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dot com, Insideanalysis dot com and now here's your host, through Eric Kavanaugh.
Ladies and gentlemen, Hello, and welcome back once again to the only
coast to coast radio show in the USFA that's all about the information economy.
It's time for Inside Analysis. You're truly Eric Kavanaugh here, and folks,
I am very excited to have frankly a legend in the industry with us.
Today we're going to be talking with Michael Bertholdt. He is the founder and
CEO of a company called NIME that's spelled k n i M and they are
an open source analytics and data science platform, a visual platform for doing data
science, which is good stuff because even though there are lots of people who
can code very well, almost anyone can look at visuals to move boxes around
on a screen. So that's what they figured out. So at that,
Michael, welcome to Inside Analysis. Tell us a bit about yourself and NIME
in which you folks are working on these days. Thanks thanks for the invite,
Eric and having us run the show. As you already said, NIME
is very much about visual programming low code for data science. And we started
that many years ago as a platform really for as a workbench almost at my
group at the University of University of Constants, to be able to kind of
deploy our research results to the real world to practitioners wanting to use that.
And it's grown from there to become one of the only open source visual data
flow platforms for doing anything you want to do with data. And I mean
I kind of I'm always a little bit careful about calling it data science because
that often scares people because they say, I just want to do data wrangling.
I don't care about the science seed beads, and that's kind of scares
them off. That's what nine does is want so a lot of applications that
we see in real life is just in large airports, just getting data in
the right shapes from many different sources. Yeah, and I will tell you
I'm a huge fan of open source. In fact, we built a website
a number of years ago and we feed it with a technology that we built
called media lens. It's called inside open source. And the reason we launched
it was because I realized there is so much happening in the open source world.
There's the Apache Foundation, as the Linux Foundation. There are lots of
other projects outside of those organizations as well, But in the origin of open
source I found fascinating. So I first started researching this in two thousand and
five when I was working for the Data Warehousing Institute as their web evangelist,
and Katrina had just struck New Orleans. I just moved out of New Orleans,
so we watched it all happen on TV. I was just, of
course terrifying. But I remember that the senators from Louisiana asked for a quarter
of a trillion dollars to rebuild southern Louisiana. And I happened to know through
a past life and past clients in the government space down there, that the
politicians are good at making money disappear. And I thought to myself, this
is a very bad situation, because if all that money just floods in,
it's going to flood right out, not where anyone intended, and a lot
of it is just going to disappear. So I went on this high horse,
if you will, and started doing research, and it took me to
open source, and I put forth this theory about open source government, about
publishing all the government data such the citizens can see where the money goes and
understand. And I basically said, look, with the sarbainez Ox the Act
in the States, which came out of the Enron tobacle, corporations had to
document their processes for how they come up with their numbers and they had to
be very transparent about that stuff. And I thought, well, if corporate
America has to do it, why doesn't the government do it as well?
And people thought I was crazy, But a couple things happened that were amazing.
One guy paid attention. Out of forty thousand people I emailed. One
guy paid attention, and he worked for the Heritage Foundation. He went and
talked to He basically testified to Congress and said, we can have citizen auditors.
This stuff really can happen. And he really leveraged the impt A tour
of TDWY and sure enough they did it. House passed the bill, Senate
passed the bill, co sponsored by a guy named Barack Obama who was a
Senator for Illinois, and then President George W. Bush, believe it or
not, signed the federal Funding Accountability and Transparency Act in September six, twenty
six, two thousand and six, and I almost fell off my chair.
I was like, Wow, they actually did it. But the reason I
bring this up is to talk about the power of open source. And I
realized at the time the Apache web server had just surpassed the Microsoft Web server
as the number one web server. And I thought to myself, well,
that's very interesting. And I was also studying service oriented architecture at the time,
and I thought, well, if you have all this open source code
and you have a service oriented architecture, you should be able to plug and
play and sort of take stuff out and put stuff in and have a very
composable environment. And I thought, well, that's not going to be very
good news for the SAPs and the Oracles of the world, because they liked
the monolith. They like control, and they have control of all that stuff.
And it took longer than I thought, but about ten years later open
source just blew up the market with Haddoub and with the Kafka and of course
you have nine. So tell me a bit about the open source foundation of
NIME and what drove your decision there and what it means for your customers.
That's a very interesting story. I didn't know about that open data movement coming
from Katrina break in the old d is so NIME is a bit atypical in
its open source models. I mean fundamentally, there are really three different spays
to do that. You can have distributions like Linox, and you're essentially making
money by packaging them up nicely and supporting them, but essentially there's not really
new code that you're adding around it. Not quite true. I mean they
are little installers and that type of stuff, but that's fundamentally the idea behind
distributions. You can also have what people often refer to as open corea where
you have something that you open source, but it's kind of it's more of
a teaser, it's more baitware, and if you really want to use this
introduction, you have to buy some commercial bits and pieces around it. And
then you have, of course also the longo debs that are essentially databases.
