The information economy as a rod. The world is teeming with models reinvent every
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dot Comside Analysis dot com. And now here's your host, through Eric Kavanaugh.
Yes, oh, yes, folks, Welcome to the future. Indeed
it is July of twenty twenty three. The future is here already, It's
just not evenly distributed yet. That's the tagline for our TV show future Proof.
Check local listings. We just got our own time slot in Washington,
DC and Fairfax County. Pretty excited about that. And timing is everything,
folks, and this show, the Inside Track, came together quickly. I
heard some exciting news I guess about a week and a half ago that Bruno
Aziza was going somewhere. Of course, he's been in Google the last few
years, and we're like, all right, what's next with Bruno. It's
always exciting. It's always interesting. That I found out he's a newly minted
partner at Capital G which means, Bruno, we're going to call you the
g Man. I'm pretty excited about that. Bruno Bruno's everybody in this business.
He's been around a long time. His passion is undying and amazing.
So we're very excited about his new gig over there, and we decided to
do a little series of shows about innovation in our space. And what a
time to live in this space. Oh my goodness. I've seen lots of
trends in the last twenty five years or so. I've seen cool things come
and go, but the size of this wave and the magnitude of what's changing
is like nothing I've ever seen before. And of course AI is at the
heart of all that. Generative AI is the topic du jour, and we're
going to talk about what to do with it, what not to do with
it, what gets us excited, what gets us concerned. There are a
lot of things to talk about with generative AI. It is incredibly powerful,
but you do have to be can you expect from it? So you have
an all star cash here, Bruno Aziza of courses with us from Capital g
We have Yashaalytics these days and San Chief Mohan or Kamo, and I'll just
say that, yes, this stuff is amazing, Yes we have to figure
it out. My big concern maybe we talk about in segment two is explainability.
If this stuff is going to be you are regulated and they're going to
be a real issue, it seems to me. But let's go around the
room, get introductions and see who's excited about what. Bruno Aziza, congratulations
on your new gig. We're all very excited for you. What gets you
excited about Generative AI? Well, I think it's really always nice to talk
to you. And so for those who don't know capital g it's Alphabet's independent
growth funds. So I'm still in the Alphabet family. Of course, if
you have my email and contact information is still the same, you could reach
out to me there For me that I've been in this did ai and analytics
market domain for what twenty five years? I have we known each other,
eric In, Yeah, we've all known each other for a long time.
And so what's exciting to me is if you remember at the beginning of our
career when we started, nobody cared about data. Right, It's a back
office topic, it's an expensive one. Why do I even need to have
to store data and so forth? And I think what jen Ai is doing
is changing the game for that. Now the interface for data has changed everybody
can work with data, which has of course advantages in terms of democratizing access
to information and letting people kind of interact us questions and saying it rate around
the answers around the data, but also has things that gotchas I think in
the world and I know we'll talk about bolts, but in the world of
gen AI, of course, if you have bad data, it's really going
to hurt you. So having high quality data I think is a big topic
of course to look at. But if you look at all the forecasts in
terms of opportunity in this space, it's there's got some really really good oportunities,
both from research firms. I think was it McKinzie recently that has said
that the opportunities and the trillions right, it's almost as much as the UK's
GDP and organizations like Walmart that are starting to work at very large scale with
AI to drive productivity for their employees and then create compelling customer experiences for their
customers. I think in the case of Walmart, last week at the venture
be Transform, we learned that approximately fifteen million, fifty million Walmart customers are
interfacing in any way, shape or form where their conversational experiences and over two
million, two million associates inside Walmart. I couldn't believe it myself. Had
to check how many employees does Walmart have? They have two point three millions,
so I guess it makes sense. But two million of them are using
this application called asks pees associates be more productive. So I'm really excited about
it. But of course, like anything in data, we got to do
it intentionally. We got to do it just like you said, Eric,
in a very open manner, so it's transparent and that it's accurate, it's
we removed the bias, and that it's also sitting on high quality data.
So these decisions that we're going to make out of the data that we can
interact with are not going to be detrimental, right right, And we have
to audit this stuff and know what happened to be able to report back what
happened. You know, there are a lot of perhaps hurdles in the future
to get over. At the moment, I think people are just trying to
figure out exactly how to use this stuff, and there are tremendous case studies.
