Stration that the House can't even vote on his resolution that condemns Hamas and supports
Israel. The Foreign Affairs Committee chair added that the House's inability to govern empowers
and Embolden's adversaries to claim that democracy doesn't work. Nine House Republicans are now
considered likely candidates for Speaker after the party dropped Jim Jordan is their nominee.
On Friday, I'm Chris Karragio NBC News Radio. You're on board kcaa's Inland
Express KCAA Home A Linda ten fifty Am, the station that needs no mess.
Year behind, the information economy has a ride. The world is teeming
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now here's your host, Eric Kavanaugh. Yes, oh yes, folks,
welcome to the future. Indeed, your host Eric Cavanaugh here on the only
coast to coast radio show in the US of Ada. It's all about the
information economy Inside Analysis, and folks, I'm very excited today to have a
very special guest in industry visionary, someone who has been working in the space
for a number of years and has always seemed to have his finger on the
pulse of where things are and where things are going, and that's especially difficult
to do in these days with this new remarkable power of generative AI. We'll
be talking to Andrew Turner. He's the general manager of a company called Proxy
Data. They're doing some very interesting things in that space. And before we
dive in, I wanted to throw out a bit of my past here as
I think about a metaphor to describe what we're seeing in the world and what
this GENAI stuff is really doing. And I'm reminded of the philosopher about whom
I wrote my thesis in college in philosophy, Jacques dere Da. He wrote
this very dense piece called Structure, sign and Play in the Discourse of the
Human Sciences, and it was deep and he starts off with this whole concept.
He says, let us assume that something has happened like an event,
if that word weren't so loaded, and he refers to as a sort of
rupture or redoubling. And what he's kind of talking about is is a movement
to another level of self awareness, almost like cultural self awareness, and how
that cultural self awareness is now shaping and sort of reshaping how we interact with
each other, how cultures are shaped around the world and change. And if
you think about this generative AI stuff, it's really what these guys have created.
What these folks have created is a reflection of the text of our world.
That's really what it boils down to. These engines like open AI and
Barred and LAMA and Lama too, and all these huge foundational models as they're
called. They have been trained on the corpus of text on the internetch maybe
it's Wikipedia or any other Twitter for example, is a very good source of
information excess, it's now called, and they are now reflecting back to us
this corpus of text, of language of communication. And Andrew will talk a
bit about the transformer models and some other things that we're enabling. En schmid
Huber, who I've interacted with, very smart guy. He wrote papers on
these transformer models and they do really interesting things. So in the earlier days
of these large language models, you could only see like a token or two
to the left or right, in other words, backwards or forwards. You
can think of a token as like half a word basically, and what these
engines are doing is just predicting text that the engine thinks you want to see
based upon your prompt. Well, there's this whole issue around embeddings you have
to use, which are like memories to a human basically, But your embeddings,
combined with the generative power of these models, this predictive capability, is
what has created these things. The transformers allowed the engine to have multiple agents,
if you will, so now you can see a number of tokens forward,
a number of tokens back and get a much clearer picture. Just think
of it as extending your view as you're driving down the highway. Before it's
like you're driving in dense fog. Now the fog has gone and you can
see pretty far forward as you're driving along. So's it helps to create more
accurate depictions of what the prompt is telling the engine to create. But it
still is an engine just predicting text basically. But the other nice thing about
these transformers is that they allow for there to be multiple agents I refer to
as almost like a peanut gallery of participants who are suggesting what the next word
should be, and then at a certain point in time, the engine goes,
Okay, I'm going to go with this word next, I'm going to
go with that word next, etc. Well that's what they're doing. But
we're just at the beginning of this I mean interactive AI I think is going
to come next, and that's going to be even more interesting. But let's
just stay focused on the JENAI and what do we mean my dark data?
So the title of today's episode includes dark data. That's all the data you
have that you don't really know about. That you have a catalog that's sitting
on servers somewhere or sitting in a file system somewhere, and that's probably more
than eighty percent of the data that any mid size or large organization has.
So you'd better be very careful about pointing this AI gun at a vast trove
of dark data because you don't know what's coming back. And this stuff is
so complex now that it's difficult to figure out exactly how the engine got the
answer that it got. And this is partly why you have hallucinations. As
a friend of mine, Eugene Burke says, these large language models don't have
an epistemological boundary, meaning they don't know what they don't know, and that
could be a very dangerous thing because that's when they start making stuff up.
Like chat GPT told me I wrote three books. I was like, did
I are you seeing in the future or something? Because not overcall writing any
books, but maybe. But anyway, someone who has focused on this for
a while now and it's working with a company that is trying to harness and
if you will team this dark data, shine a light on this dark data
is Andrew Turner of Practice Data. Andrew, welcome to Inside Analysis. Tell
us a bit about what you're working on today and how you're helping companies make
the most of this technology while mitigating the risks. Well, thank yes,
thank thanks. Firstly, thank you for letting me join you from a very
cold London tonight, so slightly different times on to where you are. Yeah,
I mean, I think, as we all know, all these technologies
go through a certain cycle, and you know, we're now in this kind
of I want to say hi, but we're in this kind of roller coaster
ride of you know, recovering from opening eyes movement of releasing chat GPT.
You know, well just just about twelve months ago. Really now is it?
