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I respective school's code of conduct.
The president of Harvard University, Claudine Gay,

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Nashville area Saturday evening. I'm Chris
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Learn more at inside analysis dot com, insideanalysis dot com. And now here's

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your host, Eric Kavanaugh. Oh
yes, folks, welcome to the future.

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Indeed, your host Eric Cavanaugh here
on the only coast to coast radio

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show in the US of ATA.
It's all about the information economy. Inside

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Analysis doing another one of our pre
records for the holiday season, and I'm

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really excited to have jig Nash Patel
with me. He's with a company called

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data Chat. He's at a couple
of companies, acquired one by Twitter apparently,

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which is pretty impressive. It's done
a lot of interesting things, and

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of course he's also at Carnegie Millon
University. But we're going to talk about

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really the trajectory of answers because that's
what we all really want. Right We've

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done data warehousing, we do data
science. All point is to get to

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answers that can help us solve business
problems. And I get the feeling that

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he's managed to find a way to
short circuit some of that stuff, but

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we'll find out. So jig Nash, Welcome to dm Radio. Thanks so

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much for your time today. Tell
us data Chats. Where did the idea

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come from and what are you working
on? Yeah, thanks Eric for having

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me on and hello to your audience. A real pleasure to be here.

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Data Chats started with an idea for
my students and I and that's what we

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do in research. We start to
look at problems that are a few years

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out. So back in twenty seventeen, when data science was really starting to

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become popular, we noticed a trend
that data science. The way was practiced

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then and to a large extent,
how it's practiced today, is to provide

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the tools and mechanisms for data science
programmers to write code from which basically allows

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them to do their analysis. And
what we realized at that time is often

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the way people wrote their data science
workflows in the form of what's called as

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notebooks, was to fill one cell
of the notebook at a time, and

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each cell might do a specific task, like I might have gotten a bunch

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of data and I want to extract
the area code from a column that has

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phone numbers information in it. And
the way people would do that is they

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would go and type in that question
saying, how do I extract area code

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information from a string that's a pull
number, and to google our sack overflow,

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find pieces of code, try to
put that into the cell, and

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then try to adapt that. That's
how they would do solve each piece of

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the data science puzzle one step at
a time, said can we automate that?

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Can we do something bigger with it? Yeah? And that's a pretty

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interesting observation because what you're looking at
is the actual de facto workflow. What

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are people doing on these machines.
How does that process look and you saw

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into the future a bit here with
the ability to leverage what we now call

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large language models. It's just a
variation of artificial intelligence. And as we

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say on the show, the lms
are really just pre divengines. They're predicting

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what text they think you want to
get based upon your prompt and based upon

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what they were trained on, which
by and large was this massive corpus of

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text out there in the real world. So you kind of saw I think

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into the future a bit, maybe
explain that and what you've built. Yeah,

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that's great. So what we notice
is there's a lot of repetition in

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what people tay to do. People
are not asking totally random questions all the

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time. They're often asking similar types
of questions, if not exactly the same.

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Similarly, when stackover, for our
Google is returning answers back to them

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in the form of yes, code, I've seen that, similar it's doing

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it based on a retrieval mechanism.
And Eric, as you pointed out,

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the lms effectively learn a ton of
information from everything they've seen, including code

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and their synthesis machine that complement what
searched us and they'll synthesize new code.

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So we started to think about the
automation that is potentially possible back in twenty

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seventeen by having the human just ask
the question and synthesize the code in one

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form or the other. And of
course at that time LLMS weren't available,

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so we were using what by today's
standard, or even by standard by the

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standard just a year ago, might
look very rudimentary, but basically using a

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lot of traditional language of technology,
passing sentences, trying to understand the grammar,

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understanding the sentence, and then trying
to produce the code. But with

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the limbs, all of that changes
in a dramatic way, and all of

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that that we were dreaming at that
point is now started to become real in

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a very big way. Yeah,
and we are at a very strange time

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right now. Let me just throw
out some context for our listeners here,

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because you get accustomed to a certain
standard or a certain paradigm, and we're

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all very much accustomed to the Google
paradigm. You Google for something, you

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get a result, you go on
about your day. Well, Google,

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of course indexes the web. Yahoo
started indexing the web with hadoop, right,

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and the HDFS had doop distributed file
system, which was then tried as

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an analytic engine by cloud Eira and
map R and hoar Works and all these

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other folks, and that kind of
went sideways after a while because there was

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too much reverse engineering that had to
be done. But there was a whole

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ecosystem that built up around this stuff. So I mentioned this because I'm watching

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as Google is straining, and Google
has all this pressure from AdWords, and

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now when you search for something,
you're going to get ten to thirty different

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AdWords listings before you get an organic
search result, which is great for their

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business. They make a lot of
money on all that stuff, but it's

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skewing what you find, and so
it's making it harder to find the stuff

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that you wanted. And then of
course we've had all this SEO search engine

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optimization stuff where people are trying to
understand the system, gain the system if

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they can. I played around with
tactics to do all that stuff. But

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my point is that what we have
accepted as a standard, which is Google

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and how Google retrieves information is really
in flux right now. And it's in

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part because of the ads, it's
in part because the web is a fast

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moving entity and lots of things are
changing, and it's in part because of

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these large language models that are severely
disrupting how people go about ascertaining information.

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So what do they do? They
hallucinate sometimes, right, they make up

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stuff. As my buddy Eugene Burke, he's working on a stealth project with

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us right now, mentions the problem
with the llms that they don't have an

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epistemological barrier, meaning they don't know
what they don't know, and that's when

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they just make stuff up. And
I've actually had a thread going with the

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go to market guy, Adam Something
from open Ai who came out and said,

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point blank, look, remember the
hallucinations are more of a feature,

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not a bug. We've designed these
engines to fuse information together and then deliver

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generate new content. Okay, So
for certain use cases, that's fine.

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For a creative copy, for short
stories, for articles that don't have to

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be really precise in terms of what
content is in them, that's fine.

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But then you get into the enterprise
space where it becomes very important to be

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correct and to know exactly what you're
doing. And it seems to me that

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you with this data chat you are
kind of filling in one of the missing

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pieces that will allow for this next
generation of technologies AI to really come to

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fruition and get us to a place
where we can trust what it's saying,

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just like we've historically trusted what an
enterprise data warehouse has told us. But

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just if you would comment on where
I'm going with that, what do you

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think about that, and what do
you think about how data chat could be

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one of the pieces to rebuild this
puzzle. Yeah, and you know,

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pick a company your thread. I
think of the technologies as two distinct collaborative

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technologies. One is search. Search
allows you to say I'm going to specify

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something and give me a precise retrieval
of information from the original database. So

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that's Google Search. You see that
in warehouses too. When you fire up

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a sequel query, you don't get
that precise result back. And you can

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think of when we go to Google
Search, probably what we want most of

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the time is search. The second
part what lms do is synthesis, which

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is different. Synthesis says I'm going
to blend together information that I know,

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sometimes making stuff up to give you
a coherent answer, which may or may

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not be based on facts in the
database over which I've learned. Search and

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synthesis are two separate components, and
synthesis is what has now become possible.