I mean, to me, a customer experience a number one fueling your
chat pots fantastic because you think about if you could point these large language models
or even small language models we'll talk about that, point them at the corpus
of your customer interactions over the last two three years, for example, and
in very little time you can have a foundation for a chat pot that will
know most of what people have been talking about with your brand. So to
start off like that, I think is a pretty powerful storyline. That's a
pretty compelling business case. But maybe we'll bring Sanji Mohan to comment on that.
I think there are still a lot of miles to be driven figuring out
where we're going to use this stuff and what the sort of rough edges are.
But Sanji, what's your take? Thank you all right, so that
you actually set me up really well, because what we have today is a
game changer in the sense that I can use open AI and I can ask
all kinds of questions. It's really democratized AI. Anybody in the world can
finally use AI. But there's one area that we still have to cross a
hood, which is how do I use LMS on my enterprise data most Because
once we can efficiently and effectively start doing that, then all of a sudden,
I can do stuff that I have never even dreamt of. Today.
I write sequel queries, I do keyboard searches or I do know. It's
on my structured data and my unsquerry. And not only am I getting the
results from my database, but I'm using a large language model to give me
some sort of recommendations, aggregations, some action plan things like that on top
of what I just got. So this combination of keyword search and semantic search
or similarity search is what is super exciting for me. Yeah, that's that's
good stuff. And I have played around quite a big with chat chypt and
it does reflect back to you some remarkable depth on even some esoteric subject areas,
because who cares. It's captured all this stuff that they've baked into this
engine basically, and then you just give it prompts to see little fragments that
come out. Of course, it's designed to fuse vectors of information, so
as the go to market from chat gpt chid to be on an email through
sorry in a Twitter thread during the data breaks show, he's like, well,
you know, remember the who listen nations are more of a feature as
opposed to a bug right, Like, the whole idea is to generate new
stuff. So the question then becomes what material do I provide you to generate
new stuff? And then how do I guide or train that? And we
can get into the challenges maybe in the second segment, But I do believe
that there is still a just whole host of value if we can figure out
how to leverage it and how to kind of move things forward. And yashall
bring you into comment on this year an old hand at this exciting enterprise software
stuff. What's your takeaway on the upside of jen Ai and where things are
going? Well, look, I kind of sit in the middle between what
you get excited about. Eric and let Sunjeeb talked about that. I mean,
you do. I've known each other, not super super in depth like
you and Bruno do, but we have no each other for quite a few
years. I've been a CMO for twenty five plus the years, but a
long time there's been like one thing that at the center of the universe for
me in every organization I've been a part of, and that the better know
who my customer is so that I can serve them in a meaningful way.
And there really is one fundamental problem with that, and that's the data that
exists inside of the organization is fragmented and it's all over the place. Fundamentally,
it's inaccessible. And it kind of doesn't matter what type of new tools
we buy, the type of things that we layer on top of it,
what new technology comes about, like, it's just not in a format that
makes it possible for me to be able to write the right kind of queries
or even forget about like get the right kind of people that can write the
right queries. We are now at a point where we'll call it jen Ai
has opened up the opportunity for accessibility to truly understand who our customers are,
to be able to create meaningful experiences. And this is really what is most
exciting in my mind right now, not months from now, not years from
now, but like right right now. We can mechanize the organization of data
inside of the organizations that we are a part of. We can use LMS
to get acts into all of that data in a way that can help us
derive the right insights to be able to create the best experiences for our current
customers. And yeah, I don't know, I mean, I've been a
rapper a long time. I've got a bunch of great here. There is
no time that I've been more excited about being able to utilize technology to create
better experiences for my customers than right now, even back in the late nineties
when the Internet was retarting, Like right now is an absolute golden age.
It is so incredibly exciting. Yeah, I have to agree. And it
is a reflection. That's the thing. These large language models reflect the world
around us, reflect the world that they have consumed. And of course they're
fast evolving too, So these things are changing. As we talk about this
on our show right now, advances are being made. What did one guy
say, I'll throw it over to Bruno to comment on. He was talking
about healthcare, and he was saying that today is the dumbest this engine will
ever be, and it already knows more than any doctor has ever known.
