WEBVTT

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The information economy as a rod.
The world is teeming with models reinvent every

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industry, industry. Inside Analysis is
your source of information and insight about how

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to make the most of this exciting
new era. Learn more and inside Analysis

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dot Comside Analysis dot com. And
now here's your host, through Eric Kavanaugh.

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Yes, oh, yes, folks, Welcome to the future. Indeed

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it is July of twenty twenty three. The future is here already, It's

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just not evenly distributed yet. That's
the tagline for our TV show future Proof.

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Check local listings. We just got
our own time slot in Washington,

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DC and Fairfax County. Pretty excited
about that. And timing is everything,

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folks, and this show, the
Inside Track, came together quickly. I

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heard some exciting news I guess about
a week and a half ago that Bruno

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Aziza was going somewhere. Of course, he's been in Google the last few

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years, and we're like, all
right, what's next with Bruno. It's

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always exciting. It's always interesting.
That I found out he's a newly minted

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partner at Capital G which means,
Bruno, we're going to call you the

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g Man. I'm pretty excited about
that. Bruno Bruno's everybody in this business.

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He's been around a long time.
His passion is undying and amazing.

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So we're very excited about his new
gig over there, and we decided to

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do a little series of shows about
innovation in our space. And what a

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time to live in this space.
Oh my goodness. I've seen lots of

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trends in the last twenty five years
or so. I've seen cool things come

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and go, but the size of
this wave and the magnitude of what's changing

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is like nothing I've ever seen before. And of course AI is at the

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heart of all that. Generative AI
is the topic du jour, and we're

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going to talk about what to do
with it, what not to do with

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it, what gets us excited,
what gets us concerned. There are a

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lot of things to talk about with
generative AI. It is incredibly powerful,

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but you do have to be can
you expect from it? So you have

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an all star cash here, Bruno
Aziza of courses with us from Capital g

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We have Yashaalytics these days and San
Chief Mohan or Kamo, and I'll just

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say that, yes, this stuff
is amazing, Yes we have to figure

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it out. My big concern maybe
we talk about in segment two is explainability.

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If this stuff is going to be
you are regulated and they're going to

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be a real issue, it seems
to me. But let's go around the

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room, get introductions and see who's
excited about what. Bruno Aziza, congratulations

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on your new gig. We're all
very excited for you. What gets you

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excited about Generative AI? Well,
I think it's really always nice to talk

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to you. And so for those
who don't know capital g it's Alphabet's independent

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growth funds. So I'm still in
the Alphabet family. Of course, if

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you have my email and contact information
is still the same, you could reach

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out to me there For me that
I've been in this did ai and analytics

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market domain for what twenty five years? I have we known each other,

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eric In, Yeah, we've all
known each other for a long time.

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And so what's exciting to me is
if you remember at the beginning of our

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career when we started, nobody cared
about data. Right, It's a back

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office topic, it's an expensive one. Why do I even need to have

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to store data and so forth?
And I think what jen Ai is doing

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is changing the game for that.
Now the interface for data has changed everybody

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can work with data, which has
of course advantages in terms of democratizing access

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to information and letting people kind of
interact us questions and saying it rate around

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the answers around the data, but
also has things that gotchas I think in

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the world and I know we'll talk
about bolts, but in the world of

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gen AI, of course, if
you have bad data, it's really going

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to hurt you. So having high
quality data I think is a big topic

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of course to look at. But
if you look at all the forecasts in

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terms of opportunity in this space,
it's there's got some really really good oportunities,

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both from research firms. I think
was it McKinzie recently that has said

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that the opportunities and the trillions right, it's almost as much as the UK's

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GDP and organizations like Walmart that are
starting to work at very large scale with

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AI to drive productivity for their employees
and then create compelling customer experiences for their

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customers. I think in the case
of Walmart, last week at the venture

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be Transform, we learned that approximately
fifteen million, fifty million Walmart customers are

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interfacing in any way, shape or
form where their conversational experiences and over two

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million, two million associates inside Walmart. I couldn't believe it myself. Had

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to check how many employees does Walmart
have? They have two point three millions,

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so I guess it makes sense.
But two million of them are using

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this application called asks pees associates be
more productive. So I'm really excited about

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it. But of course, like
anything in data, we got to do

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it intentionally. We got to do
it just like you said, Eric,

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in a very open manner, so
it's transparent and that it's accurate, it's

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we removed the bias, and that
it's also sitting on high quality data.

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So these decisions that we're going to
make out of the data that we can

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interact with are not going to be
detrimental, right right, And we have

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to audit this stuff and know what
happened to be able to report back what

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happened. You know, there are
a lot of perhaps hurdles in the future

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to get over. At the moment, I think people are just trying to

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figure out exactly how to use this
stuff, and there are tremendous case studies.

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I mean, to me, a
customer experience a number one fueling your

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chat pots fantastic because you think about
if you could point these large language models

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or even small language models we'll talk
about that, point them at the corpus

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of your customer interactions over the last
two three years, for example, and

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in very little time you can have
a foundation for a chat pot that will

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know most of what people have been
talking about with your brand. So to

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start off like that, I think
is a pretty powerful storyline. That's a

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pretty compelling business case. But maybe
we'll bring Sanji Mohan to comment on that.

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I think there are still a lot
of miles to be driven figuring out

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where we're going to use this stuff
and what the sort of rough edges are.

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But Sanji, what's your take?
Thank you all right, so that

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you actually set me up really well, because what we have today is a

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game changer in the sense that I
can use open AI and I can ask

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all kinds of questions. It's really
democratized AI. Anybody in the world can

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finally use AI. But there's one
area that we still have to cross a

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hood, which is how do I
use LMS on my enterprise data most Because

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once we can efficiently and effectively start
doing that, then all of a sudden,

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I can do stuff that I have
never even dreamt of. Today.

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I write sequel queries, I do
keyboard searches or I do know. It's

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on my structured data and my unsquerry. And not only am I getting the

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results from my database, but I'm
using a large language model to give me

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some sort of recommendations, aggregations,
some action plan things like that on top

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of what I just got. So
this combination of keyword search and semantic search

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or similarity search is what is super
exciting for me. Yeah, that's that's

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good stuff. And I have played
around quite a big with chat chypt and

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it does reflect back to you some
remarkable depth on even some esoteric subject areas,

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because who cares. It's captured all
this stuff that they've baked into this

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engine basically, and then you just
give it prompts to see little fragments that

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come out. Of course, it's
designed to fuse vectors of information, so

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as the go to market from chat
gpt chid to be on an email through

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sorry in a Twitter thread during the
data breaks show, he's like, well,

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you know, remember the who listen
nations are more of a feature as

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opposed to a bug right, Like, the whole idea is to generate new

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stuff. So the question then becomes
what material do I provide you to generate

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new stuff? And then how do
I guide or train that? And we

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can get into the challenges maybe in
the second segment, But I do believe

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that there is still a just whole
host of value if we can figure out

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how to leverage it and how to
kind of move things forward. And yashall

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bring you into comment on this year
an old hand at this exciting enterprise software

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stuff. What's your takeaway on the
upside of jen Ai and where things are

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going? Well, look, I
kind of sit in the middle between what

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you get excited about. Eric and
let Sunjeeb talked about that. I mean,

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you do. I've known each other, not super super in depth like

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you and Bruno do, but we
have no each other for quite a few

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years. I've been a CMO for
twenty five plus the years, but a

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long time there's been like one thing
that at the center of the universe for

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me in every organization I've been a
part of, and that the better know

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who my customer is so that I
can serve them in a meaningful way.

