WEBVTT

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At the moment, the information economy
as a rod. The world is teeming

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with innovation as new business models reinvent
everyvery industry. Inside Analysis is your source

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of information and insights about how to
make the most of this exciting new era.

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Learn more and inside analysis dot Comside
Analysis dot com. And now here's

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your host through Eric Kavanaugh. Yes, indeed, folks, welcome to the

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future. To the future, right. So here we are in two thousand

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and twenty three, hurtling forward.
I think the quickening has already happened because

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things are going really, really fast
these days. What do they say,

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The days are long, but the
weeks are short, and we're already knocking

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on the door of halfway through twenty
twenty three. I'm not sure I believe

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it, but here we are,
and folks, we have an all star

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cast for you today thanks to our
good friend David Sweener, who decided to

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rally some troops and get a whole
group of evangelists together. So we've got

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David Sweener, Sean Rogers, and
Nick Jewell an all star cast at analytics

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and AI experts. We're going to
talk about the business value, defining the

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business value of analytics and AI.
Well, it's a much different story now

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than it was five and in ten
years ago. Pretty much everyone understands in

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the business world that analytics is really
important. How you get there, what

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you do with it, how quickly
you leverage the power of analysis, that's

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a different story. A lot of
companies are doing real time optimizations now using

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analytics and AI. We're going to
talk about artificial intelligence as well, which

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is an interesting sidetrack. In many
cases, it tries to do the same

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things that analytics does, and ideally
you want to kind of blend these two

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worlds and get the value of AI
for pattern recognition, pattern matching, for

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understanding what's going on in complex scenarios, and of course analytics run into numbers

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under tending why that's happening. Really, if you get these two together,

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you can get a really clear picture
of what's going on in your business,

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and that's going to help you regardless. So it's it's very good to have

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these technologies. Exactly which ones you
choose will depend upon your budget, of

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course, and your use case.
But these folks know a lot about all

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that stuff. So David Swayner,
since it was your idea, I'll hand

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it off to you to kick us
off here, what do you think the

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business value is of analytics and AI
and what do you do to evangelize all

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that? Well, Eric, hey, thank you for having us on.

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Totally appreciate it. And you know, I thought of this topic. You

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know, a lot of the clients
and prospects we talked to organizations have made

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a significant investment in data, and
so we've we've done a great job of

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ingesting, collecting, and storing data. And you spent you know, millions

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and millions of dollars on these things. You know companies have, but when

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you get to the analytics side of
it, it doesn't have the rigor The

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number one question that I get is
how do we measure that value? How

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do we measure the ROI most of
the clients and customers that I speak with

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anyways, they don't know how to
articulate that beyond personal productivity, so we

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saved some time. They don't know
how to tie it back to specific business

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KPIs, and so you know,
I thought it would be great to have

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Nick Jewel and Sean Rogers here and
to discuss this topic. It's a it's

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a it's at the top of minds
for all the clients I speak with.

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Yeah, and Nick Jewel, I
think we'll bring you in next. You've

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been doing this for a long time, and you have your own company as

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well. And we should point out
that David has a whole book series,

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Tiny Guides. I think he calls
them. We'll talk about that in a

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minute too, But do you also
have your own enterprise where you're trying to

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educate people about the value of analytics? Right? Go ahead, and Nick,

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Yeah, absolutely, thanks, y, thanks for having me on the

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show. Yeah, I get to
moonlight as the co founder of a really

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cool little analytics upskilling startup that we
call Data Curious dot AI. We would

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teaching thousands of students across the world
the benefits of analytics, automation and really

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data literacy to reach their goals.
And we'll dive into this more, I'm

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sure during the conversation today. I
just wanted to throw out there to start

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with, one of my highlights of
my career was getting to interview Billy Bean,

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who was the general manager over at
the Oakland A's, the baseball team

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made famous by not being Brad Pitt
obviously in the movie money Ball, and

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the idea of value in that film
was all about trying to find cheap baseball

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players that could get on base really
easily. If you got on base,

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you scored runs, you had a
good chance to win the game. So

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I think when we talk about finding
value in analytics and AI, we can

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turn back to those ideas of big
data maybe from a decade ago, the

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big data vs. You're not going
to find value in volume anymore. I

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think the cloud service providers have probably
got that one sewn up. You're probably

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not going to find it in variety
necessarily anymore. A lot of work that

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I've done over the last couple of
years with one of the more niche players

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in the analytic space, a company
called in Quarter, is really starting to

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build out the concept of a data
lakehouse that gives us so much more flexibility

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than maybe we saw from traditional data
warehouses. But later on in the conversation,

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I'd love to dive into the topic
of that other V, the idea

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of velocity. I think there's a
lot of room for differentiation in that area.

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Velocity, volume, variety. These
are the three vs that they talked

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about, and voracity is a fourth
V that came into the equation that's not

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something that chat GPT is too good
at Sean Rogers will bring you in on

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that. It's good a lot of
things, but the voracity, yeah,

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not so much. I wanted to
be the first person to say Jack chat

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GPT. I thought there was a
prize involved, you know. I think

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right now we're at r at an
interesting inflection point. Right now it feels

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to me like it did when the
Internet arrived for the public, for the

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consumers, for mass adoption, and
suddenly things like or of AI are in

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the public eye. And was I
mentioned in the pre call. My family

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had a wedding this weekend. I
had two people asked me about chat GBT.

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They were not the normal persona I
expected to hear from on that topic.

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And it's always interesting to see what
we all know collectively as not a

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brand new technology. AI has been
around for decades, but it is really,

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you know, gaining some interesting traction. The public stuff is great.

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I think the more interesting things are
happening in the enterprise. And I agree

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with you know what Nick just said
a moment ago about velocity. How fasking

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you make a decision? How accurate
is the decision you're making? Can you

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automate that decision, and then there's
architectural challenges. Can you move that decision

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outside of your IT data center,
and can you get it on a device

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to make a decision, can you
get it out to the edge of your

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business world, and so on.
So there's a countless topics for us to

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discuss here, but it is kind
of fun and it has that feeling that

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definitely reminds me of all of the
disruption when the world discovered the Internet,

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which had been around for a while
as we all know, but it was

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this brand new thing in the nineties
where everybody thought this was this classic,

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brand new toy. I just feel
like I see the same thing with AI

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and especially large language models and generative
and so on. Some Yeah, it

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will be a fun topic to explore. Yeah, and the costs have really

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come down now. That's partially because
of open source technology. It's partially because

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of the cloud. It's partially because
of a company called Amazon that really baked

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into their corporate DNA this trajectory of
bringing costs down over time that was never

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a thing before that, I recall. I mean people would lower the costs

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of things as commodities became more prevalent, but the fact that they really baked

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that into their approach and their mission. I'll throw out to David between a

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first, I think that's one of
the reasons why costs have really come down

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and the cloud has become such a
prevalent force in our lives these days.

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What do you think, David,
Yeah, you know, I think that

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costs um you know pieces is very
interesting to me. And you know,

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to mention something that you know,
Sean had said, you know this this

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this waste that's in the analytics process, and I think this is where companies

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can tie this back to our I
if I look at an analytics process that

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starts you have some sort of business
event, you need to prepare the data

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to get analytics ready, you have
to complete the analysis, you need to

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make a business decision, and then
you need to take business action. And

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if you look across that spectrum,
there's data latency, there's analytics latency,

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there's decision latency, and there's action
latency. I think these things, you

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know that if you can compress this
cycle, that's where companies are going to

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get that that value. So that's
that I think there's there's a lot to

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be learned that if you just take
sort of a process approach to this.