So? Now? Now? I suppose what I would say is the
kind of the the door of actually, okay, let's let's stop talking about
all the things we could do with it. Let's talk about something we can
do with it. Right, So I think, you know, I think
this is where the pragmatism kicks in, and you know, the marketing's got
to stop and the actual delivery of value is going to happen. So yeah,
that whole thing, not saying that the marketing won't stop, but yeah,
I mean I think, you know, it was a really good intro.
Thanks for doing Thanks for doing the intro. I think the interesting thing
that we find is that people are still struggling with what to do with this
stuff. So they you know, they've been working on data related programs for
multiple years and then this this kind of tsunami of genreity AI happens. I
was talking to the chief their officer a very famous media company in London,
and she the positive thing was that she said that actually the CEO literally contacted
her when this whole chat GPT thing hit the hit the business and said,
what do we do from a policy point of view? Do we use it
race it that we actually you know, put you know, kind of moratorium
on it so none you can use it. So I think the positive sheets
awt of that is actually the CEO contacted her to say, how do we
govern this type of technology? You know, let's not shut it down,
let's work out how we can actually you know, coexist with it. So
but it's probably happening in lots of boardrooms at the moment. Is what do
you do with it? You know, what is the business use case?
How do you prove the how do you prove the value and make sure that
you can get something out of it quite quickly to build. It's the classic
thing about scoping something that's of you know, a reasonable scale, not too
big, you know, so you don't create this Bermuda triangle where you disappear
into Beida triangle or you never could get out. But you know how find
you know, scope something that's actually your value and then and actually go and
deliver something and then you can scale it from there. So it's back to
it's back to those kind of principles, I think, whether you're embracing generative
AI or other things. But yeah, one of the things that we did
at practice data is trying to look at how do we how do we hit
this conundrum of managing data at scale? And to the point you did in
the intro, is this whole issue of there's lots of data just set around
and being moved around your organization and being you know, stored. Yeah,
but do you actually have your arms around it, so you know, putting
a shining light and putting a real proper enterprise level discovery across that data set,
whether that's you know, unstructured data, semi structured or structured data,
which is traditionally where people have looked at just purely structured data. Obviously,
when you get into large long as one is, you're looking at unstructured and
semi structured. So how do you get that kind of over you on that
data and then actually identify where are you risks and opportunities and actually what all
of the things we've done recently and this can become more important is build effectively
business use cases and industry models to help these organizations really accelerate because you know,
lots there's lots of tools out there, but a lot of them are
very generic. So you know you're in a you know you're in a property
and casualty insurer. They don't want to know, They don't want to have
a generate tool. They want to look at metadata and particular data models that
actually they can relate to from a business point of view to make decisions.
That's the work that we've been doing at Practice Data to be different. Because
I was speaking to Mike Ferguson you probably know at Big Data a few weeks
ago. He said there's something like thirty eight different data discovery, data,
data catalog types of solution out there. So if I'm a buyer, if
I go back into where I used to work at Tesco or ge or whatever,
it's a buyer's nightmare, you know, where how do you decide what
you're going to buy? So what we do is we do something very very
narrow, but very very deep. So that's what we're focused on, is
how do we add value but not try and take, not try and say
we can do everything, but effect we do automation, do curation as a
service. So you know, we're looking at how do we use our industry
models, domain expertise and give that value add into the actual overall analytics value
check. That's what we're focused on and you know what, you just gave
me a good opportunity to bring ted Lassow in to the conversation, which of
course is taken America by storm on Netflix or maybe it's Apple TV. I
don't even know which streaming services. I don't think it's actually Apple. But
the joke there is, do you take this thing as like a college football
coach or something, and they bring them over to England to, you know,
to coach a football actual football We call it soccer team, which is
an outrageous proposition because it's like, what the heck does this guy know about
soccer? Or you know, Premier League football, which is a serious deal.
It's a very very different game. And the point being here you typically
want someone with domain expertise because you don't want to hire, you know,
a medical doctor to fix your car, for example. I mean, maybe
he or she knows how to fix cars just serendipitously, but that's certainly not
their core area of expertise. And if you're talking about trying to wrangle with
these models that have some of them one point nine three point two billion vertices
or something, this is incredibly complex stuff. You can't you can't boil the
ocean. It's an age old mantra in the IT world, don't boil the
ocean. That's kind of what folks you're starting to do. But you're not
taking that direction. You're saying, no, no, hold on, let's
focus on these specific ontologies for certain industries and businesses because that way you can
pull this off. Because if you try to use and this was my concern
very early in the game when I was listening to people talk about this,
I'm like, Okay, I get where you're going with this, but my
goodness, the training will take forever in a day, and by the time
you've done training it, you're done training it. Like, you know,
are you even in business anymore? I mean it's like, yeah, you
can get value, but geez, you got to be focused right. Well.
Yeah, the issue as well is that, you know, the I
mean, I was talking to a bank in the Naudic region of the day
and there was the issue still is around where because the technology takes so long
to deliver. Yeah, and then what you've got to do to is work
out a sustainability model. You have to actually, you've got to win the
hearts and minds of the people who are going to manage the day to going
forward. If it takes you know, multiple years to get to that point
where they can actually start adopting it, then you know that the hard stuff
is the soft stuff, actually the change management to actually engage, reward,
incentivize, train, educate the people who actually have got that deep domain expertise.