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And there really is one fundamental problem
with that, and that's the data that

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exists inside of the organization is fragmented
and it's all over the place. Fundamentally,

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it's inaccessible. And it kind of
doesn't matter what type of new tools

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we buy, the type of things
that we layer on top of it,

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what new technology comes about, like, it's just not in a format that

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makes it possible for me to be
able to write the right kind of queries

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or even forget about like get the
right kind of people that can write the

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right queries. We are now at
a point where we'll call it jen Ai

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has opened up the opportunity for accessibility
to truly understand who our customers are,

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to be able to create meaningful experiences. And this is really what is most

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exciting in my mind right now,
not months from now, not years from

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now, but like right right now. We can mechanize the organization of data

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inside of the organizations that we are
a part of. We can use LMS

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to get acts into all of that
data in a way that can help us

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derive the right insights to be able
to create the best experiences for our current

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customers. And yeah, I don't
know, I mean, I've been a

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rapper a long time. I've got
a bunch of great here. There is

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no time that I've been more excited
about being able to utilize technology to create

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better experiences for my customers than right
now, even back in the late nineties

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when the Internet was retarting, Like
right now is an absolute golden age.

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It is so incredibly exciting. Yeah, I have to agree. And it

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is a reflection. That's the thing. These large language models reflect the world

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around us, reflect the world that
they have consumed. And of course they're

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fast evolving too, So these things
are changing. As we talk about this

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on our show right now, advances
are being made. What did one guy

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say, I'll throw it over to
Bruno to comment on. He was talking

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about healthcare, and he was saying
that today is the dumbest this engine will

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ever be, and it already knows
more than any doctor has ever known.

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And so you start recognizing the power
of this breadth of information. Yes we

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have to vet things, Yes we
have to be careful about things. That's

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sort of the next phase I think
of evolution in these models, these foundational

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models, is getting focused models on
particular industries, because when you can dive

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into an industry there's a whole lot
of training you don't have to do,

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right, you get a lot of
training on the box. When I think

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about that and think about the fact
that we can now leverage that other eighty

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percent of all the unstructured data.
I mean, in our data analytics industry

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we've been focused on for a long
long time. Now, well, guess

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what that eighty percent provides all the
context that makes the rest of the twenty

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percent meaningful, And so now we
can leverage that for you know, what

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do you think absolutely? I think
using an industry like health scare. The

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Mayo Clinic is an example and organization
that's pouring through medical records to help the

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practitioners get more efficient. In another
area, as the legal domain, I

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think a company like Accenture, you
know, and they've been doing this for

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years by the way, right we're
talking to the broad markets talking about it

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now, but many organizations that have
high level of maturity within engineering have been

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working on this for a while.
So Accenture, for instance, we'll work

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for them on this program called ALICE. It stands for Accenture Legal Intelligent Contract.

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Over a million contracts added and every
month into the system and using kept

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the patterns and really help your thousands. I think it's twenty eight thousand professionals

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globally to deliver faster value by pouring
through all this instructure data and get to

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the bottom line. So there's industries
that are going to benefit from it a

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lot faster than others. I think
the main bit here and we touched it

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at the beginning as there's a lot
of confusion right now, a lot of

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hype. So the industry, I
think needs to simplify their approach. Every

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customer I talk to as a council
for figuring out okay, how do I

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power students And hopefully we'll talk about
the in the second part of this show

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here. So there's a need for
simplification, needs of an intentional and proactive

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approach with specifics this case that needs
to be made. But right now there's

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some industries you could take advantage of
this technology almost immediately. Again, if

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they've got the right technology, things
you can do. I call it a

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dynamic cliffs notes generator where you can
take a one hundred throws over to you,

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dump it into chat, GBT or
BARD or any one of these major

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financial models, and say give me
the two page summary of what this document

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says and boom, it'll do it. Then you can say, all right,

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now drill into this part, give
me more infraction about that boom.

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I mean, it really is like
the ultimate assistant. It's like an incredibly

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knowledgeable intern that make it a few
things wrong here and there you have to

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check up on, but we'll do
whatever you ask them to do and won't

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complain. What do you think,
Sage, Yeah, it's like you should

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treat or treat these llms like having
a personal butler who has who have memorized

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all of Internet, and I can
ask questions and then apply some of my

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logic and then certainly I have this
powerful helper at my back end, call

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anytime I want. So, So
where I see us heading And to the

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example Bruno give for legal professions,
what we have today these lms that are

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trained on vast corpus of data.
But in an enterprise, I mean not

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need all that. I don't need
my LM to have memorized all a stack

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overflow and read it and get hub. But maybe I've All I want is

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for it to summarize or find legal
presidents of my fully years of legal practice.

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So where I see us heading and
this is a bit controversial because we're

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not there yet. Is an ability
to train my own enterprise model in my

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domain and pointed to my documents.
So what I see happening in future is

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censure. For example, will have
a domain practice for legal, another one

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for supply chain, the one for
marketing, and it'll say, you know,

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here is a blueprint of an LM
and we can train it for you.

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Today, that infrastructure does not exist
because you need thousands of GPUs which

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how to get the very expensive No
one's got extra millions of dollars to spend

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to train it. But over time, I see this expertise the infrastructure becoming

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more prevalent, so I will have
this ability to train the model on the

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data I want, very specialized,
so I call it small form factor LM,

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and then I can start throwing questions
and have an amazing copilot or an

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assistant to help me. Yeah,
that's a good point, and I think

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you're really honest something. And I
refer to these as small language models,

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and that may not be the best
term to use, but the point is

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you don't need everything. I only
need what I need, and you don't

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want to cloud up your view of
the world with lots of extraneous concepts and

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information. I mean, this was
the thing I'm kind of thinking to myself

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as I'm watching demos at the Data
Bricks seven and granted, this very powerful

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stuff that they're building, very very
thoughtful, and they're doing it in any

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very what's the word clear fashioned way, I guess is what I would gather

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from that. But nonetheless, there's
a lot of training that's going to have

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to take place. Where is the
value in that if I have to spend