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It's not just doing analytics for the
sake of analytics. You have to take

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that business event and tie it to
business action. No, I agree with

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that, And you know, we'd
talk about big events. We've had lots

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of disruptions in the last few years. We had. It really started in

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the previous administration here in the States
with tariffs on China, which no one

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had ever done before like that,
and that threw the supply chains off,

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and then of course COVID comes around
as a huge disruption, and that really

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made us focus on, I think
the power of analytics, because we also

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had to really focus on workflows and
processes and how to get to data and

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how to do things. And a
lot of people figured out, hey,

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man, we have to change how
we're doing things. We have to take

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sort of an API first strategy maybe
whether it's security or just network connectivity or

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remote working. All that stuff kind
of just through the apple cart sideways.

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And I think we learned a lot
from that, Nick Jewel, What do

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you think, Well, absolutely,
I mean I'm not sure how it affected

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you folks over in the US,
but that big tanker that bloked up the

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sewers canal made everybody panic over here, certainly in Europe because the supply chains

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broke. We realized we didn't have
that resilience across the board. So putting

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in data programs, analytics programs,
getting to AI programs so that you could

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start to suggest alternate roots, alternate
suppliers, ask more of those what if

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questions, more sophisticated analytics became priority
number one. So I think over the

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last few years, obviously the large
shock of COVID has made data and analytics

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forefront in the minds of the public, the media, governments around the world.

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But I think that real time element, when things like supply chains get

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threatened, is such an opportunity for
more advanced analytics to enter our lives in

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a beneficial way. Yeah, and
I think the AI really comes in machine

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only comes in handy with things like
supply chain because a traditional relational database you

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can get some analysis of product amounts
and locations and things, but you really

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kind of need more of a graph
technology and you need some AI to be

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able to run all too different simulations. Right, what do we do this?

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What if we do that? What
are the costs going to be?

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You know, in the old days
twenty years ago, you would just kind

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of think about it and take your
best shot. I mean, you might

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have some decent software you're using.
SaaS, of course, has been doing

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cool stuff for a long time.
Sas out of a Kerry, North Carolina.

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But Sean Rogers the throat over to
you. I think what Nick is

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talking about, these disruptions really led
to a keen focus on different ways of

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solving problems, and I think that
led to a boom and AI machine learning.

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What do you think, Well,
I think it was certainly a cause

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of it. But we've seen this
a couple of times when these big disruptions

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come along. For those we've all
been in this business a long time.

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The financial dip that we saw in
a seven OA disrupted data change data for

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companies, and then of course the
chain reaction on up changed the analytics,

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the big ones that we're talking about
now. I have asked numerous customers,

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you know, especially in the executive
ranks, how dramatically did your data change

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in March of twenty twenty And you
don't get a clear answer because everyone was

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so busy trying to fight the higher
level issues of how do we shift our

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marketing messages, what words don't we
use? Do we want to invest here

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or there? A lot of companies
didn't notice how dramatically things were evolving and

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changing quickly, and supply chains a
great example. E commerce is another one.

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The big companies that quote unquote did
so well during COVID were not as

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prepared as they probably should have been. You guys remember early days, Zoom

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struggled as they were growing. There
was growing pains with other companies that were

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filling these gaps, and so understanding
the dynamics of your data and how you're

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applying AI to it. Models that
worked on March first did not work on

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March thirty first. They were they
were not performing at the level they needed

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to. And so that takes you
to some of these other big topics that

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I'm hoping we'll touch on, like
model ops and data ops and how do

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you bring it up to that next
level and provide the scale that's required but

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also the operational scale that's required so
that you can be nimble and so on.

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So yeah, I you know,
these big disruptors even you know,

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natural things, right, natural catastrophes
and so on, shift data all over

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the place. And then affect models
and affect the interaction of those models.

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So yeah, and we really will. We'll talk about automation too, And

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you know, automation is something that
is always helpful if you do it correctly.

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You have to be careful about what
you automate because you can automate a

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crap process and then get a really
crappy process and get very unhappy customers.

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I mean, I've seen this stuff
happen. And marketing automation is hard.

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I was talking to a guy who's
been in the industry for a long time

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and he told me, because we
were talking about HubSpot, I think and

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I just said, from my experience, it is really hard to map out

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multi touch strategies using even something like
HubSpot because of so many exceptions. There

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are so many is where someone's going
to go outside that perceived decision tree and

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the automation is going to fail when
that happens. So you have to think

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through all these little bits and pieces
and it's hard. And he said,

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yeah, it's taking us about a
year. So you've got a team together,

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it takes a year. I mean
las days like takes a year.

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What are you building a data warehouse
there? That told me don't long used

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to take to build data warehouses.
But the point is that it's hard,

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but you still have to do it. I mean, automation is going to

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be a crucial success factor, whether
that's for data ing, data cleansing,

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data provisioning, or whatever, all
along that value chain. You want to

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be automating whatever you can, right
David, Yeah, absolutely, I mean

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I think that's the power. You
know, I have this you know theory

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on AI. It's you know,
data plus analytics plus automation. That's my

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sort of basic definition. Now I
think, you know, people will certainly

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could argue with that, but I
think that's what it boils down to.

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And you know, so automating,
to your point, automating a crappy process.

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You know, you can just bad
decisions faster. So that's that's not

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the right way. I think people
forget to do is reimagine their process.

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Is there a better way to do
something? I think where most organizations fall

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down is, hey, we got
a prediction. I can predict something a

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week, a day, a minute, you know, whatever you know into

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the future. But their business isn't
agile enough to act upon that insight,

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or that they can't they can make
a decision. But they can't. The

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systems aren't just the fragile and this
ability to action as result of a prediction,

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that's where I think a lot of
companies fall down. They don't they

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can't act upon it because their business
is relying on things that their manual processes.

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They're not automated, and companies struggle
there. Yeah, well, and

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there's a speed to insight and a
speed to action that matters. Think call

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centers, think point of purchase,
things of this nature. The companies and

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00:15:50.080 --> 00:15:52.639
like some of the telcods were good
about this early in the game, delivering

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a customer value score to the person
on the front on their screen so they

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can see, Hey, waste your
time with this person. He's always in

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the bills the main sure next week, don't worry about that person so much.

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Were as a high value customer.
But you have to get that insight

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to the person right at the time
when they're on the phone. So that

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takes a lot of engineering to make
sure you get it right. So you've

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got to do the analytics, you've
got to do the automation, you've got

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to do the engineering. All these
things have to come together for it to

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work. Just right, correct,
Nick Jewel, Well, absolutely, And

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I think this is where I have
a little bit of a personal be in

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00:16:25.879 --> 00:16:29.919
my bonnet around some of the concepts
in the modern data stack, where you

240
00:16:29.960 --> 00:16:33.960
know, we're entering data into these
large, often legacy systems like ERP systems,

241
00:16:34.000 --> 00:16:38.440
big CRM systems, and the whole
infrastructure, the whole machinery that's in

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00:16:38.480 --> 00:16:41.879
place to move that data, to
ingest it, to clean it, to

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00:16:41.960 --> 00:16:45.279
prepare it, and then to get
it into a model so that somebody sitting

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00:16:45.279 --> 00:16:48.799
there with a customer can make the
right decision. That takes too long.