When they see something that's actually they can relate to very very quickly,
you're going to improve that engagement. You can improve that. You're going to
change management a lot easier as well, because you talk in their language.
If you've got a generic tool and you start with a blank canvas, then
you've got a lot of work to do before you get even to that.
You know, you could say base camp, you know from an everest or
climbing a mountain kind of analogy, right, and that that can that you
know, that's a frustrating. If you're the leader of that business unit or
that particular function, you've got a lot on your hands to sort out.
So we saw this a few years ago when we were working with Andrew and
and myself was the CEO and founder was We're working in another company and we
were doing some herculean work, piece of work in very large enterprises, but
you're just taking too long to get to that base camp. So we said,
how can we accelerate that put the effort into building the models so you
know, you can obviously get you know, free open source models from hugging
face. There's a lot that's there's been some really great work they've done,
but we believe that there's some real added value in those industry use cases and
industry models, and that's what we've been focused on. As I can have
USP well. And so the ontologies or the industry models that you start with,
they're really like roadmaps. They're like a legend if you will, this
discovery process. So at least you have hallways to go down, rooms to
look into instead of trying to you know, find something in the entirety of
the United States, which is just too big to digest, right, And
that's the challenge. If you're trying to digest too much, you just get
over overloaded and the system, the brain just crashes and you just move on
to something else. You just can't do it. So you provided pathways essentially
of discovery, and then you use the technology to look around and to curate
and you know, I have this new concept I keep talking about, I
need to write something and stick a pin, so to speak. But I
kind of view these foundational models as a second chance for data. And what
I mean by that is we spend all these years organizing, analyzing, doing
data warehousing and then data lake architectures and all this stuff, but it was
all sort of the same concept that you persist data somewhere and then you go
around and you try to analyze it and number crunch it and so forth,
and you know, we just we have a mess out there. I mean,
if you think of the the corpus of information that any mid size to
large organization has, it's just it's monstrous. It's all over the map,
it's in all sorts of formats, and there's really no way you're going to
tame that. But with these foundational models, it's like we got a second
chance to sit down and carefully curate what we want to put into our embeddings,
into our memories if you will, for the AI to learn, and
if you do that right, that I think is going to be really compelling.
What do you think we got two minutes before the break? Yeah,
I mean I think it's that you know, a phrase we use messy,
murky doubt data. You know, how do you grab your arms around it?
How do you make a real impact on that? And one of the
things we did, we've architected our solution is do it a cloud scale because
you know, that's no longer a constrained So we're going to be making it
available on you know, the cloud provider marketplaces, so you can basically try
before you buy. You can get grab your arms around this stuff. You
can apply it into your own data set, you know, get get,
get the momentum, get people on board, and then scale it from that.
Because you know that thing that no infrastructure is no longer a constraint anymore.
You can get on with that. You can you know, you can
select it from the marketplace, you know, do it by tomorrow. It's
you know, provisioning that type of instructure is like an overnight thing. It's
all. It takes you a couple of hours. It's not something you have
to work weeks and weeks and months for. And I have an army of
people to do that. You can do that from you know, the established
players. So that's that's also what we're going to be releasing as well.
To make that make it accessible and make it, you know, adoptable,
and actually then you're going to win people over and then people going to adopt
it well, right, because you think about the patience of the average person
these days, and there's no doubt that change is always difficult. Change is
always hard. Some people are better at it than other people, but everyone
has to deal with change. And you know, probably the older you get,
the less willing you are to change. It's kind of your old habits
die hard, as they say. But to your point, if someone can
make progress in a fairly short period of time or at least see where the
going, that's the key. That's the way you kind of win the hearts
and minds. And you know, it sort of reminds me of my own
writing process when I have to write something new. At first, I'm like,
oh goodness, what am I gonna do. I have to find a
starting point. I have to find where is that bass camp? Okay,
the base camp is up there, And once I find that, I can
climb my way to it and then take a few deep breaths and then prepare
for the next part of the journey. And that's kind of where we are
right now. I think everyone's trying to find their base camp. But folks,
don't touch on that. I'll be right back. You're listening to Inside
Analysis. Expected, Welcome back to Inside Analysis. Here's your host, Eric
Tabanaugh. All right, folks, take us to the future. Indeed,
I love that song that's by Black Bananas is the band. You gotta love
it. I love that song and it reminds me of William Gibson, the
futurists. I've always wanted to meet a futur rist, to be like,
are you even here right now? Man? But anyway, he's a smart
guy, and he said, the future is here already, it's just not
evenly distributed. It's like, oh, that's a good one. And all,
of course, it also means the past is still here, just in
pockets all around us. So it's an interesting dynamic. But we're talking today
with Andrew Turner, general manager of Praxy Data. That's PRXI Data, just
like it sounds. And we're talking about jen Ai and making sense of dark
data. And we wanted to talk about the different roles, things like prompt
engineering, for example, which is a real task now, it's a real
skill set and as many people have said and I think this is true.
It's not so much that AI is going to take jobs away, per se
as it is that people who learn to use AI effectively are going to have
a much better time getting jobs and doing their jobs than people who don't.
It's like any other tool. And you don't see too many yaks out there
in the fields tilling the land. We're using giant machines to do that stuff
these days because they're much more efficient. And I've always believed that machines can
do the job better than people. Let the machine do the job and let
the people do the more interesting things on top of what the machines do.