And so you start recognizing the power of this breadth of information. Yes we
have to vet things, Yes we have to be careful about things. That's
sort of the next phase I think of evolution in these models, these foundational
models, is getting focused models on particular industries, because when you can dive
into an industry there's a whole lot of training you don't have to do,
right, you get a lot of training on the box. When I think
about that and think about the fact that we can now leverage that other eighty
percent of all the unstructured data. I mean, in our data analytics industry
we've been focused on for a long long time. Now, well, guess
what that eighty percent provides all the context that makes the rest of the twenty
percent meaningful, And so now we can leverage that for you know, what
do you think absolutely? I think using an industry like health scare. The
Mayo Clinic is an example and organization that's pouring through medical records to help the
practitioners get more efficient. In another area, as the legal domain, I
think a company like Accenture, you know, and they've been doing this for
years by the way, right we're talking to the broad markets talking about it
now, but many organizations that have high level of maturity within engineering have been
working on this for a while. So Accenture, for instance, we'll work
for them on this program called ALICE. It stands for Accenture Legal Intelligent Contract.
Over a million contracts added and every month into the system and using kept
the patterns and really help your thousands. I think it's twenty eight thousand professionals
globally to deliver faster value by pouring through all this instructure data and get to
the bottom line. So there's industries that are going to benefit from it a
lot faster than others. I think the main bit here and we touched it
at the beginning as there's a lot of confusion right now, a lot of
hype. So the industry, I think needs to simplify their approach. Every
customer I talk to as a council for figuring out okay, how do I
power students And hopefully we'll talk about the in the second part of this show
here. So there's a need for simplification, needs of an intentional and proactive
approach with specifics this case that needs to be made. But right now there's
some industries you could take advantage of this technology almost immediately. Again, if
they've got the right technology, things you can do. I call it a
dynamic cliffs notes generator where you can take a one hundred throws over to you,
dump it into chat, GBT or BARD or any one of these major
financial models, and say give me the two page summary of what this document
says and boom, it'll do it. Then you can say, all right,
now drill into this part, give me more infraction about that boom.
I mean, it really is like the ultimate assistant. It's like an incredibly
knowledgeable intern that make it a few things wrong here and there you have to
check up on, but we'll do whatever you ask them to do and won't
complain. What do you think, Sage, Yeah, it's like you should
treat or treat these llms like having a personal butler who has who have memorized
all of Internet, and I can ask questions and then apply some of my
logic and then certainly I have this powerful helper at my back end, call
anytime I want. So, So where I see us heading And to the
example Bruno give for legal professions, what we have today these lms that are
trained on vast corpus of data. But in an enterprise, I mean not
need all that. I don't need my LM to have memorized all a stack
overflow and read it and get hub. But maybe I've All I want is
for it to summarize or find legal presidents of my fully years of legal practice.
So where I see us heading and this is a bit controversial because we're
not there yet. Is an ability to train my own enterprise model in my
domain and pointed to my documents. So what I see happening in future is
censure. For example, will have a domain practice for legal, another one
for supply chain, the one for marketing, and it'll say, you know,
here is a blueprint of an LM and we can train it for you.
Today, that infrastructure does not exist because you need thousands of GPUs which
how to get the very expensive No one's got extra millions of dollars to spend
to train it. But over time, I see this expertise the infrastructure becoming
more prevalent, so I will have this ability to train the model on the
data I want, very specialized, so I call it small form factor LM,
and then I can start throwing questions and have an amazing copilot or an
assistant to help me. Yeah, that's a good point, and I think
you're really honest something. And I refer to these as small language models,
and that may not be the best term to use, but the point is
you don't need everything. I only need what I need, and you don't
want to cloud up your view of the world with lots of extraneous concepts and
information. I mean, this was the thing I'm kind of thinking to myself
as I'm watching demos at the Data Bricks seven and granted, this very powerful
stuff that they're building, very very thoughtful, and they're doing it in any
very what's the word clear fashioned way, I guess is what I would gather
from that. But nonetheless, there's a lot of training that's going to have
to take place. Where is the value in that if I have to spend
six months training this huge model to my business, Well I kind of lost
six months there. And is are things going to change by the time that
six months goes by? I think so, and so I think you are
going to see I mean, it was funny. Even Sam Altman came out
and said the era of large language models is over. And he's the chat
GPT guy. Now. Someone I went and met at the Reuter's conference and
often last week said, yeah, it was a very self serving statement by
him. Don't listen to him. We think that's nonsense. We're gonna be
fus whole ecosystem built around it, just like we saw in the days of
head Dup, right, head Dup came out, everyone got excited about it,
and this whole ecosystem grew up around it, sort of ins and pieces
and fits and starts, and of course had to kind of disappeared. I
don't think the foundational models are going to disappear by any mean, but it
is a warning to everyone out there. Be careful. We'll be right back.