245
00:16:48.840 --> 00:16:52.799
There's too many touches. If it
was a kitchen, you'd have had fifty

246
00:16:52.799 --> 00:16:55.960
fingers on your food at this point, and you just can't get to that

247
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data fast enough. So I think
there's a bit of a myth that's something

248
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like a modern to stack solution solves
every problem. We really need to be

249
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very careful about how we do transformation
of data as we lift it from these

250
00:17:07.680 --> 00:17:11.279
sool systems and get it into the
systems of insights. And I think there's

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a lot of work we can still
do to improve that process. Yeah,

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and that's a that's a really good
point. One of the analysts I've had

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on the show many times, Rick
Sherman, he jokes he doesn't like the

254
00:17:21.079 --> 00:17:23.160
modern data stack. He says,
usually it's glued together with a couple of

255
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lines of code somewhere, and it's
pretty darned fragile, and fragility is not

256
00:17:27.720 --> 00:17:33.519
what you want in a data world
where you're trying to maintain the tension,

257
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if you will, from source to
target. You don't want to have to

258
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be jumping two, three, four
or five different systems because guess what,

259
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any one of those systems going slowly
breaks the whole chain right, and chain

260
00:17:44.920 --> 00:17:47.680
is only as strong as its weakest
link, and that I think is the

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achilles heel of the modern data stack
as we see it today. They're going

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00:17:51.759 --> 00:17:55.160
to be taking that stuff up.
I'm sure over time, but folks don't

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touch out that we're talking to three
experts. I can't believe the second one

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eight hundred two eight nine O four
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O four one three. That's eight
hundred two eight nine. Welcome back to

315
00:22:07.319 --> 00:22:15.680
Inside Analysis. Here's your host,
Eric Tabanaugh. Yes, in take us

316
00:22:15.680 --> 00:22:19.119
in the futures where chattt doesn't hallucinate. I love that they came up with

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00:22:19.119 --> 00:22:22.680
that term hallucination. That's pretty funny. It will make stuff up, folks.

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00:22:22.839 --> 00:22:27.400
And basically chat GPT is large language
model. It's a predictive engine.

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00:22:27.440 --> 00:22:32.039
It's designed to predict the words that
it thinks you want to hear based upon

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00:22:32.079 --> 00:22:34.559
the prompt you give it. But
I wanted to get back to this whole

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00:22:34.599 --> 00:22:38.319
concept of speed and what has to
be real time and what doesn't have to

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00:22:38.319 --> 00:22:41.480
be real time And someone who knows
a lot about that is Sean Rogers.

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00:22:41.480 --> 00:22:47.480
So Sean from Bark these days Bark
Research tell us about this collision of value.

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00:22:48.400 --> 00:22:51.119
Well, you know, it's this
idea of you know, not everything

325
00:22:51.160 --> 00:22:55.559
has to be real time or right
time. I think all too often we

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00:22:55.599 --> 00:22:59.160
get distracted by our ability to do
cool things, and then we think that

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it has to be applied everything we're
doing it. And I think, like

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00:23:02.599 --> 00:23:07.640
the big data phenomenon kind of taught
us that we started off with everybody has

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to have My hadup has to be
bigger than you're a hadup. And I

330
00:23:10.680 --> 00:23:15.240
would like a purple hadup because my
competitors have a blue one, and they

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00:23:15.279 --> 00:23:18.039
tried to do everything in it,
and then they quickly found out that it's

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00:23:18.039 --> 00:23:22.359
applicable to certain workloads. And I
think the same thing happens here around the

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00:23:22.440 --> 00:23:26.920
value of AI and the value of
automation. You've got to apply it to

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00:23:27.000 --> 00:23:32.480
the right circumstance, to the correct
workloads. A friend of mine, doctor

335
00:23:32.559 --> 00:23:36.559
Richard Hackthorne, has published an awful
lot of work on this for many years,

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00:23:36.599 --> 00:23:41.400
about the intersection of where the value
is and the timing of a decision.

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That if the timing is too long, you miss that value into and

338
00:23:45.400 --> 00:23:48.039
you give a great example of that
a minute ago, call center interactivity and

339
00:23:48.119 --> 00:23:52.960
thing of that, things of that
nature. It's all about bringing the decision

340
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and the time it takes to make
that decision to the highest value point.

341
00:23:57.160 --> 00:24:03.519
And it's not for everything you do
down there's new strategies moving your analytics to

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00:24:03.599 --> 00:24:10.319
the data instead of the data to
the analytics. Those types of finesse sophistication

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00:24:10.480 --> 00:24:15.799
strategies are allowing companies to meet that
mark, and it's especially important around you

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00:24:15.839 --> 00:24:18.319
know, like modern data stacks that
just you know, we've a greater They

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00:24:18.319 --> 00:24:22.279
can be kind of fragile, so
you can't try to do it for everything.

346
00:24:22.359 --> 00:24:25.759
Do it for the right stuff,
and then make sure you're hitting that

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00:24:25.839 --> 00:24:29.759
target of I'm in value. Yeah, well that's a great point, right,

348
00:24:29.799 --> 00:24:32.839
make sure you're hitting the target.
You always want to be focused on

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the business value and the use cases
really matter, and there are lots of

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use cases for AI these days.
You know, healthcare is a really big

351
00:24:40.480 --> 00:24:45.720
one, and population health for example, can be really aided by being able

352
00:24:45.759 --> 00:24:52.160
to analyze data at scale. I'm
actually talking to the chief analytics officer from

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CBS. She's going to speak at
an event with me for Reuters in November,

354
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and she was talking about how they're
using AI and analytics to be to

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do sentiment analysis across thousands and tens
of thousands and hundreds of thousands of comments

356
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on YouTube and Instagram and Twitter and
places where people do throw other opinions into

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the pool. Well, I mean
they're fun to read sometimes, but to

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get meaningful value out of them,
that takes some pretty serious text analytics work

359
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to identify the word clouds, identify
the cohordes. Who are these different people?