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Right, so you allude very well
to what Google's trying to do and do

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search and synthesis and how that blends
together in the search paradigm is tbd.

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But that's kind of where a lot
of the struggle is how do you blend

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ELMS with search. We still want
search for many aspects, as we might

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all potentially agree with it, right, sometimes you just want to search.

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Coming back to data chat and how
that's connected is what we do is we'll

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say point us to a database,
a structured database, even a CSB file,

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whatever is your data, and then
ask a question. We will convert

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that into an answer. Now,
remember is it totally a little while back,

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previously we were trying to figure out
how to write the piece of code

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that could produce the answer that was
twenty seventeen. We've moved well beyond that

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in data chat, where what we'll
do now is you give us a question

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and will produce you an answer.
That question might be a search question,

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it might be find me this thing
in the database, find me the customer

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that produced the highest return, highest
gross value, and sale. And that's

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a retrieval question. There might be
a synthesis question or a prediction question that

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might say, predict for me,
which is the customer that's like to be

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the most profitable over the next one
or year. To the user, we've

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simplified the interface with the UX is
just typing in that question. You'll synthesize

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that into appropriate code that might be
part CQ, part machine learning code that

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might be written in Python, bias
automatically, part of it. Visualization in

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JavaScript will translate across three different languages. The user doesn't see any of that,

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but gets an answer now. And
now the question is how do I,

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as a user take that answer and
do something with it. To do

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something with it, I have to
develop some level of trust. And this

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is where the unique aspect of data
chat comes into play, where because we

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are academics and rooted in academia,
we took the science part of data science

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seriously from day one. Science requires
at least two pillars. One is reproducibility

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and the other one is transparency.
What data chat does today is it will

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give you back your answer, often
in the form of a picture, but

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along with that, it will give
you back a step by step of the

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components that went into producing that answer. Think about a cookbook, right,

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you look at a recipe and you'd
say, I've got something on my table.

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If you're given a recipe for that, you now have an idea,

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a very precise way as to how
that was produced. If that recipe is

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correct to the food you see on
your table, that is transparent and reproducible.

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The same philosophy in data chat.
We won't give you back code.

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We've invented a new language called Guided
English Language, which is in English,

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but it's a subset of English.
And you can get into that whole theory

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next if we need to. There's
a branch of linguistics that says, if

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I want to communicate with you precisely
in natural language English, for example,

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then if I just use grammar in
the full richness of the language, there's

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a high chance that I may say
something that may be misinterpreted because language is

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not precise. It's not wrong,
sure, but there are ways of doing

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taking portions of that language, reducing
its structure so that you get precision in

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that and be able to communicate and
that's what we've done in data chat,

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where you'll get back a step by
step recipe in English, so it might

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say I did X at this point, then why, And just like a

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recipe that you see in a cookbook
which uses very few verbs to be precise

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and to avoid this miscommunication. For
example, recipes will nearly always say saute

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for that action. They won't do
different synonyms of that, and the sentence

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structure will be very simple on purpose
by designed to get that precision. That's

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exactly what we do in data chat. We'll give you back the step by

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step recipe in this language uses all
these properties of being precise and allows a

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user to induitively understand what happened at
each of those steps. We'll go further.

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You can treat that step by step
recipe itself as a program. Right,

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I gave that to you. That's
transparency. Second part was reproducibility.

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We'll give that step by step recipe
to you as a program, even though

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it doesn't feel like a program.
Imagine getting your cookbook in pressing the play

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button on step number one to sort
a garlic, and that just happens.

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And that's what we'll do. We'll
take the recipe and allow you to play

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step by step see how it's been
cooked, so that you can reproduce that.

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So we get transparency to make it
easy for anyone to understand using the

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same paradigm of simple English and recipe. And then you sability by saying you

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can actually play it step by step
to see how it was cooked to produce

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that final answer. Yeah, this
is interesting. So what you're talking about

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doing is enabling the long coveted conversation
with your data. That's the mission.

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So you've got a database, you
can use SQL to query the database.

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That's one way. Then you need
to know how to write SQ code unless

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you have a tool that does it
for you. I mean, I'm old

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enough to remember a company called Progress
Software, it's still around these days.

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It had a tool called Progress Easy
Ask. This is like sixteen years ago

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or something. And what they would
do is very interesting is you would type

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in a natural language query and it
would create the syntax of SQL for you

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so you can see what it was
doing. You're doing something similar to that

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where you're using a natural language query
and then you're underneath the covers using a

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variety of different things. This guided
English language for example, to spin up

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bits of code to solve for that
particular question. I'm going down this road

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because it's it's very interesting. It's
similar to what we're seeing with the large

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language models and enterprise versions of them. And here's my personal theory what we're

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going to see here, Every serious
company of any significant size will have their

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own AI model. They're going to
pick their poison. They're going to choose

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Google or Microsoft with open AI,
or perhaps Anthropic or someone else is going

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to come along and do AI models, and then they're going to start to

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train them. And what I've learned
in this recent journey of trying to research

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this stuff is you can either embed
data into the model, which is probably

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not the best idea, or you
embedded into a vector database and then use

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that as your anchors of truth.
Is how someone described it to me.

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And that's sort of enabling in this, of course concept of rag of regenerative

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like augmented what is it retrieval augmented
generation right where you're trying to it's like,

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Okay, I got an idea what
I want. I want to go

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to the world and on them see
what it is. They'm going to be

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all right, make sure that is
what I really wanted based upon my priorities

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or my data which is in my
probably vector database or something like that.

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Long way of saying, what I'm
really interested to see how this How what

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happens here is that these llms or
even this technology, you've got data chat

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if curated properly with the right embeddings, the right embedding strategy, the right

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rag strategy. Why do I need
to do all that heavy lifting on the

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data warehousing front anymore? Isn't this
gonna get to the answers for me?

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Well, the answer right now is
maybe not. We don't know because it

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still has these accuracy problems. But
if we solve those accuracy problems, that's

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a serious deal. And it seems
to me that you are embarking on a

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very similar journey in allowing for this
interaction with the data, and you've even

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got a dynamic app building component to
it as well. But what do you

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think about all that? Yeah?
I think this is these are there's a

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whole bunch of questions in there.
Let's break it apart. First, is

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we've been looking for this holy grail
of how do we get programmers not to

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be the gatekeepers to getting answers from
data is one example. Microsoft used to

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have something called Microsoft English that they
disbanded. Problem with all of these tools

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is twofold. One is they never
quite worked even in a single task,

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say generating sequel. The bigger problem, however, is today in the modern

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world post data sciences creation, which
is like now were about a decade into

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this data science thing. Data science
is a lot more than writing sequel.

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Squel gives you a way of getting
a report out from the data, but

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data science then requires and you could
use SQL for feature engineering portion, but

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to really get deeper insights, you
have to feed it into machine learning models,

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which is a different language. You
know you're going to do that in

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Python and stuff like that. Then
to visualize that you're probably going to put

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that into a different language too.
So we've never really solved the data science

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in an automated fashion, which is
what data chat does. As this said,

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on behalf of the users that same
recipe. Part of that might have

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been sequel, Part of that might
be some Python code that we wrote.

