WEBVTT

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NBC News Radio k c AA,
Lelanda, k q l h LP FM,

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You Kaipa, Riverside, Sandradino,
kq l h LP two, Beaumont

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Banning, Cherry Valley, new business
models, reinvent every industry industry. Inside

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Analysis is your source of information and
insight about how to make the most of

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this exciting new era. Learn more
at inside analysis dot com, Inside Analysis

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dot com. And now here's your
host, Eric Kavanaugh. All Right,

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ladies and gentlemen, it's time for
the only coast to coast radio show that's

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all about the information economy. It's
called Inside Analysis. Your host here,

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Eric Kavanaugh, and I am so
excited to have an all star cast today.

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We've got the Malcolm Chisholm, who's
got the receipts on LinkedIn promotions.

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We've got the data Scott Taylor,
who of Meta Meta Consulting. He's amazing

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and less not at least, we
have Kate Stretch Data. Kate, she

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knows where you live, she knows
where your data lives. She will find

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you. I suggest you not to
make her unhippy. Unhippy. That's what

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my Russian boss used to always say
about the people who he had stiffed on

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bills. They are unhippy. That
was his favorite term. We're talking all

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things data today. It's going to
be a fantastic show, and let's not

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waste any time. I saw this
hilarious quote from one of my favorite people

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in the business, Steve Lucas said
data is the new sand. I thought

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that was hysterical because everyone talks about
data is the new oil. You have

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to rEFInd it. It doesn't have
valent. You're find Oh, okay,

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we know all that, but data's
the new sand. It's like just gritty

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gets on your shoes, winds up
in your car. You can't ever vacuum

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it all the way out. It's
just forever with you, little tiny bits

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of sand. But it makes sense
because if you refine it, you can

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If you have a last blower,
for example, maybe you can do something

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with that, with that sand.
But let's throw it over to Kate and

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stretch dedicated. Tell us a bit
about yourself and what you think about the

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importance of data. Yes, absolutely, thanks for having me on the show.

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Hello everybody, I'm Kate Stoshna.
I can't do the accent as good

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as Eric cant for Russia, but
I am the founder of Dedicated. It's

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a media company that's focused on helping
data, analytics, machine learning, and

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AI companies reach their audience on LinkedIn. I help out with brand awareness.

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I've got my own show called Dedicated
on air, which is why I love

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Eric's background. That says on air
reminds me of my show as well.

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And you know, Eric, I
wanted to point out when you were talking

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about sand for some reason, I
started thinking of how data is the new

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water because I've heard of that,
right, all the pipelines and cleaning the

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water and making sure it all makes
sense. Then I thought of mixing sand

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and water. But then I remembered
I recently put together a video where I

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actually build a sand castle to demonstrate
the importance of data infrastructure. It was

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for one of my clients. They
wanted to talk about how if you build

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on for infrastructure and all your apps
go to crap basically and it just doesn't

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doesn't work well. And so I
built like a really bad sand castle,

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and then I scrapped it and built
a much better one. I had so

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much fun, and my kids are
like, you get to do this for

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work, and I'm like, yeah, this is what I do. For

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a living, and I explained it
to my family. My brother's like,

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I still don't know what you do
and I'm like, well, I get

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paid to make a sand castle today, and he's like, can you just

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keep making them? And I'm like, no, it doesn't work that way.

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It's all about the concept. But
yeah, data, I think data

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is extremely important. I think your
question was what I think about data.

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Data is literally my life. I
even have a license plate in my car

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that literally just says data, which
gets a lot of attention from passersbys because

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they start asking like, why do
you like data so much? So it's

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it's been fun, that's good stuff. And of course you help people get

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the word out about their own personal
brands, so you're passionate about brand.

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You've got a fine job branding yourself, by the way. Dedicated. I

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mean, come on, that's good
stuff, right, dedicated? Yes,

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thank you, thank you. Yeah. I love I love talking about personal

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branding. I've got a course out
on LinkedIn Learning on that topic as well

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for people. I think it's like
a forty five minute course of everything I

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know about personal branding. I compacted
it really really tight into a very short

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course. But yeah, I think
personal brand is extremely important in what in

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any area of data, data management, data analytics, data science, now

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AI, right, it's all about
standing out, especially with AI sort of

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doing all of our work for us. A lot of times, the more

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personal touch we can put on our
work, the better. That's interesting.

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And you had mentioned before the show
that we'd be remiss where we can not

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mention generative AI. So you've now
hinted it generative AI. And I think

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the key to success with AI is
going to be wait for it, data,

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right, you're good quality data.
In fact, let me sure this

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at you and see what you think. I believe that AI, in a

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particular large language models represent a second
chance for data. And what I mean

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by that is the first chance.
We did data warehousing and analytics and business

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intelligence and visualization, all that stuff. We moved it around. We use

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master data management to try to reconcile
systems. We've done all these different things,

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essentially torturing the data to make it
say what we want it to say.

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But it's been very expensive. There
has been certainly value for that.

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But I think that this inflection point
we're at right now is not small.

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I think this is a very significant
transformation, and that AI will give a

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second chance to data, by which
I mean if you feed your AI model,

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your corporate AI model, and I
think every big company is going to

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have one with the most trusted,
carefully curated data, you're going to get

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a very good result. And if
you don't, you're just going to get

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some random nonsense. What do you
think? Yeah, I was going to

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say it gives it a second chance, and hopefully companies and people take the

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second chance to feed it clean data
and don't lose focus and don't just focus

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on look what we could do a
I know, I think going back to

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data management and it's important, and
making sure that you focus on data quality

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and data governance, and then that
you're feeding this large language model good cookies,

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not crappy cookies, not junk food, and making sure you actually clean

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it all up. And going back
to the sand castle analogy. If you

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know, if I use sand with
twigs and random stuff and wrappers and cigarette

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butts in there, the sand castle
wouldn't be pretty, it wouldn't it wouldn't

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hold up well. Versus if I
used high quality sand, so sand and

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data could be a thing. Actually, you got me thinking, I like

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that I like that, and sand
doesn't need any branding. I think everyone

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knows what sand is. We've all
walked on the beach. But to the

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point of my one body on the
LinkedIn platform, they said, you can

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create beautiful artwork with sand if you
are very careful about what you're doing to

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it. But again, like with
data, it has to have purpose,

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it has to have contacts. And
I think part of the challenge here,

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I'll throw this at you and then'll
maybe bring in the Westburg the comment on

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it. But you think about the
old days of data warehousing and how old

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habits die hard and you had to
strip out a lot of context in order

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to get the little bits of transactional
data through thin pipes to slow processors and

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expensive storage. Well now the pipes
are fat, the processors are blazing speed,

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you can parallelize them. Storage is
cheap. So everything has changed in

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terms of the data game, which
is the big is why we went to

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the data lake. But again we
thought, oh, we're going to put

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it on in one place, which
is probably a bad idea. I think

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data should live where it is best
used and accessed as needed. But what

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do you think about this evolution and
how old habits die hard? How do

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you get your clients to make those
old habits die Yeah, I think we're

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still evolving, and I think we
are moving in the right direction, and

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the closer we can get to keeping
that business context of that data as we

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allow access to the data is extremely
important. So I'm all about, you

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know, data literacy, data accessibility, data democratization, making sure people have

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the right access. But I think
with the change in the progress in the

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data warehousing and cloud now we make
it easier for people to actually access not

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just the data, but data in
the right context for the right individual with

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personalization. So I think we're definitely
moving in that right direction. That's good.

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Lasting I'll throw at you, can
you define data mesh? Data mesh?

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No good answer needs you what you
have to say? My dog upstairs

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is trying to define data right now, and he's not very happy about it.