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six months training this huge model to
my business, Well I kind of lost

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six months there. And is are
things going to change by the time that

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six months goes by? I think
so, and so I think you are

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00:16:45.799 --> 00:16:48.200
going to see I mean, it
was funny. Even Sam Altman came out

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and said the era of large language
models is over. And he's the chat

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00:16:51.919 --> 00:16:55.279
GPT guy. Now. Someone I
went and met at the Reuter's conference and

233
00:16:55.360 --> 00:16:57.080
often last week said, yeah,
it was a very self serving statement by

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00:16:57.159 --> 00:17:00.840
him. Don't listen to him.
We think that's nonsense. We're gonna be

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00:17:00.919 --> 00:17:04.359
fus whole ecosystem built around it,
just like we saw in the days of

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00:17:04.519 --> 00:17:07.160
head Dup, right, head Dup
came out, everyone got excited about it,

237
00:17:07.200 --> 00:17:11.960
and this whole ecosystem grew up around
it, sort of ins and pieces

238
00:17:12.000 --> 00:17:15.160
and fits and starts, and of
course had to kind of disappeared. I

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00:17:15.200 --> 00:17:18.480
don't think the foundational models are going
to disappear by any mean, but it

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00:17:18.680 --> 00:17:22.519
is a warning to everyone out there. Be careful. We'll be right back.

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00:17:22.519 --> 00:17:29.160
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five four thirty two eighteen paid for
by Legal Alert Line. Welcome back to

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Inside Analysis. Here's your host Eric
two on Inside Analysis talking to a whole

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00:21:36.920 --> 00:21:40.400
host of experts. This is so
much fun. We've got Yasha from Lytos

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00:21:40.440 --> 00:21:44.599
Bruno from Capitol G and of course
Sanji Mohana, good buddy from Sanjmo.

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And on the break we were chatting
about accuracy and yes, accuracy matters.

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You have a fun side stool throw
at you. One of my good buddies

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who's also a client, a guy
who runs a company called Crowdpoint Technologies.

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He was invited into Open AI two
and a half years ago and he went

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00:22:00.640 --> 00:22:03.160
there and talked to them and signed
an NDA and said, no, no,

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00:22:03.319 --> 00:22:06.279
you got it all wrong. You're
gonna have a short term memory problem,

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00:22:06.279 --> 00:22:07.680
you're gonna have a long term memory
probably, just like outlining all these

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00:22:07.720 --> 00:22:11.279
issues they're going to have. And
so he didn't go with them, but

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00:22:11.319 --> 00:22:12.799
he couldn't talk about it because he
signed an NDA. So he said,

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00:22:12.799 --> 00:22:15.319
this is just what I'm in Texas
because I just got out of the NBA's

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00:22:15.400 --> 00:22:19.920
and he's got a concept that we'll
take intell intelligence and the ideas you apply

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machine learning to trusted corporate data,
so instead of just using whatever information was

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on the interwebs for the last ten
years to show you what's going on.

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No, it's very focused, and
that focus really matters in the enterprise SI,

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00:22:33.359 --> 00:22:37.880
but throw it over to you.
Accuracy, governance, security, trust,

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00:22:38.200 --> 00:22:42.279
these things must be in the enterprise
world. We don't have the luxury

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of consumer media and not really caring
about the truth. Right, Yeah,

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So I want to bust this out. The accuracy, which is what I'm

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00:22:53.160 --> 00:23:00.359
going to talk about. Security and
governance you cannot have. So you shouldn't

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put out there that is giving you
people's you know, personal private issue.

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The information that would be a data
privacy issue. So but let's talk about

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accuracy for a second. We have
to understand that lms are not designed.

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If you want accuracy, do a
keyword search, do a sequel search.

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If I ask a question to my
database and say show me all the top

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salespeople from Q one of twenty twenty
three, it will give me the one

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the same information every single time.
It's a very deterministic query. That's what

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we're used to. But an LM
is trained on probability. It's a statistics

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thing, and the answer can change
depending upon you know, the situation where

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people are asking a lot of like
there's a lot of world match and everyone's

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00:23:56.400 --> 00:24:02.000
talking about some new incident. Next
time I ask a question, is that

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the probability of the next world is
different. Now it's a probabilistic thing.

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So so is that a problem?
Yeah, of course, I mean if

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you cannot trust it. But the
idea is that if we understand the way

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LM's work, then we start using
it for our own benefit. There's a

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00:24:22.519 --> 00:24:26.920
question in QNA that business will get
frustrated. Yes, they will get trusted

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if they don't understand what is the
purpose of an RLM. The purpose of

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an LM is, let's say,
to aggregate the information that I've just sent

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00:24:34.920 --> 00:24:41.759
it in my prompt. If we
go with that, you can now use

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LLM for what it's good at.
It's a language model. You know.

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00:24:45.640 --> 00:24:53.039
There's that famous case where recently a
near some attorney, now you know,

332
00:24:53.240 --> 00:24:59.039
he asked a question about some presidents
and three cases came up and they were

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factually incorrect. Well, he was
not supposed to trust that data out of

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the box it was. It's supposed
to assist him, and he just presented

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it into the court and they don't
out to be wrong. So that's a

336
00:25:11.519 --> 00:25:15.000
wrong use of an ELM. Yeah, no, that's right, And that's

337
00:25:15.000 --> 00:25:18.119
what I think some of the big
prominent journalists were doing as well. The

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00:25:18.160 --> 00:25:22.599
guy from the New York Times whose
chat gbtpot set I want to marry you

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00:25:22.680 --> 00:25:26.640
leave your wife or whatever it's like, Well, you let it down the

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00:25:26.680 --> 00:25:30.359
garden path. It's the point you
were misusing the tool, and therefore you

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00:25:30.359 --> 00:25:33.599
didn't get the desired effect. But
you know, to kind of keep this

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on track and maybe I'll throw it
over to Yasha. It is generative,

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so it can get you eighty percent
of the way there. This is I

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think the proto principle is always out
there, and it seems very good at

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getting you eighty percent of the way
there, to give you some structure,

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to give you some form you can
work with, and then you kind of

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mold it like clay to get it
where you want it to be. In

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that category, it's very very powerful. But just be careful about issues that

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must require accuracy deterministic value use.
If you will just be careful about that

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00:26:00.519 --> 00:26:03.359
stuff and you should be all right. What do you think, Yoshia?

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00:26:03.279 --> 00:26:07.079
Yeah, well, I think that
the concept of copilot is maybe the big

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00:26:07.119 --> 00:26:12.799
and most important takeaway here, right
Copilot infers exactly what we need to understand,

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and that's that we're not trying to
get the final answer. We're trying

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to get support to get to an
answer that we feel competent with so that

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we can take control and take over
what's next. You know. The question

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00:26:25.240 --> 00:26:26.559
that was posed in the Q and
A, I think is a good one.