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Inside Analysis. Here's your host Eric two on Inside Analysis talking to a whole
host of experts. This is so much fun. We've got Yasha from Lytos
Bruno from Capitol G and of course Sanji Mohana, good buddy from Sanjmo.
And on the break we were chatting about accuracy and yes, accuracy matters.
You have a fun side stool throw at you. One of my good buddies
who's also a client, a guy who runs a company called Crowdpoint Technologies.
He was invited into Open AI two and a half years ago and he went
there and talked to them and signed an NDA and said, no, no,
you got it all wrong. You're gonna have a short term memory problem,
you're gonna have a long term memory probably, just like outlining all these
issues they're going to have. And so he didn't go with them, but
he couldn't talk about it because he signed an NDA. So he said,
this is just what I'm in Texas because I just got out of the NBA's
and he's got a concept that we'll take intell intelligence and the ideas you apply
machine learning to trusted corporate data, so instead of just using whatever information was
on the interwebs for the last ten years to show you what's going on.
No, it's very focused, and that focus really matters in the enterprise SI,
but throw it over to you. Accuracy, governance, security, trust,
these things must be in the enterprise world. We don't have the luxury
of consumer media and not really caring about the truth. Right, Yeah,
So I want to bust this out. The accuracy, which is what I'm
going to talk about. Security and governance you cannot have. So you shouldn't
put out there that is giving you people's you know, personal private issue.
The information that would be a data privacy issue. So but let's talk about
accuracy for a second. We have to understand that lms are not designed.
If you want accuracy, do a keyword search, do a sequel search.
If I ask a question to my database and say show me all the top
salespeople from Q one of twenty twenty three, it will give me the one
the same information every single time. It's a very deterministic query. That's what
we're used to. But an LM is trained on probability. It's a statistics
thing, and the answer can change depending upon you know, the situation where
people are asking a lot of like there's a lot of world match and everyone's
talking about some new incident. Next time I ask a question, is that
the probability of the next world is different. Now it's a probabilistic thing.
So so is that a problem? Yeah, of course, I mean if
you cannot trust it. But the idea is that if we understand the way
LM's work, then we start using it for our own benefit. There's a
question in QNA that business will get frustrated. Yes, they will get trusted
if they don't understand what is the purpose of an RLM. The purpose of
an LM is, let's say, to aggregate the information that I've just sent
it in my prompt. If we go with that, you can now use
LLM for what it's good at. It's a language model. You know.
There's that famous case where recently a near some attorney, now you know,
he asked a question about some presidents and three cases came up and they were
factually incorrect. Well, he was not supposed to trust that data out of
the box it was. It's supposed to assist him, and he just presented
it into the court and they don't out to be wrong. So that's a
wrong use of an ELM. Yeah, no, that's right, And that's
what I think some of the big prominent journalists were doing as well. The
guy from the New York Times whose chat gbtpot set I want to marry you
leave your wife or whatever it's like, Well, you let it down the
garden path. It's the point you were misusing the tool, and therefore you
didn't get the desired effect. But you know, to kind of keep this
on track and maybe I'll throw it over to Yasha. It is generative,
so it can get you eighty percent of the way there. This is I
think the proto principle is always out there, and it seems very good at
getting you eighty percent of the way there, to give you some structure,
to give you some form you can work with, and then you kind of
mold it like clay to get it where you want it to be. In
that category, it's very very powerful. But just be careful about issues that
must require accuracy deterministic value use. If you will just be careful about that
stuff and you should be all right. What do you think, Yoshia?