That type of stuff also maybe even a data breaks with Spark. They have
really cool open source technology and they make it accessible to you in the cloud
for a charge. Time is different in that we have one open source piece
that's the analytics platform that allows anybody who wants to build these workflows and execute
these workflows and pretty much do anything they want to do with data. And
then we have a commercial complement software to that, which we call the nine
Business Hub, which allows people to productionize that and collaborate. So when you
have more than one person using the analytics platform in your organization and you want
to deploy that as a web interface or a west service, or you want
to collaborate and have compliance and governments kind of features, that's when you buy
the hub from us. And the reason the open source platform is open source.
The open source analytics platform is open source is I'm not too religious around
it, but to me, it's in the data sigen and field in particular,
you can't exist with proprietary software. There's so much cool new stuff going
on on a daily basis in resourched groups and other types of environments that you're
essentially standing on the shoulder of many, many giants. A lot of functionality
inside NIME is actually based on open source libraries, so it seems kind of
unfair almost to put that into a proprietary umbrella. And also it enables us
to be fair to get in in roles, make inrolls into academic into teaching
environments much easier because they can just use the open source platform for teaching.
But it also we have of open source contributors that are contributing additional functionality into
the line platform. So it's really I'd like to see it as a win
win situation also for our customers because they essentially get a lot of maintained functionality
from us. In addition, they have access to all of those community functions
out there. Yeah, it's very cool. I mean, there are a
lot of good things about open source. One of the things that I've heard
over the years is that bad code goes away because all these eyes can see.
Now. The one shortcoming up come across is that the open source project
gets to MVP status if you will, minimum viable product, and then doesn't
typically go past that just because it works. Now we just kind of move
beyond that. But what are your thoughts about about that, in particular about
how you make sure that you have truly finished products and that you're able to
deliver robust platform analytics ongoing for all of your clients. How much effort does
that take internally from your developers to stay on top of the platform and make
sure everything's working. That's a very good compoint. I mean, I tend
to joke that ninety nine percent of all PhD projects turn into open source projects
and then they kind of die away and feel away and never really turn into
something useful in production. Now, I'm probably about half of our development team,
like Ady, developers at nine are focused exclusively on the analytics platform and
just making sure that Core works and works in a professional environment. We have
our own extensions, which are of course maintained by ourselves, so we have
the same quality assurance there. We have what we call trusted Community extensions,
where we're in close collaboration with the community contributing those extensions, so we can
also make sure there's quality insurance there as well. And then there is i'd
call it the long tail of extensions that are experimental, right. The nice
thing is that everybody can use those and play with those and explore new technologies,
and then when we see increased usage of some of these experimental extensions,
we can move them into the trusted extensions as well. Interesting that makes a
lot of sense. So you're trying out things, you've got these extensions in
trials basically, and then once you see there's a lot of activity, then
you grow some developer support behind it. To harden it is the term we
typically use right to make it sure that it's bulletproof, that it really does
what you want it to do. E makes a lot of sense, and
you do end to end. NIME does everything from data ingestion, data pipelines,
number crunching, model building, all that kind of fun stuff is in
the nine analytics platform. Is that right? Yes, that's true. So
we have everything from about the ETL part loading the data. We can access
about four hundred different data sources. You can access databases, strange file formats
x so of course we can also execute bits and pieces on different execution environments
like doing the ETL directly insider database or in snowflake or in data breaks or
in our loop in the old days. And then we go all the way
to visualization. The analytics functionality, and a lot of that, as I
said before, is of course based on each charts for the visualization a lot
of pythen libraries see libraries, our libraries, Java libraries. For some of
the machine learning functionality, we have integrations with TensorFlow if you wanted to do
that. We have integrations with the other deep learning libraries, we have integrations
with xg boosts. Pretty much everything is in there, but the other pieces.
So often when people talk about data science stainly mean this kind of from
the data to the reporter, to the endpoint or to the model. But
the business hub then also covers the rest of this journey, right, deploying
it to others, managing it, three training models when needed, and monitoring
their their performance well. Right, because at the end of the day,
you want these algorithms to connect into your business, whether it's spear of marketing
or for manufacturing or supply chain or whatever it is. You want it to
affect some outcome in the business, and so that involves connecting to operational systems,
right. It involves connecting to EERP systems or CRM systems or things of
this nature. That's where the magic happens. And a lot of times that's
the hard part, right. I Mean, I've heard many stories about models
that just don't get deployed because maybe the companies didn't have the wherewithal or they
didn't have the expertise to do it properly. But being able to plug the
algorithms into operational systems and then monitor how those models perform and switch them out
right because you've got your production model and have your challenger models that are sitting
at by the wayside waiting to get pulled in and being able to switch over
to a new model when a model that's in production starts faltering. That's a
critical piece and that, I guess, is that done in business hub with
you folks. That's something on the business subside exactly. So as business hup,
you can deploy models which really are deployed nine workflows. You can deploy
in them as a rest service or as a bad application, and then people
consume it, and you can constantly monitor what's happening in production and then potentially
replace we train, or just alert the data science team and just say,
hey, this is so out of backwith reality. We don't really know how
to fix that, do something about it. The nice thing is that you
don't have to switch code in between. Right in the old days, we
always somebody coded the model of the strains in some strange language and then it
was reprogrammed by it into some production language. On our case, on the
hubside, the workflow that was trained is also the one that runs in production.