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Right Ep writes that given the publicity
of LMS and gen AI, like,

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how does a business not get frustrated
right about the lack of a readiness

359
00:26:36.839 --> 00:26:38.519
inside of the enterprise? And I
think the question really comes down to where

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am I going to think about deterministic
information inside of my business and apply LMS

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to it behind the concept of a
copilot, so that I can apply gen

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AI in a way that's meaningful for
the business, right like you. Just

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00:26:52.960 --> 00:26:55.759
like you said, you apply a
model, you're going to get a result

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that's roughly eighty percentage accurate. And
when you start too in that model in

365
00:27:00.880 --> 00:27:03.799
a way that is built behind the
idea of a copilot, you can actually

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tune it up even better. Let
me give you a really specific example.

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The business that I said and looks
at zero party in first party data like

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00:27:11.039 --> 00:27:17.240
it's really really simple. It's all
deterministic data and the idea of a copilot

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00:27:17.359 --> 00:27:19.920
being able to look at data make
a match of an inbound set of data

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00:27:21.000 --> 00:27:26.000
into a customer profile. An LM
tuned to deterministic data, you can get

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00:27:26.039 --> 00:27:32.000
to a ninety seven percent accurate mapping
in a copilot model every single time.

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00:27:32.599 --> 00:27:36.400
So again, it depends on where
you want to look. So EP's answer

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00:27:37.079 --> 00:27:40.240
to get ready inside of the business, look at deterministic data, look at

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your customer data and zero party first
party data, and think about the copilot

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00:27:44.240 --> 00:27:47.559
metaphor. Because that's really the way
to get to the end result that you

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00:27:47.599 --> 00:27:51.400
care about. Don't expect the right
answer. You know what's interesting here,

377
00:27:51.599 --> 00:27:53.039
I'm going to tie this together.
This might be a bit abstract, but

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00:27:53.480 --> 00:27:59.720
I remember when the mobile movement really
took over, and it forced designers,

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00:28:00.039 --> 00:28:04.119
why designers to rethink their entire layout, where the functionality lives, what you

380
00:28:04.160 --> 00:28:07.880
can get access to from which window, all that kind of stuff, and

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00:28:07.920 --> 00:28:11.480
they it really forced because of the
real estate constraints. I'll throw this maybe

382
00:28:11.519 --> 00:28:15.039
to Yosha first and then to the
other guests. It forced the developers to

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00:28:15.160 --> 00:28:19.440
really think through workflow and what is
necessary at this point in the workflow that

384
00:28:19.559 --> 00:28:22.640
is not necessary at a previous point
or at a following point. Right.

385
00:28:22.960 --> 00:28:27.880
And so this copilot concept I think
can be very compelling in terms of guiding

386
00:28:27.880 --> 00:28:32.240
you down the path of what can
be done. Because if you think about

387
00:28:32.279 --> 00:28:36.319
and I always pick on Adobe Photoshop
just because in like the year, you

388
00:28:36.319 --> 00:28:40.480
know, nineteen ninety nine, you
would have all these menus and all these

389
00:28:40.519 --> 00:28:44.000
options on the menus where at any
point in time they're like eight thousand things

390
00:28:44.000 --> 00:28:47.000
you can do. Well, you
know, no one's just going to figure

391
00:28:47.000 --> 00:28:49.200
that stuff out. Especially photoshop,
you have layers and diffusion layers and all

392
00:28:49.200 --> 00:28:52.079
this kind of stuff going on,
But at any point in a given workflow,

393
00:28:52.119 --> 00:28:56.279
there's only so many things that you
can do. And so in that

394
00:28:56.319 --> 00:29:00.519
sense, I think generative can kind
of guide us through what we're complex workflows

395
00:29:00.519 --> 00:29:04.559
but can now be fairly simple,
while also educating the worker in the process.

396
00:29:04.680 --> 00:29:07.640
I'll turn it up at Bruno first. What do you think about that

397
00:29:07.680 --> 00:29:10.960
idea of Bruno? I think it's
a it's a great idea. It's a

398
00:29:10.960 --> 00:29:14.680
great example as well. I think
if you look at any adoption curve of

399
00:29:14.799 --> 00:29:17.839
any new technology, there's three big
questions we need to answer, right,

400
00:29:18.720 --> 00:29:22.240
what is this? How do I
use it? And so what? And

401
00:29:22.279 --> 00:29:23.920
I think you we've talked a lot
about what is it? Right, and

402
00:29:25.200 --> 00:29:29.839
Zanjie just explained, Look, this
is not magic, it's probability at scale,

403
00:29:29.920 --> 00:29:32.680
So that's what it is. The
question I think a lot of customers

404
00:29:32.680 --> 00:29:36.359
are asking is how do I use
it? And so I think what the

405
00:29:36.400 --> 00:29:38.920
industry needs now is a lot of
simplification, a lot of guidance on how

406
00:29:38.960 --> 00:29:44.000
do I think about using this amazing
technology everybody's talking about. I came up

407
00:29:44.039 --> 00:29:51.240
with this acronym d ACS, which
spells dates I guess, and it talks

408
00:29:51.279 --> 00:29:53.839
about what are the verbs you're looking
for when you're using GENEI. The first

409
00:29:53.880 --> 00:30:00.839
one is draft or document Right,
that's the d is answer or not invent

410
00:30:00.160 --> 00:30:07.240
answer. See it's classified. There's
a great job classifying, adit and summarize.

411
00:30:07.599 --> 00:30:10.240
But you got to think about,
Okay, now that I have these

412
00:30:10.359 --> 00:30:12.720
use cases or these verbs, these
jobs, I'm trying to use this technology

413
00:30:12.759 --> 00:30:17.279
for how do I find example across
marizations. So I'll give it an example

414
00:30:17.279 --> 00:30:21.319
of Nfair. You probably go to
Wayfair to look at a lot of you

415
00:30:21.319 --> 00:30:23.920
know, material you want to use, couches and so forth. They looked

416
00:30:23.960 --> 00:30:27.799
at, okay, how do I
help people that are creating content my website.