Yeah, well, I think that the concept of copilot is maybe the big
and most important takeaway here, right Copilot infers exactly what we need to understand,
and that's that we're not trying to get the final answer. We're trying
to get support to get to an answer that we feel competent with so that
we can take control and take over what's next. You know. The question
that was posed in the Q and A, I think is a good one.
Right Ep writes that given the publicity of LMS and gen AI, like,
how does a business not get frustrated right about the lack of a readiness
inside of the enterprise? And I think the question really comes down to where
am I going to think about deterministic information inside of my business and apply LMS
to it behind the concept of a copilot, so that I can apply gen
AI in a way that's meaningful for the business, right like you. Just
like you said, you apply a model, you're going to get a result
that's roughly eighty percentage accurate. And when you start too in that model in
a way that is built behind the idea of a copilot, you can actually
tune it up even better. Let me give you a really specific example.
The business that I said and looks at zero party in first party data like
it's really really simple. It's all deterministic data and the idea of a copilot
being able to look at data make a match of an inbound set of data
into a customer profile. An LM tuned to deterministic data, you can get
to a ninety seven percent accurate mapping in a copilot model every single time.
So again, it depends on where you want to look. So EP's answer
to get ready inside of the business, look at deterministic data, look at
your customer data and zero party first party data, and think about the copilot
metaphor. Because that's really the way to get to the end result that you
care about. Don't expect the right answer. You know what's interesting here,
I'm going to tie this together. This might be a bit abstract, but
I remember when the mobile movement really took over, and it forced designers,
why designers to rethink their entire layout, where the functionality lives, what you
can get access to from which window, all that kind of stuff, and
they it really forced because of the real estate constraints. I'll throw this maybe
to Yosha first and then to the other guests. It forced the developers to
really think through workflow and what is necessary at this point in the workflow that
is not necessary at a previous point or at a following point. Right.
And so this copilot concept I think can be very compelling in terms of guiding
you down the path of what can be done. Because if you think about
and I always pick on Adobe Photoshop just because in like the year, you
know, nineteen ninety nine, you would have all these menus and all these
options on the menus where at any point in time they're like eight thousand things
you can do. Well, you know, no one's just going to figure
that stuff out. Especially photoshop, you have layers and diffusion layers and all
this kind of stuff going on, But at any point in a given workflow,
there's only so many things that you can do. And so in that
sense, I think generative can kind of guide us through what we're complex workflows
but can now be fairly simple, while also educating the worker in the process.
I'll turn it up at Bruno first. What do you think about that
idea of Bruno? I think it's a it's a great idea. It's a
great example as well. I think if you look at any adoption curve of
any new technology, there's three big questions we need to answer, right,
what is this? How do I use it? And so what? And
I think you we've talked a lot about what is it? Right, and
Zanjie just explained, Look, this is not magic, it's probability at scale,
So that's what it is. The question I think a lot of customers
are asking is how do I use it? And so I think what the
industry needs now is a lot of simplification, a lot of guidance on how
do I think about using this amazing technology everybody's talking about. I came up
with this acronym d ACS, which spells dates I guess, and it talks
about what are the verbs you're looking for when you're using GENEI. The first
one is draft or document Right, that's the d is answer or not invent
answer. See it's classified. There's a great job classifying, adit and summarize.
But you got to think about, Okay, now that I have these
use cases or these verbs, these jobs, I'm trying to use this technology
for how do I find example across marizations. So I'll give it an example
of Nfair. You probably go to Wayfair to look at a lot of you
know, material you want to use, couches and so forth. They looked
at, okay, how do I help people that are creating content my website.
