WEBVTT

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Into his death is underway. The
United Autoworkers Union is expanding its strike against

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GM, shortly after the union announced
a tentative deal with Stillantis. Yesterday,

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the UAW said it will order four
thousand workers to walk out of GM's spring

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Hill assembly plant in Tennessee. The
move comes the same week UAW reached a

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tentative agreement with Ford. The auto
workers have been on strike since September fifteenth.

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I'm Chris Caragio, NBC News radiomor
or on board kcaa's Inland Extress KCAAA

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M Linda ten fifty am, the
station that needs notice Here behind, the

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information economy has a ride. The
world is teeming with innovation as new business

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models reinvent every industry industry. Inside
Analysis is your source of information and insights

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about how to make the most of
this exciting new era. Learn more at

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inside analysis dot com, insideanalysis dot
com and now here's your hope to Eric

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Cavanaugh. All right, ladies and
gentlemen, Hello, and welcome back once

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again to another episode of Inside Analysis, the only coast to coast radio show

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in the US of A that's all
about the information economy. Yours truly,

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Eric Kavanaugh, here and what a
treat. I'm actually dialing in from way

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across the pond. I'm over in
Vietnam, big thanks to FPT Software.

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Their headquarters is right here in Hanoi, Vietnam, so I'm here for their

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big annual events what they call their
own cees if you will, and they're

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doing some amazing things. They're gonna
announce a big partnership later on tomorrow with

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Landing Ai, which is a huge
AI company. So just like lots of

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other firms like Data Bricks and Amazon
and Microsoft, these folks are going all

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in on AI. Google of course
as well is doing the same. Oracle

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as talking about it, IBM is
talking about it. Everybody is going all

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in on AI and why shouldn't they? And for today's show, we're going

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to take a little bit of a
twist. I'm going to be interviewed myself,

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that's right, yours truly will be
the subject of an interview with DJ

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Murphy of our ex Global. They
are the folks who recently took over the

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Big Data London conference in of course
the UK, and they're also planning right

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now Data Universe, which is going
to be a nice big event on data

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and AI and analytics and so forth
in New York City next April eleventh and

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twelfth. I'm very excited about that
because we missed the Strata conference and these

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folks have come along to fill that
void. And my good buddy and longtime

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friend and colleague, Mike Ferguson is
the one who got me connected with this

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group. He was the conference chair
over at Big Data London, and I

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can tell you it was a huge
success. They had something like twenty thousand

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registrants and fifteen thousand people show up, and they were very clever. They

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gave the tickets away to the delegates. I don't have to pay to go

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into Big Data London. Anyone can
go in, and from what I heard,

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the audience was absolutely fantastic. A
lot of senior level people, a

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lot of practitioners out there learning about
different technologies. What's going on in AI

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and what is not going on in
AI? My goodness, it's everywhere these

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days, between these large language models, so called foundational models, and the

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transformers. Of course, I've got
a thread going with Jurgen schmid Huber.

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He's the guy who wrote the papers
on the transformers, and you'll hear about

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that in this interview with DJ Murphy
and you hear a lot more. But

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if you want to find out more
about Data Universe, send me an email

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info at insidanalysis dot com. And
with that, here's my interview with DJ

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Murphy. Take it away. So
I'll give you a quick background on DM

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Radio. I call it the longest
running show in the world about data.

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We've been broadcasting since two thousand and
eight. It was first just online and

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then about nine years ago, I
guess we cracked onto the actual airwaves and

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brought the show to radio stations.
It's we're at about twelve or fourteen markets

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around the country, some big one
San Francisco, La Chicago, Atlanta,

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et cetera, a bunch of different
markets. And the whole point is to

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just talk about issues related to data
as a resource. So we talk about

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data warehousing, analytics, data ingestion, data movement, data cleansing, data

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quality, anything to do with using
data at some significant scale. And we

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typically get three guests per show,
and the whole idea was to create this

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roundtable style discussion where each guest gets
their own segment of about eight to nine

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minutes to talk about what they're working
on, what they see in the marketplace,

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and then we all do the roundtables
at the end, so it allows

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everyone to get their perspective out within
the context of this conversation, and then

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the roundtables allow us to kind of
tie it all together. So we're in

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year sixteen. Next year is going
to be our seventeenth year, so we

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talked to lots and lots of folks. A lot of things have changed obviously

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over the years. And then we
have another show, inside Analysis, which

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is really all about the information economy, new jobs, new roles, new

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titles, new business models, new
ways of doing things. Anything along those

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lines qualifies for that show, so
it's a bit more broad and focus.

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And then about four years ago,
I guess I got nudged into creating a

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TV show out of this stuff.
We have a show called future Proof,

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which is really just repurposed clips from
the radio shows. So we have a

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student in Chicago the records. They
do all sorts of different camera angles to

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make it feel like a TV show, and then we just carve that out

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and put it onto about six or
seven markets now around the country, including

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Washington, DC. So we just
got our own time slot in DC on

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Channel ten. Which is like one
of the cable companies there, Channel ten.

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We got our own time slots,
so we're pretty excited about that.

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That's crazy. Yeah's great. These
are all things that we can used to

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promote the show and obviously get speakers, get luminaries, advisory board members,

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really whoever. That's fantastic, what
a platform it is. And I'm we're

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so really so happy that you're you're
able to help out on this. Yes,

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it's great. Are you based in
Chicago? No, I'm from Chicago,

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but these days I'm based in Pittsburgh's
okay, I asked, we were

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just my family was just out there
last weekend. My son is a freshman

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at De Paul, so oh,
congratulations. Yeah, No, it's great.

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Where I am. We're in New
England, so it was kind of

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a trip for us. But I
love it. I went to school in

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the Midwest, so familiar with Chicago
and loved and I was really happy when

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he decided to let's agree that.
Yeah, it's great. It's an awesome

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neighborhood, love the area and so
forth. So yeah, so you kind

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of got into this just a little
bit there. Do you do you have

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sort of a basic message or fundamental
themes that you hit when you you know,

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each time you get on the air, whether it be radio or TV.

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I mean, what yeah, okay, go ahead. The standard approach

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I take is that we do topics
so and I can send you if I

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haven't already, I'll give you a
list to the edcal And we actually just

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folded the last five years of Bettertal
calendars into one Google sheet, so you

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can kind of go back in time
and see what was last year, what

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was two years ago, three years
ago, all that stuff and all the

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guests. So if there's anyone you
ever want introductions to, just let me

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know. Connected to probably almost all
the people who've been on these shows.