417
00:30:29.119 --> 00:30:33.079
That's an example of documentation or maybe
creation of content of a body of

418
00:30:33.559 --> 00:30:37.359
data that already have and accelerate the
productivity of my copywriters in the case of

419
00:30:37.359 --> 00:30:42.480
Wayfair, and they're looking to double
triple the productivity of people creating MIDA data,

420
00:30:42.519 --> 00:30:45.319
if you will, off of data
they might already have. You look

421
00:30:45.319 --> 00:30:49.839
at developers. You know, developers
can benefit from JENA at least in three

422
00:30:49.960 --> 00:30:55.119
different areas. This actually came out
from McKinsey research. One is existing meaning

423
00:30:55.119 --> 00:30:59.039
if you're a coder today, you
probably are going to a lot of manual,

424
00:30:59.400 --> 00:31:03.519
repetitive work to document your code.
Well, McKinnon do that in about

425
00:31:03.559 --> 00:31:07.759
half the time. If you're a
developer and you want to jumpstart, I

426
00:31:07.759 --> 00:31:10.799
think, yesha, I was talking
about this idea of copilot. I like

427
00:31:10.880 --> 00:31:14.400
the idea of companionship. You know, if you're a content creator or even

428
00:31:14.440 --> 00:31:18.799
a coder, often when you get
started, you have a blank canvas,

429
00:31:18.119 --> 00:31:22.920
so you can play within gen AI
and start getting started on how you know,

430
00:31:22.960 --> 00:31:26.680
what should I create and so forth, an accelerate update to existing code

431
00:31:26.720 --> 00:31:30.400
and so forth. In this case, you know what McKinsey were code refactoring,

432
00:31:30.519 --> 00:31:33.279
content refactoring at about two thirds of
the time. And then finally,

433
00:31:33.319 --> 00:31:37.440
I think the third example here we
keep talking about Jeni. You know taking

434
00:31:37.480 --> 00:31:42.799
over the situation is more a human
without that tool versus a human with the

435
00:31:42.839 --> 00:31:47.240
tool. And so an example is, you know, in McKinsey's example,

436
00:31:47.319 --> 00:31:52.880
they found that developers using GENI tools
are twenty five to thirty percent more likely

437
00:31:52.920 --> 00:31:56.599
to complete complex task than people that
don't have it. So you can now

438
00:31:56.680 --> 00:32:00.759
kind of unleash imagination with the assistant
of that theology. Interestingly enough, you

439
00:32:00.799 --> 00:32:06.759
know, we think about these as
this nice element that if Eugenai tool effectively,

440
00:32:06.839 --> 00:32:09.880
it becomes this human companion that you
can kind of run ideas with.

441
00:32:10.480 --> 00:32:15.839
But again, the invention comes from
you. You shouldn't expect them to invent

442
00:32:15.640 --> 00:32:21.319
for you. You iterate with them. I think that's really kind of how

443
00:32:21.640 --> 00:32:24.400
because we confuse what they are and
use them for those verbs I mentioned,

444
00:32:24.440 --> 00:32:29.359
and how you can get value from
them. Yeah, well guardrails, right,

445
00:32:29.359 --> 00:32:30.960
I mean, that's what you're getting
here. And with the copilot stuff,

446
00:32:31.000 --> 00:32:35.079
I mean, and this is not
terribly new, right, Google was

447
00:32:35.119 --> 00:32:37.480
finishing sentences in your emails like about
a year and a half ago, so

448
00:32:37.519 --> 00:32:40.799
that was kind of an early indicator
of what's coming down the pike. Right.

449
00:32:42.160 --> 00:32:45.079
So now I really like on the
copilot thing, and maybe I shall

450
00:32:45.119 --> 00:32:46.920
throw it to you. We've got
to It can help you understand where to

451
00:32:46.960 --> 00:32:50.400
go. It's like having someone at
your side all the time says, now

452
00:32:50.400 --> 00:32:52.000
you're going to turn left. Now
you're gonna want to turn right. Oh,

453
00:32:52.079 --> 00:32:55.160
maybe you should do this, and
you're gonna see these suggestions come out.

454
00:32:55.559 --> 00:33:00.440
But I'll ask you quite eving this
more every day that the manifestation of

455
00:33:00.480 --> 00:33:04.720
AI will be in the form of
suggestions and as long as you accept that's

456
00:33:04.759 --> 00:33:07.960
what they are, and then you
take accountability for the action based upon the

457
00:33:07.039 --> 00:33:12.079
suggestion, then you're fine. Now
I'm the human who is accountable. What

458
00:33:12.119 --> 00:33:15.160
did someone say one of these events
recently, The question was like, oh,

459
00:33:15.200 --> 00:33:17.519
what is responsible AI? And she
says, there's no responsible AI,

460
00:33:19.160 --> 00:33:22.440
and there's not going to be.
There are people. Only people can be

461
00:33:22.480 --> 00:33:25.480
responsible for decisions they take. And
so that's where the accountability comes in.

462
00:33:25.519 --> 00:33:28.599
It's in the first it's not in
the AI. But Yasha, what do

463
00:33:28.680 --> 00:33:30.759
you think? Yeah, but okay, I think you brought up a that

464
00:33:30.920 --> 00:33:36.759
we should call back into when you
talk about the paradigm shift that happened a

465
00:33:36.799 --> 00:33:39.359
popular out to consumers. Right we're
going through that right now. But we

466
00:33:39.400 --> 00:33:45.319
have this really really difficult challenge to
work out of the rapid like mass mass

467
00:33:45.440 --> 00:33:51.680
adoption of jenai that's happening right now
is coming behind one single user metaphor and

468
00:33:51.759 --> 00:33:55.559
it's chat. So like everything that
we're dealing with is behind what we've been

469
00:33:55.599 --> 00:33:59.839
taught over the first of the last
month and a half. We all have

470
00:33:59.880 --> 00:34:02.400
to work our way out of that. And it's like it's trivial that that

471
00:34:02.480 --> 00:34:06.839
sounds amongst the four of us right
now and those that are listening like that

472
00:34:07.079 --> 00:34:09.199
is not trivial, Like we have
all been taught that the way that we

473
00:34:09.239 --> 00:34:14.880
interact with jen Ai is through a
chat interface, and it is not that.

474
00:34:15.000 --> 00:34:16.320
It is everything that you just said, Eric, it's everything the Bruner

475
00:34:16.440 --> 00:34:22.079
just said. It is about providing
access into other information behind these models.

476
00:34:22.440 --> 00:34:25.880
The user metaphor has to change.
But that's going to take a lot of

477
00:34:25.920 --> 00:34:30.480
thought by different enterprises to be able
to provide that kind of coopilot recommendation,

478
00:34:30.880 --> 00:34:35.000
whatever that experience needs to be.
We're just kind of caught in this user

479
00:34:35.039 --> 00:34:38.760
experience trap right now. That's very
interesting, a user experienced trap. I

480
00:34:38.760 --> 00:34:42.360
got to write that down. I
think you're right, and I'll just throw

481
00:34:42.440 --> 00:34:45.519
one other interesting observation out there.
One thing I thought that open Ai did

482
00:34:45.559 --> 00:34:51.360
that was very compelling is the workstation
in which you operate. And I've always

483
00:34:51.360 --> 00:34:53.800
wondered about Google and how they just
don't use the real estate that you have

484
00:34:53.880 --> 00:34:58.719
on that Google search page. And
now they do cool stuff with the visuals

485
00:34:58.800 --> 00:35:02.119
and sharing history of individuals and things
of that nature, and of course you

486
00:35:02.199 --> 00:35:07.039
had I think it was Project Hummingbird
maybe where it makes all these recommendations of

487
00:35:07.039 --> 00:35:09.480
what you think of what it thinks
you want to look for. But nonetheless

488
00:35:09.480 --> 00:35:14.320
it wasn't really a workstation. It
was just a window into the Internet that

489
00:35:14.360 --> 00:35:17.400
has been indexed for me. Whereas
what chat GPT has done in bar and

490
00:35:17.480 --> 00:35:22.159
these others now is create these workstation
environments where you can kind of tell that

491
00:35:22.199 --> 00:35:25.119
you're at work now and you can
kind of piece things together, because that's

492
00:35:25.119 --> 00:35:29.840
what you're supposed to do with this
is use it as your workstation to piece

493
00:35:29.880 --> 00:35:31.480
things together and make sense of your
business. Bruno, what do you think?