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Is JavaScript. We go across three
languages and you don't have to know either

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of those languages. The other big
aspect is who is the user progress and

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all of these other tools. Even
today, a large amount of effort is

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being spent by companies like data Breaks
and Snowflake and everyone else to write seql

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code. But the user then is
a programmer, right right. We'll tell

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you what. Let's pick this up
after the break. We got a break

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coming up here, folks. Don't
touch that down. You were listening to

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

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All right, folks, but having
a fascinating conversation with jig Nesh Patel

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with data chat. He's done many
other things too. And I'm getting all

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excited because you know, I've watched
the world of data warehousing, I've watched

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the world of data science. I've
been amazed that those are a very different

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world. These people often don't even
talk to each other, which makes exactly

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zero sense to me. But I
think that the economic pressures bearing down on

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us right now, plus the power
of these new AI models is going to

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force is going to catalyze, basically, and it's force organizations to get a

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lot smarter about what they're doing and
how they're doing it, and paying attention

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to the value that they get from
all this stuff because what's happening. And

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I have an old buddy, Dave
Wells. He was the education director at

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the Data Warehousing Institute years ago,
and he used to always refer to something

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he called under the hood technology,
and his point was that you don't have

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to know how your car engine is
working. To know that it works and

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to have faith in it and to
drive it to work in drive it places.

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You don't have to know all that
stuff. The same is becoming true

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in this data space. And it
seems to me, Jignest that what you're

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doing is really pushing that all forward
because you're saying, look, we're to

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handle the assembly of code where you're
going to choose which language is necessary for

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this particular use case, and just
take care of that stuff for you so

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you get the result that you want. Because all anyone wants are the answers.

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That's the all point, the all
point of doing data warehousing, and

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the whole point of doing data science
is to get to answers to more and

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more interesting and compelling questions. And
that's really where you're focusing your attention.

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So continue where you were. I
know why, I'll cut you off dealing

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the last segment. Go ahead.
Oh that's great. You exactly got it.

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Is the whole point of collecting data
is to get value from it.

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You get value from it when the
data actress in your organization, including an

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especially the non programmers who can make
business decisions, can directly ask the questions

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in a way that is trust that
they can trust. And that's really what

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data chat does right. And there's
a whole bunch of technologies that now makes

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it possible. But so far,
even when we've gone into warehousing, we've

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said, okay, we'll all this
money into getting an enterprise data into a

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warehouse. Then what we'll do is
to allow every data actor to get access

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on it. We'll put a dashboard
up for you, or we'll tell you,

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hey, why don't you become a
sequel programmer. Oh, by the

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way, if you're a SEQL programmer, good luck, you can do data

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science. You also have to become
a Python programmer. And oh by the

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way, if you're a Python program
if you want to do some visualization stuff,

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you have to become a programmer and
some visual language maybe Tableau, maybe

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JavaScript or something else. So it's
become impossible for data actors that are not

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necessarily programmers to do that. And
that's kind of what we are trying to

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do with data chat is saying,
forget about the language will derive code for

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you in different languages. We just
focus on the question and now that the

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programming language is English, and it's
just not English to seqel, it's English

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to plus Python plus JavaScript for us
really more powerful than what you hear at

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the industry talk about saying will generate
sequel for you. That's just a small

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part of the puzzle for the data
science. We've lost all of these three

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and allow anyone in your organization,
not just programmers, to really directly derive

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the value of data without saying I
need to find a ticket with the Tableau

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team to create a dashboard for me
to use it right right, right,

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right right. So, and the
other interesting thing here is to your point,

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a SEQL query is going to access
bits and pieces of data that are

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important for telling your story. But
they're just component parts. They are not

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the mosaic, they are not the
picture of what's really happening. It's a

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part of what's happening. But you
know, I even joke about this on

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the show that you know, data
warehousing was born forty odd years ago.

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When processors were slow, pipes were
thin, storage was expensive. All of

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those factors have completely changed. Now
processors are incredibly fast, we can parallelize

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them, the pipes are fat,
storage is cheap, so all those dynamics

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are inverted quite frankly. And what
we did back then is we had to

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strip out all this context just to
get the stuff through the thin little lines

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into a model that we can then
analyze an understanding query, like an O

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lap cube for example. And then
you have to reconstruct all that context,

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either in your report about it or
a dashboard, or in some way you

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describe the situation to your board or
your stakeholders or whatever. The point is,

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we had to strip out all this
context, and it seems to me

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that now you really don't have to
do that as much. And what you're

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enabling is this conversation with a data
set. Now, I'm curious to know

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what kinds of data sets can you
tackle. Can I connect you to an

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ERP for example, or a clickstream
analysis system? What can I connect data

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chat too? Specifically? Yeah,
so data chat works with tabular data,

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so you can connect us to a
warehouse, you can connect it to CSV

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files, s park files, so
that sort. And sometimes people might take

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data that might come from ritual data
sets like images, convert that into features

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and that becomes tabular data that you
could point to and build models on.

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So have you focused on the tabular
data at the point in a journey.

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At some point in the future,
that may change, but that's the big

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portion of the market that we are
addressing right now. Yeah, that's that's

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interesting, that's good. And then
just to help our audience understand these large

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language models, they use vectors,
so you get embeddings and they vectorize,

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which basically means they convert text to
numeric values. And it's my understanding the

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reason you do that is because it's
very easy to use existing technologies to compare

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and contrast and find similar numeric values
across this expanse of embeddings, and that's

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how they kind of operate. So
your approach is that we're to focus on

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the tabular data because it's very well
known, it's very common. Everyone understands

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Excel and spreadsheets and all that kind
of stuff. So you're really tackling what

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is a major component of the data
world, right Yeah, And that's what

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the business analyst. Ultimately you're going
to need to connect that to some tabula

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data on which you make decisions.
Right, more than ninety percent of decisions,

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believe it or not, even today
get made in Excel. Yeah.

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When people can't make a decision and
Excel because the data is too large or

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something else, they'll start to make
it fit within Excel. And so,

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in spite of decades of us working
on data warehouses, faster, sequel,

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paralyzation, all of that stuff,
sadly a lot of decision making still doesn't

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happen with all of these tools because
ultimately, the decision makers are not necessarily

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programmers, and they are not necessarily
even if you point them to a data

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breaks or a snow if they can
give them an access to the database,

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what are they going to do?
Systems are complex, right, And that

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is the challenge. It's like the
bottomneck to making fast, agile decision in

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a correct fashion based on the truth
and the data. Today is not how

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fast your database engine goes, it's
how fast the human goes, right,

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And it's all about human empowerment in
a safe manner that allows you to move

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forward faster. Yeah, that's interesting, and so let's talk about again,

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someone uses your technology, they use
an English language question of a particular data

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set, and under the covers.
What you're then doing is parsing the meaning

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of the words of the syntax,
then mapping that to the data, and

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then using a combination of languages.
And this is very interesting to generate your

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visual answer. So it'll do SQL, but it'll also do Python on some

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other various languages too, like JavaScript
or something. And what's interesting there is

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that I try to explain to folks, languages and programming are a lot like

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languages in human languages, meaning they
have different syntax. They are not just