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But we do specific topics, so
we did yesterday we did a show on

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the modern data stack versus hyper scale
data warehousing. Right. We had the

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guy from OC and Tom, we
had an analyst on. I know a

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lot of these independent analysts too,
so we can get those guys involved however

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you want. I know, Stephanie
said that you're not doing a call for

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speakers this time. It's all going
to be curated, which is probably smart.

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You know, go with people you
know and people you can trust to

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do a good job. So I'm
happy to make some introductions there. But

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basically the shows all have roughly the
same calculus, if you will, and

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that we define a topic, we
go out and find speakers who can speak

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to that topic, and then I
just tell guests, think of two or

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three key points you want to make
on this topic. We'll discuss them in

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the pre call, which is about
fifteen minutes, and then I'll use those

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as guideposts and navigate through a natural
conversation. So we never want to thing

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scripted. Obviously, we do want
to know what we're going to talk about,

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and then what we find is that
there's always something interesting. So a

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lot of times we'll pivot halfway through
because we just pick up on this interesting

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thread. And what I figured out
and this I was hoping this would be

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true and it's been very true,
is that when you get several experts in

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the same domain on the same show, they get excited about that because usually

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they know each other. I mean, I can't tell you how many times

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I've had people go, oh,
I've always loved your work so and so,

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and it's great to be on the
show with you and stuff. And

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what's interesting is that to me one
of the real values is just the experience

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itself. So we're kind of in
the experience management business, and that we

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just give really cool audio visual experiences
to experts in data, to experts in

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technology. We get them all excited
about stuff and then go on about our

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day. Right, right, Why
data? Way back when, when you

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were sort of putting this idea together, why did you land on data or

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you know, what was your experience
at the time. Sure, So I

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worked for a company called Damon Consulting
back in two thousand and one, and

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they were a data warehousing consultancy,
so doing ETL jobs, extract, transform,

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load, filling your data warehouse,
et cetera. And the analyst in

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me just thought, I mean,
this is going to be huge because there's

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so much information. And it's funny
because I knew it would be big,

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and I've been blown away by how
big it got. I mean, I

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didn't even realize because I didn't back
then see where the nexus would occur between

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data and AI. Right, And
that's really the bottom line is that to

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train models, whatever model you want
to train, whatever you want to do

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with AI, you need some kind
of data as the foundation to train it.

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To use it to execute it.
So data is going to be crucial.

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Well how do you get to how
do you understand that data? How

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do you get to data literacy?
So I saw that back in two thousand

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and one at Damen Consulting, and
then I went on my own for a

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couple of years and I figured what
I wanted to do is represent consulting firms

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in data because the consultants are the
intersection between the clients and the technologies,

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right, so they have to understand
business speak, the business problems, the

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challenges also the data, and then
also know that which technology is to use

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and when. And that obviously is
a very challenging situation because you can look

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at a vendor's website and still have
very little idea what they do. I

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mean, I've had many experiences where
I'm like, oh, you sound like

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these guys like are you competitors?
Like no, we're partners. I'm like,

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all right, did not get that
from looking at your website. So

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you have to get into the weeds
a bit to understand. So I focused

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on doing that for a couple of
years, and then I wound up getting

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the Data Warehousing Institute as a client, and then I wound up working for

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the Data Warehousing Institute as an employee
and help them launch their webinar series back

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in two thousand and five, which
has been going ever since. And with

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that we took the same formula,
right. I love having formulae for creating

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content because that way it's consistent and
people know what to expect. And so

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the formula back then was, Hey, you guys have this whole roster of

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consultants who come and teach at your
shows. They're not employees, but their

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partners. I said, let's get
abstracts from them of what they're working on,

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what they want to talk about,
and then we'll shop those abstracts to

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the vendor community to matchmake basically because
TWI is a big list, and these

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vendors they all want demand gen right, that's I mean, they want content

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and they want demand gen. There's
pretty much two things they're always going to

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want. So we just created this
beautiful marshaling area, if you will,

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for conversations around data and technology and
that's been rocking ever since. And then

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after that, I actually pitched them
rady to them. They didn't want it,

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so I was like, Wow,
I'm going to do it. You

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know or know you want it.
So I jumped ship and wound up partnering

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with Source Media, which is based
in New York. Media company you know,

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I know Source Media, Yep,
American Banker, all these guys.

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They were my partner for the first
I don't know, four or five years

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or so, and then that went
away and I worked with the Diversity for

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a while and now we're just kind
of on our own. But that's where

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the data journey began, was like
twenty two and a half years ago,

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and it's just going full full speed
ahead. Well you you were way ahead

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of that. I mean, it
is all about what you foresaw at that

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point. So let me and you
talked about AI and how they relate is

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that. I actually had two questions
here. One was like, what's the

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hottest topic you're talking about on the
show right now? I get the feeling

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that it's AI. Right. Is
AI sucking all the oxygen away from other

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important topics or is it just that
important? Or how are you managing you

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know those discussions both? YEA,
yes, it is absolutely sucking the oxygen

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out of the room, without question, also sucking the money out of the

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DC right. Really, because I
was talking to a guy who was a

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really sharp guy. I would recommend
as a speaker Brian Raymond from Unstructured Io.

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And what they do they call it
et L for LLMS, so extract

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transform for large language models, and
all this stuff is really geared around embeddings.

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And embeddings, as you may know, are in human terms, they're

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like your memories. They're like what
you've learned, right, y'all walk around

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with this whole stack of embeddings in
her mind. And as you can imagine,

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the embeddings from your earliest days are
the most powerful because they're formative.

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Right as you're born into this world. You just have all this capacity to

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learn and figure stuff out, and
your embeddings it's like you're schooling, right.

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Those are embeddings, Like children go
to school and they get embeddings all

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day. And I'm kind of driving
this point home. So with AI,

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it's the same kind of thing.
They have these large language models, which

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are absolutely fascinating. I mean,
there's like fifteen or sixteen of them now,

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and they are these modern era deep
learning neural networks basically, and so

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what they can do is fabricate text. And you probably know this, but

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all they're really doing is predicting what
text they think you want based upon your

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prompt. Right. The problem that
they have is that they will make stuff

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up, and they make stuff up
because that's what they were designed to do.

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They're basically designed to fuse vectors of
information into a string of text or

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into an image according to your instruction. Well, how do you avoid the

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hallucinations is you train the models on
your data, on your trusted corporate data.

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So one of my fun topics here, and I'll even throw this out

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is potential session is large language models
represent a second chance for data. And

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what I mean by that is that
we have spent forty to fifty years now

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doing all sorts of calisthenics around trying
to organize data, analyze data, integrate

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it, curate it, do all
these things in lots of systems. And

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there are, of course databases,
there are analytical systems. There are data

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catalogs, which are a fairly recent
entrance to the market, well fairly recent

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fifteen years or so, twelve years, and there are all these wonderful things.