But Andrew, we are going to have to deal with some pretty heavy duty
change management. Right in our deep dive earlier, you talked about this McKinsey
quote where they said that we now believe, or they now believe, we'll
be able to automate many of these processes ten years sooner than we had previously
thought. That's a pretty big deal. I mean, that's a step change.
You're like skipping a decade basically, and it's going to require us all
to get our sea legs. Right. What do you think? Yeah,
I mean I think it's absolutely I mean that quat quote came out of the
fourth of October, so it's only a few weeks ago, and it's absolutely
you know when you think about the ramifications of it's absolutely staggering. You know,
you talk about, you know, I remember the days of business process
reengineering, but that's like, to your point, it's like BPR on steroids,
you know, times ten. So you know, it's it's kind of
like it's saying, you know, you it's a real opportunity to redesign and
really step change and reorganize the organization. And any CEO that's listening to this
session that kind of says, oh, you know, we can put it
on the back burner. We could ignore the impact of generaty of AI.
You know, it's literally it's it's a it's that would be a mistake.
The question. But the question I think is then about, you know,
how do you then sit down with your people team, you know, what
is the organization of the future, because I think it is that point you
made a bit earlier. It's not just replacement, it's it's coexistence and actually
how do you shift the work into that new type of role and then it
is a piece around upskilling and you know, how do you upskill the right
roles in the future. And you know, one of the things that I
was sat in the audience at this recent session where you know, the couple
of CTOs presenting their take or you know, from a technical perspective about their
view on generative r and they said, you know, it's pretty much the
whole organization structure is going to be impacted at different levels. And people that
we you know, we traditionally call you know, developers that the rock stars
of the future or you know, the the rock stars of the of the
today and the stars of the future. But then you know, I think
it's going to change that that role because people are spending a lot more money
on people in this kind of area, and actually, how do you get
the productivity out of those you know that that you know, everybody wants to
be a data scientist and then you know, you pay, you pay,
you pay market rate to attract the best, but then actually, how do
you put the tools around them to actually become productive and impactful in that organization.
So in generality, it puts that the next layer of complexity on that
I think there's an opportunity for organizations to help other, you know, enterprises,
to actually educate them on prompt engineering. I think it's a key,
it's a core competency going to be going forward. And if you're not done
prompt engineering or interacted with bar GPT, et cetera, then you know,
it seems like talking, you know, talking interacting with a real person.
It's quite it's quite remarkable. Yeah, it really is. And you know,
I had Matt McClarty, the CTO of Boomy, on the show a
few weeks ago, and he had this great line where he referred to what
era we're in in a certain sense, and we talked about machine learning and
AI and what's really happening now, and he said, it's a time of
learning, meaning we humans are now learning what this stuff can do and how
it works, and we're learning about how to make it hum we're learning about
what the challenges are. And it kind of reminds me of something I worked
with a couple of Russians years ago, and it was a good Russian and
the bad Russian. The good Russian Niko and Minchugol. He was a very
smart guy in Nikolai Minchugo. He ran the first ever advertising agency in Moscow
because when Pedestoika came out, all of a sudden, you had a need
for ad agencies. There was no need before in the communist system because why
would you have an ad agency? Right, So he was the first to
do that. He's a designer but a very good coder too, and he
built out this system. But he would say funny things to me, like
one day said, the funny thing about the web is a no one knows
how it works. All we know is it works, right, And that's
kind of where we are at this lom stuff. I mean, we do
know more about how it works. You can get into the weeds, and
the embeddings are crucial, right because the models have been trained on this corpus
of text that is vast. It's very very large. So there's good stuff,
there's bad stuff, there's truthful stuff, there's not truthful stuff, and
so it has all this material. But the AI engine itself is kind of
like a teenager with a tremendous vocabulary, but it still doesn't know the sort
of more rays and the rules and what it should do versus what it shouldn't
do, And these are all important things. To know and so to me,
those embeddings are crucial. And I get back to this sort of second
chance for data concept because once your organization understands what these things are and you
kind of wrap your head around some of the use cases, like customer support,
for sure, it's a big one. News gathering, news dissemination,
news generation I think is going to be revolutionized by this stuff. Once you
wrap your head around it, then you can sort of start the process and
figure out, well, what do we want. How do we want to
teach our teenage child. Do we want to send it to Catholic school?
Do we want to send it to public schools? We want a home school
basically, And you really have to know like what your appetite is, and
that gets into the training side, right, So you don't want to be
trying to boil the ocean. Don't try to, you know, do two
full time jobs and educate your five kids by yourself. You're probably going to
burn yourself out if you do that. So you have to kind of understand
the scope and the appetite and where you're going. And if you do that
and you use tools I think like praxy data for example, I think you're
going to have a really good chance of success. But what do you think,
Andrew, Well, I mean, I think what you touched on there
was that what i'll call data toxicity. You know, so that thing about
you know, because of you know, there was a scenario that will slightly
the week where if you get it wrong the way you connect these things together,
which you can actually generate data and then it actually publishes that data and
then it feasts on itself, like it becomes like a carnival for its own
data sets. So the danger there is that you you know, you go
back to origin, what's the origin, what's the syndicated origin, and what's
true and what's not true? Yeah, right, there's a piece around that,
around you know, lineage and and the kind of the inheritance of that
data, which becomes back to some kind of fundamental how you manage data at
scale principles you need to get you need to get right. So you know,
that's the point you said a bit earlier about this. You know,
what we see is this messy, murky data. You've got all this kind
of patchwork quilt. You know, people have gone out and bought lots of
tools and they've evolved over time. But actually what we see has being this
kind of like I suppose you know this, I think we talked about it
that you know, it's backplane, you know, like a backplane concept.