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a different set of words for the
same set of objects. It's really a

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different way of viewing the world.
And they each have their own strengths and

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weaknesses. You know, SQL is
very good at getting discrete bits of data

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from a database, but Python is
very good at weaving things together. I

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remember learning about ten years ago that
in the risk management world, where I

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spent many years doing webinars for the
Global Association of Risk Professionals. In risk

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management, Python is very popular because
the risk managers have five, seven,

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twelve different systems that they're supposed to
be monitoring well, the signal from any

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given one of them might be hard
to understand or what it might be such

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that it's difficult to know and there's
a problem. But when you aggregate them,

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when you look at the big picture, that's when you can start understanding,

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oh, wait a minute, if
this is going up and that's going

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down, we've got a problem.
And so Python is very good at that

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capacity to kind of weave stuff together. Is that one reason why you chose

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to use these different languages under the
hood to be able to tackle just about

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any natural language query you come across. Yeah, great question, going back,

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and this is a perfect way to
connect to the earlier half of we

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talked about sequels. Very good at
retrieval stuff and putting together reports from that

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retrieved stuff. That's what it excels
at. It's good at ETL where you're

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transforming the data a very structured way. Python which is a whole ecosystem,

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and the machine learning part of that
is what's super exciting, including in the

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example you mentioned, it can build
models on the data. The models can

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then explain patterns in the data.
It can synthesize beyond that nlm M and

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that is a model. So you
need data, you need to be able

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to extract it and search it,
but you also need to synthesize models.

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And that's NOTQL. Sql can't build
models. They can't build machine learning models.

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It can barely build a regression model. Forget about the more complex models

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that you need to build. You
say, what's interesting in my data?

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Right? Right? It can do
predictions. So all of that comes from

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modeling data plus model has been like
oil and water. And that's why people

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have to learn both SQL and Python
vs say you have to learn neither.

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Let's take care of all of that
stuff for you and you and you will

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reveal what the recipe was. Right. That's the transparency side. And this,

401
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you know, one of the big
challenges that the lms are going to

402
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have in terms of getting enterprise penetration
for very serious decision makee is the lack

403
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of transparency. And if you don't
have transparency, then you don't know how

404
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it got the answer. And if
you don't know how I got the answer,

405
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you know and you got the right
answer. It's a very difficult thing

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to do. And I remember watching
as the data bricks folks were demonstrating how

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they were curating these models and just
thinking to myself, man, you know,

408
00:29:18.400 --> 00:29:22.759
we always say, don't boil the
ocean. So if you're going to

409
00:29:22.799 --> 00:29:26.200
go out there and try to like
throw all your data into a large language

410
00:29:26.200 --> 00:29:27.440
model and they, oh, we'll
just curate it, will you, and

411
00:29:27.480 --> 00:29:30.960
it'll be done by the end of
the thirty third century or something, it's

412
00:29:30.960 --> 00:29:36.680
going to take forever to do that. So you need bite sized chunks to

413
00:29:36.759 --> 00:29:41.200
be able to handle. And that's
why folks are talking about frequently asked questions

414
00:29:41.440 --> 00:29:45.319
or customer service for example at chatbots. There are some really good use cases

415
00:29:45.359 --> 00:29:48.680
for these kinds of technologies, but
they are discreet and they are not the

416
00:29:48.720 --> 00:29:53.200
sort of bigger, broader, what's
really happening in my business kinds of questions.

417
00:29:53.680 --> 00:29:57.240
And that's what you get when you
synthesize, to your point, the

418
00:29:57.319 --> 00:30:02.079
sequel the data that pulled out,
but the model that I can build on

419
00:30:02.160 --> 00:30:06.000
top of the data, and just
to explain to our audience, maybe models

420
00:30:06.160 --> 00:30:08.079
they're also recipes, right, they're
like, okay, look for this,

421
00:30:08.160 --> 00:30:11.440
once you find this, do that, once you see this, do this,

422
00:30:11.519 --> 00:30:14.200
once you see that, do that, and it's sort of a process

423
00:30:14.319 --> 00:30:17.359
then shows you what the world looks
like Okay, it's not really what I

424
00:30:17.359 --> 00:30:18.920
wanted to try. Again, you
train the model, you play around with

425
00:30:18.920 --> 00:30:22.759
the model. But that's kind of
what the models are, right. There

426
00:30:22.759 --> 00:30:26.480
are complex recipes for how to interpret
data and then generate an answer. Right,

427
00:30:26.799 --> 00:30:32.400
yeah, perfect. And it's even
more exciting now because it's imagine I'm

428
00:30:32.440 --> 00:30:37.000
working on a problem and maybe it
is predicting sales. Now, maybe that

429
00:30:37.200 --> 00:30:41.599
we say there's a model for it. Right. It might be a regression

430
00:30:41.640 --> 00:30:44.880
model, or it might be some
of the more complex model. But that's

431
00:30:44.920 --> 00:30:48.920
not enough. What you really want
is to say, on my data,

432
00:30:48.440 --> 00:30:52.359
which probably looks different than someone else's
data trying to solve the same problem,

433
00:30:52.559 --> 00:30:56.599
Right, how do I build a
model on the fly that fits to my

434
00:30:56.680 --> 00:31:00.799
data to answer the question? And
that's what we do. We'll build a

435
00:31:00.839 --> 00:31:07.359
model on your data on the fly, use your specifically created model for you

436
00:31:07.799 --> 00:31:11.400
to answer the question that is really
relevant to you. So interesting that requires

437
00:31:11.400 --> 00:31:15.920
all kinds of rocket science, data
science, see wizardry to build models.

438
00:31:15.920 --> 00:31:18.279
We're saying, don't worry about it, we got that covered for you.

439
00:31:18.319 --> 00:31:22.960
Wow, data models for on your
data on the fly, and explain that

440
00:31:22.000 --> 00:31:26.000
to you intros you can understand and
that's that's the magic, and that's that's

441
00:31:26.160 --> 00:31:32.680
it's all centered around human productivity is
the goal. You know, I'm gonna

442
00:31:32.720 --> 00:31:34.599
throw a bit of a curve ball
question at you and folks, Jig did

443
00:31:34.599 --> 00:31:37.759
not get any of these questions in
advanced just so you know, we don't.

444
00:31:37.759 --> 00:31:41.359
We don't roll that way. But
is there some calculus or some math

445
00:31:41.480 --> 00:31:44.720
underneath all of this? The reason
I asked that is I remember from calculus

446
00:31:44.720 --> 00:31:48.599
class. The key is that you
had to learn to identify the pattern of

447
00:31:48.640 --> 00:31:52.440
the problem and then know which formula
to apply, and then of course you

448
00:31:52.440 --> 00:31:56.279
have to do the work to apply
it and to see if it reasons out.