360
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What are the demographics we care about? But she's telling me that they

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are intently focused on that day after
day after day. And that's a pretty

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interesting use case in and of itself. Nick jul, what do you think

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about that or some other good use
cases for AI? Well, absolutely,

364
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I was just my mind was drawing
back to some work I did a couple

365
00:25:38.680 --> 00:25:42.200
of years ago with the Path organization
out of San Francisco, which does a

366
00:25:42.279 --> 00:25:45.960
lot of work with the Gates Foundation
to sort of trends, spread as much

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goodwill as they possibly can with the
combination of data and boots on the ground

368
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to actually solve problems like eradicating malaria. Another really simple example in this area

369
00:25:56.119 --> 00:26:00.720
was during COVID governments were trying to
give to the most needy people in those

370
00:26:00.759 --> 00:26:06.039
countries, and one great example is
a program called NOVESI, which was out

371
00:26:06.079 --> 00:26:11.160
of Togo, very small country over
an Oceania, and basically they wanted to

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give out aid. They really struggled
to work out who needed the aid the

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most. They didn't have a really
good socioeconomic registry across the country. So,

374
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as we were talking about earlier,
they took really interesting sources of data

375
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IoT data. They took satellite images, they took mobile phone metadata the information

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about the calls rather than the calls
themselves, to actually identify the poorest geographic

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regions, the poorest mobile subscribers based
on their behavior, and could actually target

378
00:26:40.799 --> 00:26:42.720
the groups that were most at risk, and they actually managed to hit the

379
00:26:42.799 --> 00:26:48.839
twenty nine percent most poorest economic groups
in that country as a result of using

380
00:26:48.839 --> 00:26:52.960
these modern data sources with advanced machine
learning models in ways they just couldn't have

381
00:26:53.039 --> 00:26:56.240
dreamed of just a few years before. Wow, and you know, you

382
00:26:56.319 --> 00:27:00.599
brought up something that I think is
absolutely fascinating, and maybe bring David to

383
00:27:00.680 --> 00:27:04.359
comment on it, and then seun
is this wealth of information sources that we

384
00:27:04.400 --> 00:27:10.279
have these days. IoT in particular
is so powerful because you can track things

385
00:27:10.319 --> 00:27:15.240
and you can understand the movement of
people of objects in the factory, for

386
00:27:15.279 --> 00:27:19.680
example, even football to be expected
at shopping malls based upon the traffic and

387
00:27:21.039 --> 00:27:25.119
coming into the city that morning.
These are real use cases that some very

388
00:27:25.160 --> 00:27:29.839
forward thinking companies have now put into
play and that can be very very Now

389
00:27:30.119 --> 00:27:33.920
we're leveraging what I call real world
data at scale, and I think we're

390
00:27:33.960 --> 00:27:38.480
still in the period of time of
trying to reconcile what we thought was the

391
00:27:38.559 --> 00:27:41.559
case with what we're now seeing as
the case. But David Sweinter, what

392
00:27:41.599 --> 00:27:45.559
do you think? Yeah? No, I think you know we're all walking

393
00:27:45.599 --> 00:27:49.839
IoT devices these days. Yeah,
that's right, all phones and they're in

394
00:27:49.839 --> 00:27:52.880
our car. You know. It
brings up an interesting question to me,

395
00:27:53.000 --> 00:27:57.880
though, is you know, who
owns this data? Are there concerns about

396
00:27:59.440 --> 00:28:03.119
privacy? See insecurity? And you
know, what is that limit? You

397
00:28:03.160 --> 00:28:07.039
know, we we've certainly seen that
big tech companies they're not going to police

398
00:28:07.079 --> 00:28:10.799
themselves. I think you know,
even you know, Google had written at

399
00:28:10.839 --> 00:28:15.359
in her original research paper, if
you're motivated by by profit, that sort

400
00:28:15.359 --> 00:28:17.559
of always wins, right, And
so now we got all these ads.

401
00:28:17.599 --> 00:28:22.680
So I appreciate the value of having
all this data. I have a little

402
00:28:22.720 --> 00:28:26.559
concerned on sort of who owns it, who controls it? You know in

403
00:28:26.599 --> 00:28:32.240
that classic who Watches the Watchman?
Yeah, how do you how do you

404
00:28:32.240 --> 00:28:36.680
you know? I would love to
hear hear thoughts on that. Maybe Sean

405
00:28:36.799 --> 00:28:42.319
Rogers quis custodiat ipsos custodes who watches
the watch David knows this and uh.

406
00:28:42.400 --> 00:28:45.480
And the last book I wrote,
I have a chapter called the Innovative or

407
00:28:45.519 --> 00:28:52.440
Ikey, and it was an entire
chapter about how far is too far?

408
00:28:52.720 --> 00:28:57.400
How far you know? When does
insight and helpfulness or automated AI start to

409
00:28:57.400 --> 00:29:00.640
make you feel a little uncomfortable?
And the weird thing is or maybe not

410
00:29:00.759 --> 00:29:06.480
weird. It's a different threshold for
everybody. All of us feel differently.

411
00:29:06.720 --> 00:29:11.160
I have expectations, and I want
AI to help me do things. I

412
00:29:11.240 --> 00:29:15.720
want Amazon to tell me what's shirt
to buy with whatever slacks I just got.

413
00:29:15.759 --> 00:29:19.640
I want to know what other people
ordered when they went to that restaurant.

414
00:29:19.680 --> 00:29:22.640
I want all of those things.
Some people don't care for that.

415
00:29:22.759 --> 00:29:27.119
And so I think one of the
balances to David's point about privacy and so

416
00:29:27.200 --> 00:29:33.359
on, is going to be how
we're going to find what works for the

417
00:29:33.480 --> 00:29:38.720
majority and giving controls to the others
to limit their interaction. But I think

418
00:29:38.759 --> 00:29:42.680
just as the Internet disrupted many years
ago, or the Worldwide Web, as

419
00:29:42.680 --> 00:29:48.839
we were talking during the commercial,
it exposed this great technologies existed a long

420
00:29:48.880 --> 00:29:55.240
time ago. So I think there's
going to be acceptance and rejection on these

421
00:29:55.279 --> 00:30:00.039
types of interactions, And it will
also depend upon the the aider you're in.

422
00:30:00.519 --> 00:30:04.480
Right. If I'm in a business
environment, I have an expectation that

423
00:30:04.759 --> 00:30:08.200
a bi tool is going to use
AI to augment the view of the things

424
00:30:08.240 --> 00:30:12.960
that I'm doing. And I may
not have that same level of expectation with

425
00:30:14.319 --> 00:30:18.319
my sports favorite sports website. So
yeah, I don't. I don't know,

426
00:30:18.400 --> 00:30:19.799
Guys, does that make sense to
me? Do you see this collision

427
00:30:19.839 --> 00:30:26.000
of innovation, innovative and IKEY coming? Yeah? Absolutely, Suan, I

428
00:30:26.039 --> 00:30:27.200
think I think for me, this
is something I've been watching for a couple

429
00:30:27.240 --> 00:30:32.240
of years, as I watched Jeff
Bezos get closer and closer to William Shatner's

430
00:30:32.319 --> 00:30:34.079
orbit, you know, sending him
into space doing all that cool stuff.

431
00:30:34.359 --> 00:30:38.400
William Shatner has been sitting down with
Amazon and basically pouring his heart and soul

432
00:30:38.599 --> 00:30:41.519
into a model right now, so
that when he dies, you'll be able

433
00:30:41.559 --> 00:30:45.759
to talk to William Shatner. How
close do we get to IKEY When you

434
00:30:45.839 --> 00:30:48.519
can do that with your mother,
grandmother, a significant other, that won't

435
00:30:48.519 --> 00:30:52.119
be that far away, Right,
It'll be David Sweener as a service,

436
00:30:52.279 --> 00:30:55.920
and we'll be able to call and
tap in on his expertise. But seriously,

437
00:30:56.240 --> 00:31:00.640
for this we are talking about,
I think the need for privacy and

438
00:31:00.799 --> 00:31:04.039
ethics, but also synthetic data.
I think when it comes to these niche

439
00:31:04.119 --> 00:31:08.680
very sensitive areas where we do have
privacy issues, healthcare data, finance data,

440
00:31:08.880 --> 00:31:14.839
we need to be investing more in
topics like synthetic data production to be

441
00:31:14.880 --> 00:31:18.880
able to train these models on domain
areas rather than necessarily just harvesting everyone's data

442
00:31:18.960 --> 00:31:22.200
for that purpose. So I got
an expansion for you on this one,

443
00:31:22.279 --> 00:31:25.680
Nick, I'm not sure you would
have seen it on TV where you live.