494
00:35:32.840 --> 00:35:37.639
You know? I think sometimes the
beauty of the UI is no UI,

495
00:35:37.800 --> 00:35:44.119
right, is to be really invisible
to you. So I personally I

496
00:35:44.519 --> 00:35:46.800
really like the interface of Google Search. Maybe I'm a little biased on that

497
00:35:46.840 --> 00:35:51.199
one, but I do think that
it gets out the way of everything else

498
00:35:51.239 --> 00:35:54.639
that could be busy here. So
I don't know, I think you'll see.

499
00:35:54.639 --> 00:35:58.400
I mean, even Claude, I
think just came out yesterday. So

500
00:35:58.440 --> 00:36:01.159
I think we're still in the experimentation
phase of which is going to look like

501
00:36:01.199 --> 00:36:05.159
a use case that's going to be
the most helpful to me. I think

502
00:36:05.239 --> 00:36:07.719
y Aasha has got a great point. You had and thought about it,

503
00:36:07.760 --> 00:36:10.920
but you're right that we think about
the interface today as being chat whereas we

504
00:36:10.960 --> 00:36:15.159
really should think about it much broader, where it's everywhere and it could now

505
00:36:15.199 --> 00:36:19.880
start pushing decision out to you so
you can react to it rather than have

506
00:36:19.920 --> 00:36:23.119
to ask questions to it. I'll
be curious to see what people think about

507
00:36:23.119 --> 00:36:28.119
it, but I certainly think today
lots of noise, lots of confusion,

508
00:36:28.440 --> 00:36:34.559
potentially a lot of misuse that when
you turn this consumer AI trend into an

509
00:36:34.639 --> 00:36:38.360
enterprise AI trend, you're gonna have
to figure out what the flamewoark case will

510
00:36:38.360 --> 00:36:43.880
adopted it. Yeah, now we're
talking about generative AI and the power of

511
00:36:44.000 --> 00:36:46.920
data in the enterprise and around the
world will be right back. You're listening

512
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Terms and conditions apply equal housing opportunity. Welcome back to Inside Analysis.

564
00:40:55.360 --> 00:41:07.880
Here's your host, Eric Tabanaugh.
All right, let us and gentlemen back

565
00:41:07.000 --> 00:41:10.679
here on Inside Analysis. We're talking
to a whole bunch of experts here.

566
00:41:10.679 --> 00:41:15.119
We've got Yasha from Lytos Bruno from
Capital G which is part of the Alphabet

567
00:41:15.199 --> 00:41:19.320
Group. But of course Sangmo and
Yasha. You had a really good point

568
00:41:19.320 --> 00:41:24.119
in the break there around sovereignty data
sovereignty. Obviously the Europeans are big on

569
00:41:24.159 --> 00:41:27.960
this stuff. You have to be
careful about where the data is persisted,

570
00:41:27.960 --> 00:41:30.599
where it's processed, what your intentions
were. We're starting to see more of

571
00:41:30.599 --> 00:41:34.960
that in the US, certainly in
California with's CCPA. And you said there

572
00:41:35.039 --> 00:41:38.239
is one cloud vendor that is promised
it is not going to be naughty with

573
00:41:38.280 --> 00:41:40.920
your data. Tell us about that. Yeah, I mean, but let's

574
00:41:40.920 --> 00:41:45.920
even talk about at one level higher
up, like everything that we think about

575
00:41:45.960 --> 00:41:50.880
in the enterprise, we have to
think about behind our data strategy and our

576
00:41:50.920 --> 00:41:54.159
data strategy has to be cognizant of
all of the complexities of consumer privacy laws.

577
00:41:54.440 --> 00:41:58.599
You mentioned CCP. We have like
six new states in the US that

578
00:41:58.639 --> 00:42:04.239
now have their own privacy a lot
like there are. There is no slowing

579
00:42:04.280 --> 00:42:07.840
down of consumer data protection, no
slowing down. So when we think about

580
00:42:07.840 --> 00:42:12.880
the application of gen AI, like
every single person watching and listening like you

581
00:42:12.920 --> 00:42:15.320
have to be thinking about how lms
are going to have a relationship into consumer

582
00:42:15.400 --> 00:42:21.679
data privacy period like period right now, there is only one cloud provider that

583
00:42:21.760 --> 00:42:24.519
will kind of back up the fact
that when you are utilizing their models and

584
00:42:24.679 --> 00:42:29.400
their model garden, the data that
you input in the corpuses that you train

585
00:42:29.440 --> 00:42:31.280
on aren't going to get reshared.
And that's Google, It's protects Ai.

586
00:42:31.760 --> 00:42:36.239
There are lots of announcements that have
just happened, and I suspect that over

587
00:42:36.280 --> 00:42:39.199
time others are going to follow because
the acknowledgement of the importance is there.

588
00:42:39.480 --> 00:42:45.280
Right enterprises have to operate with confidence
that they can work within the data sovereignty

589
00:42:45.440 --> 00:42:47.639
rules that are required. But as
Google right now, So if you're going

590
00:42:47.679 --> 00:42:51.440
to go out and experiment right now
and you want to have confidence, protects

591
00:42:51.480 --> 00:42:53.800
AI is really the place to go. M that's a good point. And

592
00:42:54.039 --> 00:42:59.000
you do want to have confidence.
Yeah, this is concept of what psychological

593
00:42:59.039 --> 00:43:01.920
security that they talk about some of
these events these days. And the idea

594
00:43:01.960 --> 00:43:07.119
is that you want to empower your
people with their creativity and their confidence.

595
00:43:07.119 --> 00:43:10.480
You want them to be confident enough
to take chances to look for things.

596
00:43:10.920 --> 00:43:14.280
You know, as I'm sitting here
listening to these experts, I'm wondering to

597
00:43:14.320 --> 00:43:17.920
myself and you maybe throw it over
you first and then Bruno. This whole

598
00:43:19.039 --> 00:43:23.800
concept around the hidden context in your
data, the hidden meaning in your data.