That's interesting. So one of the other hurdles that people are running into
is when they use Jupiter notebooks to write their model or to build their model
to test with data, and then they want to go put that into production,
and it's just this step by step tedious process of copying over code and
values and all these things, and that falters often. That, from what
I understand, is a really serious problem. But I guess do you not
have that challenge because you're not using the Jupiter notebooks typically, and people are
just in the environment in the analytics platform building out their models after they pull
in their data, et cetera. So you're already production ready when the process
begins. Is that about right? That's a very nice summary. Yes,
So what you use, what you use on the creation site when you actually
train the model is exactly that piece of the workflow gets then moved into production
and executed by exactly the same engine, so you don't also have a translation
issue there. The other piece that people often lose in this going from training
to production is all of the feature engineering that you did, all the feature
transformations that you tend to lose them. So you only can take the model
and move that in production, but you can't do the transformations. And in
Lime you can grab automatically the part of the workflow that has the transformations and
the model applying the model, take that rep that automatically and deploy it to
line business up. Well, that's pretty cool. And you cover also two
different industries, right, so you do insurance healthcare? Would imagine financial services
all sorts of different industries because it's more of a horizontal solution instead about right,
Yes, that's absolutely true. We have customers and users in pretty much
every industry. Yeah. Well, and we're going to talk about large language
models here in a minute in our next segment, but before we get there,
I'll just throw out one of my theories to you and see what you
think about this. To me, this explosion of AI through foundational model,
including large language models, is really a major call to action for organizations to
get their data house in order. And what I mean is that data governance.
If you don't have data governance, if you don't even know what data
governance is from an organizational perspective, you're going to have a hard time responsibly
leveraging AI. Would you agree that companies really do need to take a very
hard look at their end to end data management life cycles processes, understand governance,
who gets access to what data? Even understanding a broad inventory of your
data sources. Would you agree that is paramount to do that before pulling the
trigger on some AI. I totally agree. And the funny in the way,
it's funny that we've been preaching this for also data science processes for a
long time, this government's topic, and nobody really cared, and now people
really care by breaking up. Isn't that fascinating? I mean, I just
I've been in this business a long time. I've been talking about data governance,
analytics, AI, all this stuff for twenty years, right, And
we talked about data governance twenty years ago and fifteen years ago and ten years
ago, and basically nobody was doing it. I mean, you couldn't even
it wasn't even easy to do because you could either control access at the database
level, which is hard to access controls, or at the application level.
But there's nothing in the middle. And really it's in the middle. And
now with the cloud, that's one of the nice things about the cloud is
that it is this de facto marshaling area for functionality and data. And now
we have the capacity to apply very fine grain controls on things, on data
sets, on types of data. For example, we can scan and find
PII and then know, okay, flag this as sensitive. There are lots
of things we can do these days that we just kind of couldn't do ten
years ago. Real quick, one minute, what do you think is that
about? Writer? We finally can do this stuff. Until we are doing
it, what do you think. I'd probably explained it slightly differently and say
we could have done it probably before as well, at least some of those
aspects, but people just did didn't care enough because there was not enough arm
in it. Now But now that everybody who does anything with JENNYI is the
danger of sending data anywhere people are really really baking up and seeing the pain
there. That's right. Well, it is like I say, it's a
call to arms, it's a call to action that your organizations have got to
do it because you don't want to wind up in the crosshairs of an audit.
You don't want to wind up with a breach, you don't want to
wind up getting sued by someone because their information has now been leaked to sensitive
the sensitive resources out there. Well, folks, don't touch that doll.