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I have two German shepherds, and
of course they chose this moment to

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chime in because we are trying to
figure out what data mesh is all about

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as well. Well, let's let's
hand it over to Scott Taylor, the

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proverbial data whistpar of meta, meta
and You've been talking about data and the

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business value of data for a long
time, and they deal with data mesh

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is you want the business groups to
govern their own data. To help with

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that process, but tell us a
bit about your thoughts and the value of

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data on the data mesh. You
hear about the data mesh here about the

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data fabric. Eric I asked you
what about the data spanks? Help could

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tighten the data spanks? Thanks like
the clothing, tighten that integration. Scott

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Taylor, the data whisperer here,
I help people calm data down. That's

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what data whispering is all about.
I'm thirty something years in the data management

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space and now I'm just a content
creator, part of Kate's posse of the

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data a Vengers as well out there
trying to get people excited and fired up

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about the data management part of the
space. You're talking about feeding stuff into

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data second chance. That's a nice
way to put it, But are we

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just talking about the same thing We've
always talked about. Garbage in, garbage

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out. Put the bad data into
llms, you get hallucinations. So put

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garbage into jenn Ai. You can
get garbage out at scale now, which

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is really well right, I'm actually
so one of my good buddies, Eugene

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Burke, who's been on the show
a few times Digital Strategies Group, we're

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doing a bit of a stealth project
now. He had the greatest line I

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think about this limitation of large language
models. He said, they don't have

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an epistemological barrier, which means for
non philosophers out there, they don't know

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what they don't know. And it
reminds me. I won't say which country,

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but there are certain countries where I
was told don't ask people in the

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street for directions because they'll be too
embarrassed and they'll just tell you something you

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know that they don't know. And
that's kind of like what these large language

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models do. If you don't have
a good embedding strategy, if you don't

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populate either a vector database or your
own model itself with enough curated data,

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and if you don't train it properly, then guess what, it's just going

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to make things up. Like when
it said I've written three books, and

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I was like, are you seeing
the future? So you're right that we

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need to curate carefully. But that's
always been the mission. It's always been

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the case there's nothing new about that. There's nothing new about need for a

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well structured, expertly stewarded, wonderfully
governed data that you know you're gonna bring.

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Malcolm on here has got metadata,
it's got master data, it's got

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reference data, mdm rdm RIM,
PIM, damn. All these foundational activities

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that enterprises must commit to if they
want to leverage data across their organization.

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And what enterprise doesn't want to leverage
data across their organization. So these are

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the same principles we've been dealing with. We got to shine them up,

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we got to rephrase them a little
bit to get people's attention. But it's

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just maddening sitting in the data space
for so many decades. Again, I

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go back pre to K seeing the
same story over and over again with different

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characters. Well, I think part
of the challenge is that you have this

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long tail of legacy software and hardware
and mindset, by the way, and

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it's very difficult. So when new
technologies come in, you try to move

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over to the new technology, but
you still have this long tail of legacy

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to contend with. And in fact, we want to promote the Data Universe

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conference, it is coming April tenth
and eleventh to New York to the the

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Javit Center, which I Javit Centritism
does not like. He's not a fan

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of the Javit Center. I'll find
that out in the next segment. Well,

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I'll be there, Kate will be
there. We'll let Malcolm decide whether

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he wants to be there or not. Given is going to be a party

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his feeling about that location. But
I think it's going to be a blast.

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Yeah. Well, so I wrote
I wrote an abstract. I don't

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know if it will get approved or
not yet, but it's said the fourth

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word is in parentheses forever in your
technical debt. That's what I read about.

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Oh and that's pretty cue to Kate
laughs, so she likes it.

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The headline basically leads into the abstract, which has something like what's the definition

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of a legacy system? And the
answer is any system in production. The

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joke is that the moment you drive
off the lot, you are accruing technical

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debt, like literally the second you
leave the lot, tactical debt is like

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bare death. That's a good way
to think about it. Yes, well,

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you look at like doctor's offices,
I mean they still want me to

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fill out pieces of paper on a
on a notepad. I'm like, are

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you kidding me? I have to
write my name seven times? Like you

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can you just you know, could
you just give me the iPad? And

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then they give it the iPad and
it's like eight hundred and seventy five questions.

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It's like, do you people know
anything about surveys? Like when you

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get to question five hundred and twenty
one, the quality of the answer goes

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way down because no one wants to
write that stuff right. But we can.

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We can port that to the data
world and say ask just enough questions

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to get just what you need and
move on. What do you think?

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I think so? Yeah, it's
just I hate that a doctor's offices as

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well, filling it and over and
over again. It's like, haven't they

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kept anything? Don't they have any
legacy data about me? Yeah? They

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do. It's en a folder that
they put in the in the filing cabin

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in the back there some rdy here. You have some interesting folders too,

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including folders on stretchedy. I have
folders. Yes, I've got all kinds

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of interesting stuff here. Do you
want to talk about the puppets yet?

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Or you want to bring Malcolm on. First, let's bring in a puppet

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real quick, show your puppets so
we can see them. This is about

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my character, the CDO, the
Chief Dog Officer, and he partners up

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with If you haven't seen the Data
Puppets on YouTube, you got to see

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him out there. There's a preview
out there. Journey to the Center of

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the single version of the Truth,
the greatest data story ever told, starting

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the CDEO, the Chief Dog Officer
and also appearing the CEO and Chief Elephant

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Officer played by Kate. There's a
CMO. Guess what he is? A

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mouse? A CFO is a fish? I mean, just stuff rights itself.

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How has nobody else come up with
that? Before? They hire a

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cat sultant from meol Kinsey who borrows
their watch to tell him what time it

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is. Watch. We've got a
whole crowd of business users who will be

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out there trying to do but take
a look at that. It's crazy stuff.

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I'm having a ball. This is
what I'm doing now, Data Puppets

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events, the funnest possible stuff and
similar decay. If you can get paid

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for making sand castles or doing puppets, then welcome to our world. You

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gave me a segue to bring in
a fantastic lyric by coy Leroy or Coyle

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Ray I think her name is.
She says time is money, so I

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spent it on a watch. Hold
on, It's like what that's pretty clever,

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And that's the semantics side of the
equation. We do want words to

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go along with all these numbers.
I mean, the numbers obviously are the

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transactions and how many widgets we're selling
and so on and so forth, or

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clicks and all this kind of fun
stuff. But the words are the semantics.

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That's probably gonna be a good segue
to bring Malcolm chi isn't into the

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second segment here, But maybe we'll
just finish up with Scott, and maybe

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I'll go ahead and throw a curveball
and bring Stretch back in the conversation Gates

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the puppets. Why you play with
games with puppets with your data? What's

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the question? Your accent is very
difficult there. She doesn't take puppet questions.

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She doesn't. I'm not allowed to
speak about puppets for our contract with

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Scott. That's that's his thing.
We can it's my area, all right.

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But I had a comment when you
were talking about the doctor's office and

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the iPad. So they do have
iPad now, or they send you a

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link on your phone. But the
worst, most, most most knowing part

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is when you have to fill out
Let's say I recently had an appointment for

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my kids doctor checkup and they ask
for the kids date of birth at least

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eight times on eight different within one
form. Like I filled it out.

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You can't. You don't have the
systems to just why it's so compat the

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model. It's a system's integration issue. It's a data model, right.

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I mean I remember with some of
the smart people online they figured out,

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hey, if we have someone enter
the zip code, we can dynamically look

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up the town that they're in.
I'm like, who's thinking, huh,

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who's thinking put the zip code there? It's like the people like USAA figured

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this out. You call in,
they say, I see you're donally from

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a number in our system. If
that's the phone you usually used to be

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a press one, Like, yes, that's right, now you know who

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I am. I don't have to
tell five people in a row. This

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is Eric Cavanaugh. I'm calling about
this. Oh yes, yes, that's

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called enterprise integration. That's from a
lot twenty two years ago, enterprise integration.

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We've come a long way, but
data is front and center. I

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suppose data should lead the charge.
Let the data talk to you, let

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the data tell you what's going on. I mean, that's a standard thing

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to say in our industry. But
folks, we're just going to have a

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lot of fun here on our show
today. Talking to of course, Kate

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Stretch. I'm dedicated. I'll never
be able to say your name normally,

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just so you know, because I
now know like you know, and you

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laughed. So come on, a
guy gets the growth laugh, keep saying

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that stuff. Keep her laughing.
That's the joke. And then have you

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00:17:56.920 --> 00:18:02.680
introduced me at like conferences? Now
back to bring Homer Simpson back into it.

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One time on an episode he made
he got marg mad and then he

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made her laugh. He goes,
ah h, I made you laugh.

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I win. He walked away.
Oh we're not on that bad. Don't

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touch that doll. Folks will be
right back. You were listening to Inside

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Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.

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All right, folks, take us
to the future. Indeed, you're on

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Inside Analysis talking to an all star
cast. Scott Taylor, the data whisperer,

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Keate Stratch of Dedicated and next up
one of my favorite people in the

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business. He's just rocking and rolling
on the LinkedIn platform these days with thought

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provoking posts about receipts. He has
all the receipts and he knows about the

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metadata on the receipt one. Malcolm
Chisholm of Data Millennium, welcome back to

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DM radio. Tell us about the
receipts post that got three hundred thousand views.