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But it's a mess out there,
and there are all these different ways

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of doing things. And if you
were to look at the if you were

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to appreciate the corpus of data for
any organization, it's a mess. It's

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all over the map, it's in
all sorts of different formats. It's in

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your email, it's in your powerpoints, all these different things. Well,

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Brian, what unstructured does is they
will take your powerpoints, your PDFs,

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your word documents, your email,
whatever, They'll extract the semantic meaning out

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of that and then publish it as
an embedding in a large language model or

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in a vector database. Basically,
so they are tackling that side of the

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equation. Right. This is not
a perfect metaphor, but if you think,

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let left brain, right brain.
Let's say, let's say the left

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brain is your ability to talk and
to have vocabulary into no words. Your

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right brain is everything you remember.
It's the basically, it's the repository of

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information you use to answer questions.
And that's what we're seeing here with these

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large language models. So what you
want to do is use the LLM for

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its text generative abilities, for its
image generative abilities in that case, but

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you really want to make sure that
it's leveling your trusted data. So what

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does that mean? It means all
these companies have to find a way to

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get their data into embeddings, and
what I'd suggest is only put the good

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stuff in. It's like, you
know, I'll break my own rule and

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letting our kid watch TikTok. We
try to make sure we know what he

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sees. But those are all little
embeddings that are coming in. They're picking

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up all sorts of little things,
and it's like, you want to be

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careful about what goes into that that
matrix. Right. So he is a

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good guest, and he used this
term that I just absolutely love. He

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said these embeddings are anchors of truth. And I was like, oh man,

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yeah, that's good, right,
because you want they're basically moorings.

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They're gonna they're gonna bend the wave, like bend the light wave toward them

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such that it'll pick up the interesting
things and give you good content on the

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other side. Right, So,
and then other you know. So just

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the other day on a radio show, I learned half of what I know.

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On these radio shows. We had
a Bill French who talked about how

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Google's next thing is they have built
a vectorization layer underneath their entire architecture such

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that if you're using bards. It's
not in everyone's account yet, but I

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think it's coming. You can type
at drive and then at Gmail and it'll

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automatically point to your corpus of pre
vectorized data in your g drive and we'll

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pick that up right after the break. You are listening to Inside Analysis.

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

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back here Inside Analysis, and here
is the continuation of my interview with DJ

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Murphy of RX Global and Data Universe. So here Unstructured is trying to make

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their money telling you, hey,
we can vectorize all your content. Google

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is like, if you're on GCP, we already did it for you.

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Don't worry about it. Like what
I mean, think about how crazy that

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is? So long story short,
Yes, LLLMS and AI and the Foundational

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models are absolutely sucking in all the
energy. And it was Brian Ramy who

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told me that there's like thirty billion
dollars in investment in AI related to LLMS

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in like the last nine months or
something. So that's clearly because you know,

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the vcs are always trying to predict
the future, right, and they

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always they always move in packs.
There's usually a first mover and then all

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the rest go in behind. And
you just saw this with Amazon throwing four

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billion at Anthropic. How much that's
a lot of money to throw it at

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something. So they all see it, and I see it too, I'll

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tell you honestly, even though the
hallucinations are a problem. And again,

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and that's when you don't have good
enough moorings, don't have enough angers of

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truths, that's when you get the
hallucinations. My personal opinion, there will

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probably be people who disagree with me
on this. I don't think there's anything

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that's not in the crosshairs right now. What gets done because when you get

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down to brass tacks, access to
data. So data retrieval, data persistence

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are crucial components of every application,
no matter what you do, any software

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you use must have data otherwise what
is it doing. You know, it

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needs data as it's fuel. It
has some sort of form of data as

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its output. So what's not in
the crosshairs. You can use these llms

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to write your emails, you can
use it to write articles. I mean

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you have to fine tune it.
I think if you're going to use that

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responsibly, you have to be careful
about that. But even in my opinion,

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and this is very controversial, I
think a lot of the value that's

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being wrought from data warehousing environments,
which is very expensive stuff, can be

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delivered via these new foundational models.
So, in other words, in data

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warehousing, they put tremendous amounts of
effort into extracting data from source systems,

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transforming it into a schema that can
fit into the warehouse, then managing the

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warehouse doing analysis on that aggregated data. Well, you know that's very expensive,

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it's very time consuming, and the
bottom line is you're in terms of

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what gets actually used, what actually
generates value for the business is probably five

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percent of that data. So in
other words, you're theoretically wasting ninety five

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percent of your time in this old
model. Now, the one person if

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you look at the big companies,
you've got Terearra Data, You've got Alterics,

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you've got Data Bricks, you've got
Snowflake. These are all the sort

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of big data warehouse data analytics vendors. Of those, The one who seems

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to really know what's happening here is
Ali Gatzi of Data Bricks, because he's

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got who threw one point two billion
to buy Mosaic whatever. It was six

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seven, eight months ago, and
the week that deal cleared, I had

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Navin raw on Damn Radio. So
I interviewed the CEO of Mosaic like that

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week, and he's very smart guy, as you can imagine, and he

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was saying that you know, right
now it is the marketing is the content

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creation stuff. But I'm here to
tell you that is just the beginning.

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And then interactive AI is what they're
really talking about. Because I'll throw this

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out at you and then I'll be
quiet for a minute. If you think

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about the complexity of these models learning
to reflect back fully syntactically correct, accurate

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pros, it's pretty complex, well
machine to machine conversations, even though it's

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very difficult for most people to understand
what they're doing. They're much less complex

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than that, which means a foundational
model should be very powerful in terms of

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optimizing your current information and systems architecture
in my opinion. So what does that

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mean? I mean you're already seen
a couple of companies do interesting stuff here.

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Hammer Space is one. I was
on the Data Unchained podcast just earlier

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this week, and they're doing They
are the company that is training Lama too.

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So the new model that's coming out
of Facebook. They're actually doing the

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training of the model with their data, and they do this hyper efficient data

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movement where they only move this piece
of data or that piece of data,

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which is a stark opposition to ETL. In the ETL world, you extract,

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transform, and load huge sums of
data into your data warehouse. And

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the point I'm making here is that
I think we're going to see crazy efficiency

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gains using foundational models in AI to
get just to the nuts that you want,

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so you don't have to shape the
whole tree, gather all the nuts,

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move them across town to a warehouse, then package them, then ship

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them out to people. No,
you're just going to use teeny tiny little

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little drones to deliver eight acorns to
this address in ten acorns to that address.