You know, how do you have a backplane across your enterprise that you can
actually rely on, that you you look after properly, that's going to take
in these different types of dat source So you have that I don't want to
it's an age your phrase about that golden record definition and people still struggling with
that. So you know, all we're trying to do is is not not
say you're going to throw away all the thity investments you've already made, but
how do you actually you know, tap into this this model, these models,
this this industry insight to to add value to your you know, getting
those people on board and then dealing with the change management we talked about a
bit earlier, because that's that is really really the sustainability model. That's the
biggest issue we can see out there. You know, people can keep buying
these tools, but until you actually get work out a path to get people
engaged, that's that's the key to it. So you know, we can't
you know, I don't know what we have situations in the future where we
have robots managing the data in our organizations. There's that tacit knowledge that's been
hearing the brain, right, that people have got experience about that is difficult,
difficult to codify. I'm sure work out through bring computer into face a
way of codifying at some point in the future. But for the for the
you know, for the next few years, I think that's where we're trying
to add some value in this, in this kind of this ocean of dates
that we're dealing with. Yeah, and I think, you know, for
use cases, certainly chatbots are going to get a lot better. You know,
a lot of times in enterprise technology, stuff comes out a bit too
soon. Right when the chatbots first came out, I think they just they
weren't quite ready for prime time, and so they didn't do as well as
as we needed them to do. And you know as well as I do,
that people have a very short attention span these days and very little patience.
So when something doesn't work out, like oh, that didn't work,
and then these impressions last, right, these impressions linger for periods of time,
and it's hard to sort of shake off that old mindset and realize,
no, this is a new tool, this is a new way of doing
things. But I think that the key is to help people understand that used
properly, this new suite of technologies is going to make your job a heck
of a lot easier, and it's going to it's going to make your job
more interesting and more fun, is I guess a way I'd put it,
because in general, machine learning algorithms are very good at tackling very tedious tasks
like scanning fifty thousand purchase orders and finding the twenty seven that have some error
in them. Well, a human being a record after record after record,
forget about it. Man, it's a nightmare and it's terrible for morale.
So as long as people understand, and this this is my beef with the
media writ large, is that I have this expression I throw out there that
the narrative is always wrong because the narrative is a story, and the story
is not reality. The narrative is some story I've spun up around components of
reality. But the reality with AI is that it is making our jobs easier,
or making certain easier. It's not necessarily taking jobs away. And it's
not the red eyed robot that's going to take over the world and all that
stuff. But the media loves to focus on that. They love to focus
on negative because that sells, It gets eyeballs and so forth. And I
think it's important that people recognize this as a whole new tool set. You
know, I mentioned dere Dodd is sort of redoubling. It's a reflection on
everything that we've seen already. So now it's almost more like riding a wave
instead of trying to create the waves. We have the waves now now we
have to figure out how to ride them. What do you think, Yeah,
yeah, I mean I think that what you were touching on a bit
earlier about that the chatbot situation was that you know, you obviously the the
data that feeds the chat bot, right, So you touching on this thing
about there's been some scary situations happened recently with you know, what you do
privately and what you do publicly. Yeah, so you know, how do
you deal with that, the risk around data privacy, preserving privacy around that,
you know, building your own private you know LLLM models, your own
data models, so putting your domain expertise with recognize other external data sources,
and that's where you're going to have that differentiator in service because if you create
that kind of that data source that then is an input into your chatbot,
you're then going to have to stand a much better chance of having a very
a delighter as a customer experience. That's going to drive loyalty. Yes,
So you know, one of the use cases we've been looking at recently is
with an organization that's dealing with insurance and roadside recovery and that kind of thing
where you kind of combining lots of you know, are you getting complaint letters
in from into a call center, how is the call center agent dealing with
and linking and saying who is that particular customers that's affected by that? So
you're getting different types of data input, but you've got to connect these dots
and how do you put an instruction in place to allow you to do that.
You've got to then, you know, you've got to work. It's
a complex object. It's a complex structure of how you sat in a CRM
system or something like that. But then actually you've got a different type of
interaction of data coming in through through letters and call center records, how do
you make sure you connect those dots to make the right decision to look after
that customer or deal or deal with that service intervention. So those are the
types of thing where we can see that having this kind of foundational capability is
very important where you're actually connecting the different types of data that before were siloed'.
That's a really good point. Yeah, that's very interesting because you're right
when you think about the old way of connecting systems, and I'm old enough
to remember EAI Enterprise Application Integration right where we would connect one application to another.
Well, in the old days, you would look at things like primary
keys and foreign keys and stuff like this as a a very what not what's
its declarative and what's the other one? I can't even remember the name.
