kott Or. Engineers are Sarah Brugerer 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
(30:27):
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.
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glapwell. 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
(00:25):
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 Mariam Ashuri,
the Director of Product Management and Head of Product for
IBM's Watson x dot AI, where she spearheads the product
(00:46):
strategy and delivery of IBM's Watson x foundation models. She
is a technologist with more than fifteen years of experience
developing data driven technologies. The conversation folks on how enterprises
can use technology to build and deliver greater transparency in
AI With Granite. Meriam explained how Grantite can be utilized
(01:11):
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. Meriam provided a
(01:31):
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
(01:52):
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.
Speaker 2 (02:18):
Let's just start with your background. How did you come
to work at IBM.
Speaker 3 (02:22):
I joined 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
(02:45):
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 generative
II has been changing the market. The way that I
see that is JENAI has been perhaps one of the
largest paradigm shifts when we think about productivity. The same
(03:07):
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
(03:29):
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
(03:50):
enterprise AI.
Speaker 2 (03:51):
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?
Speaker 3 (04:09):
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,
(04:32):
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
(04:53):
your response time. That translates to increased cost. That translates
to increase carbon, food print, energy consumption. So think about that.
At the scale of enterprise in production, some of them
can be a showstopper. Because of this reason, what actually
c is emerging in the market is instead of focusing
(05:15):
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
(05:38):
a target use case for a fraction of the costs.
Speaker 2 (05:41):
Uh huh. So let's talk a little bit more specifically
about what you're working on. Let's talk about Granite. First
of all, tell me what is Granite.
Speaker 3 (05:51):
Granite is our industrial leading family of models, flagship IBM models.
These are the models that we 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,
(06:12):
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.
(06:34):
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 right class.
So very powerful models.
Speaker 2 (06:53):
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. And you give me any
specific examples of how these models are being used in
businesses in the real world right now.
Speaker 3 (07:08):
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,
(07:29):
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
(07:51):
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 them 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
(08:14):
human agent, and then if they verify with the sources
of aries coming from, they can just translate it directly
to the customer. This is a very simple example of
how it's impacting the customer care.
Speaker 2 (08:27):
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
(08:48):
fitting into your kind of real world typically enterprise GENAI work.
Speaker 3 (08:53):
When it comes to model strategy, our strategy is really
focused on two pillars, multimodel and multidiplom 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,
(09:15):
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
(09:36):
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 avoid 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
(09:57):
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
(10:17):
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 dat choice, either multi
cloud it can be gcpaws as youre IBM cloud, or
on premises. The same for mistrall Ai. Mistrall Ai recently
(10:41):
released the model misroll large two on the same day
we delivered that through the platform. That's an example of
a commercial model Lama but open source, but large two
is a commercial model that we made available through the platform.
Speaker 2 (10:56):
Great. So I want to talk about enterprise grade foundation models,
just to get into it briefly, what's a foundation model.
Speaker 3 (11:06):
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 jewispecial models. So foundation model, as the term suggests,
(11:29):
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 is a
good example of that as a subset for language that
you can further customize on your specific data to get
the model to do other works. So the core of
(11:52):
these foundation models, they are basically trained on an ab
third amount of data r data sets that 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,
(12:15):
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 we
are comfortable of fering client protections on the models that
are available in the market. And guess what, for a
majority of these models. There is absolutely no visibility into
(12:36):
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.
Speaker 2 (12:46):
So just to be clear, that is presenting like potential risk,
real potential risk to a company that is using these models.
Speaker 3 (12:54):
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 persons of its data
(13:16):
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 haytype
use and profanity detectors.
Speaker 2 (13:32):
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?
Speaker 3 (13:44):
By using Granite and Whatson X, you get two things.
The first one is the client protection and in themification
that we talked about. You get that if the model
is consumed through our platform. And the second one is
really the equos of platform capabilities that we are offering
to help you create value on top of those data,
(14:06):
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,
(14:28):
they can nodge 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
(14:52):
the lineage. When you use the grantite within the platform,
you get all of those you can have the end
to end governance, you can and 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
(15:15):
avoid range of model customizations approaches, promptuning, fine tuning, retrieval,
augmented generations agents. There is a series of them available
to use an apply to your model.
Speaker 1 (15:27):
This distinction between large language models and foundation models is
eye opening. Miriam 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 risk, especially in highly regulated industries
(15:50):
like finance. Essentially, by using Granite and watsonex together, enterprises
get powerful and customizable tools.
Speaker 2 (16:00):
So let's talk about the future a little bit. What
do you think are some of the big developments we're
likely to see in the realm of AI models?