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Wow, it is moving. Well, it's certainly moving faster than I

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have any sort of conception of I'm
glad there are folks out there like you

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that have some sort of conception of
it. And that actually leads me to

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kind of what I wanted to ask
next about business users and kind of the

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state of their expertise or literacy on
average, Like what you see in across

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businesses. Are people ready to use
this stuff and leverage these advancements? Yeah,

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yeah, even the ones that are
available now, and not even what's

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coming down the pike. I mean, just rough aest me. I'm a

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big fan of the Peretto principle,
right, the eighty twenty rule probably,

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and there were always breakdowns from there. Right, It's like the twenty percent

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of the twenty percent, so probably
four percent of workers are all in now.

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They totally get it, they're working
on it, they're focused on it,

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and they're they're excelling. Overall.
I'd say twenty percent are open to

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it, they've kind of tested things
out, and the other eighty percent are

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probably just too busy doing other stuff, right, And that's the problem,

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right, is like, one of
my favorite people in the world is a

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guy who got me in fact,
a guy who signed the forum to get

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dm Radio launched financially back in two
thousand and seven. I guess it was

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David Greeno. He had this term, and I just love these expressions because

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they communicate so much. They're so
pithy. He referred to the tyranny of

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urgency. I was like, it's
painful to hear that you know, and

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that's what most people are dealing with, is the tyranny emergency. They got

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to get through their emails, they
got to get through all the stuff they

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have to do today, you know, then they got to get the kids

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to Google and all this stuff.
And so that's what's sort of driving the

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reluctance or the inertia on being able
to leverage these technologies. But I think

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that is that's going to change pretty
quickly. I think it's going to be

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you know, thirty seventy within three
months. And you know, Microsoft is

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a reason for that. Google is
a reason for that. You know.

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The fact that you don't have to
go out and buy some software and install

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it, well that's big news.
I mean, honestly, if you if

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you wont my honest opinion on one
of the most important things to have right

358
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now in the software world for survival. It's called install base, like just

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people who are already using your stuff. Right. So, I got an

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email from some girl at Google yesterday
saying, Hi, I'm your account executive.

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I've never heard from any account executive
Google first time ever, and she's

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like, oh, prices are going
up and this is why et cetera.

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Et cetera. But she pointed out
that if you're logged into your Google account,

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you're just going to get all this
new functionality. Right, So the

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fact that you don't have to go
out and buy something like you used to

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in the on prem days, that's
a big deal. So that's going to

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reach the tracks to usage. Now
the question becomes, you know, how

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useful is it. Have they sorted
out the guardrails? I mean, that's

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one of the issues, is the
guardrails on this stuff, and so what

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are those? Well, to a
certain extent, those are embeddings. You

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know, you can do things different
ways. There's this RAG model that came

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out of Facebook and it has to
do with how you actually persisted. So

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retrieval augmented generation is what it's called
IF the strategy that helps address both l

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and hallucinations and out of date training
data. So basically, you use you

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go into the language model, you
pull out your stuff, and at the

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last minute you're like, all right, let's check and make sure that's right,

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and you know, let's throw another
guardrail, if you will, into

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the picture. And so guardrails are
going to be really important. But the

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problem is everything is moving so fast. It's like people just want to go,

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go, go and use this stuff. You do have to be careful

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that you know, it's not hallucinating, it's not making stuff up, because

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you know, for example, chat
GPT. The first time someone searched me

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that I saw, they showed me
what it said and it was very accurate.

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I was like, wow, that's
pretty impressive. The second time I

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did it, it said I'd written
three books. I was like, are

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00:28:22.000 --> 00:28:26.880
you sure. The usually don't write
a book and then black out, but

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you know, yeah, So it's
just you know, that is going to

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be a real serious issue going forward
because you know, think about it.

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We've had to deal with fake news
and now we have this issue of hallucinations

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where these powerful engineers is making stuff
up. And when I was watching the

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Data Bricks conference, there was a
girl doing a great demo about how they

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are allowing the training of these models. And that's what you do, is

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you train it basically. And here's
one of the sort of dirty secrets,

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if you will, well of old
deep learning modules and old AI and new

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as well. I had a guy
in the show. I'd have to look

396
00:29:06.559 --> 00:29:08.759
up his name. He's a very
smart guy. To Boston somewhere and he's

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one of the experts on deep learning
and AI, and he told me a

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00:29:12.119 --> 00:29:15.480
stat that blew my mind. He
said, when you're training this stuff,

399
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and that's when you have human beings
just saying yes or no. You may

400
00:29:18.920 --> 00:29:22.279
have seen the thing is as a
blueberry muffin or a dog, that whole

401
00:29:22.279 --> 00:29:26.279
thing you you come across that because
like the AA engines have a hard time

402
00:29:26.839 --> 00:29:30.119
person figuring it out. He goes, As it turns out, very few

403
00:29:30.160 --> 00:29:36.279
people are good at that task because
it requires you to be very focused and

404
00:29:36.319 --> 00:29:41.440
to always answer correctly. It's like
those stupid captures that you'll get when you're

405
00:29:41.519 --> 00:29:44.240
trying to log into a system and
like, okay, which are these pictures?

406
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Has a motorcycle in it? Like
really, what do I And that's

407
00:29:48.559 --> 00:29:52.400
a fun story too. Did you
know that you're actually training models when you

408
00:29:52.440 --> 00:29:55.960
do that? I did not know
that. Yeah, that's Google and they're

409
00:29:55.960 --> 00:30:00.240
doing They had this crowdsourced effort basically
to train models to identify stuff. So

410
00:30:00.319 --> 00:30:06.000
you're when you do the capture,
you're actually helping train a bit model for

411
00:30:06.119 --> 00:30:10.200
the future. Do you think that's
become its primary purpose and not not the

412
00:30:10.200 --> 00:30:14.559
security or whatever you know, that's
a very very good question. Honestly,

413
00:30:15.119 --> 00:30:18.680
I absolutely detest those captures and I
understand why they're it's necessary, but they

414
00:30:18.720 --> 00:30:22.480
always force me back, I don't
know it's it's and they're they're vague sometimes

415
00:30:23.240 --> 00:30:26.200
to to the to the point of
of you know, them using it to

416
00:30:26.319 --> 00:30:30.359
maybe that's what they're experimenting with as
far as the training goes. But right,

417
00:30:30.880 --> 00:30:37.240
But yeah, so that's kind of
interesting. So like, aside from

418
00:30:37.240 --> 00:30:42.839
AI, where are do you see
trends or anything where people are investing their