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Thank you so well, that was, you know, something I always

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wanted to do, which was I
mean, you know, how do you

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bring home data to people and the
different types of data? Well, do

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data analysis of a receipt. We
all deal with a receipts, I mean

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just normally throw them away or even
refuse to accept them. But if you

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look at them, there's a lot
of data on it, and some of

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it I frankly, I don't know
what it means. Quite frankly. It's

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all encoded, but a lot of
it you can make out and then you

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can classify and it's met to data. It's transaction data, it's mass to

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data, it's reference data. And
it's you know, useful way, useful

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tool for teaching people about data.
I didn't think it would go very far,

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but it like took off and went
viral, which is quite bizarre.

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So and it's still going strong out
there in the LinkedIn sphere, So there

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you go. Gave me hope that
stuff actually can go viral because I've learned

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some hard lessons over the years that
a lot of what goes viral was paid

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to go viral. So yeah,
I don't think you paid any money to

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promote that, right, That was
no, No, I paid no money.

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Well, I mean except for the
lettuce and whatever else was I bought.

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It cost me about six bucks something
like that to get the receipt,

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so you know, the local supermarket
and the folks of the supermarket were very

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happy because it served as three hundred
thousand views of unpaid advertising for them too,

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So there you go. But I
thought it was very clever because you

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were looking at the receipt and you
pointed out, here's metadata about the product,

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here's metadata about the skew. All. This information is very important information,

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which is a window into the operational
system that is actually generating the receipt,

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right right, and all systems.
It's a composite that's all ending up

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in the receipt of all this data. So you can think about you would

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have to have different systems to handle
different bits to it, but they're all

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coming together there. So it's a
view of data, you know, and

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something you know quite common, but
it's it's it shows the importance of data,

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yeah, and trying to understand where
things come from. The thing I

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like about it is that it is
a reflection of what is coming from the

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inside. There are all these systems
that are tracking the price of products,

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of course, the date and things
of this nature. If you have your

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your magic code or your you know, your rewards number for example, I'm

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like Bill Burr, I don't really
trust the rewards programs. They're always changing

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their minds and pulling the string on
things. But there is some value and

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all that stuff, and it allows
you to kind of see what's inside,

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you know. I remember years and
years ago I worked on a project where

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we connected to Fanny May's deshtop underwriting
system. We're the first company ever to

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do that, and I remember that
the developers having a hard time, and

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I thought to myself, you know, in the office. They use a

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system, a desktop underwriter. It's
an application and if I just did something

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like you know, print to PDF
or something. I think pds are around

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00:22:03.240 --> 00:22:04.480
back then, but it's year two
thousand. I was like, look,

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let's just print out what it needs
and that those would be the fields you

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would look to capture through this web
form. So it's like, how the

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hell hard is that? It's just
the data stream Now you have to securely

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connect to something but in terms of
the data itself, and that's what we

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did, and they freaking it worked. I'm like, well, duh,

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why don't you pay me a thousand
dollars an hour or more? And just

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00:22:25.119 --> 00:22:29.759
for those that are no Desktop Underwriter
is a way to get mortgage applications approved

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so that the government will securitize them
and put them into mortighbacked security. So

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it's pretty important part of our economy
actually, So yeah, that was that's

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a big deal. So even if
you own a house, you've been probably

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been through it, and even if
you don't know, yeah, it's it's

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00:22:48.599 --> 00:22:52.960
painful. I remember the collateralized debt
obligations and what was Bucket's comment, don't

336
00:22:52.960 --> 00:22:57.559
believe the experience formulas or something like
that. It's like watch out for that

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00:22:57.680 --> 00:23:03.000
kind of stuff. Yeah, those
are the days. Yeah. But the

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00:23:03.359 --> 00:23:07.599
important thing is the data that's in
these systems. And I'm curious to know

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00:23:07.720 --> 00:23:11.000
your thoughts. You've been around for
a number of years here that have seen

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highs and lows and movements like Haddo
coming along, everyone getting excited and now

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of course AI. But to our
point earlier, AI is not going to

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do you much good if you don't
have really high quality data, right,

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00:23:22.160 --> 00:23:25.799
Yeah, I mean talking about the
historical I always thought, you know,

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00:23:26.000 --> 00:23:29.680
slogan of it ought to be we
build the legacy systems of the future,

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you know, so that people truly
understand the value that they add. That

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said, I mean, okay,
AI data is going to be important,

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00:23:40.599 --> 00:23:44.599
but it's a different take on it
is going to because these things work off

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of sentence form you're going to have
to get It's much more about getting unstructured

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data as we would call it,
into a shape that you can push through

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00:23:56.720 --> 00:24:04.200
in data preparation into the into the
you know, AI black hole beast basically,

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00:24:07.119 --> 00:24:11.279
and that data preparation, having looked
into it, is an enormous number

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00:24:11.319 --> 00:24:15.160
of steps. There's all kinds of
things you've got to do. And there's

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things that AI doesn't do in terms
of normal data management practices, like data

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00:24:21.720 --> 00:24:23.799
attention. You forget about it once
it goes in. That's it. It's

355
00:24:23.880 --> 00:24:26.799
not you know, it's like it's
like Las Vegas. It's well, it

356
00:24:26.839 --> 00:24:30.119
comes out again, but I mean
it's you can't make it forget. There's

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00:24:30.119 --> 00:24:34.000
no forgetting mechanism. So if you
put something in like PII, it's not

358
00:24:34.039 --> 00:24:38.799
like you can scrub it. There's
no bleach for AI. So that's a

359
00:24:38.839 --> 00:24:44.559
problem. But this enormous set of
steps to break up things into maybe propositional

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00:24:44.599 --> 00:24:49.920
format. Yes it'll read some structural
data, but mostly it's getting it into

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00:24:49.960 --> 00:24:53.680
that format. And then on the
other side of it. It's not like

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00:24:53.759 --> 00:24:57.119
the thing in Star Trek where you
ask it anything and it'll just tell you

363
00:24:57.200 --> 00:25:03.200
whatever, and it'll like this super
brain. It's it's going to have to

364
00:25:03.240 --> 00:25:06.720
be coupled to use cases, and
those use cases are going to have to

365
00:25:06.759 --> 00:25:11.799
be expressed as prompts. So you've
got these prompt libraries that will turn,

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00:25:11.920 --> 00:25:18.480
you know, the gibberish question that
Malcolm asks the AI into the closest prompt

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and give it the answer associated with
that prompts together question answer pair. So

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all of that is a huge amount
of data management, data preparation, and

369
00:25:27.680 --> 00:25:32.440
you better get it right. Be
goes what I have found in the past.

370
00:25:33.119 --> 00:25:37.200
Maybe this doesn't apply to ai IS, but with documentation is that as

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00:25:37.200 --> 00:25:42.880
soon as somebody finds a bit of
documentation they don't trust like one percent of

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00:25:44.039 --> 00:25:47.640
some threshold like that, they don't
trust any of it. Okay, So

373
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if you get garbage answers out of
AI, then it's going to make people

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thinking. And although they probably you
know, use the day, it's probably

375
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conditioned to think that AI is you
know, going to solve all the problems.

376
00:26:00.559 --> 00:26:07.039
So maybe be a bit more generous
with it than documentation, but they

377
00:26:07.160 --> 00:26:10.359
won't, you know, they'll come
to a point where they don't trust any

378
00:26:10.359 --> 00:26:14.880
of it if this isn't done correctly
right. Well, that is actually a

379
00:26:14.880 --> 00:26:18.319
pretty interesting point, and there is
a trust issue that we're going to have

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00:26:18.359 --> 00:26:22.119
to get around it. There's also
a mathematical issue, because as I understand

381
00:26:22.160 --> 00:26:26.119
it, what these models have done
is first of all, they vectorize,

382
00:26:26.240 --> 00:26:30.319
by which they mean they take words
and turn them into numbers, into a

383
00:26:30.440 --> 00:26:36.359
raise of numbers, and then they're
doing statistical analysis on the numbers, and

384
00:26:36.400 --> 00:26:38.759
then when you sort of decode it
on the other end, it turns it

385
00:26:38.799 --> 00:26:44.839
from numbers back into letters. So
you do have this very interesting transformation like

386
00:26:44.920 --> 00:26:49.920
reminds me of asking, remember asking, Or it switches from letters and sentences

387
00:26:51.000 --> 00:26:56.680
and paragraphs and so forth to numeric
values and then back. So the thing

388
00:26:56.720 --> 00:27:00.240
that kind of strikes me is the
other shoot to drop here is it's going

389
00:27:00.279 --> 00:27:03.720
to be the cost of this stuff
now. Right now, there are billions

390
00:27:03.720 --> 00:27:07.960
of dollars going in by Microsoft and
Google other guys to train these things,

391
00:27:07.400 --> 00:27:11.200
and companies are all going to want
to train their own models. But I

392
00:27:11.200 --> 00:27:14.359
don't think we really have any idea
what the actual cost is going to be

393
00:27:14.759 --> 00:27:17.359
to the user. And I think
that's going to be another shoot to drop.