That's an example of documentation or maybe creation of content of a body of
data that already have and accelerate the productivity of my copywriters in the case of
Wayfair, and they're looking to double triple the productivity of people creating MIDA data,
if you will, off of data they might already have. You look
at developers. You know, developers can benefit from JENA at least in three
different areas. This actually came out from McKinsey research. One is existing meaning
if you're a coder today, you probably are going to a lot of manual,
repetitive work to document your code. Well, McKinnon do that in about
half the time. If you're a developer and you want to jumpstart, I
think, yesha, I was talking about this idea of copilot. I like
the idea of companionship. You know, if you're a content creator or even
a coder, often when you get started, you have a blank canvas,
so you can play within gen AI and start getting started on how you know,
what should I create and so forth, an accelerate update to existing code
and so forth. In this case, you know what McKinsey were code refactoring,
content refactoring at about two thirds of the time. And then finally,
I think the third example here we keep talking about Jeni. You know taking
over the situation is more a human without that tool versus a human with the
tool. And so an example is, you know, in McKinsey's example,
they found that developers using GENI tools are twenty five to thirty percent more likely
to complete complex task than people that don't have it. So you can now
kind of unleash imagination with the assistant of that theology. Interestingly enough, you
know, we think about these as this nice element that if Eugenai tool effectively,
it becomes this human companion that you can kind of run ideas with.
But again, the invention comes from you. You shouldn't expect them to invent
for you. You iterate with them. I think that's really kind of how
because we confuse what they are and use them for those verbs I mentioned,
and how you can get value from them. Yeah, well guardrails, right,
I mean, that's what you're getting here. And with the copilot stuff,
I mean, and this is not terribly new, right, Google was
finishing sentences in your emails like about a year and a half ago, so
that was kind of an early indicator of what's coming down the pike. Right.
So now I really like on the copilot thing, and maybe I shall
throw it to you. We've got to It can help you understand where to
go. It's like having someone at your side all the time says, now
you're going to turn left. Now you're gonna want to turn right. Oh,
maybe you should do this, and you're gonna see these suggestions come out.
But I'll ask you quite eving this more every day that the manifestation of
AI will be in the form of suggestions and as long as you accept that's
what they are, and then you take accountability for the action based upon the
suggestion, then you're fine. Now I'm the human who is accountable. What
did someone say one of these events recently, The question was like, oh,
what is responsible AI? And she says, there's no responsible AI,
and there's not going to be. There are people. Only people can be
responsible for decisions they take. And so that's where the accountability comes in.
It's in the first it's not in the AI. But Yasha, what do
you think? Yeah, but okay, I think you brought up a that
we should call back into when you talk about the paradigm shift that happened a
popular out to consumers. Right we're going through that right now. But we
have this really really difficult challenge to work out of the rapid like mass mass
adoption of jenai that's happening right now is coming behind one single user metaphor and
it's chat. So like everything that we're dealing with is behind what we've been
taught over the first of the last month and a half. We all have
to work our way out of that. And it's like it's trivial that that
sounds amongst the four of us right now and those that are listening like that
is not trivial, Like we have all been taught that the way that we
interact with jen Ai is through a chat interface, and it is not that.
It is everything that you just said, Eric, it's everything the Bruner
just said. It is about providing access into other information behind these models.
The user metaphor has to change. But that's going to take a lot of
thought by different enterprises to be able to provide that kind of coopilot recommendation,
whatever that experience needs to be. We're just kind of caught in this user
experience trap right now. That's very interesting, a user experienced trap. I
got to write that down. I think you're right, and I'll just throw
one other interesting observation out there. One thing I thought that open Ai did
that was very compelling is the workstation in which you operate. And I've always
wondered about Google and how they just don't use the real estate that you have
on that Google search page. And now they do cool stuff with the visuals
and sharing history of individuals and things of that nature, and of course you
had I think it was Project Hummingbird maybe where it makes all these recommendations of
what you think of what it thinks you want to look for. But nonetheless
it wasn't really a workstation. It was just a window into the Internet that
has been indexed for me. Whereas what chat GPT has done in bar and
these others now is create these workstation environments where you can kind of tell that
you're at work now and you can kind of piece things together, because that's
what you're supposed to do with this is use it as your workstation to piece
things together and make sense of your business. Bruno, what do you think?
You know? I think sometimes the beauty of the UI is no UI,
right, is to be really invisible to you. So I personally I
really like the interface of Google Search. Maybe I'm a little biased on that
one, but I do think that it gets out the way of everything else
that could be busy here. So I don't know, I think you'll see.