419
00:30:42.920 --> 00:30:49.319
time as far as professional growth goes
for businesses and employees within these businesses,

420
00:30:49.359 --> 00:30:55.559
what they're kind of looking at,
Well, clearly they want to educate themselves

421
00:30:55.559 --> 00:30:57.480
on AI. That's that's got to
be the biggest thing. But well,

422
00:30:57.599 --> 00:31:03.920
data is data can the important thing. So data lineage, data quality,

423
00:31:03.559 --> 00:31:10.839
data movement, data pipelines. That's
a very very hot topic because data pipelines

424
00:31:10.880 --> 00:31:15.680
are these as the name would suggest, connections from sources to targets. So

425
00:31:15.720 --> 00:31:22.240
what used to be just ETL extract
transform load is now much more sophisticated where

426
00:31:22.599 --> 00:31:27.119
you'll pull from this system and what
they do is they'll use a graphic interface

427
00:31:27.160 --> 00:31:32.640
where you have different nodes on a
pipeline, and one note will be to

428
00:31:32.720 --> 00:31:36.200
access the data. One no will
be to check the quality of the data.

429
00:31:36.240 --> 00:31:40.279
One note will be to transform the
data into a different scheme of for

430
00:31:40.319 --> 00:31:44.400
example, one note will be to
enrich the data, so add some information

431
00:31:44.440 --> 00:31:48.559
from another source. Basically, data
pipelines are really really important. Real time

432
00:31:48.880 --> 00:31:55.519
is really important. That's a big
deal. There are other technologies too that

433
00:31:55.599 --> 00:31:59.559
are still important even though they're kind
of on a back burner. Blockchain is

434
00:31:59.599 --> 00:32:07.279
still interesting things going on in that
space around durability but also immutability is a

435
00:32:07.279 --> 00:32:12.480
big one. But they blockchain has
had trouble, you know, finding its

436
00:32:12.799 --> 00:32:15.640
its relevant use cases. But I
think it's it's still out there. It's

437
00:32:15.640 --> 00:32:21.079
still an important thing to have and
we'll probably come in handy. And then

438
00:32:21.200 --> 00:32:25.000
just analytics in general, being able
to number crunch hyper scale is an interesting

439
00:32:25.240 --> 00:32:30.680
topic in and of itself. We
had Chris Gladwin of Oscient on our show

440
00:32:30.839 --> 00:32:35.640
yesterday, and they're based out of
Chicago. In fact, he's from Lamont,

441
00:32:35.720 --> 00:32:39.000
where I used to be the reporter
and editor for the Lamont Metropolitan newspaper.

442
00:32:39.039 --> 00:32:42.400
That was my first job right out
of college. They stumbled into a

443
00:32:42.400 --> 00:32:46.319
newspaper job. But they're doing hyperscale
data warehousing where we're talking, and they're

444
00:32:46.400 --> 00:32:52.799
using these new ENVM cards which are
so much more powerful than the old ones.

445
00:32:52.920 --> 00:32:57.400
So he was talking about how when
you retrieve data it's in packets,

446
00:32:57.440 --> 00:33:00.359
it's like, you know, four
bits basically, Well these they can two

447
00:33:00.400 --> 00:33:05.079
thousand, so you're talking about five
hundred x. You're talking about orders of

448
00:33:05.200 --> 00:33:09.519
magnitude greater. And they're just doing
crazy stuff, these huge, huge data

449
00:33:09.519 --> 00:33:15.079
warehouses petabytes of information, you know. So hyper scale is an issue.

450
00:33:15.440 --> 00:33:16.920
Cloud is an issue. Of course, security is always going to be an

451
00:33:16.920 --> 00:33:22.039
issue, you know that. And
with that, will take a quick break

452
00:33:22.240 --> 00:33:25.599
and we'll pick up the conversation once
again. Right after these messages, you

453
00:33:25.640 --> 00:33:42.640
were listening to Inside Analysis. Welcome
back to Inside Analysis. Here's your host,

454
00:33:43.160 --> 00:33:50.039
Eric Tavanaugh. Okay, folks are
back here on Inside Analysis in Media

455
00:33:50.160 --> 00:33:54.359
Race as it, were talking to
DJ Murphy about hot trends like hyperscale and

456
00:33:54.400 --> 00:34:00.359
cloud and security. And here are
my thoughts on security and passwords. I

457
00:34:00.359 --> 00:34:05.599
think passwords are the biggest pain in
the freaking universe and I'm still not entirely

458
00:34:05.640 --> 00:34:07.639
sure why they haven't solved it.
And they're starting to with the biometric stuff.

459
00:34:08.199 --> 00:34:12.639
You started to see more apps on
your phone leverage that. So,

460
00:34:12.719 --> 00:34:16.000
you know, security, even though
the audience is pretty small, it's always

461
00:34:16.079 --> 00:34:22.599
important. And governance. Governance at
the other really big important topic. How

462
00:34:22.639 --> 00:34:27.119
do you govern access to these data
assets? Because in the old days you

463
00:34:27.159 --> 00:34:30.599
had two options. Basically, you
could govern it at the database layer,

464
00:34:30.039 --> 00:34:34.639
meaning you're allowed to access the database
or you're not. Or you could govern

465
00:34:34.679 --> 00:34:38.519
it at the application layer meaning okay, user, your role is X,

466
00:34:38.639 --> 00:34:43.039
you're a manager or whatever, I'll
govern it that way. Both of those

467
00:34:43.039 --> 00:34:45.920
are very inefficient ways to do it. Certainly the database one is incredibly inefficient

468
00:34:46.280 --> 00:34:51.119
because you only want your database administrators
touching that. And you know, if

469
00:34:51.119 --> 00:34:54.840
you have four thousand people who are
allowed to use this analytics software, you

470
00:34:54.840 --> 00:34:59.480
know, having Bob go in there
every day and take out Susie, Okay,

471
00:35:00.119 --> 00:35:02.440
go out, I mean, you
know, automating that, that process

472
00:35:02.440 --> 00:35:09.760
of governance is huge. So I
want to turn to the event a little

473
00:35:09.800 --> 00:35:13.800
bit. You're a broadcaster. You
want to get your message out to as

474
00:35:13.800 --> 00:35:19.039
many people as possible. How important
do you think like a micro sort of

475
00:35:22.199 --> 00:35:27.840
situation like a face to face event
is or it's like the propagation of knowledge

476
00:35:28.119 --> 00:35:30.960
or or whatever. I mean,
you're obviously in this, you want to

477
00:35:30.079 --> 00:35:34.400
you're helping us out and you're advising
us on this. How important are the

478
00:35:34.559 --> 00:35:37.119
are the face to face events?
Oh, they're hugely important, you know,

479
00:35:37.440 --> 00:35:42.880
And we're seeing that come back now, roaring back. Obviously with COVID

480
00:35:43.199 --> 00:35:46.559
everyone went into hiding for a period
of time and for lots of different reasons,

481
00:35:46.559 --> 00:35:51.760
for good reasons, obviously. But
virtual will only get you so far.