Different kinds of programming. It's very specific instruction go here, do this,
go here, do that. But it's it's rigid. It's a very rigid
connection. And what you want is more of a fluid connection that can accept
inputs from multiple sources, like you say, from the call center, from
text messages, from mobile, from your laptops or whatever. They are all
these different sources which to manually connect. Yeah, you can do it,
but it's cluegy and it's also not very performance and then you wind up spending
all this time and effort trying to figure out how to optimize the performance.
So let's add some hardware over here, let's add some caching over there.
These are very specific tactical things that you can do tactical, but it's hard
to pull off. Whereas with these foundational models, that's what they do for
a living. That's what they're designed to do, is to take sources of
information and fuse them and then generate some output. And if you can train
that to do something using only your trusted data, the second chance for data
that we're talking about, then you're really going to get somewhere because those things
can do it really really really fast, and they just kind of figure stuff
out about how to do that dynamically, instead of having to give such specific
instructions to your systems people about how to get this connected to that and not
connected to this. They're kind of solving that out of the box. But
folks don't touch that that. I'll be right back. You're listening to inside
Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
All right, folks, welcome back to our show today, the Dark Data
Challenge. Solving the Dark Data Challenge with Practical jen Ai. You're truly Eric
Havanat talking to Andrew Turner of Praxy Data Praxy Practical. I don't know if
that was the logic behind it, but you know. And now let's talk
a little bit about public verse is private. Right, So when you're on
Google, you're just when you're using chat GPT, let's say the original one.
That's the public version that's trained on all this data that's out there,
but it hallucinates, it does all kinds of different things. What you really
want is a private instance that you then very carefully train by curating only the
data that you want, only the trusted data. It's like teaching your kid
how to grow up. You want to be careful about what you expose them
to. Not too much YouTube, not too much TikTok. Maybe we screwed
up on that one a little bit with our kid, but anyway, we
try our best. But tell us about your thoughts. I think every company
is going to want to have their own private instance of one of these models
and just over time build it out and let it support things like customer support,
let it support things like even supply chain other stuff. But what are
your thoughts, Andrew, Yeah, I mean I think it's I think people
originally thought there was only one model, right, They only thought there was
only a public one. And you've probably heard about that. You know,
there's been various examples where you know, the high tech company based in South
Korea, you know, uploaded their source code into chat, GPT, et
cetera, et cetera. So you know, but the I think that you
know, this is where the CISOW comes down into. This is where they
kind of you know, the IT security compliance piece and that the chief data
officer around you know, data privacy is you've got to govern your assets as
you will govern you know, any other assets. So you've got to it's
just a different type of asset that you're having to manage here. So yeah,
there's there's a raging debate. But the net net is you've got to
build your own private you know, generative AI asset asset library, you know,
your set up models to address that problem. Because you know, we
know there's some there's some class actions going on with some litigation going on with
certain tech companies at the moment around you know that they're the things that they've
done prior to coming out to market, and you know it's obviously causing a
lot of concern for a lot of organizations. So I think getting the right
advice around how you architecture what you're going to be put in place. But
yeah, you've got to have something that's protected, it's it's it's like a
it's kind of like sleep at night factor that you've got to think about,
and you've got to get the right people around the table. And I think
that you know, the key thing I think is also if you're a CEO
or a CFO, is looking at the the leadership team you've got working for
you to give you briefing so you understand the ramifications of just putting this piece
thing, this this thing together in the right way. I think then then
you can really exploit it. So that's the kind of that's the thing that
we're seeing. There's there's people are definitely speeding up now. They you know,
they've they've been watching and waiting, they've been dealing with this kind of
this massive tsunami and all the all the kind of you know, as you
mentioned the kind of media and PR around this, but people, now what
I wanted to go into, get into brass tax gett into real use cases,
you know, work out you can actually make some real big impact in
their organization, reshaping their organizations we talked about earlier with their organization structures.
And then you know, I think it's raising the importance of that chief Data
Officer role, the chief Digital office, so the CIO, the CTO to
be at the top table again, you know, and actually to really help
the CEO and CFO and top team to make those really really strategic decisions.
Yeah. Well, you think about digital transformation, which we've been talking about
for twenty years or so, and we've been doing digital transformation, but this
is almost like wholesale organizational transformation. Is what can happen if you do this
stuff? Right? What do you think? Yeah? No, definitely,
I mean I think I mean it was I was literally two hours away from
presenting something and that that McKinsey quote out came out. I mean it's absolutely
mind blowing, right, you know, taking ten years off an organization's evolution
with in one stroke. So you know, obviously you know, is is
that a precursor for you know, oh please please bring up a very very
large strategic consulting firm to actually help you with that transformation. But you need
some help. I think, you know, you need to get the right
people around the table, and you need external help. There is a and
that's what's driving up the cost of resources because there is a finite resource of
people who know what what the pass should be. So you know, that's
that's the key thing now is actually how do you get the right team on
board? How do you accelerate with this stuff? Don't don't wait, You've
got to get your arms around it. So yeah, at least the planning
side of things right, You've got to get the c suite and key directors
on board to recognize that this is a direction for almost everyone for the future.
In me, to not use this stuff is a bit crazy. I
think you're just going to get steamrolled if you don't. It's like the dot
com age when the dot com came out and everyone realized, oh wow,
so now I can sell everything online. I don't have to have all these
brick and mortar stories. Well that was a pretty big change in terms of
how we view the internet, and now you have this new, incredibly powerful
set of technologies. And to your point, you can use different models.