449
00:31:56.839 --> 00:32:00.000
But there are X number of formula
basic formula that you're using calculus to

450
00:32:00.359 --> 00:32:04.960
unravel problems, and it sounds to
me like there's something like that underneath the

451
00:32:04.960 --> 00:32:07.519
hood with your technology. Is that
correct? Absolutely? And this is well

452
00:32:07.559 --> 00:32:12.240
known in literature. So we try
a bunch of different models, and the

453
00:32:12.319 --> 00:32:15.440
number of models you can try and
your data is not just like what model

454
00:32:15.480 --> 00:32:21.039
classes like what it sets of equations
to try, but also parameters for each

455
00:32:21.079 --> 00:32:24.279
one in that space is extremely large. All kinds of intelligence built into a

456
00:32:24.279 --> 00:32:28.799
platform that will look on the fly
at your data, look at what you're

457
00:32:28.799 --> 00:32:30.640
trying to do, and say,
you know, I don't mean to try

458
00:32:30.640 --> 00:32:34.920
the billion options because to your point, that will come back in the twenty

459
00:32:34.920 --> 00:32:38.480
fourth century, right if we just
we will then use intelligence built into the

460
00:32:38.519 --> 00:32:44.519
platform to understand the context from the
data from your questions, to build a

461
00:32:44.599 --> 00:32:47.400
small number of models, not just
one small number of models on the fly,

462
00:32:47.880 --> 00:32:52.960
raise them against each other to see
which one there's better an answering your

463
00:32:52.000 --> 00:32:54.720
question, and then give you the
answer from that, and all of that

464
00:32:54.720 --> 00:32:59.079
will be fully automate for you.
Yeah, So that reminds me of what

465
00:32:59.200 --> 00:33:02.000
data robot would doing a few years
ago before they ran into their very strange

466
00:33:02.000 --> 00:33:07.359
problems. But what I loved about
data robots approach was this auto mL.

467
00:33:07.799 --> 00:33:10.160
So you would load your data in
like let's say it's mortgage loan data,

468
00:33:10.519 --> 00:33:15.160
and you want to just see a
regression mode you understand, okay, where

469
00:33:15.200 --> 00:33:19.440
did loans go bad versus where did
loans stay good? And what they would

470
00:33:19.480 --> 00:33:24.559
do is automatically run that data against
like seven or eight different algorithms and figure

471
00:33:24.599 --> 00:33:28.359
out what one is best. So
you're doing the same thing that they're doing

472
00:33:28.400 --> 00:33:32.079
in that sense, but you're actually
dynamically spinning these things up because again,

473
00:33:32.119 --> 00:33:37.599
when you consider the permutations, when
you consider all the different ways you can

474
00:33:37.640 --> 00:33:42.480
get covariance between functions, right,
there are so many possible answers. So

475
00:33:42.519 --> 00:33:45.680
if you can automate that early process
and get to a handful of models,

476
00:33:46.240 --> 00:33:52.400
that's a tremendous advance in being able
to get to that specific model that's going

477
00:33:52.480 --> 00:33:55.680
to answer your question, right,
Yeah, absolutely, And you bring up

478
00:33:55.680 --> 00:34:00.640
an extra excellent point. Industry has
been focused on very small things. So

479
00:34:00.680 --> 00:34:05.359
data robot was trying to solve part
of the data science problem, but still

480
00:34:05.400 --> 00:34:09.320
targeted towards largely programmers, but not
SEQL, not visualization, not that.

481
00:34:09.400 --> 00:34:13.760
And really the point is you want
to do all of that and do that

482
00:34:13.800 --> 00:34:15.440
in a way that is accessible to
everyone. Yeah, you know, we're

483
00:34:15.480 --> 00:34:19.400
going to pick up after the break. This is a fantastic conversation. Don't

484
00:34:19.440 --> 00:34:30.039
touch that doubt you are listening to
Inside Analysis. Welcome back to Inside Analysis.

485
00:34:30.519 --> 00:34:37.360
Here's your host, Eric Tabanaugh.
All right, folks, back here

486
00:34:37.400 --> 00:34:43.360
Inside Analysis. What's fun is we're
moving through the whole evolution of data management

487
00:34:43.360 --> 00:34:47.000
and data warehousing into data science and
AI and capturing all this stuff, and

488
00:34:47.039 --> 00:34:51.559
I promise you folks, it's all
turning around. Right now. We are

489
00:34:51.599 --> 00:34:54.719
at the ultimate inflection point in this
industry, which is very exciting. It's

490
00:34:54.760 --> 00:34:59.679
this is very very good news because
all the plumbing and the hammering away.

491
00:35:00.119 --> 00:35:02.239
Let me tell you, it's not
exciting, okay. Like the poor data

492
00:35:02.280 --> 00:35:07.480
engineers who are building these pipelines and
like waiting for the data to get there,

493
00:35:07.719 --> 00:35:08.920
they're pulling their hair out if they
have any hair left. It's a

494
00:35:09.000 --> 00:35:12.840
very difficult job to do. But
a lot of that is going to be

495
00:35:12.920 --> 00:35:15.679
solved with this new era of tools
and technologies. It I'll bring jig nsh

496
00:35:16.039 --> 00:35:22.920
Ptel back in from data chat.
You know that I completely understand the value

497
00:35:22.960 --> 00:35:27.800
the synergy. In fact, there's
a German concept of gestalt, which says

498
00:35:27.840 --> 00:35:30.719
that the sum is greater than the
whole of its parts. And when you

499
00:35:30.760 --> 00:35:34.400
get these things together, really cool
stuff can happen. I'll just throw one

500
00:35:34.480 --> 00:35:37.440
last thought at you. I came
up with this idea a couple of years

501
00:35:37.480 --> 00:35:40.440
ago when folks were saying, oh, it's AI versus data warehousing, Like,

502
00:35:40.440 --> 00:35:44.840
it's not AI versus data warehousing,
and a way, data warehousing is

503
00:35:44.880 --> 00:35:47.519
like your left eye and AI is
like your right eye. And when you

504
00:35:47.519 --> 00:35:51.360
get those two, then you get
depth perception because if you only have one

505
00:35:51.360 --> 00:35:53.639
eye, you don't have depth perception
because you have no way to triangulate where

506
00:35:53.679 --> 00:35:58.840
things are right. And so what
Jignesh has been working on here with data

507
00:35:58.880 --> 00:36:01.599
chat really, I think is the
next step in that process, and it

508
00:36:01.639 --> 00:36:07.519
really is that synthesizing component that will
take your natural language query, figure out

509
00:36:07.519 --> 00:36:12.639
what the data is telling you,
then build a series of models that they

510
00:36:12.639 --> 00:36:15.320
compete with each other to figure out
the answer, and then you get the

511
00:36:15.360 --> 00:36:17.719
answer and you open the commander to
show them the whole process. Right,

512
00:36:17.760 --> 00:36:22.360
go ahead, Yeah, and that's
exactly right, you know, coming back

513
00:36:22.400 --> 00:36:25.559
to the different the journey that the
industry has gone through. First of it's

514
00:36:25.599 --> 00:36:30.039
all about can we even afford to
keep all of this data together and make

515
00:36:30.039 --> 00:36:35.559
it That was the data warehousing the
cloud are doupe world that then trusted it

516
00:36:35.679 --> 00:36:39.679
into big data stuff, which is
all about moving that often to the cloud

517
00:36:39.719 --> 00:36:44.519
so that you didn't you could make
that economies of scale even better. And

518
00:36:44.639 --> 00:36:47.320
then came the data science world,
which is about saying, hey, sqel's

519
00:36:47.360 --> 00:36:51.360
pretty dumb, it doesn't It only
knows data, not models. It's two

520
00:36:51.400 --> 00:36:55.159
models and all this really clugy gluewar
and companies that just did one versus the

521
00:36:55.199 --> 00:37:00.760
other? And yet now and that's
all twenty ten right solution, and as

522
00:37:00.760 --> 00:37:05.400
we look forward, it's about neither
of those. These are all just foundational

523
00:37:05.440 --> 00:37:09.840
blocks on top of which the goal
is to enable the humans to make decisions.