482
00:35:51.760 --> 00:35:53.400
And I'm a virtual guy, so
I do virtual stuff all the time.

483
00:35:53.519 --> 00:35:59.599
I love virtual it's really important.
But person to person, shaking hands,

484
00:35:59.719 --> 00:36:02.400
talking of people, sitting down,
it's always important. It's always going

485
00:36:02.480 --> 00:36:08.920
to be important. And these these
focused events are are really crucial, you

486
00:36:08.920 --> 00:36:13.159
know, because I've gone to a
bunch of different conferences over the years.

487
00:36:13.280 --> 00:36:16.639
I've been involved in promoting conferences and
speaking of conferences, et cetera. And

488
00:36:17.000 --> 00:36:22.079
the sort of broader consumer technology ones
aren't so interesting to me because I'm a

489
00:36:22.119 --> 00:36:25.719
data guy. So you have to
have a very focused conversation. And this

490
00:36:25.800 --> 00:36:30.119
is what trade press is all about. And you know, frankly, trade

491
00:36:30.119 --> 00:36:31.800
press, in my opinion, is
dying. It's been dying for a number

492
00:36:31.840 --> 00:36:37.800
of years now because largely of the
Internet, because trade publications used to make

493
00:36:37.880 --> 00:36:42.000
all their money on print dads in
magazines and now those are gone. So

494
00:36:42.840 --> 00:36:45.719
take an industry and just rip the
financial carpet out from under their feet.

495
00:36:46.119 --> 00:36:50.639
That's a problem. So you know
a handful of being able to pivot to

496
00:36:50.840 --> 00:36:53.960
webinars, which are one of the
biggest ones, and obviously events, but

497
00:36:54.519 --> 00:37:00.079
absolutely events are huge, They're very
important. The session design is always important,

498
00:37:00.079 --> 00:37:02.880
you know, making sure you've covered
all the bases of you know,

499
00:37:02.880 --> 00:37:07.639
who wants to learn about what?
But I mean no doubt in person events

500
00:37:07.760 --> 00:37:12.719
are awesome. Do you think there's
a takeaway for this particular event that's going

501
00:37:12.760 --> 00:37:17.400
to be important for the people that
attend. Granular knowledge, you know,

502
00:37:17.559 --> 00:37:24.039
workshops like have a combination of sessions. Round Tables are always fun. I

503
00:37:24.079 --> 00:37:30.360
love roundtables and I'll have to moderate
roundtables in part because you get the chemistry

504
00:37:30.400 --> 00:37:36.719
of the group involved, right and
with a single presenter it's just one person,

505
00:37:36.800 --> 00:37:39.199
even two. But when you have
a round table, especially if they're

506
00:37:39.280 --> 00:37:44.800
you know, open minded to how
that gets managed, you can sort of

507
00:37:44.840 --> 00:37:49.760
build up a momentum and a chemistry
that you can't get in other sessions.

508
00:37:49.840 --> 00:37:52.960
Now you have to have people who
moderated effectively obviously and really know what they're

509
00:37:52.960 --> 00:37:58.239
talking about. But that's that would
be one of my suggestions is have numerous

510
00:37:58.360 --> 00:38:02.800
roundtables that are really interactive and dynamic
right where you go in. I always

511
00:38:02.800 --> 00:38:06.639
tell people, I always joke,
we'll talk about what we're going to talk

512
00:38:06.639 --> 00:38:07.960
about, but once the bell rings, all bets are off. Yeah,

513
00:38:08.320 --> 00:38:13.320
it's live conversation, so you know, we'll see where things go. So

514
00:38:13.320 --> 00:38:15.400
I'd be sure to do that.
And then you know, meet and read

515
00:38:15.480 --> 00:38:22.000
sessions and free cocktails. That's always
that's the best part of a live event,

516
00:38:22.119 --> 00:38:23.079
isn't it. That's what I tell
my wife. I go to these

517
00:38:23.079 --> 00:38:27.840
events for the free food and the
free cocktails and like joking, half joking,

518
00:38:27.880 --> 00:38:30.239
but it does helps people, you
know, especially at the end of

519
00:38:30.239 --> 00:38:34.400
the day, like the six o'clock
thing, the seven o'clock thing, you

520
00:38:34.400 --> 00:38:37.320
know, that's when everyone has been
talking all day business. They want to

521
00:38:37.360 --> 00:38:40.000
go, you know whatever, drink
wine or even a coke or something and

522
00:38:40.079 --> 00:38:45.320
just chat about what's going on.
All that stuff is really important. As

523
00:38:45.320 --> 00:38:50.880
someone advising on the program, What
do you hope that this event will deliver

524
00:38:51.199 --> 00:38:55.159
to the community that you communicate to
on a you know, daily or weekly

525
00:38:55.199 --> 00:39:00.840
basis, that may they may not
be getting elsewhere. That's a hard fact,

526
00:39:02.039 --> 00:39:06.360
you know. So Mike Ferguson,
he's the guy who suggested that you

527
00:39:06.400 --> 00:39:09.119
all talk to me, who's an
incredibly knowledgeable guy. Like getting a dozen

528
00:39:09.199 --> 00:39:15.599
folks of that caliber too. I
won't call them fact checkers because I don't

529
00:39:15.599 --> 00:39:20.519
like all fact checking thing, but
they what's really cool is that when someone

530
00:39:20.559 --> 00:39:24.679
when these vendors know that there's someone
in the room who knows all their weaknesses,

531
00:39:24.800 --> 00:39:31.119
boy do they behave they just know
we learned this from experience that you

532
00:39:31.119 --> 00:39:32.639
know, if you get a round
table. When I get the end,

533
00:39:32.639 --> 00:39:37.880
I always want an analyst on the
call. If I can three vendors like,

534
00:39:37.920 --> 00:39:43.119
it's amazing how constructive the conversation becomes
because number one, they don't take

535
00:39:43.119 --> 00:39:45.719
pot shots at each other. Number
Two, they don't talk about stuff they

536
00:39:45.760 --> 00:39:50.039
don't have, you know. I
mean I often joke that talking to especially

537
00:39:50.280 --> 00:39:53.440
a bigger enterprise software executive, is
like talk to a politician. I mean,

538
00:39:53.480 --> 00:39:57.559
they have their messaging down, they
have their talking points, and you

539
00:39:57.639 --> 00:40:00.320
got to find some way to get
them off with that message, to get

540
00:40:00.360 --> 00:40:06.360
some gritty detail, and that's usually
a curve ball of some kind or you

541
00:40:06.360 --> 00:40:08.000
know, some question that pops up
and you have to wait and let it

542
00:40:08.159 --> 00:40:13.199
kind of flow out, as opposed
to you can't badger someone, right,

543
00:40:13.239 --> 00:40:16.280
this is all it's all friendly media. I don't do sixty minutes type stuff,

544
00:40:16.360 --> 00:40:19.960
right, I'm always doing friendly media, But I want to get to

545
00:40:20.000 --> 00:40:22.400
the meat. I want to get
to the hard facts that people need to

546
00:40:22.440 --> 00:40:25.480
know, like, oh, you
want to use this technology that's out knowledge.