I mean, even Claude, I think just came out yesterday. So
I think we're still in the experimentation phase of which is going to look like
a use case that's going to be the most helpful to me. I think
y Aasha has got a great point. You had and thought about it,
but you're right that we think about the interface today as being chat whereas we
really should think about it much broader, where it's everywhere and it could now
start pushing decision out to you so you can react to it rather than have
to ask questions to it. I'll be curious to see what people think about
it, but I certainly think today lots of noise, lots of confusion,
potentially a lot of misuse that when you turn this consumer AI trend into an
enterprise AI trend, you're gonna have to figure out what the flamewoark case will
adopted it. Yeah, now we're talking about generative AI and the power of
data in the enterprise and around the world will be right back. You're listening
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Terms and conditions apply equal housing opportunity. Welcome back to Inside Analysis.
Here's your host, Eric Tabanaugh. All right, let us and gentlemen back
here on Inside Analysis. We're talking to a whole bunch of experts here.
We've got Yasha from Lytos Bruno from Capital G which is part of the Alphabet
Group. But of course Sangmo and Yasha. You had a really good point
in the break there around sovereignty data sovereignty. Obviously the Europeans are big on
this stuff. You have to be careful about where the data is persisted,
where it's processed, what your intentions were. We're starting to see more of
that in the US, certainly in California with's CCPA. And you said there
is one cloud vendor that is promised it is not going to be naughty with
your data. Tell us about that. Yeah, I mean, but let's
even talk about at one level higher up, like everything that we think about
in the enterprise, we have to think about behind our data strategy and our
data strategy has to be cognizant of all of the complexities of consumer privacy laws.
You mentioned CCP. We have like six new states in the US that
now have their own privacy a lot like there are. There is no slowing
down of consumer data protection, no slowing down. So when we think about
the application of gen AI, like every single person watching and listening like you
have to be thinking about how lms are going to have a relationship into consumer
data privacy period like period right now, there is only one cloud provider that
will kind of back up the fact that when you are utilizing their models and
their model garden, the data that you input in the corpuses that you train
on aren't going to get reshared. And that's Google, It's protects Ai.
There are lots of announcements that have just happened, and I suspect that over
time others are going to follow because the acknowledgement of the importance is there.
Right enterprises have to operate with confidence that they can work within the data sovereignty
rules that are required. But as Google right now, So if you're going
to go out and experiment right now and you want to have confidence, protects
AI is really the place to go. M that's a good point. And
you do want to have confidence. Yeah, this is concept of what psychological
security that they talk about some of these events these days. And the idea
is that you want to empower your people with their creativity and their confidence.
You want them to be confident enough to take chances to look for things.
You know, as I'm sitting here listening to these experts, I'm wondering to
myself and you maybe throw it over you first and then Bruno. This whole
concept around the hidden context in your data, the hidden meaning in your data.
These language models, these foundational models can be extremely powerful once you trusted
to put some of your corporate data in there to just look around and find
stuff you think about discoverability. To me, that is the most important part
of analysis as being able to discover what's happening. So companies like thought spot
for example, have a great UI that a great concept. You start throwing
natural language query, I think all of that is going to open up to
tremendous value because of these foundational models, because you'll be able to ask much
more complex questions in just natural language of your existing documentation and then be able
to kind of guide the conversation to understand how this fits into certain corporate regulations
or policies or directives. For example, you think about the marketing world where
you have these themes that you want to deploy, So you could explore your
data and look for where these themes map with something you already have. My
point is we have this very powerful mirror now that we can point at our
data to export in whole new ways that I think is going to be really
compelling. Sanchi, what do you think now you're mute? If I may
give an example, a real life example, So every organization has to be
with this problem that the customer man will renew. So today what we do
is we create these machine learning models to determine customers chilling. For example.
The problem is that the machine learning model can detect and predict which customers are
at the risk of not renewing. But then what you know, we've all
these strategies we try. We kind of like throwing stuff on the wall and
see if it sticks or not. So there is a huge potential where lms
can now come to our rescue. And the way it works is a combination
of predictive AI and generative AI. So so it's a two step processes.
The first step is is because I remember, LM has got no clue about
sorry I was going to use a bad word, has no clue about who
my customers are. So I ask a query to my database and I say,
okay, predict tell me which customers are at the risk of our journey.
Once it retreats that information, I can now put that as a prompt
into an LM and I guess everything that you know on this data, give
me some strategies of how I should go about And this is hugely power strained
to do this kind of work. It can come up with some amazing recommendations
that are even beyond a human beings capability, at least in a given amount
of time. So you can instantly get that. But this, by the
way, what I just explain is what is going around in the market.