We're talking all about AI and analytics platforms, and next up we're going to
dive into these large language models that are just taking the business world by absolute
storm. It's really quite fascinating to watch. But don't touch out. That
will be right back. You're listening to Inside Analysis. Welcome back to Inside
Analysis. Here's your host, Eric Tabanac. All right, folks, back
here on Inside Analysis talking to Michael bertthol. He is the CEO and founder
of a company called NIME. That's k n I M Look these folks up
online, an open source analytics platform. It's wonderful stuff. It's like a
giant candy store for analysts to go play and have fun. But I wanted
to talk to you about these large language models, Michael, and in particular,
first of all, the open source side of the equation. So Meta
comes out with Lama and lama too. Open source. Open AI used to
be open now it's not. Now it's the ironically named open AI because it's
a black box. And with the technology this powerful, I believe we need
we need open source. I don't know that I would get behind a mandate
that they must be open source, but there needs to be some transparency into
how these things are working, just so that we can have our peaceful sleep
at night to know that there are bad actors involved somehow. I mean certainly
for regulated industries like financial services. If you bring it into some workflow for
loan approval or something like that, then you have to be able to explain
how you came up to your answer. But what are your thoughts in general
about open source versus closed source? With these large language models, I think
there's a lot of value in it. The problem is that, in my
opinion, there's open sourcing large language models isn't just about open sourcing the code,
but you also need to open source how it was actually trained. So
in a sense you also need to at least give open access to the data
that was used for training. Because even if I give you a model and
it was trained on half copyrighted material that it's going to spit out again when
you use it, you wouldn't know if you didn't have access to the training
data, right that That's part of that is is was it supposed to be
used? And then I think the other piece is that what some companies are
open sourcing is only the code to use the model for predictions later actually apply
it, still don't know how it was trained. So that's the third element
that needs open sourcing. And then I believe one of the key proprietary ingredients
that a lot of these companies now have is safeguarding code around it so that
some types of answers don't get produced, some types of inputs aren't being accepted,
and open sourcing that as well would really really reveal their secret sauce.
And I think that's why they are the open eyes of the world are shying
away from that one. Right, No, it does make sense. I
mean, we have proprietary code. It's not new, but again, these
are very very powerful engines. And then there's another whole side of this equation,
which is the RAG model Retrieval augmented Generation, which upon great reflection,
I believe will be the layer of functionality for governance, for privacy, to
a certain extent, for security, for management. You know, a lot
of that's going to get baked into the RAG model, where you could bring
example, Apple, before you hit your prompt, before your prompt goes up,
to the large language model. Have a layer in between the checks and
sees. Okay, and this is already happening. Like I asked Gemini a
couple of weeks ago how many electoral votes are in Georgia and Arizona and some
other state, and a thought for a second. It said elections are complex
and fast moving. We recommend you use Google. It was a guard rail.
That's a guardrail. They exactly built that in to say no, no,
no, no, we don't want to touch that. Right, And
that's in the RAG model. Right. That's not like trained in the model.
That's outside the model, But it's the workflow you have around the engine
that's very very important. Right. I totally agree with that, man.
I mean that's what I call the safeguards before. And I think sometimes it's
probably not even part of the context that's part of the RAG models, but
it's really part of some safeguarding code even around it. I mean we use
that ATNAM as well. So we have built in what we call KAY inside
the analytics platform that allows you to have a QA mode you ask questions do
this and this, and excel out does that look in a nine Doug fim
and then it gives you X shows you a couple of notes in nine,
and we're of course filtering that these notes do actually exist, because every now
and then open AI, which we use underneath the hood, hallucinates and invents
notes that NIE probably should have but we don't have it. That doesn't help
the pro use. But that's a very very simple way of code around the
KAI that is just making sure that what it spits out is reasonably useful.
Mm hmm. Yeah, that's interesting, and you're going to see more and
more of these AI agents. That's what everyone is talking about now, are
AI agents, which are like little bots, semi autonomous bots that can do
various things, and they can check on each other and they can do all
kinds of stuff. I mean, it's very interesting to me when we talk
about data science. We talked about it before, it all seems to be
getting subsumed now into AI. In conversations about AI. Even though there are
lots of different versions of AI, right, I mean, there are traditional
models, regression models like also of old fashioned aif you well, it's still
very powerful and still works, but the new stuff is sucking all the auction
out of the room. Isn't that about right, Yeah, we see that
as well, and sometimes I mean, I'm an old guy, but now
I've seen this in the past. Right when back propagation came along, everybody
was suddenly using gradi into cent for every problem. We just thought, hey,
you can solve this directly, you don't need to do Gradi intosst.
Then there was support vector machines, and then somebody else, and now then
with deep learning, and now it's AI. So sometimes we see people building
workflows for even very simple things. They're reaching out to some AI and we
just say, hey, there's a no denignment that does that computationally a lot
less expensive. I don't use that. So I think, to me,
it's it's a bit of a hype right now. It's just a new kid
in the block. Everybody wants to play with it and use it. But
the augmented really mixing it and matching it right with traditional techniques, I think
that's where the true value lies. Yeah. Well, and so I'm just
guessing here that one of the nice things about your platform is that it is
an end to end platform for building models, designing models, training models,
pulling the data in all these things, and it's adjacent to this business hub,
So you have a marshaling area for ideas and for testing algorithms and for
testing models. Then you connect it through the business hub and see what happens
and see how it operates. And it's important to have this one environment where
that takes place because when you have multiple tools, it just takes longer and
it's disjointed and there are connections between the tools and things change, So it's
important to have that main marshaling area to It's like a giant analytics sandbox.