Speaker 3 (16:07):
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.
Speaker 2 (16:23):
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.
Speaker 3 (16:34):
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.
(16:55):
You ask the model to call those and 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
(17:15):
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 callings. So for example, today we
(17:38):
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 calling. 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.
Speaker 2 (18:00):
So thinking now from the point of view of buyers
and users of AIS, really people who are listening kind
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?
Speaker 3 (18:21):
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
(18:42):
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 The thirting
is go and evaluate the regulations. Does regulation allow you
(19:07):
to applyoy 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
(19:27):
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 aiit.
Speaker 2 (19:47):
And 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?
Speaker 3 (20:00):
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
(20:26):
adopting the new ideas.
Speaker 2 (20:29):
And when you think about the future and GENAI, is
there a particular, say problem that you are most excited
to solve.
Speaker 3 (20:38):
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 percents 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
(21:01):
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 their employees to higher
value items.
Speaker 2 (21:20):
Great. Okay, a couple of Granite specific questions. So what
are like the key things you want the world to
know about Granite.
Speaker 3 (21:29):
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.
(21:52):
We have released Granite 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
(22:14):
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 specific enterprise needs. And that's Granite, so open, trusted
and targeted.
Speaker 2 (22:33):
So there are a lot of models out in the
world all of a sudden, right, it's a crowded market.
Where does Granite fit in that universe? What is the
market for granted?
Speaker 3 (22:43):
We talked about the enterprise market shifting away from very
large general purpose models to targeted, 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 alsuited
(23:07):
as a small domain specific business ready tailored for business
and trained on enterprise data to solve enterprise questions.
Speaker 2 (23:17):
You mentioned small as one of the things that granted
is why is that useful in some contexts for enterprise
for businesses.
Speaker 3 (23:27):
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 the scale of enterprise transactions,
(23:49):
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 assumption can be a
serious thing, and the latency is depending on the application,
can be a showstopper and blocker because for longer, larger models,
(24:11):
more powerful models, it just takes a way longer time
to process and calculate the output.
Speaker 2 (24:17):
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.
Speaker 3 (24:31):
Will be invisible.
Speaker 2 (24:33):
Ah, I like that. What do you mean by that?
Speaker 3 (24:36):
Today? AI is everywhere, But if you ask my kids
at home, they know AI. But if you say very a,
I 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
(24:57):
of next generation and the years to 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.
(25:19):
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. Internet is
completely invisible today.
Speaker 2 (25:40):
Right, Like we used to talk about logging on, right,
and you don't log on anymore because you're always logged on.
Speaker 3 (25:46):
Yep, you're always connected.
Speaker 2 (25:48):
Yeah. What's the number one thing that people misunderstand about AI?
Speaker 3 (25:53):
AI is an irritable but should not be feared.
Speaker 2 (25:59):
What advice would you give yourself ten years ago to
better prepare you for today?
Speaker 3 (26:05):
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.
Speaker 2 (26:14):
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.
Speaker 3 (26:26):
AI influencers generating content.
Speaker 2 (26:29):
Huh. How do you use AI in your day to
day life today?
Speaker 3 (26:33):
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
A five b the post that I announced on LinkedIn
on hey, IBM is releasing the model on the same
day it was generated by Lama three point one four
A five billion. So using the same model to post
(26:56):
the generate the announcement note very elegant.
Speaker 2 (27:00):
Is there anything else I should ask you?
Speaker 3 (27:02):
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
(27:23):
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 higging face today and look for Lama, there are
(27:45):
about fifty thousand different lamas 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 find
you need for your own purpose. We are the method
that we call instruct lab to be able to collectively
collect all that information and contribute to the base model
(28:08):
and enhance. 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
(28:31):
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 feel like that's really
the adoption that the market is going to emerge to
moving forward.
Speaker 2 (28:50):
Yeah, this interesting, I think, maybe naively unintuitive, but it
makes sense once you think about it. Thing that open
source things are safer. Evily 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.
Speaker 3 (29:10):
Right, That's exactly what's going to happen. Yeah.
Speaker 2 (29:13):
Great, it was lovely to talk with you. Thank you
so much for your time.
Speaker 3 (29:17):
The same here, thanks Jacob.
Speaker 1 (29:21):
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
(29:45):
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
(30:06):
IBM is produced by Matt Romano, Joey fish Ground, Amy
Gains McQuaid, and Jacob Goldstein, who are edited by Lydia
Jean kott Or. Engineers are Sarah Brugerer 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
(30:27):
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.
Host
Malcolm Gladwell
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com