394
00:27:17.359 --> 00:27:18.599
But what do you think about them? Well, right, I mean

395
00:27:18.640 --> 00:27:22.359
I think that it depends on the
apps that are made from them. So

396
00:27:23.319 --> 00:27:27.279
you know, I'm looking to this
right now because I don't want to use

397
00:27:27.359 --> 00:27:32.640
one of the hosted ones because why
should I give it my intellectual property?

398
00:27:33.160 --> 00:27:34.359
So I'll get one of the ones
I can have on prem Well, it's

399
00:27:34.400 --> 00:27:38.880
going to cost me about five thousand
dollars to buy the equipment, you know,

400
00:27:40.039 --> 00:27:44.319
just to be able to run the
you know, the base version of

401
00:27:44.359 --> 00:27:48.079
one of these. But then I
could do things like, you know,

402
00:27:48.640 --> 00:27:55.079
create something around a curated set of
knowledge where people would understand it will do

403
00:27:55.160 --> 00:28:02.160
that and give you answers to that. But I think a more interesting set

404
00:28:02.160 --> 00:28:07.799
of use cases to me is one
is knowledge integration. So you know,

405
00:28:07.799 --> 00:28:11.599
we talked about data integration like you
know you're talking at MDM, like integrating

406
00:28:11.640 --> 00:28:15.960
customer data earlier. Okay, but
we've got knowledge as well, So I

407
00:28:15.079 --> 00:28:19.720
could theoretically think about nobody likes being
a data steward, So let's have an

408
00:28:19.759 --> 00:28:26.640
AI one. So this AI data
steward is told who looks after you know,

409
00:28:26.720 --> 00:28:30.480
these reference data tables, these code
tables. And then it's also let's

410
00:28:30.480 --> 00:28:37.720
say, coupled to the HR system, and it recognizes the feed from the

411
00:28:37.920 --> 00:28:42.279
HR system, so it says,
okay, Eric is or Malcolm is the

412
00:28:42.440 --> 00:28:47.799
data steward for the country code.
Okay, that's good to know. And

413
00:28:47.839 --> 00:28:51.759
then Malcolm gets fired because he's useless
and is replaced by Eric. But that's

414
00:28:51.759 --> 00:28:56.279
an announcement from HR. Now today
you'd have to go into a data catalog

415
00:28:56.359 --> 00:29:00.279
and manually some pushchmucks data slash data
steward has to update it. But now

416
00:29:00.319 --> 00:29:07.039
we can integrate these various unstructured data
sources and it'll realize who is the true

417
00:29:07.079 --> 00:29:11.279
day's steward of the country coast.
So we've got that as a possibility to

418
00:29:11.279 --> 00:29:15.240
get all this integration of knowledge,
which is something a little bit different to

419
00:29:15.279 --> 00:29:18.000
the structured data integration. See,
so that's a good one. And having

420
00:29:18.039 --> 00:29:25.799
these you know, data steward bots
really you know agents out there doing this

421
00:29:25.920 --> 00:29:29.680
work for us so that we don't
have to do all the donkey work which

422
00:29:29.799 --> 00:29:32.599
nobody wants to do. We keep
telling them they have to do it.

423
00:29:32.680 --> 00:29:36.359
No, no, they don't want
to do it, so and I don't

424
00:29:36.359 --> 00:29:40.000
blame them. So those are there's
a lot of promise there too. If

425
00:29:40.000 --> 00:29:42.720
we can get this to work properly, well, and you know, let

426
00:29:42.720 --> 00:29:45.000
me throw this curve ball at you, and then I'll bring in the other

427
00:29:45.039 --> 00:29:48.079
guests and throw it at them as
well, or some variation of it,

428
00:29:48.119 --> 00:29:52.759
and maybe a hard slider. When
I think about the power of these models

429
00:29:53.240 --> 00:30:00.599
to upon prompt grab information from all
these different sources and fuse it together to

430
00:30:00.640 --> 00:30:03.920
give you some narrative, which is
what they're doing. I wonder to myself

431
00:30:04.440 --> 00:30:10.519
think about all of the heavy lifting
that's done even still today for data warehouse

432
00:30:10.559 --> 00:30:15.359
and extract transform load extract load transform, all these batch windows, which in

433
00:30:15.440 --> 00:30:18.480
large organizations are in the hundreds,
if not thousands. And you have to

434
00:30:18.519 --> 00:30:22.279
know that it's not all change data
capture. There's just a lot of bulk

435
00:30:22.359 --> 00:30:26.799
movement of data picking it up fork
maker over here, pick it back up

436
00:30:26.799 --> 00:30:30.480
again, fork lifted over there.
That's I think that's going to be looked

437
00:30:30.480 --> 00:30:34.279
at as a tremendous waste of time
in the new world. But the key

438
00:30:34.400 --> 00:30:38.480
is can these models short circuit all
of that and just get me the answer

439
00:30:38.519 --> 00:30:41.960
I want somewhat reliably so I don't
have to move all that stuff around anymore.

440
00:30:41.960 --> 00:30:45.400
What do you think I think you're
on something that I think that one

441
00:30:45.400 --> 00:30:51.960
of the impetuses if you look away
the AI audiences that coming from is to

442
00:30:51.960 --> 00:30:55.400
get rid of codeer is to get
rid of data. Engineers don't need them

443
00:30:55.400 --> 00:31:00.440
anymore, we don't want them.
They're a huge expense, So could we

444
00:31:00.519 --> 00:31:03.279
replace them. So you've got you
know, copilot and things like that.

445
00:31:03.640 --> 00:31:10.240
You've got assistance to generate code,
But why not let it do the code

446
00:31:10.279 --> 00:31:12.000
generation. If you could just show
it the data, okay, and do

447
00:31:12.119 --> 00:31:15.640
exactly what you're saying. I mean, how complicated is it? I mean

448
00:31:15.720 --> 00:31:22.519
ETL is you know, the the
ETL products are, or traditionally have been

449
00:31:22.799 --> 00:31:26.440
a replacement for the more expensive kind
of programmer. This is just taking that

450
00:31:26.480 --> 00:31:30.720
evolution a step further. So then
then you know, if you could like

451
00:31:30.960 --> 00:31:36.559
just get rid of the you know, legions and legions of coders that are

452
00:31:36.599 --> 00:31:40.519
out there, you know it's not
good for them, but and replace them

453
00:31:40.519 --> 00:31:45.640
with some skilled people who are driving
this and who you know, constrain the

454
00:31:45.720 --> 00:31:48.319
AI and let it generate the code
based on what it's seeing in the data.

455
00:31:48.359 --> 00:31:55.440
That would be that would be a
massive you know, efficiency gain in

456
00:31:55.480 --> 00:32:00.480
the economy. Yeah. Well,
well and the yeah, I mean,

457
00:32:00.920 --> 00:32:04.920
I asked myself, we have a
show planned next year. What's not in

458
00:32:05.000 --> 00:32:08.960
the crosshairs of this stuff? And
you know the fact is we have lots

459
00:32:08.960 --> 00:32:15.359
and lots of legacy processes, legacy
mindsets, legacy systems. So just give

460
00:32:15.359 --> 00:32:20.799
you one example real quick before the
break, think about MRI scans. My

461
00:32:20.920 --> 00:32:23.319
wife who has to deal with MS, which is a real pain. She

462
00:32:23.599 --> 00:32:28.319
has to go get these MRIs every
once in a while. Those files are

463
00:32:28.559 --> 00:32:30.200
massive. I mean, you have
you can fit them on a cdond,

464
00:32:30.279 --> 00:32:36.400
but it's a huge file. If
you vectorize something like that, number one,

465
00:32:36.440 --> 00:32:40.160
it's a form of compression. But
number two, it facilitates statistical analysis.