Is that about right? That's a very nice sbscription. Absolutely. I tend
to say that data scientists doesn't necessarily need to know how the method does something,
but it needs to know what the method does. So if it's reaching
out to a Python library or in our library or sea libry underneath it,
it's not that important, but you still need to understand what the method actually
does underneath it to be able to interpret the results. It's a simple example.
If you don't know what a regression coefficient is, you won't be able
to interpret it, but you don't necessarily need to understand how it was derived
from the data. Yeah, no, that's very interesting. Let me throw
this concept at you and see what you think about it. I wrote up
an article just last week I guess about this. I was flying to a
conference in Denver just thinking about these large language models and analytics and AI and
all this stuff that have been covering for a long long time, and I
thought to myself about this concept I call the executive cockpit. And the idea
is that I think very forward looking organizations are going to deploy a small language
model that is aligned with their business, like if it's manufacturing or healthcare or
whatever, in their data center, so on prem, possibly in the cloud
as well, but I have my thoughts wrapped around this on prem small language
model. Then you're going to train it on your ERP, on your salesforce,
on your CRM, on your customer support for example, your tickets,
like any of your core enterprise systems. You're going to train this model on
your data, on your business business data. Then what you'll do is set
up copa topics coming from those systems into a vector database adjacent to this interface
for the small language model, and that is where the executives will spend their
day running their business. Because then you could ask any question at all,
how is our marketing working? In APAC Who can we let go if we
have to save some money? Where are we weak in our organization right now?
Just all kinds of different questions and you'll get all these answers. And
I actually mentioned to a CEO of this one company because I was trying to
get him to help me do sales enablement for them, because I have this
big audience I've been marketing to for years. And one person turns out to
be the next deputy Chief data officer for the IRS. And I sent this
email saying, Hey, this is the lady I've known for a long long
time. This is what I mean by sales enablement. Do you guys have
the IRS account? And he fire back, he said, don't. I
don't know for the other's accounts. I thought to myself, well, you
would know if you had the executive cockpit, because you would just ask it,
do we have the IRS account? Who is the account rep? What's
the latest of this account? Because you're getting information from all these systems in
your private environment. But what do you think about this concept? Is that
is that doable? Is that pie in the sky or what do you think
about all that? It's an interesting idea, I thought about similar. I
mean, at the end of the day, you're personalizing a large language model
around your own infrastructure in house data. I think the challenge there is that
in order to get a really really good model like that one that's really useful,
you need to train it on a lot more data than just your own.
So in a sense, you need to benefit from your competitor's data without
actually seeing that, but kind of learning the general structure and the general insights,
and then you customize it on your own, which in return kind of
means that you should also be providing your data to other organizations. It's almost
like that's kind of pre competitive training of these models so that they're useful for
everybody. I think just training it on your setup, you need some bigger
context than that. Or maybe you're a company and you have enough context anyway,
But for every small company, I don't think you have enough data to
really get meaningful insights. That's very interesting. That's a good that's a good
point because I'm just I'm wondering to myself and I'm gonna throw this one at
you too. So one of my AHA moments with these large language models is
when I realized that when you train them on a corpus of data. They're
not actually persisting the data verbatim. It's not like they're taking strings of text
and storing it in a record somewhere, but rather, in the training process
that data you use will adjust the weights and biases and the parameters of the
model. So in other words, it's like, huh, well, that's
that's very interesting that it can train in that fashion and then reflect back to
you such remarkably granular detail about things. And you know, what I've seen
is that if there is a subject area that has been published about widely,
like how computer processors work, or how an irrigation system works, anything that
has a lot of content on the web that these engines were trained on,
it does very well. It knows all that stuff. It's when you get
to the fringe where there's not that much published. And I guess that's kind
of your point about having enough data to train the models. If you don't
have enough, you're not going to get the contours right, and it's going
to be skewed in one direction or other. Is that about right? I
think that's a very good summary. The contrast is right. I mean,
a colleague of mind want summarized, since it essentially it's a consensus engine.
It's getting the consensus around what a computer programming is, learns that from the
data and can repeat that. But if it's just one isolated outcome, it's
not going to be able to recall that one. Interesting. Yes, So
Craig schmid Huber I think his name is. He's the guy who wrote the
papers on the transformers, and he's based I guess he's actually he Arabia these
days, but I want to say he's German of German origin. And I
was amazed when I realized he wrote those papers in like the nineteen nineties or
something. And it's just just now we have the compute to be able to
can you explain that? Is that what happened is that just the timing was
right now to be able to understand this and put it into play, because
that was one of the big changes. And now it's able to see like
you know, ten twelve tokens left or right as opposed to just like two
or three, and you also have this, like you say, like a
consensus right where so they are like I call it almost like an ai Greek
chorus where one is saying I think it should be an A. I think
it should be a B. I think it should be a C. And
then the Okay, I'm going to pick this one. That's very interesting.