There's the chat GBT, there's barred you know, GitHub as co pilots,
you know, Microsoft, they all have their own version, if you will.
So you have to start somewhere, so at least start the planning process,
get people to sit down and understand what's possible. And the best way
to do that on an individual basis is just to play with this stuff,
right, Just get in there and start playing it, giving it prompts.
Ye see it to create your lab, create, you know, create,
create your you know, your your data lab. But it's it's it's reinforcing
that type of innovation lab. And you know what we're quite impressed with actually
what Meta are doing with Lama too, you know, that's what we've been
pretty impressed with that as a as a foundational capabilities because they've been you know,
we see been through some quite interesting you know, pr the last few
years about data privacy and preserving that kind of dimension. But you know,
we think that we think they've done a really good job around the stuff they've
been doing around armors. So I think that's something to think about as you
you know, you put that reference architecture in place, and you get these
right people in the room, and you get on with a sprint, you
know, get some value done in and for a few weeks get that into
the back into the board and show show what's possible with this stuff, and
that that's the way you're going to transform your industry rather than being transformed or
put in a difficult position where your competitors are actually doing something. You know,
we were we were looking as you know we were looking at recently,
was you can use these types of tool to do competitive analysis and identifying you
know, what the what the strengths and weaknesses of your cells and competitors that's
been indexed by these types of model. That's right. If you're not leveraging
that kind of stuff, you're missing out. Well. And I heard and
I haven't verified this, but I've heard it from a sources now and it
makes complete sense that Google has been quietly building a vectorization layer underneath g Drive
and Gmail such that in your prompt at BARB you can do at Gmail at
Drive and it will then point to your vectorized information and use that to generate
its answer. And that is pretty darn cool, I mean to already have
it there and just to explain to folks what these vectors are, what these
embeddings mean. And embedding can be any piece of information. It can be
a press release, it could be any number of things that you're using to
build out the foundation for your personalized engine, if you will. And then
you turn it into mathematical or numeric values basically, and that way you can
number crunch and do comparisons and do nearest neighbor and k meines distant differences basically,
And that's how these things run. And they're just doing all these calculations.
These just tremendous amounts of calculations. But you think just in terms of
and them use cases. You know, I sit there and think to myself,
MRIs. The MRI, the actual file of someone's MRI is a massive
file. It's huge. I mean, it barely fits onto a cd RAM.
But if you vectorize it, you probably collapse it by ninety nine percent
in terms of size. And now you can also analyze it more effectively.
So this can solve all kinds of problems, all the data storage issues that
we have. We can now vectorize this stuff and then put those big fat
files in just ice cold storage. That's very very cheap. Its depends what
kind of industry you're in them. I mean, we we're getting involved in
a lot of healthcare stuff and synthetic data. You know, actually you know,
creating synthetic data sets which you've got the characteristics of the actual data.
That's where you've got to You've got to go down that route as well.
That's another dimension that people need to think about. It's not just about privacy,
right, it's actually about synthetic data as well. Is an important you
know principle you've got to work out as well in certain industries because there's obviously
a you know, a patient record, there's a there's a lot of you
know, biometric data, et cetera, et cetera. You've got to work
out how to manage that data at scale as well. So that's that's another
thing that you know, it's it's a lifelong kind of thing to sort out.
But I think you know, generally of AI is giving the industry a
leg up. It's how it's but it's a you know, it's a ten
headed hydra. How do you how do you manage that in a smart way?
A lot of maturity, but also a lot of risk. So it's
it's it's back to basics. It's back to basics, but it's also back
to you know, getting executive sponsorship on this kind of stuff and actually getting
the right people around the table and then getting you know, try trial and
error. You know sometimes in enterprises that we see today is there's still a
bit scared of failing. But you know, you've got to do sprints.
You've got to apply agile principles. You've got to get to the point where
you can you know, you're going to learn something out of the back of
something, doing something quickly, and then you're you're going to iterate on that.
And it sounds like an obvious way of approaching it, but we still
see some food factor around them. Yeah, that's exactly right. Well,
folks, look these guys up online praxy Data Andrew Turner. You've been listening
to Inside Analysis. All right, folks, time for the podcast bonus segment
here on Inside Analysis, talking today with Andrew Turner of praxy Data, all
about jen Ai and solving the dark data challenge. So Andy take it away.
Yeah, so we were talking about the the situation with a lot of
these models, and you know, there's obviously a huge advantage you can get
if you get this right, but also sometimes they get it wrong. So
you know, we've got this situation where you've probably heard about this thing called
hallucinations. You know, you've heard the thing where we get an incorrect data.
We're getting bias put into this data set, and the thing is then
and actually responses from the from the from the platform. So you know,
how do we keep learning this data, how do we keep improving this data
accuracy and ensure that we get a good output, a good quality output can
rely on. The Other thing that we've see in as well, is that
there's this whole debate, raging debate about business data with public products, public
generator AI products. So you know, how do you make sure you're compliant?