444
00:31:26.680 --> 00:31:29.880
The CEO of Alben Ai was in
front of our Congress a week or

445
00:31:29.920 --> 00:31:33.839
so ago trying to teach all the
seventy five year old senators what AI is,

446
00:31:33.039 --> 00:31:37.720
which should have been built as a
comedy show. But they did use

447
00:31:37.759 --> 00:31:41.720
a piece in that where the setup
for part of the conversation was supposed to

448
00:31:41.759 --> 00:31:45.599
be one of the senators speaking and
his lips weren't moving and the words were

449
00:31:45.680 --> 00:31:55.480
coming and it was a very eloquent
paragraph of text and it was AI copying,

450
00:31:56.000 --> 00:32:00.559
and so the AI was speaking in
the senator's voice. Wow, And

451
00:32:00.920 --> 00:32:06.640
it went into the record here in
Congress, says AI gave testimony, kind

452
00:32:06.640 --> 00:32:10.680
of made a statement, so,
you know, wow, where does that

453
00:32:10.839 --> 00:32:15.400
go? I think the senator did
it because Ai was a lot more eloquent

454
00:32:15.400 --> 00:32:19.160
about AI than he was going to
be. But but you know, it's

455
00:32:19.240 --> 00:32:24.200
it's the synthetic idea of synthetic data
and aim, you know, coppying people's

456
00:32:24.279 --> 00:32:28.960
voices. I mean, there's gonna
there's gonna have to be some guardrails here

457
00:32:29.000 --> 00:32:31.799
pretty soon. So this brings up
an interesting question those shot. So you

458
00:32:31.839 --> 00:32:36.839
know, the US has always sort
of been behind, you know where nickolabs

459
00:32:36.880 --> 00:32:40.880
in the world in terms of regulations
on AI and privacy. Now, if

460
00:32:40.640 --> 00:32:45.559
we stepped back, we say,
okay, Europe has this sort of regulations,

461
00:32:45.640 --> 00:32:49.759
the US has this sort of regulations. China, who knows what China

462
00:32:49.839 --> 00:32:52.240
is going to do, They have
their own regulations. So is there like,

463
00:32:52.240 --> 00:32:58.119
like, what does that balance like
if we're really strict over here,

464
00:32:58.680 --> 00:33:01.400
not strict here and doing something totally
weird over here. You know, I'm

465
00:33:01.440 --> 00:33:06.039
curious about you know, your thoughts
on that. You highlighted the balance,

466
00:33:06.200 --> 00:33:10.519
right, it's the extremes of both
are going to be curtailing and the inside

467
00:33:10.599 --> 00:33:15.240
lane with whatever that is, I
had the privilege David, when you and

468
00:33:15.279 --> 00:33:20.240
I worked together years ago. I
was at the EU and I got to

469
00:33:20.279 --> 00:33:25.200
have a conversation, gave a talk
there about would would the strict governance over

470
00:33:25.319 --> 00:33:30.839
AI and big data limit the ability
for the EU to actually keep pace with

471
00:33:30.920 --> 00:33:37.599
crazy countries like the US who were
not really doing any guardrails at that point.

472
00:33:37.640 --> 00:33:39.359
And it was an interesting debate.
And I can tell you I had

473
00:33:39.440 --> 00:33:45.960
that conversation in twenty sixteen. I
wrote the chapter in my book about innovative

474
00:33:45.039 --> 00:33:50.759
or ikey in two thousand and seventeen. And we're not there yet. And

475
00:33:50.799 --> 00:33:54.720
then when something like chat GBT shows
up in the public market and is being

476
00:33:54.880 --> 00:34:00.599
adopted by college kids to do their
homework and everybody else in kind of odd

477
00:34:00.599 --> 00:34:05.240
ways, it's just opening up.
I think it's going to be a forcing

478
00:34:05.279 --> 00:34:08.760
factor David. Right, it's gonna
it's gonna make us find the guard rails,

479
00:34:08.800 --> 00:34:14.000
and it's gonna make us identify that
inner spot because both ends of it

480
00:34:14.079 --> 00:34:16.719
probably aren't the way to go.
Yeah. No, these are really interesting

481
00:34:16.760 --> 00:34:21.039
points too. And Nick I was
talking to a guy from England. He

482
00:34:21.159 --> 00:34:24.599
runs an organization called the Data City
and they're doing some really cool stuff around

483
00:34:24.639 --> 00:34:30.599
transparency. And I'm pretty sure Sean
knows about my evangelism in the era of

484
00:34:30.639 --> 00:34:36.440
transparency in this for transparency in federal
spending, but also in corporate transparency.

485
00:34:36.480 --> 00:34:38.719
And he was telling me that in
England, if you have over million pounds

486
00:34:38.760 --> 00:34:42.800
a year of revenue, you have
to open your books, whether you're a

487
00:34:42.800 --> 00:34:45.760
public company or not. And I
was like, that's pretty cool. I

488
00:34:45.800 --> 00:34:52.079
mean, I really like this focus
on especially again as AI start taking off

489
00:34:52.079 --> 00:34:55.320
and getting a life of its own
and being able to imitate people extremely well,

490
00:34:55.639 --> 00:35:01.480
we need transparency and we need trusted
voices, trusted channels to be able

491
00:35:01.519 --> 00:35:06.480
to deliver trusted information. What do
you think, Nick, Well, absolutely

492
00:35:06.480 --> 00:35:08.480
so. I've done some work with
a data science charity in the UK called

493
00:35:08.559 --> 00:35:13.880
Data Kind and we did a big
hackathon all around exactly what you said.

494
00:35:14.079 --> 00:35:16.840
A company called Open Corporates opened the
books and all the different companies that were

495
00:35:16.840 --> 00:35:22.480
based out of the UK, based
out of the various communities in the islands

496
00:35:22.519 --> 00:35:24.840
around the UK, Jersey as a
tax haven for example, Bermuda and other

497
00:35:24.880 --> 00:35:29.320
areas like that, and we crawled
that data looking for patterns. But without

498
00:35:29.360 --> 00:35:31.880
that transparency, none of that machine
learning would have been possible. We built

499
00:35:31.880 --> 00:35:37.360
an amazing graph database that allowed us
to capture who was opening serial companies day

500
00:35:37.400 --> 00:35:40.039
after day, why were they doing
that. We had a whole team of

501
00:35:40.119 --> 00:35:45.400
data journalists just to look into the
things that the model produced. So transparency

502
00:35:45.440 --> 00:35:49.719
leads to some amazing benefits. But
if I may one last point around just

503
00:35:49.760 --> 00:35:53.039
the general concept of bias inside these
models. Myself and my co founder a

504
00:35:53.039 --> 00:35:57.519
couple of months ago, just as
GPT was really hitting the hype cycle,

505
00:35:57.840 --> 00:36:00.039
we did some frivolous tests and we
were like, tell me a joke about

506
00:36:00.039 --> 00:36:02.480
the Irish. GPT would give you
a joke, Tell me a joke about

507
00:36:02.519 --> 00:36:06.280
the Polish, no problem at all. Give me a joke about the Chinese.