599
00:43:24.000 --> 00:43:29.639
These language models, these foundational models
can be extremely powerful once you trusted

600
00:43:29.719 --> 00:43:32.159
to put some of your corporate data
in there to just look around and find

601
00:43:32.360 --> 00:43:37.840
stuff you think about discoverability. To
me, that is the most important part

602
00:43:37.119 --> 00:43:42.760
of analysis as being able to discover
what's happening. So companies like thought spot

603
00:43:42.840 --> 00:43:45.679
for example, have a great UI
that a great concept. You start throwing

604
00:43:45.760 --> 00:43:49.960
natural language query, I think all
of that is going to open up to

605
00:43:50.119 --> 00:43:54.480
tremendous value because of these foundational models, because you'll be able to ask much

606
00:43:54.519 --> 00:44:00.760
more complex questions in just natural language
of your existing documentation and then be able

607
00:44:00.800 --> 00:44:06.719
to kind of guide the conversation to
understand how this fits into certain corporate regulations

608
00:44:06.800 --> 00:44:10.719
or policies or directives. For example, you think about the marketing world where

609
00:44:10.719 --> 00:44:15.880
you have these themes that you want
to deploy, So you could explore your

610
00:44:15.960 --> 00:44:19.760
data and look for where these themes
map with something you already have. My

611
00:44:19.880 --> 00:44:22.880
point is we have this very powerful
mirror now that we can point at our

612
00:44:23.000 --> 00:44:27.880
data to export in whole new ways
that I think is going to be really

613
00:44:27.920 --> 00:44:32.920
compelling. Sanchi, what do you
think now you're mute? If I may

614
00:44:34.000 --> 00:44:37.760
give an example, a real life
example, So every organization has to be

615
00:44:38.039 --> 00:44:45.159
with this problem that the customer man
will renew. So today what we do

616
00:44:45.320 --> 00:44:49.960
is we create these machine learning models
to determine customers chilling. For example.

617
00:44:50.400 --> 00:44:57.239
The problem is that the machine learning
model can detect and predict which customers are

618
00:44:57.400 --> 00:45:01.000
at the risk of not renewing.
But then what you know, we've all

619
00:45:01.000 --> 00:45:05.840
these strategies we try. We kind
of like throwing stuff on the wall and

620
00:45:05.880 --> 00:45:09.679
see if it sticks or not.
So there is a huge potential where lms

621
00:45:09.760 --> 00:45:15.360
can now come to our rescue.
And the way it works is a combination

622
00:45:15.599 --> 00:45:22.480
of predictive AI and generative AI.
So so it's a two step processes.

623
00:45:22.679 --> 00:45:29.000
The first step is is because I
remember, LM has got no clue about

624
00:45:29.239 --> 00:45:32.440
sorry I was going to use a
bad word, has no clue about who

625
00:45:32.480 --> 00:45:37.239
my customers are. So I ask
a query to my database and I say,

626
00:45:37.639 --> 00:45:43.679
okay, predict tell me which customers
are at the risk of our journey.

627
00:45:44.280 --> 00:45:50.239
Once it retreats that information, I
can now put that as a prompt

628
00:45:50.039 --> 00:45:55.719
into an LM and I guess everything
that you know on this data, give

629
00:45:55.800 --> 00:46:01.480
me some strategies of how I should
go about And this is hugely power strained

630
00:46:01.559 --> 00:46:07.239
to do this kind of work.
It can come up with some amazing recommendations

631
00:46:07.280 --> 00:46:13.000
that are even beyond a human beings
capability, at least in a given amount

632
00:46:13.039 --> 00:46:15.719
of time. So you can instantly
get that. But this, by the

633
00:46:15.760 --> 00:46:20.599
way, what I just explain is
what is going around in the market.

634
00:46:20.679 --> 00:46:27.719
We call it rag RAG, which
is retrieval augmented generation. So this is

635
00:46:27.760 --> 00:46:34.280
a great use case of an ELM
working on my because you have to know

636
00:46:34.400 --> 00:46:38.039
that these elms were the big ones
were trained like open air once in September

637
00:46:38.039 --> 00:46:43.039
of twenty twenty one. They don't
even know anything that's happened in this year,

638
00:46:43.519 --> 00:46:45.599
So how are they going to help
an enterprise if they're so out of

639
00:46:45.679 --> 00:46:52.400
date. So that's how these lms
are now being used. Yeah, that's

640
00:46:52.440 --> 00:46:53.840
fascinating pronoun. You want to comment
on that, Well, I'm going to

641
00:46:53.880 --> 00:46:59.119
see if I can alcronym sound.
Gee. I love that rag. That's

642
00:46:59.159 --> 00:47:04.639
incredible. There's three things I would
say. The first thing is the state

643
00:47:04.679 --> 00:47:07.440
of readiness today. As looking at
the twenty twenty three I Readiness Report,

644
00:47:08.039 --> 00:47:13.480
twenty one percent of organizations stay there
in production. Nineteen percent of them have

645
00:47:13.599 --> 00:47:15.760
no plans. So you can see
in between there's a lot of people that

646
00:47:15.800 --> 00:47:20.360
are experimenting, planning, trying to
figure it out. And I think a

647
00:47:20.400 --> 00:47:22.440
lot of it is related to some
of the issues we talked about, obviously

648
00:47:22.559 --> 00:47:25.599
quality, the input of the data. But a big one that you just

649
00:47:25.599 --> 00:47:29.400
talked about, Eric, which is
really interesting, is the refinement of the

650
00:47:29.480 --> 00:47:32.440
understanding of the question. You refer
to. Thoughts product company I'm very familiar

651
00:47:32.519 --> 00:47:37.199
with, came out of this product
called Sage, where the benefit of it

652
00:47:37.280 --> 00:47:42.239
is understanding the question itself. Now, what is the real application of that.

653
00:47:42.599 --> 00:47:46.199
You take an organization like seventy five
percent of people that are ordering from

654
00:47:46.199 --> 00:47:50.599
when he's they do it through their
drive through, and so that happens is

655
00:47:50.719 --> 00:47:57.199
Wendy's as this amazing menu and there's
billions of possibilities that you can order.

656
00:47:57.480 --> 00:48:01.519
Everything is customizable, and so having
a model that is able to understand and

657
00:48:01.639 --> 00:48:07.239
the refinement behind the question is what's
going to make the answer really interesting.

658
00:48:07.639 --> 00:48:10.440
You know, talking about acronym,
I was trying to think about what would

659
00:48:10.480 --> 00:48:15.079
be the attributes in the ideal world
of the best in AI type of application.