You know, if you're in a regulated industry, you know, how
do you deal with data leakage, data theft? You know, how do
you how do you understand that the third party dat you may be integrating is
actually syndicated and actually good to integrate with your model? And how do you
do with the usual things around you know, risks around hacking. So you
know, we've got a strong position here that we believe that you've really got
to build, you know, your own private models, and that's where we
see we can augment that with our industry models to help you get a leg
up even further. One of the things that we see as have been a
core competence of organizations and individuals and teams within another organizations is this thing of
what we're saying prompting so or prompt engineering as people starting to call it,
where you know, you develop this kind of style to interact with the models
and to get your desired output, and you have to you know, you
can actually then start to request a certain way of interacting with it, even
a certain character of how you interact with it as well. You've got to
provide the context. You've got to understand that the temperature on that particular prompt
that you're actually integrating as well, and also make sure that you actually start
to build a library up so you build the value with library that you can
use as a reference model a bit like we're done with industry models from our
data point of view, to help you your organization evolve and become more competent
over time. So you know, looking at this kind of the magic would
say of generative AI and how you can combine that with you know, corporate
data. You've got to look at how do you integrate that so you know,
you can use it individually, you can go into that consumer version that
chat, GBT, bar et cetera, and music like that. But obviously
ultimately you're going to move from an individual use case into an integrated business scenario.
You know, this example of you know, the kind of power you
can do today. You know you can actually start to say, okay,
you know, asking these questions. You know, how could I create a
direct competitor to a particular company, either your own company or a company that
you've seen a preer on your radar. I'd yet you know, use the
models to find out what their strengths of weakness is doing interactive SWAT analysis,
you know, dynamically with prompt engineering, what IP or trade secrets do they
have or they have published that you can actually then catalog understand, create a
you know, a way of actually dealing with that and creating a battle card.
You know, how do you deal how do you sell against that particular
position they have in the market. This is the kind of things where you
can actually really use it from a marketing and strategic point of view that really
make a difference to your day job. You know, to talk about public
and private, you know, so that what that means is that you've got
to build this competency internally. You've got to look at you know, we
talked about prompt engineering, we talked about upskilling, we talked about helping not
only train the models, but train your organization evolve those roles that new organization
structure of the future. But also you've got to look at think about how
do you manage that data at scale and apply some kind of the privacy concepts
of you know, compliance, security and control. Because whether you're in a
regulated industry a non regulated industry, you've got to think about building your own
model. So how do you put these these little pieces of the recipe together
in a way that's going to help you get to that next level of capability
In terms of the future of generative A you know, what we see as
a number of things here happening is you know, with the point when this
is already starting to happen where you can actually interact with it through voice,
you know, where you can actually start to evolve this into a what we
say in multimodeal capabilities where you can combine you know, video with images,
with texts, with audio, with actual code, so you actually create this
kind of multimodal type of output and the evolved in that ultimately into some kind
of platform that you can use internally and that's you know, access to corporate
and personal data, obviously respecting the kind of you know, GDPR, CCPA
and all different regulations you got across the world. And the whole point is
about boosting the performance of your organization. That you've got to think about think
this through about not only from a people point of view, but also how
do you put the process in place and how do you build the products and
data platforms that you need to do for the future, So, you know,
finishing off of what we've do practice data. As I mentioned, we're
trying to make sense of this kind of messy, murky dark data out there.
There's been a lot of investment in lots of different tools, lots of
different platforms, but we're still seeing a real huge problem. So our kind
of area that we're focusing on is really the domain knowledge and the industry libraries
that we've built to allow you to have discover data, you know, curate
data at scale, you know, have a curation as a service, and
also have conversations with your data. So that's we see as an important thing,
whether that's structured, semi structured, or unstructured data. And we're saying
that we're effectively delivering that, you know, a narrow type of capability,
but being very deep, So not trying to be a suite of products,
not trying to be a Microsoft, trying to be a Google, trying to
be specifically focused on those industries we're looking at looking at financial services, banking,
insurance, looking at healthcare, look at the government use cases, and
ultimately one moving into telco and retail going downstreaming from there. But we're trying
to be part of that analytics and insight value stream and also not only delivering
industry libraries, but also delivering business rules out of the box that you can
apply to identify risks, identify opportunities. That's fantastic, both folks. Yeah,
go ahead, one, let's lie go ahead, so you know,
it's very fast. I suppose a big watershed moment is that on the sixth
of November, there's going to be the first Opening Eye Developer conference over in
San Francisco, So it sounds like something that people should consider going to.
Obviously, it's going to be an evolution of the GitHub co pilot, which
has been you know, raging, raging capability that people can coexist with developers.
And I suppose the key thing is about, as I said a bit
earlier, skills, compensation and dealing with that, you know, the food
factor, the fear, uncertainty, in doubt. I'm going to grab this
initiative by both both with both hands and see how you can embrace it and
apply it to your organization. A few books for you to read and think
about as you go forward. There's a particular one that the one on the
right hand side AI twenty forty one that was written by the ex CEO of
Google in China, Mustafa Suliman is the original founder of deep Mind, which
is now part of Google, and a couple of other interesting things. If
you've got some bedtime reading for you to finish off. If you want to
know more about praxy data, my email address is at at practi data dot
com and we'd love to hear from you to speak to you about our use
case and our capability. Thanks for your time. Good stuff. Another deep
dive on Inside Analysis. Look at these guys up online at at praxidata dot
com. We'll talk to you next time you've been listening to Inside Analysis.
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