It's a very interesting development. But why do you think it took so long?
Is it just because we now have the compute to do that? I
think it was a computer party issue as well. And then some science tends
to have a little bit more of fun, needs a little bit of time
before it truly has an impact, but mostly waiting for complete power. I
don't know that one way of looking at what this consensus really does is I
don't know if you watch these YouTube videas about JGBD playing chess now and the
interesting part is that at the beginning it does extremely well and does very sensible
things, and part of that is these opening libraries are all over the place,
so that's extremely well established consensus. And then somewhere in the middle it
starts inventing bizarre moves and suddenly new figures pop up on the on the board
out of nowhere, right, and it has always meaningful explanations for that,
And the problem there is that that data is so sparse that there's no consensus
to learn. So at the beginning it sounds it almost looks like it understands
chess rules. But the only reason it does follow the chess rules is that
they're so deeply ingrained in all of the common material that you see that the
kind of the likelihood of going outside of the world book is too small.
But somewhere in the middle of the game it goes completely off the books.
That's interesting, that's wild. So one of my good friends in the business
as a gentleman named Usama Fayad. You may have come across from at some
point. He was the first chief data officer for Yahoo way back in the
day, and now he runs the Institute for Experiential AI over at Northeastern University
here in the States. And I had him in the show, and he's
very funny, he's very candid. He said, these large models, they're
too big. They're not supposed to work. We don't know why they work.
What are you talking about, this guy who runs this whole operation.
He's joking, we don't even know how they work. I mean, how's
that for transparency, right, I mean there's some truth, right. We
don't really know how they come up with these answers. Right, it's a
wild mix of it. It's a highly distributed model. We don't know why
a particular answer comes. We can come up with kind of proxies for an
explanation by wiggling with the inputs and trying to figure out what happens, and
we can say, ah, this probably had a lot of influence on the
decision, but we don't know for sure. That's so wild. I mean,
that's just such a big deal that you know, but we do.
So now we have all this observability in the data space, right, You've
got Data Relic and Data Dog or new Relative today Data. All these different
companies are doing observability which I think spun out of Kubernetes primarily. But it's
very interesting and we need that kind of observability in these large language models.
I think. I think that's going to be one of the keys to success.
But folks don't touch that dot. Will be right back. We're talking
to Michael berthold from NIME on Inside Analysis. Standby, Welcome back to Inside
Analysis. Here's your host, Eric Tabanac. All right, folks back here
on Inside Analysis with Michael Bertholdt, founder and CEO of nime K n I
m e. Looked them up online and Michael I was mentioning to you in
the break that I'm wondering to myself this whole business intelligence industry and there are
hundreds of players these days, hundreds of companies doing some form of analytics.
Of course, NIME is an whole analytics platform and open source platform end to
end, but there are lots of point tools, whether it's visualization or number
crunching, OL app roll, app all this kind of stuff, and I
wonder is all of that in the crosshairs of these foundational models? What do
you think? That's a very very interesting question, and we of course asked
ourselves that as well. And I think for some of the some of the
tools that you mentioned, like generating visualizations, that type of stuff, I
do think they are pretty replaceable by AI type models, because at the end
of the day, you're doing something, you're generating a code that generates the
visualization based on data, and you judge the output of that code by just
looking at it and saying this is quite right. So I think that type
of stuff will go away. And we have in NIME actually built in what
used to be an each chart scripting editor that has now an AI element and
you don't need to touch the code anymore. So those types of wills I
believe will go away. The eye tools trying to really find surprising, interesting
new insights in I think that type of stuff is a lot harder to replace
because fundamentally you're trying to find something new. And like we discussed before the
break, these GENEI models are consensus engines, right, so they kind of
try to gravitate towards something they've seen more and more often before. Interesting.
That's right, that's an excellent point. Really, that's that's exactly right.
So it's good for understanding the well trodden path basically, Like that's what it's
very good at doing, is saying, Okay, there's a highway, it
goes that direction, but I want to go wandering around the forest, and
it's not as good on the fringe basically, so you will use it.
But I mean, so I read an article some guy on LinkedIn talking about
how he connected I don't know by ODBC or JDBC or something in his model
with data sources, and he asked it to queer the data source and it
did. It reached into the database, pulled the information out and delivered it
and you're like, Okay, that's pretty interesting. And then when I think
to myself, what's what could be happening here? Is in the data warehousing
space, for example, we move so much data around. It's all the
data that's from your core systems that you've decided to put in, which is
a tremendous amount. Very little of that data ever gets used a lot of
times it's the it's the summaries or the aggregates or the roll ups that are
used for various purposes, but a lot of it just doesn't even get used
at all. And I think that what these large language models are going to
do is kind of turn the entire model inside out of how we viewed moving
data and analyzing data and doing things with data because they don't really care.