We call it rag RAG, which is retrieval augmented generation. So this is
a great use case of an ELM working on my because you have to know
that these elms were the big ones were trained like open air once in September
of twenty twenty one. They don't even know anything that's happened in this year,
So how are they going to help an enterprise if they're so out of
date. So that's how these lms are now being used. Yeah, that's
fascinating pronoun. You want to comment on that, Well, I'm going to
see if I can alcronym sound. Gee. I love that rag. That's
incredible. There's three things I would say. The first thing is the state
of readiness today. As looking at the twenty twenty three I Readiness Report,
twenty one percent of organizations stay there in production. Nineteen percent of them have
no plans. So you can see in between there's a lot of people that
are experimenting, planning, trying to figure it out. And I think a
lot of it is related to some of the issues we talked about, obviously
quality, the input of the data. But a big one that you just
talked about, Eric, which is really interesting, is the refinement of the
understanding of the question. You refer to. Thoughts product company I'm very familiar
with, came out of this product called Sage, where the benefit of it
is understanding the question itself. Now, what is the real application of that.
You take an organization like seventy five percent of people that are ordering from
when he's they do it through their drive through, and so that happens is
Wendy's as this amazing menu and there's billions of possibilities that you can order.
Everything is customizable, and so having a model that is able to understand and
the refinement behind the question is what's going to make the answer really interesting.
You know, talking about acronym, I was trying to think about what would
be the attributes in the ideal world of the best in AI type of application.
And it's not as great as as an acronym as Sanji did, but
let me try it on you. I called MTKAC, MTKC, so M
and T. It has to be multimodal, multimodal and input and output photos,
text, audio. It's got to be able to handle that because that's
how we live. T Trustable why and we talked about the multiple dimensions.
Obviously there's bias, there's ability to trust the output. Trustable is a key
notion that you know, we everything we do in data the foundation is trust.
See is what Sanji was talking about. You know, if you give
me insights from five years ago, not really helpful, right, So I
need currency on the data. I need you know that your work of data
that's recent enough a is for applied to a workflow. I think that's what
yah I was saying earlier today, is that it's got to be applied specifically
to my workflow. And see it's contextual. You know how many of these
experiences we've had actually even with other humans, where we have a conversation and
we lost the flow of that conversation. So contextual is a big value if
I think, if you hit all these dimensions, you end up having the
real capabilities that you need to take your company to the next kind of generational
change here, but we're not there yet. We're at the beginning. Yeah,
and I am looking forward to this the single instance future where you can
point your corporate data at it, you trust this engine. Once you can
do that, you can learn tremendous things about what's going on in your organization.
I mean you'll learn things that you do never even knew existed. There
are workflows that you didn't even know about that are getting the job done.
One of my favorite analysts friends from like twenty years ago, said something I
thought that was funny. He goes, if there's something in a large organization
that must happen. It is happening, you just don't know how it's happening.
You have to find out how it's happening. And we're going to be
able to kind of see. And it's really this nexus of things, this
nexus of analytics, of good quality data, of foundational models, and of
things like process mining. To me, that is the real magic is to
be able to understand what your processes really are, how long they really take.
Because I think we're going to see a whole lot of optimization on actual
run time workflow with things. I think that's the big benefit that's going to
come in the next couple of years, is this all sort of comes together,
is we're going to figure out there are whole masses of work that we're
doing that are just not necessary and shouldn't be done and we shouldn't be wasting
time on things like that. I think that's going to be one of the
hallmarks out of this whole pandemic era is we all figured out quickly there are
things that have to get done and there are things that do not have to
get done. And when you're trying to get the business afloat or trying to
stay afloat during difficult times. You do not want to be wasting time at
all. So you want to be leveraging every tool in your toolbox. You
want to be leveraging every piece of data that you have. And I think
that is going to be the hallmark of the future is being able to piece
all of this together. And that's what these foundational models are good at,
right, They're good at fusing vectors of information to create something new. And
I think we're going to see an interesting array of solutions come out from all
of that. But folks, you've been listening to Inside Analysis recording stop Legacy
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