524
00:37:10.159 --> 00:37:15.800
Whether it is using data warehousing,
sequel technology or data science technology.

525
00:37:15.159 --> 00:37:19.800
No one cares. What you care
about is did you get the answer that

526
00:37:20.159 --> 00:37:23.000
could move your business in a different
direction That would have been very hot for

527
00:37:23.039 --> 00:37:27.280
you to determine without the data being
present. So of course you need the

528
00:37:27.280 --> 00:37:32.119
warehouse, but you don't necessarily need
to deploy an army of programmers now that

529
00:37:32.159 --> 00:37:37.199
has to do an el pipeline and
a melt uning pipeline and then presentation stuff.

530
00:37:37.480 --> 00:37:43.119
It's like, can you bring it
up to the front where the humans

531
00:37:43.119 --> 00:37:46.519
are in charge and the humans don't
have to delegate to other humans to get

532
00:37:46.559 --> 00:37:51.119
even simple stuff done. It's the
true empowerment of the data actors, the

533
00:37:51.199 --> 00:37:54.840
human data actors in an organization is
to free them from having to go to

534
00:37:54.880 --> 00:37:58.960
school for six, eight, ten
years to become programmers, because that's just

535
00:37:58.960 --> 00:38:00.760
a mean to an end. The
end is to get insights. Yeah.

536
00:38:00.800 --> 00:38:05.840
No, that's exactly right. And
it's almost like you're delivering what a lot

537
00:38:05.880 --> 00:38:07.360
of people back in the day thought
Tableau would, right. I remember the

538
00:38:07.360 --> 00:38:12.079
first I actually saw. I think
I saw the first ever public demo of

539
00:38:12.079 --> 00:38:15.320
Tableaux because I worked at the Data
Warehousing Institute and five and there was a

540
00:38:15.320 --> 00:38:20.000
conference in Seattle where they were just
part of one event after the show,

541
00:38:20.079 --> 00:38:22.920
like one of these parties that you
have and give way free beer. And

542
00:38:22.000 --> 00:38:24.480
I watched, and I saw the
first ever. I was the first person

543
00:38:24.519 --> 00:38:27.679
in the room because I worked there, and I watched, I was like,

544
00:38:27.679 --> 00:38:29.559
oh, that's going to be good, you know, because no one

545
00:38:29.559 --> 00:38:32.519
has done this yet. That was
just one tiny little piece of the puzzle.

546
00:38:32.920 --> 00:38:39.280
But when you can synthesize all these
various engines to generate again what we

547
00:38:39.320 --> 00:38:43.800
want, which is the answer,
right, And it's like there is such

548
00:38:43.800 --> 00:38:47.840
a tremendous opportunity cost to doing things
the old fashioned way because, as you

549
00:38:47.920 --> 00:38:52.679
suggest, if you have to ask
for permission from this person to do this,

550
00:38:52.840 --> 00:38:54.039
wait for that person to do that, Oh, that's going to be

551
00:38:54.079 --> 00:39:00.480
a week. He's on vacation,
like all of these pauses just rush creativity,

552
00:39:01.000 --> 00:39:06.480
crush innovation, and just kill the
spirit. Whereas if I can just

553
00:39:06.679 --> 00:39:10.840
answer or get a question answered in
this sort of visual format and then understand

554
00:39:10.880 --> 00:39:16.239
how it got there, now you're
teasing the brain. You're getting the creativity

555
00:39:16.719 --> 00:39:21.599
rolling, you're getting those juices flowing
in your mind. That's when good things

556
00:39:21.639 --> 00:39:23.039
happen. That's when you figure things
out and go, oh my goodness,

557
00:39:23.079 --> 00:39:25.440
this is I know what we have
to do. Now. It's not this

558
00:39:25.519 --> 00:39:29.280
product, it's that product. Okay, Bob, let's go do X y

559
00:39:29.480 --> 00:39:34.800
Z. That's the so called aha
moment that only happens if you're in this

560
00:39:34.920 --> 00:39:38.239
fluid conversation with the data. And
that's what you're enabling right exactly, and

561
00:39:38.440 --> 00:39:43.880
the whole vision behind data chat.
Take those words data chat first. That

562
00:39:44.079 --> 00:39:46.840
chat portion always meant had two meanings
to it. One is, I,

563
00:39:46.920 --> 00:39:52.280
as a human, should be able
to talk to my data and get trusted

564
00:39:52.320 --> 00:39:57.400
answers that I can verify and reproduce. But the other part of that is,

565
00:39:57.760 --> 00:40:00.760
Eric, I could chat with you
about an insight that I've found,

566
00:40:00.239 --> 00:40:05.559
and this is a human to human
chat that it enables give you that visualization

567
00:40:05.639 --> 00:40:07.599
that came to along with the recipe. And now, because we have a

568
00:40:07.639 --> 00:40:14.480
common grounds for saying how did I
arrive at this insight in a fact driven

569
00:40:14.519 --> 00:40:17.239
way, you have a better human
to human conversation. And you might say,

570
00:40:17.280 --> 00:40:21.440
you know what, step number three
in this recipe looked at all the

571
00:40:21.519 --> 00:40:23.760
data. Oh, but you know
what we should take away twenty twenty and

572
00:40:23.760 --> 00:40:28.320
twenty one because there were aberrations and
let's go read this stuff. So let

573
00:40:28.320 --> 00:40:32.159
me go change that step in the
recipe. So it's just as powerful in

574
00:40:32.280 --> 00:40:37.679
terms of what we do is enabling
an individual to talk to the data directly

575
00:40:37.800 --> 00:40:42.239
without any intermediateary. But it's also
about what we as humans can do because

576
00:40:42.280 --> 00:40:45.840
now we are coming at it in
a factual, data driven fashion, in

577
00:40:45.880 --> 00:40:50.000
a truly data driven, factual fashion. Right, we are not guessing what

578
00:40:50.039 --> 00:40:52.880
are we talking about? You can
look at a Tableau dashboard and what does

579
00:40:52.920 --> 00:40:54.440
it mean? You're just gonna assume
it. This means what it means?

580
00:40:54.599 --> 00:40:58.360
Can you interrogate it? Can you
say did it miss a step? No?