508
00:36:06.400 --> 00:36:08.000
I'm sorry, I'm an AI model. I can't do that. And

509
00:36:08.039 --> 00:36:12.039
there were certain countries where it was
forbidden, and it was really interesting to

510
00:36:12.079 --> 00:36:15.159
see. Guardrails are obviously in place, but they're not applied consistently, you

511
00:36:15.199 --> 00:36:19.159
know. And I had this thought
because there is this big question, and

512
00:36:19.159 --> 00:36:22.119
I predict there are going to be
some heavy duty legal battles going on in

513
00:36:22.159 --> 00:36:27.679
the very near future going up against
open AI and any of these large language

514
00:36:27.719 --> 00:36:34.039
models because it seems to me you
could create a machine learning algorithm to request

515
00:36:34.360 --> 00:36:40.000
text created by chat GBT and then
do a handful of exact phrase searches from

516
00:36:40.039 --> 00:36:45.079
that document to the interwebs to see
because it's getting this information from somewhere,

517
00:36:45.239 --> 00:36:51.039
So is it violating copyright? I
have to believe that it is at some

518
00:36:51.119 --> 00:36:53.159
point, right, it can't.
There are only so many ways to say

519
00:36:53.239 --> 00:36:58.199
things, which is why the chat
GPT is even possible. But to me,

520
00:36:58.280 --> 00:37:00.800
that's a very interesting shoe yet to
fall. What do you think,

521
00:37:00.800 --> 00:37:02.639
Sean real quick, Well, I
think it's I think some are doing it

522
00:37:02.679 --> 00:37:07.199
better than others, being for instance, if you go there and use their

523
00:37:07.239 --> 00:37:09.719
system, they will give you footnotes
at the bottom of the response and they'll

524
00:37:09.719 --> 00:37:15.239
say I pulled this segment from a
report that was written and on one of

525
00:37:15.239 --> 00:37:20.159
the searches I did recently, it
took me back to a report at TDWY,

526
00:37:20.639 --> 00:37:23.159
a renowned source or respected source.
I was happy to see that I

527
00:37:23.239 --> 00:37:27.920
knew the man who it was,
an older piece of research. Philip wrote

528
00:37:27.960 --> 00:37:30.719
it, and and so you know, I felt good about that. So

529
00:37:30.760 --> 00:37:34.400
I think some of that transparency here, you're going to start having to see

530
00:37:34.440 --> 00:37:37.320
that like a Sean wrote two books
and here's the titles. And I would

531
00:37:37.320 --> 00:37:42.800
have liked to have known why they
why why chat got one of my titles

532
00:37:42.840 --> 00:37:46.519
wrong and attributed me to a book
I didn't write. So it's coming.

533
00:37:47.119 --> 00:37:51.760
Yeah, that's that's a very very
good point. These folks, these organizations

534
00:37:51.840 --> 00:37:54.920
are really moving quickly to I mean, really interesting things are happening right now.

535
00:37:54.920 --> 00:37:59.280
They're addressing about these concerns. So
it's going to get better faster.

536
00:37:59.360 --> 00:38:02.800
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eight Welcome back to Inside Analysis.
Here's your host, Eric Kabanaugh. All

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right, folks back here on Inside
Analysis talking to an all star cast of

592
00:42:17.000 --> 00:42:22.119
analytics and AI experts. Are good
buddy, Nick Jeweles dining in from across

593
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the pond, David Sweener from up
in New Hampshire, I believe, north

594
00:42:24.480 --> 00:42:29.639
of here, and Sean Rogers way
out west in the Colorado regions. We're

595
00:42:29.639 --> 00:42:32.280
talking about the business value and we
should get back to that ROI. So

596
00:42:32.320 --> 00:42:36.239
I'll throw it over to David.
I mean, clearly, you can save

597
00:42:36.360 --> 00:42:39.440
time, you can do much more
effective research, and it's also changing the

598
00:42:39.519 --> 00:42:45.159
mindsets about how you do things.
But being able to define the specific value

599
00:42:45.360 --> 00:42:49.679
that's always going to be hard.
But if you can tie a decision to

600
00:42:50.039 --> 00:42:52.800
an analytic, for example, and
then show hey, this is why we

601
00:42:52.960 --> 00:42:57.760
sold that deal that million dollar project, well that's the best kind of roy

602
00:42:57.840 --> 00:43:00.440
where you can actually tie it to
moneycutting in the door or what do you

603
00:43:00.480 --> 00:43:02.800
think? David? Yeah? Absolutely, And I wrote a blog that it's

604
00:43:02.840 --> 00:43:06.960
getting quite a bit attraction on this, and I call it the art of

605
00:43:07.000 --> 00:43:09.320
the AI KPI. So you know, I really think of this in sort

606
00:43:09.360 --> 00:43:15.360
of five five different dimensions. So
there's the business impacts of data, analytics

607
00:43:15.400 --> 00:43:17.000
and AI, so we can tie
you know, where we're getting more customers,

608
00:43:17.000 --> 00:43:21.239
We're going to market faster. That's
great. I think the second one

609
00:43:21.360 --> 00:43:24.480
is time from you know, data
creation to insight and action. So we

610
00:43:24.559 --> 00:43:28.760
talked about that a little bit earlier. How can we compress that cycle so

611
00:43:28.800 --> 00:43:32.800
we can make decisions faster. I
think the third dimension people can measure is

612
00:43:32.920 --> 00:43:37.639
data quality. How is your data
quality? That a crappy data quality,

613
00:43:38.199 --> 00:43:40.920
you know you're not going to get
great decisions. You can look at the

614
00:43:40.920 --> 00:43:46.320
impact on your organization, so data
literacy levels within your organization, the more

615
00:43:46.400 --> 00:43:51.760
people that can participate in the data
and analytics process within your organization, those

616
00:43:51.800 --> 00:43:58.039
companies outperform companies that have a lower
percentage population. And the last one is

617
00:43:58.039 --> 00:44:01.079
really risk. You can measure your
risk of your AI application. So those

618
00:44:01.119 --> 00:44:05.480
are sort of the five sort of
dimensions. I tend to think about how

619
00:44:05.559 --> 00:44:09.760
companies can constart to measure the value
of their analytics investments. Yeah. I

620
00:44:09.840 --> 00:44:13.840
like the data quality example, Nick
Joel, I'll throw it over to you

621
00:44:14.480 --> 00:44:19.119
because again, AI and machine learning, these are very good at tackling tedious

622
00:44:19.159 --> 00:44:22.559
tasks at scale, very simple things
that a human can usually do, but

623
00:44:22.840 --> 00:44:28.199
very very very slowly. And folks, anyone who out there in the enterprise

624
00:44:28.239 --> 00:44:30.440
world who thinks that your data quality
is very very good, all you got

625
00:44:30.440 --> 00:44:34.519
to do is ask someone to show
you one database. Just pick any database

626
00:44:34.559 --> 00:44:37.320
at all and open it up to
any page, and you're going to see