660
00:48:15.360 --> 00:48:19.760
And it's not as great as as
an acronym as Sanji did, but

661
00:48:19.840 --> 00:48:23.000
let me try it on you.
I called MTKAC, MTKC, so M

662
00:48:23.079 --> 00:48:28.920
and T. It has to be
multimodal, multimodal and input and output photos,

663
00:48:28.960 --> 00:48:30.679
text, audio. It's got to
be able to handle that because that's

664
00:48:30.679 --> 00:48:36.920
how we live. T Trustable why
and we talked about the multiple dimensions.

665
00:48:37.280 --> 00:48:42.079
Obviously there's bias, there's ability to
trust the output. Trustable is a key

666
00:48:42.199 --> 00:48:45.800
notion that you know, we everything
we do in data the foundation is trust.

667
00:48:46.280 --> 00:48:49.719
See is what Sanji was talking about. You know, if you give

668
00:48:49.719 --> 00:48:52.800
me insights from five years ago,
not really helpful, right, So I

669
00:48:52.880 --> 00:48:57.079
need currency on the data. I
need you know that your work of data

670
00:48:57.159 --> 00:49:00.880
that's recent enough a is for applied
to a workflow. I think that's what

671
00:49:00.960 --> 00:49:05.239
yah I was saying earlier today,
is that it's got to be applied specifically

672
00:49:05.280 --> 00:49:07.599
to my workflow. And see it's
contextual. You know how many of these

673
00:49:07.639 --> 00:49:12.639
experiences we've had actually even with other
humans, where we have a conversation and

674
00:49:12.760 --> 00:49:16.280
we lost the flow of that conversation. So contextual is a big value if

675
00:49:16.400 --> 00:49:20.920
I think, if you hit all
these dimensions, you end up having the

676
00:49:21.039 --> 00:49:24.280
real capabilities that you need to take
your company to the next kind of generational

677
00:49:24.400 --> 00:49:30.400
change here, but we're not there
yet. We're at the beginning. Yeah,

678
00:49:30.599 --> 00:49:34.719
and I am looking forward to this
the single instance future where you can

679
00:49:34.760 --> 00:49:38.119
point your corporate data at it,
you trust this engine. Once you can

680
00:49:38.199 --> 00:49:43.440
do that, you can learn tremendous
things about what's going on in your organization.

681
00:49:43.440 --> 00:49:45.440
I mean you'll learn things that you
do never even knew existed. There

682
00:49:45.440 --> 00:49:50.679
are workflows that you didn't even know
about that are getting the job done.

683
00:49:50.679 --> 00:49:52.800
One of my favorite analysts friends from
like twenty years ago, said something I

684
00:49:52.800 --> 00:49:57.360
thought that was funny. He goes, if there's something in a large organization

685
00:49:57.440 --> 00:50:00.880
that must happen. It is happening, you just don't know how it's happening.

686
00:50:00.960 --> 00:50:04.199
You have to find out how it's
happening. And we're going to be

687
00:50:04.280 --> 00:50:06.719
able to kind of see. And
it's really this nexus of things, this

688
00:50:06.920 --> 00:50:12.039
nexus of analytics, of good quality
data, of foundational models, and of

689
00:50:12.119 --> 00:50:15.480
things like process mining. To me, that is the real magic is to

690
00:50:15.480 --> 00:50:20.679
be able to understand what your processes
really are, how long they really take.

691
00:50:20.880 --> 00:50:23.599
Because I think we're going to see
a whole lot of optimization on actual

692
00:50:24.199 --> 00:50:29.719
run time workflow with things. I
think that's the big benefit that's going to

693
00:50:29.800 --> 00:50:31.599
come in the next couple of years, is this all sort of comes together,

694
00:50:31.800 --> 00:50:37.159
is we're going to figure out there
are whole masses of work that we're

695
00:50:37.199 --> 00:50:40.440
doing that are just not necessary and
shouldn't be done and we shouldn't be wasting

696
00:50:40.480 --> 00:50:43.480
time on things like that. I
think that's going to be one of the

697
00:50:43.519 --> 00:50:47.000
hallmarks out of this whole pandemic era
is we all figured out quickly there are

698
00:50:47.039 --> 00:50:50.800
things that have to get done and
there are things that do not have to

699
00:50:50.800 --> 00:50:53.320
get done. And when you're trying
to get the business afloat or trying to

700
00:50:53.599 --> 00:50:58.199
stay afloat during difficult times. You
do not want to be wasting time at

701
00:50:58.239 --> 00:51:00.920
all. So you want to be
leveraging every tool in your toolbox. You

702
00:51:00.920 --> 00:51:04.840
want to be leveraging every piece of
data that you have. And I think

703
00:51:04.840 --> 00:51:07.840
that is going to be the hallmark
of the future is being able to piece

704
00:51:07.960 --> 00:51:10.800
all of this together. And that's
what these foundational models are good at,

705
00:51:10.880 --> 00:51:15.519
right, They're good at fusing vectors
of information to create something new. And

706
00:51:15.559 --> 00:51:20.679
I think we're going to see an
interesting array of solutions come out from all

707
00:51:20.760 --> 00:51:32.559
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733
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Yesterday and back in time. We
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734
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Because of the success of ABCTVS Bewitched
this past season, TV's copycats are

735
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concocting at least three new series with
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736
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mind is from the same folks who
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737
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present Eye Dream of Genie this fall, and from this time in nineteen seventy

738
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two, looks like CBSTV is going
to put the Waltons on the fall schedule.

739
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It's about the life of a rural
family in the South, set about

740
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nineteen twenty nine, and from this
time in nineteen eighty three, Sesame Street

741
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releases a rock album titled Born to
Add The Jacket is a takeoff of Bruce

742
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two ninety three CF Burrito Valley,
NBC News Radio. I'm Chris Garagio.

797
00:59:28.679 --> 00:59:31.960
The massive heat wave continues across the
western US. The National Weather Service as

798
00:59:32.000 --> 00:59:37.880
Phoenix reached one hundred and eighteen degrees
Saturday, shattering the previous daily high record

799
00:59:37.159 --> 00:59:40.719
of one sixteen set back in two
thousand and six. It was also the

800
00:59:40.880 --> 00:59:45.360
twenty third consecutive day of temperatures above
one ten in Phoenix. A record breaking

801
00:59:45.400 --> 00:59:50.679
temperature of one oh six was reached
in Salt Lake City yesterday. Excessive heat

802
00:59:50.719 --> 00:59:54.000
warnings are being extended across the Southwest
at least through midweek. At least six

803
00:59:54.079 --> 00:59:58.880
people are dead following a series of
weekend shootings in the Chicago area. The

804
00:59:59.000 --> 01:00:01.800
violence began Fry, when a man
was fatally shot during an armed robbery and

805
01:00:01.920 --> 01:00:06.519
found inside a vehicle in an alley. Since then, more than twenty others

806
01:00:06.599 --> 01:00:08.400
were injured in the shootings, with
the victims ranging from the