581
00:40:58.920 --> 00:41:01.760
But that human to human chat portion
is a big part of a vision,

582
00:41:01.800 --> 00:41:07.960
and it all connects if you have
this way of explaining how you came

583
00:41:07.320 --> 00:41:13.320
to your answer in the first reproducible
way. It enables both these modalities of

584
00:41:13.360 --> 00:41:16.159
communication, and that's what is super
exciting, right And you know, I'll

585
00:41:16.239 --> 00:41:21.519
end with a bit of an analogy
here. So online and social media and

586
00:41:21.599 --> 00:41:23.719
via email, people get to these
big fights. It appens all the times.

587
00:41:23.719 --> 00:41:27.679
Where all this hate is online is
because people are kind of going at

588
00:41:27.719 --> 00:41:30.599
each other. But if you just
sit down and talk to a person,

589
00:41:30.119 --> 00:41:35.079
you'll always be amazed at how much
you actually agree on. And a lot

590
00:41:35.119 --> 00:41:43.800
of that friction results from misunderstanding,
misinterpretation, and really from superimposing your beliefs

591
00:41:43.840 --> 00:41:45.960
onto someone else when you don't really
know what they think. You just know

592
00:41:46.000 --> 00:41:50.159
what they say or what they've posted
on a chat, and you can't read

593
00:41:50.280 --> 00:41:52.719
sarcasm in chats and things of this
nature. It's hard to So these are

594
00:41:52.840 --> 00:41:58.559
very serious problems in communication that as
solved if you just talk to someone and

595
00:41:58.639 --> 00:42:01.960
what you're doing is enabling kind of
conversation with the data, but then also

596
00:42:02.000 --> 00:42:07.679
with your counterparts and the organization,
so you can collaborate on working through what

597
00:42:07.760 --> 00:42:12.679
we just saw here. That's a
whole different story from debating whether or not

598
00:42:12.760 --> 00:42:15.320
it's true. And then Okay,
next month's meeting, we'll try to figure

599
00:42:15.320 --> 00:42:19.079
it out again. Well, you
just lost another month, and we can't

600
00:42:19.119 --> 00:42:22.239
afford to lose months these days,
folks. I'm telling you like that the

601
00:42:22.239 --> 00:42:27.920
pressures from the economy, from global
forces, from artificial intelligence, all this

602
00:42:27.960 --> 00:42:34.199
stuff is really bearing down and forcing
change, forcing companies to get real about

603
00:42:34.239 --> 00:42:37.400
what they're doing. And I think
you've got a pretty powerful technology here to

604
00:42:37.639 --> 00:42:45.679
enable those data driven, data focused
conversations where you have the transparency, you

605
00:42:45.840 --> 00:42:49.559
have the recipe so you can see
under the hood what it was doing.

606
00:42:49.920 --> 00:42:53.000
That's very powerful. What do you
think? Yeah, absolutely, And I

607
00:42:53.039 --> 00:42:59.159
think it's the confluence of all these
technologies that are coming together. We have

608
00:42:59.280 --> 00:43:04.280
the processing that was simply not available
ten years ago to run any of these

609
00:43:04.440 --> 00:43:07.639
modeling things in an economical fashion.
And I know that some of these things

610
00:43:07.679 --> 00:43:13.360
need. We've got the data because
we've been collecting it now for twenty years.

611
00:43:13.480 --> 00:43:17.000
Nobody throws up a data and that's
awesome. And now we've got the

612
00:43:17.920 --> 00:43:23.320
business drive like every CEO has heard
of LLLM. Every CEO is investing in

613
00:43:23.400 --> 00:43:28.199
that as one of the top priorities
in the company. So the opportunity exists

614
00:43:28.440 --> 00:43:31.239
so super exciting time for all of
us who are in this data world,

615
00:43:31.320 --> 00:43:37.599
which is taking different forms databases,
big data. Now everything is AI.

616
00:43:37.039 --> 00:43:43.039
But at the end of the day, data plus algorithms plus processing power that

617
00:43:43.079 --> 00:43:46.159
becomes available the synthesis of that is
what we are living and it's fascinating.

618
00:43:46.559 --> 00:43:49.840
Well, and you know, we've
got about one minute left here. The

619
00:43:49.880 --> 00:43:52.800
other interesting thing is I want more
and more business users, including myself,

620
00:43:52.800 --> 00:43:58.039
to better understand the different algorithms in
what they do, Like a K means,

621
00:43:58.079 --> 00:44:00.960
for example, what exactly does that
do? I think the more business

622
00:44:00.960 --> 00:44:05.639
people can wrap their heads around how
those things operate, the easier they'll be

623
00:44:05.679 --> 00:44:07.599
able to map their own thoughts to
their own world of data. What do

624
00:44:07.639 --> 00:44:12.840
you think? Real quick? Yeah, I agree. I think it's going

625
00:44:12.880 --> 00:44:15.360
back to your car example is that
you don't need to be to know how

626
00:44:15.360 --> 00:44:20.280
to build an engine or fix an
engine to understand a car to drive a

627
00:44:20.320 --> 00:44:22.199
car, and that's totally fine.
But if you're a race car driver,

628
00:44:22.320 --> 00:44:25.599
even though you're not fixed engines,
you probably need to at des understand a

629
00:44:25.599 --> 00:44:29.760
little bit of how the engine works
because guess what, you'll get a little

630
00:44:29.760 --> 00:44:32.119
bit more out of that now,
a lot of automation is making that easier.

631
00:44:32.159 --> 00:44:36.719
So I think there's always value in
learning something even though you're not going

632
00:44:36.760 --> 00:44:38.920
to be using that in a direct
fashion, because it allows you to appreciate

633
00:44:38.920 --> 00:44:44.559
at a deeper level. So fan
of learning things absolutely, Yeah, well,

634
00:44:44.559 --> 00:44:46.079
folks, that's what we're doing.
That was a great quote I heard

635
00:44:46.119 --> 00:44:49.239
from the CTO of Boom Media the
day. I asked him, you know

636
00:44:49.320 --> 00:44:52.079
what's the most important thing in this
era of AI, and he said learning

637
00:44:52.599 --> 00:44:57.679
and he meant human learning, Like
we need to learn about these things and

638
00:44:57.719 --> 00:45:00.599
what they do and how they operate, and be very crystal clear about what

639
00:45:00.679 --> 00:45:06.239
we're absorbing in terms of information and
insight from these systems. And folks with

640
00:45:06.320 --> 00:45:10.599
a fantastic conversation with jig Nash Patel
of data chatook them up data chat dot

641
00:45:10.639 --> 00:45:15.320
AI. We'll talk to you next
time. You've been listening to Inside Analysis.

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is the Man from Yesterday and back
in time to this time in nineteen sixty

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eight, Merrily Rush and the Turnabouts
will appear at Disneyland at New Year's Eve.