They're just going to once they're trained on a certain space. And again,
if you train it on your data, or if you're in your vector database,
you have a lot of embeddings of your corporate data and you point your
RAG model there, well, you can get answers to things very quickly that
before would have required running reports and doing ETL and doing all this stuff.
And I think that in many use cases. These models are going to short
circuit all all that stuff and you're just not going to have to do as
much that stuff anymore at all. But what do you think about that?
I think there's some truth to it, because fundamentally, what these models won't
necessarily do is actually look at all of the data, but they're going to
apply a lot of common standard practices to that. And standard sounds a little
bit too limiting. I think there's a huge wealth of standard practices that people
do apply to the data, and that's part of this consensus engine, and
so that the AI models will try out a lot of those things a lot
faster than you ever would. So absolutely, and there's a good chance that
some of these insights that will be generated are interesting to you. But then
continuing the exploration and saying, I mean, how do they always say the
Eureka moment is usually preceded by oops, that's strange. I think you'll have
these moments right, And AI doesn't do that. AI doesn't say this is
weird, I should diggle in a little bit deeper because that's outside of the
consensus. So it will continue doing kind of like you said, will will
continue the normal path and that's why I believe the human intuition curiosity OOPS detection
capability is going to be relevant for a long time. I like this oops
detection. That's good stuff. Well, there was a gentleman I had on
the show years ago who did something. He said something a lot like that.
He basically said, AI doesn't have to be the ability to be like,
hmm, that's kind of weird. What's going on with that? Right?
Because it's just processing information and doing what it's been told to do,
which is just reflect backwards based upon a prompt and its training. It's a
very simple thing. I mean, it's very complex in terms of how it
got there, but nonetheless it you know, one thing that did annoy me,
I will say is in the early days when that New York Times reporter
was getting deep with the with CHATGBT and trying to like tease out of it
whether it's sentient or something. I'm like, dude, that is a misuse
of the technology, Like that is not whether you should be using this thing
for to try to like what trick it into revealing that it's really alive and
you know, what are you even talking about? And I think that's part
of the downside these days is that. And I'm a media person myself,
but a lot of times the media will just sort of glom onto some narrative
about something and it's very hard for them to decouple from that and get down
to brass tacks. And that's what we do in the show. In fact,
I used to say at the beginning of every show, the show,
it's all about getting down to the brass tax of what actually happens in the
data world and what you do with this stuff. And I think it is
important that people keep in their minds the purpose of this technology, Why are
you using it? Where is it appropriate to use it, and where is
it not appropriate to use it? And that's just basic common sense, right,
Yes, I totally agree. I mean I go pretty much in line
with also the European AII that they just passed. I mean, if it's
not mission critical, if it's not safety critical, you can trust a system
that is wrong in I don't know, point one percent of all cases.
If it's controlling nuclear power plants, better not be wrong in point one percent
of all cases, right, that's right. You got to watch out where
it's so where do you see a lot of use case of your clients.
I mean, obviously some of your clients are using large language models. Where
are you seeing success stories in that space right now? So there's a lot
of success stories in other areas of the business, as you probably probably know.
Undoubtedly, no checking legal contracts, doing marketing material, that type of
stuff. There's a lot of value in applying GENEI on the data analytic space.
Honestly, we don't. We see a lot of interest. There's a
lot of people that say, oh cool, I can build a customized chatboard
using name that's not really our core business. And then the real applications tend
to be around text processing, which is where jennai is really strong. And
then instead of using outdated antique libraries for sentiment analysis or text segmentation, you're
just handing it over to an AI model and say, hey, segment this,
or extract the key components or create a summary. But that type of
stuff, it's amazing. So I see. I'm also as image mining extensions.
I think that's the next setup where we can use image processing capabilities of
Jennai for a lot of the number crunching. I mean, we've all seen
these cases where you can't add two numbers doesn't really know what the prime numbers.
This is the understanding of the concept of a number. Right. So
there. I think it's more as a tool to help you build workflows,
build dataations, but only as a helper, right. So that's actually an
excellent point I wanted to get into. I believe that we're just scratching the
surface of using these models as a component in a workflow. So you mentioned,
for example summarization. That is hugely powerful. I mean, you know
you can enter especially for policies, for complex policies for law, for example,
for legal protocols and when to file motions, what motions you can file,
what you have to do according to I mean, you used to have
to pay lawyer's a lot of money to tell you that stuff. Now if
you just get access to the rules, will load them into a large language
model and just start asking questions. That is an incredibly powerful use case because
it used to take a lot of time to sort through the process of how
to do something. Now you just ask it, how do I fire someone?