627
00:44:37.360 --> 00:44:40.679
problems or it's depressing quite frankly.
But these little algorithms they can just kind

628
00:44:40.679 --> 00:44:45.880
of churn through this stuff and what
they can do is flag for you all

629
00:44:45.880 --> 00:44:47.280
the things that you can do to
clean up. And you're starting to see

630
00:44:47.280 --> 00:44:51.599
this, like in Google Sheets for
example, Demo is doing this. A

631
00:44:51.599 --> 00:44:55.519
bunch of vendors are now all offering
these sort of recommendation engines. Hey do

632
00:44:55.559 --> 00:45:00.400
you want to clean up your state
characteristics in this com Do you want to

633
00:45:00.400 --> 00:45:01.800
clean this up? Clean that up? And a lot of times they're right,

634
00:45:01.960 --> 00:45:05.679
And I'm like, that's pretty good, Nick Jewel, what do you

635
00:45:05.760 --> 00:45:07.800
think? Well, absolutely, I
think this actually touches on two of David's

636
00:45:07.800 --> 00:45:10.840
points, So I think you've got
the essence of automation. So once you've

637
00:45:10.880 --> 00:45:15.480
identified a problem, how quickly can
you get it out of a human's hands

638
00:45:15.519 --> 00:45:19.480
into the playbook that you run every
single night, every single load. But

639
00:45:19.599 --> 00:45:23.119
also actually it goes after bottom line
use cases, so cost savings. It

640
00:45:23.159 --> 00:45:27.840
could be you know, you improve
your supply chain management because you fix some

641
00:45:27.920 --> 00:45:31.239
of these look up codes that are
always causing the system to fail. Bottom

642
00:45:31.239 --> 00:45:36.440
line stuff's all about going after friction
within a business process. If you can

643
00:45:36.440 --> 00:45:39.159
oil that machine so it runs faster, you are going to make the case

644
00:45:39.239 --> 00:45:44.719
for analytic investment. That's a That's
a great example, Sean Rogers, I'll

645
00:45:44.760 --> 00:45:46.559
throw it over to you. There
are lots of ways that you can improve

646
00:45:46.639 --> 00:45:50.599
efficiency, like Nick just said,
just finding the look up codes that are

647
00:45:50.599 --> 00:45:54.280
causing you trouble prioritization. This is
something I came up with last year.

648
00:45:54.599 --> 00:45:58.599
I was trying to figure out what's
a good methodology I can put in place

649
00:45:58.639 --> 00:46:02.159
to explain the importance of knowing what
to do and knowing what to do at

650
00:46:02.199 --> 00:46:05.920
any given moment. Kind of a
big deal. I mean, if you're

651
00:46:05.920 --> 00:46:07.840
a firefighter, it's pretty obvious what
you do you put the fire out.

652
00:46:08.119 --> 00:46:12.920
But for lots of other jobs,
especially running small businesses man that you could

653
00:46:12.960 --> 00:46:15.519
be doing any number of things at
any point in time, where will you

654
00:46:15.519 --> 00:46:19.920
get the most value from it?
I think AI can help in that category.

655
00:46:19.960 --> 00:46:21.719
Two, what do you think,
Sean, Well, yeah, I

656
00:46:21.760 --> 00:46:23.679
think it's going to get us to
a finish line a whole lot quicker than

657
00:46:23.679 --> 00:46:27.559
we used to be able to.
In my role, I get to be

658
00:46:27.679 --> 00:46:31.039
with a lot of different software companies
and get these kind of behind the scene

659
00:46:31.039 --> 00:46:35.480
briefings, And I spent a couple
of days in Las Vegas a week or

660
00:46:35.480 --> 00:46:39.599
two ago with Informatica, for instance. They're automating an awful lot of the

661
00:46:39.760 --> 00:46:44.960
things that are around being able to
be literate and understand your data, being

662
00:46:45.000 --> 00:46:49.079
able to have better data quality,
and being able to leverage the metadata that

663
00:46:49.199 --> 00:46:52.119
exists in your environment in a way
that you couldn't before, kind of pulling

664
00:46:52.159 --> 00:46:57.599
everything from data catalogs all the way
up to make sure that there's continuity between

665
00:46:57.679 --> 00:47:01.840
the systems and the stack that you're
operating. And so it's I think it's

666
00:47:01.840 --> 00:47:07.880
one of the most disruptive automated aspects
of data management then that we've ever seen.

667
00:47:07.960 --> 00:47:10.760
And I think we're just going to
keep seeing it get better and better

668
00:47:10.800 --> 00:47:15.079
and better over the coming year or
so. And I think it's going to

669
00:47:15.159 --> 00:47:20.840
be extremely disruptive, but in a
really positive way. So I talk about

670
00:47:20.880 --> 00:47:24.159
this idea of everything old is new
again quite often, and by that I

671
00:47:24.159 --> 00:47:29.159
mean MBM has been around a long
time, data quality a long time,

672
00:47:29.599 --> 00:47:32.360
and projects get started and projects were
abandoned, and a lot of times they

673
00:47:32.360 --> 00:47:37.599
were abandoned because someone saw another bright, shiny toy of technology that they wanted

674
00:47:37.599 --> 00:47:43.960
to go chase and they shifted budget. It's the new opportunities to AI brings

675
00:47:43.960 --> 00:47:50.239
to data management as a foundational layer
to better analytics and automated AI. It's

676
00:47:50.280 --> 00:47:53.639
incredible and it's really exciting. So
I think we're going to see a lot

677
00:47:53.679 --> 00:47:58.239
there. And to David's first comment
is that where the value is that where

678
00:47:58.320 --> 00:48:02.760
the return on investment is out absolutely
well. And also, like one last

679
00:48:02.840 --> 00:48:07.400
question I'll throw out to the team
here and David maybe over to you first

680
00:48:07.400 --> 00:48:09.800
about where these things are headed.
And in my opinion, one of the

681
00:48:09.840 --> 00:48:14.000
most important things to note is that
the value of AI, I think is

682
00:48:14.079 --> 00:48:17.400
largely going to manifest in the form
of suggestions, Hey, why don't you

683
00:48:17.440 --> 00:48:21.440
do this, why don't you do
that? And when that suggestion comes in

684
00:48:21.519 --> 00:48:23.679
some sort of enterprise portal where you're
doing your work every day, well,

685
00:48:23.719 --> 00:48:28.559
guess what the machine can track when
you said yes, when you said no,

686
00:48:28.719 --> 00:48:31.079
when you went with certain suggestions,
and then if you have if you're

687
00:48:31.079 --> 00:48:35.079
selling goods, for example, you
should be able to track that. I

688
00:48:35.119 --> 00:48:37.480
think you should raise the price fifty
cents. Okay, let's do it a

689
00:48:37.920 --> 00:48:39.559
month later. Look, we made
more profit, so we'll be able to

690
00:48:39.639 --> 00:48:45.159
calculate that roim much more accurately in
the future because of AI, because everything

691
00:48:45.280 --> 00:48:46.840
is captured in the cloud. David, what do you think? Yeah,

692
00:48:47.039 --> 00:48:52.800
I certainly think rapid iteration is definitely
something to keep an eye on. You

693
00:48:52.800 --> 00:48:55.159
know where if I just was to
look in the future, I think right

694
00:48:55.159 --> 00:49:00.480
now we're talking about mainly software and
using it to predictions and dashboards and things

695
00:49:00.480 --> 00:49:05.400
like that. I think where this
will be heading is there's gonna be more

696
00:49:06.039 --> 00:49:07.599
robotics out there, and you know, I'm gonna just take an example.