547
00:40:25.519 --> 00:40:29.599
You know, they don't actually work
well, or they are maybe they're

548
00:40:29.679 --> 00:40:32.760
very complimentary. You don't know that
stuff until you've talked to someone who has

549
00:40:32.840 --> 00:40:36.400
used it, who has built the
solution, you know, who's worked with

550
00:40:36.480 --> 00:40:38.840
it. That's where you find the
bugs and the sort of rough edges on

551
00:40:38.960 --> 00:40:43.079
things. And that's that's what I
would love people to get more of,

552
00:40:43.239 --> 00:40:45.639
is you know, what are the
rough edges in fact that could even be

553
00:40:45.679 --> 00:40:52.440
an interesting show or an interesting conversation
is you know, what do people think

554
00:40:52.519 --> 00:40:58.599
is the case versus what really is
the case? AI? And it's like

555
00:40:58.719 --> 00:41:02.519
we learn that with that New York
Times guy who interviewed chat GBT when it

556
00:41:02.519 --> 00:41:08.280
first came out, and I try
to tell people he is responsible for that

557
00:41:08.360 --> 00:41:14.280
conversation going sideways because he misunderstood what
the tech is. He's treating it like

558
00:41:14.320 --> 00:41:19.559
a sentient being, trying to,
you know, get it to say colorful

559
00:41:19.559 --> 00:41:22.559
things, which it clearly did.
But again, all it's doing is reflecting

560
00:41:22.679 --> 00:41:28.039
back from the corpus of techs that
it was trained on what it thinks you

561
00:41:28.119 --> 00:41:30.760
want to hear. So you know, prompt engineering, for example, is

562
00:41:30.920 --> 00:41:36.840
absolutely do workshops on that all day
long on prompt engineering, because that is

563
00:41:37.480 --> 00:41:42.440
it's really crucial to get what you
wanted of something, to get very precise

564
00:41:43.039 --> 00:41:45.519
instruction, and it will take that
instruction like. That's the thing is,

565
00:41:45.599 --> 00:41:50.320
it's like a little assistant who will
do everything you ask it to do as

566
00:41:50.360 --> 00:41:52.599
best as they can do it,
you know, And I always tell it

567
00:41:52.639 --> 00:41:58.400
please and thank you always. It
won't filter out your worst impulses. It'll

568
00:41:58.480 --> 00:42:01.400
just always be nice to the machines. You know, they may wake up

569
00:42:01.400 --> 00:42:04.199
one day, I don't know.
There you go, There you go,

570
00:42:04.639 --> 00:42:07.639
Eric, thank you very very much. What I'm gonna do is I'm going

571
00:42:07.719 --> 00:42:13.000
to go through and do this up
sort of as a Q and a I

572
00:42:13.039 --> 00:42:19.119
will shorten your most of your answers
tragically unfortunately, but I am going to

573
00:42:19.159 --> 00:42:22.000
send it to you before we publish, just in case I've edited something the

574
00:42:22.000 --> 00:42:27.639
wrong way or whatever. And let's
shut. Okay, Thank you so much.

575
00:42:27.679 --> 00:42:30.960
I appreciate you recording it. Send
me the transcript when you have a

576
00:42:30.039 --> 00:42:35.800
chance. And this has been great, and hope to have a chance to

577
00:42:35.800 --> 00:42:44.320
meet so. And that concludes my
conversation with DJ Murphy from our ex Global

578
00:42:44.559 --> 00:42:49.559
Read Exhibitions. The folks who are
putting on the Data Universe conference in New

579
00:42:49.639 --> 00:42:53.320
York City April eleventh and twelfth,
be sure to check that out. We're

580
00:42:53.360 --> 00:42:57.639
working on the speakers right now,
working on the themes, working on all

581
00:42:57.639 --> 00:43:00.159
that kind of fun stuff. Should
be a fantastic show. And look for

582
00:43:00.199 --> 00:43:04.440
yours truly to be walking around.
If you want to offer some advice on

583
00:43:04.480 --> 00:43:07.320
what you think the show should cover, by all means, send me an

584
00:43:07.360 --> 00:43:12.239
email info at insideanalysis dot com.
You know, I'm always keen to find

585
00:43:12.280 --> 00:43:15.239
out what folks are looking to learn, and these days it's going to be

586
00:43:15.280 --> 00:43:19.519
a lot about those foundational models.
It's going to be a lot about how

587
00:43:19.599 --> 00:43:22.360
to train large language models. It's
going to be a lot about what else

588
00:43:22.599 --> 00:43:25.599
is in the crosshairs, And to
be honest, folks, I don't think

589
00:43:25.719 --> 00:43:30.320
anything is not in the cross hairs
of these foundational models. We're going to

590
00:43:30.400 --> 00:43:35.719
find out. But at the moment, according to Navin Raw of Mosaic mL

591
00:43:35.840 --> 00:43:39.079
and several other experts I've talked to, yes, it's mostly marketing copy.

592
00:43:39.119 --> 00:43:45.440
It's mostly copy for blogs and articles
and emails and all sorts of other things.

593
00:43:45.840 --> 00:43:47.199
So a lot of that stuff's going
to be going on, but I'm

594
00:43:47.239 --> 00:43:52.239
telling you other things are in the
cross heres. Interactive AI is going to

595
00:43:52.280 --> 00:43:55.480
be huge. Interactive AI is where
we're going to get much deeper in terms

596
00:43:55.480 --> 00:44:02.000
of letting AI run systems and optimize
information architectures, optimize how things get done

597
00:44:02.159 --> 00:44:07.880
in the real world. That's coming
soon to a an enterprise near you.