466
00:32:40.640 --> 00:32:47.640
So isn't that a use case for
vectorization to vectorize graphic images big ones

467
00:32:47.680 --> 00:32:52.640
into numbers, which is much smaller
and allows statistical analysis. What do you

468
00:32:52.680 --> 00:32:57.759
think? Well, I think we're
we' I don't. I think you're right,

469
00:32:57.799 --> 00:33:00.200
But I think we're part of the
way there already with the image processing

470
00:33:00.279 --> 00:33:06.680
for features that we have that are
available in current libraries which are pre AI.

471
00:33:07.240 --> 00:33:09.480
So a lot of that can be
done already. I've seen it done,

472
00:33:10.440 --> 00:33:14.519
you know, building data pipelines to
do just that, like has the

473
00:33:14.759 --> 00:33:20.839
has this you know, tumor progressed
regress whatever. So so we've got a

474
00:33:20.839 --> 00:33:22.640
lot of that, So that would
be taking a step further. Why not?

475
00:33:24.119 --> 00:33:28.759
Yeah, I mean I think again, what isn't in the crosshairs of

476
00:33:28.839 --> 00:33:31.440
these new technologies, you know,
and I think all you you're in the

477
00:33:31.440 --> 00:33:37.680
process. We could have a you
know, we could have an AI illusion

478
00:33:37.400 --> 00:33:43.680
of a host who could ask the
questions you know you can get you can

479
00:33:43.720 --> 00:33:46.839
get Jeni jen a Ai to ask
to you know, give you questions.

480
00:33:47.480 --> 00:33:52.359
And you couple that with one of
the you know, the video generator for

481
00:33:52.440 --> 00:33:58.119
things that they have and they are
you've got a new personality. Yeah.

482
00:33:58.160 --> 00:34:00.839
I think in terms of its storyteller, it's still pretty formula, but it's

483
00:34:01.000 --> 00:34:05.000
you know, you have to figure
it's learning. It's going to learn.

484
00:34:05.039 --> 00:34:07.279
It's going to get better and better
at these things, and you're going to

485
00:34:07.359 --> 00:34:12.960
have your choices. Maybe maybe they'll
plateau. Yeah, hopefully we'll see,

486
00:34:13.119 --> 00:34:15.519
well they'll touch the doubt. Folks
will be right back. You are listening

487
00:34:15.559 --> 00:34:27.639
to Inside Analysis. Welcome back to
Inside Analysis. Here's your host, Eric

488
00:34:27.840 --> 00:34:35.039
Tabanaugh. All right, folks,
back here on Inside Analysis, talking all

489
00:34:35.079 --> 00:34:38.079
about all things data. Time to
refine. You have to refine the data

490
00:34:38.159 --> 00:34:42.320
to get value from it, just
like oil, just like water. I

491
00:34:42.320 --> 00:34:45.079
heard the water analogy at a conference
this past week and I was joking,

492
00:34:45.320 --> 00:34:49.360
we're all thirsty. Look at the
oceans. They're huge, but you can't

493
00:34:49.440 --> 00:34:51.920
drink that water. You got to
desalidate it. You got to clean it

494
00:34:51.960 --> 00:34:53.119
out, You got to get the
fishes out of there, You got to

495
00:34:53.119 --> 00:34:57.760
get all kinds of fun stuff out
of there. So we understand that refined

496
00:34:57.960 --> 00:35:02.760
data. But I'll throw it over
to Kate's that of dedicated to share with

497
00:35:02.840 --> 00:35:07.239
us her thoughts on on best practices
going forward. We have a second chance

498
00:35:07.280 --> 00:35:10.519
for data. I think our audience
has agreed with me on that. What's

499
00:35:10.559 --> 00:35:15.559
your advice for companies to build their
own AI model with refined data? How

500
00:35:15.559 --> 00:35:19.760
do you get that process started?
Yeah, you know, when you said

501
00:35:19.800 --> 00:35:22.480
best practices, I was actually going
to take it into the direction of data

502
00:35:22.800 --> 00:35:29.920
privacy because something Malcolm shared earlier when
we was talking about receipts reminded me of

503
00:35:30.079 --> 00:35:32.440
a story I had from a couple
of months ago. I went clothes shopping,

504
00:35:32.480 --> 00:35:36.320
So raise your hand if you've been
clothes shopping in the past few months,

505
00:35:36.559 --> 00:35:39.559
right in store, not online.
And I think we share so much

506
00:35:39.639 --> 00:35:44.639
information online that when I don't really
do in store shopping. But when I

507
00:35:45.159 --> 00:35:47.400
got there, they said, do
you want to put your email or phone

508
00:35:47.440 --> 00:35:51.239
number into the system? Literally every
store, and I'm like, no,

509
00:35:51.880 --> 00:35:52.960
I don't want to put my information
in. I just don't like it.

510
00:35:53.000 --> 00:35:55.719
I don't like all the spam.
It was fine, and then I get

511
00:35:55.760 --> 00:36:00.280
to one store I'm not going to
name and shame them, but they're like,

512
00:36:00.320 --> 00:36:01.719
okay, do you want to receive
After our hole back and forth of

513
00:36:01.719 --> 00:36:05.239
are you sure you don't want to
sign out, like yeah, I'm sure,

514
00:36:05.760 --> 00:36:07.000
fine, okay, so they're like, okay, so in order to

515
00:36:07.000 --> 00:36:09.960
get a receipt, you're going to
need to give us a phone number or

516
00:36:10.000 --> 00:36:14.480
an email address. So I'm like, okay, well, I guess I

517
00:36:14.559 --> 00:36:16.599
won't get a receipt then, and
so they come back and they're like,

518
00:36:16.599 --> 00:36:20.800
well, you can't return your stuff
with that a receipt. And these were

519
00:36:20.800 --> 00:36:22.679
shoes for my kids, So I'm
like, okay, well, if they

520
00:36:22.679 --> 00:36:24.440
don't fit, I'm gonna have to
come back. So they sort of put

521
00:36:24.480 --> 00:36:29.000
me in this position where I won't
be able to return it, but they're

522
00:36:29.039 --> 00:36:34.079
sort of forcing me into sharing my
data. When you were talking about receipts

523
00:36:34.079 --> 00:36:36.599
Welcome, that was all I could
think about, Like, Yeah, we

524
00:36:37.039 --> 00:36:42.239
have all this information that they're collecting, but sometimes I just didn't want that

525
00:36:42.360 --> 00:36:45.719
to happen. I ended up not
getting my receipt. But I think one

526
00:36:45.719 --> 00:36:50.599
of the best practices that I can
think of for organizations and data is making

527
00:36:50.599 --> 00:36:57.239
sure that the people they're taking data
from feel comfortable enough sharing that and sort

528
00:36:57.280 --> 00:37:00.679
of allowing them to opt out of
that process if that thing that they want

529
00:37:00.719 --> 00:37:04.199
to do well. You bring up
a very interesting point, which is getting

530
00:37:04.280 --> 00:37:07.760
forced into using a certain system,
and this some thing is the dark side

531
00:37:07.760 --> 00:37:13.159
of digital transformation. You know.
I went to a hotel in Austin,

532
00:37:13.199 --> 00:37:15.880
Texas last October and there was no
one at the desk. There was no

533
00:37:15.920 --> 00:37:19.719
one to talk to to give me
my keys or anything. It was all

534
00:37:19.840 --> 00:37:23.800
this this online process of using your
phone and I had to like show it

535
00:37:23.840 --> 00:37:29.800
a picture of my driver's license to
capture that. I'm like, how arwellian

536
00:37:30.039 --> 00:37:31.559
is this weird situation? And it
was a real pain in the rear.

537
00:37:31.639 --> 00:37:35.599
I'm not gonna lie. It was
like you really can't afford to have one

538
00:37:35.639 --> 00:37:38.280
person sitting there and like get paid
twenty bucks an hour to take information,

539
00:37:38.880 --> 00:37:42.519
Like that's how far we're going to
go? Or I'll throw to Kate and

540
00:37:42.519 --> 00:37:45.599
then we'll get the other gentleman in
on this. The McDonald's kiosks where they

541
00:37:45.639 --> 00:37:49.639
want you to go and find your
food. Look, if you just do

542
00:37:49.760 --> 00:37:52.800
the guarden variety something, okay,
But if you have special orders, it's

543
00:37:52.880 --> 00:37:55.079
like, oh my god, hold
on this menu, over to that menu

544
00:37:55.239 --> 00:37:59.880
and this other now wait which I
lost it. It's just a total nightmare.