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They have a huge hit here in
nineteen sixty eight with Angel of the

693
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Morning Just call Me jos times.
And from this time in nineteen ninety one,

694
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actor Richard Gear, who is forty
two, marries his longtime girlfriend Cindy

695
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Crawford, who's a model. It's
the first marriage for both. And the

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thing is is, I don't think
it's that uncommon to one of you married

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for a year or two before all
of a sudden you make another change and

698
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have a baby. And I mean, your life keeps changing and changing and

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changing. And from about this time
in December of nineteen seventy eight, this

700
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Tuesday Night on ABC TV, The
Carpenter's Karen and Richard in a Smith's Portrait

701
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features Gene Kelly and Jimmy and Christy
McNichol. But I and in my Dream,

702
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I'm Christmas with more at Man from
Yesterday dot Com. I'm Rick Smith,

703
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host of the Rick Smith show,
inviting you to listen to my show

704
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during the noon hour every weekday right
here on KCIA. My show is sponsored

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locally by Teamsters nineteen thirty two,
a strong union with fourteen thousand members in

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and get started now. America exists today

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as a bizarre anomaly. We profess
to be an electoral democracy, yet we

710
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are ruled by a governmental plutocracy.
One especially gross example of this incongruity is

711
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the overwhelming power of big money over
the people's will by a wide margin.

712
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Americans of all political stripes want to
ban the distorting force of huge electoral campaign

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donations by favor seeking corporations and ultra
rich elites. Yet nothing. National and

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state lawmakers take the plutocratic money and
promptly bury democratic reforms that would stop the

715
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gusher of corrupt cash. An analysis
of donations in this year's congressional races shows

716
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that this plutocratic perversion of our politics
and government has reached absurd levels. Historian

717
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Nancy Maclin and public interest advocate Frank
Clemente have documented that the two main super

718
00:51:47.079 --> 00:51:52.559
PACs trying to put Republicans in Congress
got about half of their one hundred and

719
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eighty eight million dollar election fund from
just twenty seven billionaires. Also, a

720
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corporate front that backs gop CAN candidates, Club for Growth, got nearly thirty

721
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five million dollars from only three billionaires. Thanks to years of congressional stonewalling and

722
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the steady partisan stacking of our top
courts with corporate ideologus, there is essentially

723
00:52:13.559 --> 00:52:17.199
no limits on this purchase of lawmakers. Indeed, the biggest donors are even

724
00:52:17.239 --> 00:52:23.440
allowed to undermine democracy in secret,
not revealing their identity. Lawmakers elected with

725
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this money not only support the billionaire's
special interests agenda, which the people don't

726
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want, but also are fierce opponents
of any reforms to increase voter participation in

727
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our governing process, which people do
want. This is Jim Hitier saying that's

728
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why, for example, efforts to
guarantee every eligible American a constitutional right to

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vote have not moved forward in Congress, even though the great majority of people

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airs Tuesdays at four pm right here
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That's eight six six to zero one
zero one five six. Then you can

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say DNA from the Bureau of Economic
Geology. This is Earth Day. When

775
00:56:19.039 --> 00:56:22.920
did our ancestors leave the trees and
begin to lead a life on solid ground.

776
00:56:22.639 --> 00:56:27.920
Some real life CSI has given us
a big clue. Lucy, the

777
00:56:27.960 --> 00:56:31.159
three million year old skeleton of one
of our oldest known human relatives, was

778
00:56:31.199 --> 00:56:36.440
recently on a museum tour of the
United States. During her visit, the

779
00:56:36.519 --> 00:56:40.559
University of Texas, scientists examined her
skeleton with geological CT scanners, similar to

780
00:56:40.639 --> 00:56:45.639
medical cat scans, but with higher
resolution. Fossil bones often break as they're

781
00:56:45.639 --> 00:56:52.199
buried, but Lucy's upper arm showed
something unusual. It was compressed with sharp

782
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fracture lines and tiny bone fragments intact. The team called in an orthopedic surgeon,

783
00:56:57.880 --> 00:57:00.960
who confirmed that this injury in modern
human only occurs when they extend their

784
00:57:01.039 --> 00:57:06.159
arms to try to break a fall
from considerable height. So they began to

785
00:57:06.199 --> 00:57:09.239
look for other fracture evidence of a
fall and found it in her ankle,

786
00:57:09.559 --> 00:57:15.800
knee, pelvis, and ribs.
They then look to modern chimpanzees, which

787
00:57:15.800 --> 00:57:20.000
are about the same size as Lucy. Chimps nest in trees at heights of

788
00:57:20.039 --> 00:57:22.639
up to thirty five feet high,
enough that a fall could result in the

789
00:57:22.679 --> 00:57:28.599
same type and degree of fracturing.
Found in Lucy. Further studies using the

790
00:57:28.639 --> 00:57:32.159
ct data showed that her ratio of
arm strength the leg strength was more like

791
00:57:32.320 --> 00:57:38.679
chimps than humans. Both finding suggests
that three million years ago, our ancestors

792
00:57:38.679 --> 00:57:44.760
may have lived a significant part of
their lives in trees. I'm Scott Tinker

793
00:57:44.880 --> 00:57:50.519
and this Spaniard date with Earth.
Earth Date is produced by the Bureau of

794
00:57:50.559 --> 00:57:54.000
Economic Geology at the University of Texas
at Austin. Earth Date is researched by

795
00:57:54.079 --> 00:57:58.800
Julie Hennings, written by Harry Lynch, and distributed by Mark Blunt and Casey

796
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Walker Stories. Follow us on Facebook
or visit Earthdate dot oorg. Are you

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tired of the same old conversations that
everyone keeps talking about? Want to hear

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something new, fun and exciting.
Listen to Whatever Works here on KCAA every

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com or our website KCA Whatever Works
dot Com to find upcoming and previous episodes.

803
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We like to thank all the KCAA
listeners for supporting our show. Did

804
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you know here at KCAA ten fifty
am that we developed an app for all

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your Android devices. We're talking about
your smartphone, your tablets, you name

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it. You have an Android format. You can take KCAA with you everywhere

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you go. We're talking about our
audio stream, our video stream, and

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even our podcast. Go to KCAA
express dot com. That's KSECAA express dot

809
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com. KCAA express dot com.
Listina KCAA Lomolinda at one O six point

810
00:59:17.880 --> 00:59:23.920
five FM, K two ninety three
CF, Brino Valley, NBC News Radio.

811
00:59:24.039 --> 00:59:28.960
I'm Chris Gragio. Former President Trump
now says he won't testify in his

812
00:59:29.039 --> 00:59:32.320
fraud trial in New York tomorrow.
He posted his decision on truth Social today,

813
00:59:32.559 --> 00:59:37.159
adding that he's already previously testified.
Trump was set to make his second

814
00:59:37.159 --> 00:59:39.320
appearance on the witness stand to be
questioned by his own attorney, says the

815
00:59:39.360 --> 00:59:44.519
final witness for the defense. The
office of New York Attorney General Letitia James,

816
00:59:44.679 --> 00:59:46.840
filed the two hundred and fifty million
dollar lawsuit against the Trump family and

817
00:59:46.880 --> 00:59:52.920
the Trump Organization for allegedly inflating financial
statements by billions of dollars in an effort

818
00:59:52.920 --> 00:59:57.519
to receive more favorable loans. Secretary
of State Anthony Blincoln believes Israel does not

819
00:59:57.639 --> 01:00:01.480
want to harm Palestinian civilians, but
also should do more to avoid civilian casualties

820
01:00:01.480 --> 01:00:06.519
and allow humanitarian aid. Speaking on
CNN stead to the Union, Blincoln said

821
01:00:06.559 --> 01:00:08.320
Israel needs to close the gap between
intent and results.