697
00:49:07.599 --> 00:49:14.119
If you look at Japan, thirty
percent of the population in Japan is over

698
00:49:14.159 --> 00:49:17.159
sixty five, so it has the
oldest population on Earth with the exception of

699
00:49:17.480 --> 00:49:22.679
Monaco. The median age is about
forty eight point seven and it's the world's

700
00:49:22.719 --> 00:49:28.800
median age is thirty point two.
So there's a lot of elderly people that

701
00:49:28.920 --> 00:49:31.679
need help they need help getting around, they need help getting things from the

702
00:49:31.760 --> 00:49:37.400
store, they need help with healthcare, they need companionship. And so I

703
00:49:37.400 --> 00:49:40.840
think we're gonna see a lot of
this really embedded in robotics that's gonna help

704
00:49:40.880 --> 00:49:46.440
people for the better. And the
other I think big area is physics based

705
00:49:46.480 --> 00:49:52.159
AI. So right now, these
algorithms spit out things without context to the

706
00:49:52.280 --> 00:49:57.800
environment. And if you can take
a little bit about the environment, here's

707
00:49:57.840 --> 00:50:00.440
the guardrails, here's how this environ
it behaves and works. And if you

708
00:50:00.480 --> 00:50:04.599
think about process steps, these things
are sort of connected process step one,

709
00:50:04.639 --> 00:50:07.119
two three. I think this is
where we're going to see AI. We're

710
00:50:07.119 --> 00:50:10.239
going to infuse it with physical system, the physics of it, and we're

711
00:50:10.239 --> 00:50:14.000
going to get better predictions there.
And that's sort of where I think AI

712
00:50:14.119 --> 00:50:17.039
is headed. I like that better
predictions. Nick Jewel, closing thoughts from

713
00:50:17.039 --> 00:50:20.960
you where these things are heading?
Oh fantastic. Well, Dave Sweener went

714
00:50:20.960 --> 00:50:22.639
down the rout of physics. I'm
going to go back to math all the

715
00:50:22.639 --> 00:50:27.599
way back to the beginnings. So
I've been looking really closely into a thing

716
00:50:27.639 --> 00:50:31.280
called causal AI recently. So Eric, to your point about making better recommendations.

717
00:50:31.719 --> 00:50:37.800
The kind of classical statistics that we've
used in advanced analytics for years centuries

718
00:50:37.800 --> 00:50:40.079
really are actually quite limited by what
they can do. So you want to

719
00:50:40.119 --> 00:50:44.320
make a prediction, what happens when
I raise the price by three times?

720
00:50:44.320 --> 00:50:49.159
Will people still buy it? It's
actually incredibly hard for classical models to answer.

721
00:50:49.440 --> 00:50:52.480
So causal AI is all about really
getting into this hard space of cause

722
00:50:52.480 --> 00:50:55.719
and effect. What would happen if
I changed the price by three times?

723
00:50:55.840 --> 00:51:00.639
Would people still buy it? What
happens is to find a relationship, you

724
00:51:00.679 --> 00:51:04.639
know, between smoking and death.
That was a question that was incredibly hard

725
00:51:04.679 --> 00:51:07.840
for traditional statistics to answer. There
are some amazing startups in this space.

726
00:51:07.880 --> 00:51:13.280
There's a lot of work going on
bringing in graph databases to map these causal

727
00:51:13.320 --> 00:51:16.039
models. I think we're going to
see you disruptive, staggering potential in this

728
00:51:16.079 --> 00:51:22.440
space. Yeah, I like that
staggering potential. Buckle up, Sean Rodgers,

729
00:51:22.480 --> 00:51:25.719
what do you think? One minute? Last thing I will add is

730
00:51:25.760 --> 00:51:30.480
I think going back to David's literacy
idea of understanding the information that we're getting,

731
00:51:30.480 --> 00:51:35.599
I'm seeing a lot of the VII
vendors bringing to market things like key

732
00:51:35.679 --> 00:51:37.800
drivers, right, So you can
do the query what did we sell on

733
00:51:37.880 --> 00:51:42.559
the western region, and you can
ask it a question of why sales are

734
00:51:42.599 --> 00:51:46.519
down and now the systems are servicing
here's the four things and the number one

735
00:51:46.679 --> 00:51:52.960
key reason that this occurred, and
it drives that literacy idea so that you

736
00:51:52.000 --> 00:51:57.760
can do more and get greater insights. And that's that's been coming for a

737
00:51:57.800 --> 00:52:01.360
while, but I think the new
things that we're seeing with AI and mL

738
00:52:01.440 --> 00:52:05.440
are certainly going to drive it.
Yeah, I think so too. And

739
00:52:05.519 --> 00:52:10.119
these large language models going single tenants
right where you can basically have one and

740
00:52:10.199 --> 00:52:15.159
connect it to your information system,
so you're not just plugging it into the

741
00:52:15.719 --> 00:52:20.000
greater large language model, which of
course is a leaky way to give your

742
00:52:20.000 --> 00:52:22.800
trade secrets to everyone in the world
who puts the right prompt Q in there.

743
00:52:23.320 --> 00:52:27.480
That is going to change things as
well that I'm hearing that's coming down

744
00:52:27.559 --> 00:52:31.480
the pike right now. But the
cool thing is that the speed at which

745
00:52:31.480 --> 00:52:37.320
you can get answers these days is
going to do wonders for the analytics industry

746
00:52:37.639 --> 00:52:39.719
because we all know we're in this
business. We all know how exciting it

747
00:52:39.760 --> 00:52:44.280
is to figure something out, make
a decision and watch it work. It's

748
00:52:44.360 --> 00:52:46.880
amazing stuff, and many, many, many more people are going to do

749
00:52:46.920 --> 00:52:50.800
that because it's going to work a
lot faster. Look these guys up on

750
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LinkedIn. We're talking next. A
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Tune into The Ferran Dozier Show Musical
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on all available streaming platforms and one

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six point five a FM from ten
fifty am. The ferrando Zier Show on

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kc A Radio. CACAA Radio has
openings for one hour talk shows. If

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you want to host a radio show, now is the time. Make CASEAA

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your flagship station. Our rates are
affordable and our services are second to nine.

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We broadcast to a population of five
million people plus. We stream and

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podcast on all major online audio and
video systems. If you've been thinking about

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broadcasting a weekly radio program on real
radio plus the internet, contact our CEO

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at two eight one five nine nine
ninety eight hundred. Two eight one five

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nine nine ninety eight hundred. You
can skype your show from your home to

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our Redlands, California studio, where
our live producers and engineers are ready to

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work with you personally. A radio
program on CACAA is the perfect work from

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home avocation in these stressful times.
Just type CASEAA radio dot com into your

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browser to learn more about hosting a
show on the best station in the nation,

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or call our CEO for details.
Two eight one five nine nine ninety

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eight hundred Kristina k c AA,
Lowlinda and one oh six point five FM

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K two ninety three CF Merino Valley, NBC News Radio