545
00:38:00.159 --> 00:38:02.320
There are some things that should not
be digitally transformed. Real quick,

546
00:38:02.400 --> 00:38:05.880
key, what do you think?
Yeah, so real quick, I'll tell

547
00:38:05.880 --> 00:38:08.239
you. I think there are preferences. Some people, probably about half the

548
00:38:08.280 --> 00:38:13.679
population, would want to avoid eye
contact and people, and they would love

549
00:38:13.719 --> 00:38:16.679
it. So I think just providing
the option for someone like you who wants

550
00:38:16.679 --> 00:38:20.840
that social interaction of like, here
you check my idea, I don't want

551
00:38:20.880 --> 00:38:24.480
to scan this, And then I
think just just long story short, have

552
00:38:24.639 --> 00:38:29.159
both options because I know that could
speed things up if there's a really long

553
00:38:29.199 --> 00:38:30.960
line and you've got that one person
who just maybe is new and there's know

554
00:38:30.960 --> 00:38:35.119
what they're doing, or one system
crashes. You have all these other options

555
00:38:35.119 --> 00:38:37.760
of people doing it themselves. But
I personally like, if I'm going to

556
00:38:37.800 --> 00:38:40.920
get do food shopping, I'm not
going to do the self checkout when I

557
00:38:40.960 --> 00:38:45.679
see even if there's a line.
I just why there are people a right

558
00:38:45.719 --> 00:38:49.679
there who are going to do it
for you. Where my mom would insist

559
00:38:49.800 --> 00:38:52.719
on self checkout, even if it's
a cart full of stuff, I'm like

560
00:38:52.719 --> 00:38:54.599
you might you might as well work
here. Now you're just you're doing it

561
00:38:54.639 --> 00:38:59.480
all, so there should be at
least a discount for that service. Shoper

562
00:38:59.559 --> 00:39:01.280
makes that joke. It's like,
why don't I just check it out myself

563
00:39:01.280 --> 00:39:04.159
and I get a discount? Or
what's going on? I will? Yeah,

564
00:39:04.199 --> 00:39:07.519
exactly when did I sign up to
dive an Intel insurance plan? What's

565
00:39:07.519 --> 00:39:09.239
going on here? Oh? Why
am I working for you? Well,

566
00:39:09.239 --> 00:39:14.239
Scott Taylor, I'm sure you don't
mind hitting all the different buttons in the

567
00:39:14.280 --> 00:39:16.320
menus. I don't, I don't
mind. I guess my big question is

568
00:39:16.360 --> 00:39:20.760
based on the conversation we just had. Kate, did the shoes fit your

569
00:39:20.840 --> 00:39:23.639
kids? Yes, the shoes fit. I did not have to return,

570
00:39:23.719 --> 00:39:30.079
but I'm not going back to this
youw okay? That's good? Looks like

571
00:39:30.199 --> 00:39:34.920
radio did. I had the opposite
experience recently at a panera where they had

572
00:39:34.920 --> 00:39:37.599
a bunch of things on the menu
and I was sitting there, going,

573
00:39:37.679 --> 00:39:40.159
I have to ask these people what's
in this salad? Like three different salads,

574
00:39:40.800 --> 00:39:45.000
and they had a kiosk and they
had the nutrition information and they listened

575
00:39:45.039 --> 00:39:46.400
to every single item that was in
there was like, all right, that's

576
00:39:46.440 --> 00:39:52.039
a lot easier. So yes,
if you have a choice, but sometimes

577
00:39:52.079 --> 00:39:54.079
you feel like not because I don't
want to have eye contact with somebody.

578
00:39:54.239 --> 00:39:58.639
But it just might be a smoother
experience to go this way rather than that

579
00:39:58.679 --> 00:40:02.719
way. So as much as these
things can augment our existence, that's good.

580
00:40:02.800 --> 00:40:07.800
But when it starts to replace it
as an either or or that ridiculous

581
00:40:07.800 --> 00:40:13.760
story you're talking about, Kate of
demanding certain data otherwise you don't get proof

582
00:40:13.880 --> 00:40:17.519
of the transaction we just had.
That's if that's even crazy. That's I

583
00:40:17.599 --> 00:40:23.559
was wondering if it's legal as well, because maybe I don't know. But

584
00:40:23.880 --> 00:40:27.360
if you know, if you spend
some time and you're not from Russia,

585
00:40:27.400 --> 00:40:30.599
you're from Tatika's standard, I guess, so it's not not the same.

586
00:40:30.119 --> 00:40:32.559
You'll probably just say don't shop here
if you don't like it, right,

587
00:40:32.599 --> 00:40:36.559
but I don't know, don't shop
here if we can't get your data.

588
00:40:36.719 --> 00:40:39.239
Yeah, pretty much, that's pretty
strangel malth Im. I'll throw it over

589
00:40:39.280 --> 00:40:45.559
to you for Yeah, I would. I would try my favorite Soviet rule,

590
00:40:45.599 --> 00:40:50.440
which is the rules of there to
be avoided. And you want my

591
00:40:50.519 --> 00:40:54.119
emails, okay, say at gmail
dot com. Okay, so I'm just

592
00:40:54.239 --> 00:40:58.840
lye or you know, we just
said you were the most honest man a

593
00:40:58.840 --> 00:41:01.960
moment ago. You've upset then no, no, no, no breaking Now

594
00:41:02.079 --> 00:41:08.840
you heard it here first open in
this kind of ethical dilemma, you're forcing

595
00:41:08.840 --> 00:41:13.840
me to do something against my will. So this is what you're going to

596
00:41:13.880 --> 00:41:16.360
get. Well, now then you
you still you won't get the receipt which

597
00:41:16.880 --> 00:41:20.679
they will give you. They will
print it out and give it you even

598
00:41:20.679 --> 00:41:24.159
that they don't have only email.
Okay, in that case, what would

599
00:41:24.159 --> 00:41:28.920
do it was a single point of
failure. So this is like when they

600
00:41:28.920 --> 00:41:32.960
had a colonial pipeline ransomware a few
a couple of years back, where they

601
00:41:34.039 --> 00:41:37.679
said they said, well, okay, the system screw up. Well,

602
00:41:37.679 --> 00:41:40.800
why didn't you go back to managing
this pipeline, you know, the mechanical

603
00:41:40.920 --> 00:41:44.840
way that you used to do it, because it's quite old. And the

604
00:41:45.280 --> 00:41:47.039
guy said, well, everybody knew
how to do that is either dead or

605
00:41:47.079 --> 00:41:53.400
retired, and so you know we
are. You can't read maps, you

606
00:41:53.440 --> 00:41:58.880
don't want to make eye contact with
anybody or talk to anybody. Okay,

607
00:41:58.920 --> 00:42:07.199
this is okay until something happens with
this great shaky edifice of technology that makes

608
00:42:07.239 --> 00:42:12.880
our lives easier in the sense that
we don't have to think very much or

609
00:42:13.400 --> 00:42:16.400
engage in too much physical labor anymore. Yeah, but what happens when you

610
00:42:16.440 --> 00:42:21.800
need to so you've got no backup, You're you know, you're making your

611
00:42:21.840 --> 00:42:27.480
civilization extremely vulnerable. So we will
we will see. Well we're talking about

612
00:42:27.480 --> 00:42:30.559
in the break right. So you
have Jena I create the questions for an

613
00:42:30.559 --> 00:42:34.199
interview that you have jen AAI create
the answers for the interview that you haven't

614
00:42:34.320 --> 00:42:39.920
watched the show, and generate a
summary do we need as part of the

615
00:42:39.960 --> 00:42:45.800
show. There bots, they're accounted
as visitors, as viewers to my website.

616
00:42:45.880 --> 00:42:49.559
Look at all the viewers I have. Yeah, I had a nasty

617
00:42:49.679 --> 00:42:52.159
spell, an experience. I don't
know, but I was contacted in by

618
00:42:52.440 --> 00:42:58.400
over LinkedIn by It was a legitimate
company. I'm pretty sure it was a

619
00:42:58.400 --> 00:43:05.400
bot that was text me on LinkedIn
messages and then somebody another bot on email.