598
00:44:08.119 --> 00:44:17.920
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With sixty years of fascinating facts. This

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is the man from yesterday and back
in time to this time. In nineteen

628
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sixty three, it's announced that First
Lady Jackie will join President Kennedy in a

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Democratic Party fundraising swing into Texas November
twenty first and twenty second. It'll be

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Jackie's first political trip with President Kennedy
since he took office as it itorerary include

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swings into Houston, Dallas, Fort
Worth, and Austin. It's funny.

632
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I used to worry about going into
the White House. It'll be a goldfish

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bowl of secret service. I'll never
see my husband. Then you've found out

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that, you know, it was
really the happiest time in my life.

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And from this time in nineteen eighty
two, filming raps for a new Dustin

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Hoffman movie, Tutsi, which sees
Hoffmann disguised as a woman to get acting

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parts. Also in that movie,
Jessica Lang, Terry Garr and Bill Murray.

638
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I'm afraid, doctor Booster, that
you have underestimated me. If you

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want to win me over here,
you don't have to deal with my mind,

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not my lips. Got it,
and stop tape. I was supposed

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to kiss her. And from November
of nineteen seventy two, fourteen year old

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Donnie Osmond is losing his youthful trouble. His voice is changing into manhood.

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Donnie Osmond says that he just can't
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more at Man From yesterday dot Com, It's that time of year again.

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No, not the holidays, medicare
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you want to learn and get answers
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then you want to hear my show
business Game Changers with me, Sarah

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Westall. I have conversations with thought
leaders who have the courage to address off

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limit topics, so you and your
family have the tools to make the right

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decisions. Join me Wednesdays at four
pm on ten fifty AM and one oh

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six point five FM right here at
KCAA, that station that leaves no listener

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behind. Last July, several GOP
senators combine their five want intellects to charge

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that inflation was rising because of quote
insane tax and spendings free of President Biden

659
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and the Democrats. Never mind that
the insane spending is for such sensible,

660
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productive and enormously popular national needs as
childcare and childless benefits. Mitch McConnell's rapidly

661
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partisan flock saw the chance to politicize
the public's legitimate worries about rising prices.

662
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You, poor consumers are made to
pay more for basics they squawked because of

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socialist Joe's investment, and grassroots people
follow the ricocheting pinball of the GOP's logic.

664
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One, they say that helping hard
hit families induces them to refuse to

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go to work. Two, this
creates blockages in the global supply chain.

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Three this causes shortages of everything.
Four This forces corporate bosses to raise all

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prices, which five slams the middle
class and poor. So six, lazy

668
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workers cause inflation. Who Rube Goldberg
couldn't have dreamed up a more fantastical diagram

669
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to deflect attention from what's really happening, namely that instead of an inflation problem,

670
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we have a corporate greed problem.
Of course, the greed moisters insist

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that their pursuit of excess corporate profits
has not driven any price surges in our

672
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economy of free market competition. They
snap prices are established by the law of

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supply and demand. It's the magic
of the marketplace, they explain. But

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magicians don't do magic, they perform
illusions, and the illusion of free market

675
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competition implodes when it hits the reality
that our economy doesn't remotely resemble a competitive

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marketplace. This is Jimhiitwer saying,
for some forty years, corporate directed government

677
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policies have been transforming America into monopoly
nation, letting the feud gout the many.

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of the Your Personal Bank Show.
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super raw. Okay, a gimmicky
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life insurance, but I'm sure it
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three pm, It's The Uncommon Sense
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when his people talk about how old
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a couple of years behind him.
You'll get the best political commentary and stuff

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like this. Good night, don't
well. Join us for the Uncommon Sense

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Democrat every Wednesday at three pm on
the stations that leave no listener behind casey

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AA ten fifty am, Man one
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segment is sponsored by press Print,
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zero eight Get the Union Bug.
Are you tired of the same old conversations

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that everyone keeps talking about? Want
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Listen to Whatever Works here on CASEAA
every Sunday at seven pm. We

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have amazing guests with interesting and unique
topics. We do giveaways, and we

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have sponsorship opportunities. It's more than
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You can also find us on CACA
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Works dot Com to find upcoming and
previous episode. We like to thank all

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the KCAA listeners for supporting our show. For over a century, AM radio

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has evolved to meet the needs of
our community. More than eighty million listeners

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depend on AM radio each month.
It's also the backbone of the emergency alert

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system keeping us safe in dangerous times. A new bill in Congress would ensure

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this free, reliable service remains in
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eighty six and tell Congress to support
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message in data rates may a play. You may receive up the four messages

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a month, and you may text
stop to stop this message. Furnished by

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the National Association of Broadcasters, KCAA
Radio has openings for one hour talk shows.

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CEO at two eight one five nine
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ninety nine ninety eight hundred. You
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our Redlands, California studio, where
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or call our CEO for details.
Two eight one five nine nine ninety

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eight hundred. The Tri City Shopping
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cool ice cream at lemcho Acana.
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relax while they get treated like royalty
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then pampered at Text Your Hair.
The Tri City Center filled with retailers who

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care about you. Shop at the
Tri City Center in Redlands and see why

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they call it the Mall with a
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No, not the holidays. Medicare
open enrollment and if you have questions

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about Medicare, you should talk to
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business, their agents are trained to
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you. Hi on food critic Allenborgan, and I'm excited to tell you about

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Raised Shanghai Bistro, located next to
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Shanghai Bistro offers the largest, hym
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dishes available in you An Empire.
Some of my favorite dishes are the house

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beat hot Stickers, the chris Port
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bamboo, the sweet and tangyed deep
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Basos spicy shrimp, plus lots of
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pick up the food, or have
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you will see why ray spelled r
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the best Chinese restaurant in the Mumpire. Their website is Raised Shanghai Bistro dot

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net. That's Raised Shanghai Bistro dot
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net. Happy eating, You won't
be disappointed. From the KCAA Weather Center,

790
00:59:44.679 --> 00:59:46.880
we have a high one warning and
effect till ten pm Monday. This

791
00:59:46.960 --> 00:59:50.880
afternoon, it's gonna be sunny and
windy where there is a blowing dust.

792
00:59:50.920 --> 00:59:53.280
The high temperature seventy four, winds
gustin the forty miles per hour up to

793
00:59:53.320 --> 00:59:58.599
seventy five miles per hour through cannons
and passes. Clear Tonight, low forty

794
00:59:58.639 --> 01:00:05.119
six Mondays sunny, high eighty Tuesday
sunny. I'm atronologist Germinoli, broadcasting live

795
01:00:05.159 --> 01:00:08.440
from the Tri City center of the
ten to ten Freeways. We are the Trifective Talk