First step, send them a letter saying they're not performing properly. Second
step, you know, monitor their behavior. Third step. With all this
stuff, it's like there it is like, Wow, talk about saving time.
I mean, it saves time. And here's another big soapbox issue.
It improves morale because nobody wants to spend their time scratching their head reading through
just dreadful documentation. Nobody likes doing that, nobody, So all that stuff
is going to go away, right, what do you think? I totally
agree. Yah. Your examples center a lot around firing people, by the
way, But I think I tend to say, and people ask me if
Jenny, I A, you going to make data science lives easier? I
say no, I don't think so. But it's going to make it nicer
because it's going to remove all of that boring stuff and we can now focus
on the really interesting but more complex stuff. So it's going to make it
more interesting, more complex. That's interesting. Yeah, wow, And I
think you do want to document things, and you can have it document things
for you too, Right, you can just throw a whole bunch of stuff
and document this, okay exactly. I mean, we we now have a
component on the hub that takes a nine workflow and explains what the nine workflow
does, and we do that by just shipping it off to Jennai, it's
perfect for that. Wow, that's amazing. All right, folks, Well
podcast p A segment coming up next. We're listening to Inside Analysis. All
right, folks back here on Inside Analysis talking to Michael Bertholdt is the founder
and CEO of nine k N I m and Michael, I know what it's
like in a software company. There's always a roadmap. You're always working on
something, and we talked about a couple of key things. Governance. There's
model governance, there's data governance, there's it governance. What are you working
on in the governance space? Thanks for asking that, Like, like you
know, all the road maps are changing all the time. But what we're
currently working on. We had actually this model government's topic on the work right,
brend I've been working on that for a couple of years now. So
the idea of being able to monitor what models are doing automatically we train them.
We talked about that and I think the first episode, but what we
added now is the ability to also govern the AI usage of people that are
creating nine work clows. So first of all, then somebody is creating nine
workplos using the NIME analytics platform and uses this built in AI. We call
it KAI for NIME AI. We need to make sure that gets channeled to
an IT approved AI. Right, maybe that's just for expense purposes. You
know, I'm going to have too much consumption in the cloud. Want you
make that in house or it's really a data privacy issue. But the more
worrisome part for people is that you I mean, one of the springs of
the NIE Analytics platform, the workflow concepts, is that everybody can use any
technology they want. Right, they can reach out to experimental libraries, they
can reach out to our stuff, to Python stuff, to whatever. But
by now they can of course also connect to various different AI providers, and
we need a way for them for Central Light Tea Governance to be able to
make sure that the nine workflow users inside the organization can only use approved AIS.
So maybe the maybe marketing can use an AI in the cloud, but
maybe legal shouldn't or HR shouldn't, right, And that's something we have built
in into the name hub now that we can limit the types of AIS,
you can their users can reach out to from the nine Ndredrix platform and they
get to choose from one of the approved AIS that it central Light he set
up and said, okay, here's an AI that's consumption light, that's for
the easy tasks. Here's the one for whatever the tech team. Here's the
one that's for compliant data. And we also allow they're set up on the
hubside of safeguarding workflows so that you can before the data gets sent out to
say a cloud AI provider, it gets screened for private information or maybe the
data automatically it gets anonymized before it gets sent out. Yeah, that's very
important stuff. And the use are you also able to do some finops on
that, In other words, see how much it is costing to leverage this
AI engine versus that AI engine and do some cost optimization. Is that something
you can do. We can do that as part of a nine workflow and
you could build that. You could build that in there as well. But
we are currently offering to our customers the abilities to monitor our consumption so they
have a bit of an eye on that. But it's not automatically re routing
to different AIS. But that's just an added functionality under the wood m hmm.
Yeah, it's all in the workflow is basically, and that's where we're
going. In the last segment where I think that we're just the beginning of
leveraging these technologies because what they're really very good at is pattern recognition, right
even just the vectorization the embedding is basically how it stores it as a point
in array basically, and just understanding how it can map those two things.
It's not just word not just text generation. I think we're going to get
some really interesting things in terms of pattern recognition and then recommendations. I mean,
I think that these little AI agents, these assistants are going to be
extremely helpful in all facets of business. You know, to be able to
very quickly give you a customer profile when you're on the phone with someone,
or to be able to give you summarization of text on demand. I mean,
really, I think the hardest challenge is going to be changing mindsets and
changing day to day behaviors and workflows. What do you think final thoughts?
I totally agree with that. But we see that inside NIM as well.
I mean, the developers were the first ones that really said we want to
use this, you want to use this, but then getting the rest of
the organization to as seriously think about It's like what can HR do? But
can legal really do with that. It's a huge time saver, and it's
largely untipped. I totally with you as a check and they're interesting times a
hit yes, to say the least. Well, what a fantastic conversation,
folks, look them up online. Michael Berthold B E R T H O
L D from nine K N I M E. We'll talk to you next
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