620
00:43:06.239 --> 00:43:08.199
And I think we ought to at
least have transparency. Say Hi,

621
00:43:08.280 --> 00:43:12.639
I'm a bot, I'm emailing you
on behalf of you know, blah blah

622
00:43:12.679 --> 00:43:16.360
blah company. There's no there is
no transparency. It's like I had to

623
00:43:16.440 --> 00:43:21.039
prove I had to go through this
LinkedIn certification, where I did have to

624
00:43:21.079 --> 00:43:25.280
do facial recognition stuff to get LinkedIn
to believe I was a human, whereas

625
00:43:25.320 --> 00:43:30.800
no bot has to declare themselves to
be a bot on LinkedIn and doesn't have

626
00:43:30.960 --> 00:43:35.840
to prove that they're not. But
I had. I had to prove I

627
00:43:35.960 --> 00:43:37.559
wasn't. I had to prove I
was human. So that kind of irked

628
00:43:37.639 --> 00:43:45.360
me. Is there a need for
like a reverse Turing test? And yeah,

629
00:43:45.440 --> 00:43:49.320
you're onto something though, because you
get these phone calls too, like

630
00:43:49.679 --> 00:43:52.519
Hi, it's Cindy, I'm calling
you about the new online program. Like

631
00:43:52.559 --> 00:43:57.039
okay, I'm sure there was a
Cindy who recorded that at some point in

632
00:43:57.039 --> 00:44:00.519
time, but you are not on
the phone. N No, there's not

633
00:44:00.519 --> 00:44:06.880
even a Cindy. It's totally generated. True. Oh that's just bizarre.

634
00:44:06.920 --> 00:44:09.480
I think I like opening up one
chat bot and then getting into a conversation

635
00:44:09.559 --> 00:44:13.840
with the other chat bot, So
you get two chat bots talking to each

636
00:44:13.840 --> 00:44:17.760
other. Yeah, and you still
don't get your order through. But it's

637
00:44:17.840 --> 00:44:22.119
fun. It's you know, plenty
of time with my hands in between puppet

638
00:44:22.119 --> 00:44:24.679
takes. Yeah. At a certain
point, customer service does come into play,

639
00:44:25.039 --> 00:44:29.639
and you will remember as Kate will
not to go get that store anymore.

640
00:44:29.880 --> 00:44:32.719
But I have to shout out to
old Radio Shack because in the nineteen

641
00:44:34.559 --> 00:44:38.480
eighties, Radio Shack had the foresight
to ask you for information. They have

642
00:44:38.519 --> 00:44:42.400
a little form you'd fill out when
you bought your batteries or whatever it was.

643
00:44:42.440 --> 00:44:45.199
It was actually very annoying, But
in retrospect, I was like,

644
00:44:45.239 --> 00:44:47.639
wow, they were very forward looking. But they weren't. There was a

645
00:44:47.679 --> 00:44:51.960
comedian who did a bit during that
time about having to give your because they

646
00:44:52.039 --> 00:44:55.239
forced They were really militant about getting
your phone number. They were remember this,

647
00:44:55.280 --> 00:44:59.800
and you need your phone number to
buy batteries. It's a corporate dictate.

648
00:45:00.199 --> 00:45:02.000
Yeah. How's how's radio Shack doing
these days? By the way,

649
00:45:02.079 --> 00:45:06.360
so well, folks who've been listening
to the only coast to coast radio show

650
00:45:06.599 --> 00:45:09.440
here in America about the infationied economy. It's called Inside Analysis. Talk to

651
00:45:09.480 --> 00:45:16.119
you next time. All right,
folks, time for the podcast bonus segment.

652
00:45:16.199 --> 00:45:22.840
We said, stretch and we can
talk about her in different ways.

653
00:45:22.039 --> 00:45:25.159
We will get the information from it. I love Russians like I just love

654
00:45:25.199 --> 00:45:29.400
the Russian accent. It just cracks
me up. Hey, you're bringing me

655
00:45:29.440 --> 00:45:34.320
back to my bow ankle days here
with your with your that's right order?

656
00:45:36.719 --> 00:45:39.159
And they finally and what's Natasha's last
name? And it's talking about trivia since

657
00:45:39.280 --> 00:45:45.000
the bonus? You know Natasha Boris
and Natasha No, what is Natasha fatal?

658
00:45:45.480 --> 00:45:49.960
It's Natasha? Like yeah, so
I like, did not see me

659
00:45:50.039 --> 00:45:52.800
look anything up for that one?
I know that one. Look Trivia Pursuit

660
00:45:52.880 --> 00:46:01.039
days. You are a walking,
talking, large language model. I've certainly

661
00:46:01.079 --> 00:46:07.599
hallucinated given my Berkeley background. That's
pretty funny. I like that. Well,

662
00:46:07.880 --> 00:46:13.440
speaking of hallucinations, I mean,
goodness, gracious, we do have

663
00:46:13.519 --> 00:46:16.320
to get down to business here and
get serious about these AI models. And

664
00:46:16.599 --> 00:46:21.480
this is my prediction that every major
corporation is going to have their own AI

665
00:46:21.559 --> 00:46:22.960
model. Many of them were working
at it already, a lot of them

666
00:46:23.000 --> 00:46:27.480
aren't. In fact, you'll probably
have multiple models where I did multiple data

667
00:46:27.480 --> 00:46:30.280
warehouses. And I get back to
is anything not in the crosshairs here?

668
00:46:30.320 --> 00:46:34.239
I'll throw at first to Scott and
then over to Malcolm. You know,

669
00:46:34.400 --> 00:46:39.320
if you launch one of your own
models, your private, single instance,

670
00:46:39.360 --> 00:46:44.920
if you will single tenants AI model
and start feeding it with your data,

671
00:46:45.280 --> 00:46:47.039
you're going to be able to get
interesting stuff. From that, from a

672
00:46:47.119 --> 00:46:52.920
senior executive standpoint, you'd be able
to ask who's working on this right now?

673
00:46:52.880 --> 00:46:55.360
It's just going to pull this stuff
in. It's kind of crazy how

674
00:46:55.400 --> 00:46:59.239
well it works. I don't fully
understand it. I don't think anyone does,

675
00:46:59.800 --> 00:47:02.840
but Scott'll throw it over to you. I really think that every last

676
00:47:02.840 --> 00:47:07.440
bit of legacy technology is to some
extent in the crosshairs. Now, what

677
00:47:07.480 --> 00:47:13.440
do you think. Don't you hope
so everybody complains about so much of this

678
00:47:13.480 --> 00:47:16.679
stuff, you hope that it gets
transformed in a way. Meanwhile, more

679
00:47:16.719 --> 00:47:21.360
than you know half the world,
if not more, it's still tied to

680
00:47:21.559 --> 00:47:23.320
Excel and things like that, where
they've got to go kind of do their

681
00:47:23.320 --> 00:47:28.360
own little skunk works. But you
know, I hear about what I kind

682
00:47:28.360 --> 00:47:31.960
of want to remind people of is
all this stuff's cool. But how's it

683
00:47:32.039 --> 00:47:38.199
driving the business regardless? How is
this enabling the strategic intentions of your enterprise?

684
00:47:38.400 --> 00:47:42.519
How is it helping you your company
get to where it needs to go?

685
00:47:43.199 --> 00:47:46.440
And that's still the nature of business, right, human interaction, bringing

686
00:47:46.559 --> 00:47:53.239
value to your relationships through your brands
at some form of scale, right,

687
00:47:53.320 --> 00:47:58.760
that still has to happen, right, and and I think, you know,

688
00:47:58.840 --> 00:48:00.480
while Malcolm was talking about coders,
I mean, it wasn't that long

689
00:48:00.519 --> 00:48:02.320
ago, right that they were saying, okay, what you got to do.

690
00:48:02.320 --> 00:48:06.800
Everybody's got to learn to code,
and now it's you know, that's

691
00:48:06.840 --> 00:48:08.280
the equivalent of we all have to
learn how to use a slide role.

692
00:48:08.880 --> 00:48:13.679
So just you know, having the
creative aspects. And what I would counsel

693
00:48:13.719 --> 00:48:15.280
anybody going to the space is make
sure you, you know, you beef

694
00:48:15.400 --> 00:48:22.800
up your creativity interesting capabilities there because
that is going to be tough to to

695
00:48:22.280 --> 00:48:27.639
replace. Yeah, that's despite what's
going on in Hollywood and then you know,

696
00:48:28.360 --> 00:48:30.800
discussions and during the strike about the
use of AI and that sort of

697
00:48:30.840 --> 00:48:36.079
thing. Uh, it seems to
be okay to tell a kind of mediocre

698
00:48:36.159 --> 00:48:38.320
story, but it can't. Hasn't
come up with great stories yet, has

699
00:48:38.360 --> 00:48:42.000
it. Yeah? I don't know, man, this is interesting, Malcolm.

700
00:48:42.000 --> 00:48:44.679
I'll throw it over to you.
You've been tracking this industry for a

701
00:48:44.719 --> 00:48:49.760
long time. There are countless inefficiencies
in how we manage data, how we

702
00:48:49.800 --> 00:48:53.039
move data, what we do with
data. I think these new models,

703
00:48:53.079 --> 00:49:00.480
these new AI models are going to
be truly revolutionary in terms of information persistance

704
00:49:00.599 --> 00:49:04.800
and retrieval. But we do have
to be careful because they do make stuff

705
00:49:04.880 --> 00:49:07.239
up. So how do you know, how do you build an epistemological barrier

706
00:49:07.400 --> 00:49:09.880
into these things? I don't know. Do you have any ideas now?

707
00:49:10.719 --> 00:49:14.559
I'm not sure, but I would. I think one of the things that

708
00:49:14.599 --> 00:49:19.360
we we're going to have that is
a bit difficult to conceive of, is

709
00:49:19.440 --> 00:49:23.280
it's going to replace legacy. I
mean, what we have is an accumulating

710
00:49:23.639 --> 00:49:28.920
layer after layer after layer of legacy
technology. There's going to be a new

711
00:49:28.920 --> 00:49:31.519
one on top of that. It's
kind of the law of primitive survivals.

712
00:49:31.719 --> 00:49:37.119
You've still got you know, I
looked at jobs for Cobolt programmers on LinkedIn

713
00:49:37.159 --> 00:49:40.480
a couple of days ago. You've
got people, you know, still writing

714
00:49:40.519 --> 00:49:46.360
Cobolt and it's not necessarily going away. So we're getting this layered architecture built

715
00:49:46.400 --> 00:49:51.559
up with these things on top of
it, which is becoming increasingly difficult to

716
00:49:51.599 --> 00:49:53.119
manage. It's not like we've had
this before, and you generation, your

717
00:49:53.119 --> 00:49:57.480
technology is supposed to replace everything else. It doesn't. It just becomes another

718
00:49:57.559 --> 00:50:00.719
layer. You know, what's happened
to her? Do that's legacy now?

719
00:50:00.920 --> 00:50:06.840
So I think that's part of it. The other part is this is different

720
00:50:06.920 --> 00:50:10.559
in some ways. It addresses a
different set of use cases. And as

721
00:50:10.679 --> 00:50:14.800
you know what Scott was saying in
terms of the value it brings, I

722
00:50:14.840 --> 00:50:19.519
don't know, but I think it's
like one of these moments where everybody's paralyzed

723
00:50:19.519 --> 00:50:23.119
with fear again that if their competitor
gets hold of this thing and does something

724
00:50:23.159 --> 00:50:25.400
with it, they're going to be
driven out of business. So we got

725
00:50:25.400 --> 00:50:30.039
to do something too, and we'll
spend money on it up until the point

726
00:50:30.280 --> 00:50:34.559
where we've disproven the technology that it
isn't a threat to us. Okay,

727
00:50:34.679 --> 00:50:38.800
like you know the books and mortar
crowd. Then if you remember rebelled against

728
00:50:38.960 --> 00:50:45.440
the dot com people back in the
early two thousand once the dot com bubble

729
00:50:45.480 --> 00:50:50.639
burst, and they took a pretty
terrible revenge on them. Now everything's been

730
00:50:50.719 --> 00:50:54.320
cured since then, but it's it
kind of feels a little bit to me

731
00:50:54.440 --> 00:50:59.079
like that again, Well, everything's
got a label now right slapped on it.

732
00:50:59.199 --> 00:51:05.360
Now with jen it's just like you
know now with Extra Zest. It

733
00:51:05.599 --> 00:51:08.599
just becomes this practization for a lot
of these technology companies, you know,

734
00:51:08.599 --> 00:51:13.239
they rip the data mesh label off
their box and put GENAI on it because

735
00:51:13.239 --> 00:51:15.719
that's the hottest thing now. So
there's it's hard not to be cynical about

736
00:51:15.719 --> 00:51:19.000
some of this stuff. If you've
been in the space for long enough.

737
00:51:20.440 --> 00:51:25.039
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It's downtown waiting for you. She
has not a thing that really has

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00:57:47.480 --> 00:58:00.719
to mean she's going anywhere. I
haven't seen it show in the yes but

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00:58:00.920 --> 00:58:07.800
a sweary NBC News Radio. I'm
Chris Karragio. A young Israeli American child

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00:58:07.880 --> 00:58:12.840
is among the hostages being reunited with
family members after being released on the third

814
00:58:12.960 --> 00:58:15.960
day of a ceasefire in the Israel
Hamas War. President Biden confirmed four year

815
00:58:15.960 --> 00:58:22.159
old Abigail Edon arrives safely back in
Israel today in exchange for Palestinian prisoners.

816
00:58:22.440 --> 00:58:25.280
Speaking to reporters in Nantucket, Biden
said he's hopeful a four day truce can

817
00:58:25.320 --> 00:58:30.320
be extended so all of the hostages, including other Americans, can safely return

818
00:58:30.360 --> 00:58:34.920
to their families. The Israel Prison
Service confirmed the thirty nine Palestinians were released

819
00:58:34.920 --> 00:58:38.840
today in accordance with the temporary peace
Agreement. A Republican congressman says he supports

820
00:58:38.840 --> 00:58:44.400
how Speaker Mike Johnson despite voting against
Johnson's recent spending bill to prevent a government

821
00:58:44.400 --> 00:58:49.039
shutdown. I don't think that most
Republicans blamed Speaker Johnson for the challenges he's

822
00:58:49.079 --> 00:58:52.559
facing. Those were created during the
McCarthy time period. Speaking on CBS's Face

823
00:58:52.599 --> 00:58:57.320
the Nation, Ken Buck of Colorado
says he believes Johnson is trying to reduce

824
00:58:57.320 --> 00:59:00.480
government spending and is going about it
the right way. Buck added that,

825
00:59:00.599 --> 00:59:05.239
unlike former Speaker McCarthy, Johnson will
not face a Republican backlash for reaching across

826
00:59:05.280 --> 00:59:08.760
the aisle to get his spending meal
passed. Congress returns from Thanksgiving break tomorrow

827
00:59:08.880 --> 00:59:13.400
with a full load of legislative business
to take care of before the end of

828
00:59:13.440 --> 00:59:16.039
the year. Pope Francis delivered his
Sunday prayer and message from the chapel of

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00:59:16.039 --> 00:59:21.559
his Vatican residence instead of from the
balcony overlooking Saint Peter Square Today. The

830
00:59:21.599 --> 00:59:24.280
Pope's remarks were televised on a JumboTron, so he didn't have to venture outside

831
00:59:24.280 --> 00:59:28.400
in the cold. While suffering from
the flu. The eighty six year old

832
00:59:28.440 --> 00:59:32.239
Francis went to a Rome hospital yesterday
for a scam that ruled out lung complications.

833
00:59:32.400 --> 00:59:36.559
The pope is still scheduled to travel
to Dubaid later on in the week

834
00:59:36.760 --> 00:59:39.880
to attend the UN Climate Change Conference. Tomorrow is cyber Monday, and the

835
00:59:39.960 --> 00:59:45.360
FBI is warning online shoppers to beware
of scammers. Agents say scams range from

836
00:59:45.519 --> 00:59:51.159
untrustworthy or fake websites to fake emails
offering great deals to phony charities. Smaller

837
00:59:51.239 --> 00:59:54.159
cyber scams run by individuals and groups
are common. The FBI says it's good

838
00:59:54.159 --> 00:59:57.800
to remember the old adage, if
it looks too good to be true,

839
00:59:57.840 --> 01:00:01.000
it probably is. I'm Chris Garagio
News right, you're on board k c

840
01:00:01.159 --> 01:00:07.760
AA's Inland Talk Express k c AA
Romelinda ten fifty am, the station that

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01:00:07.840 --> 01:00:08.000
needs no listener behind