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analysis dot com, Inside Analysis dot com and now here's your host, Eric
Kavanaugh. Well, 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 or 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 Berthold. 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 beats, and that's kind of
scares them off. And that's what Nie does as well. 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 and 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 peer 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 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 debacle, corporations had to
document their processes for how they come up with their numbers and 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 of 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 imprimiture of
TDWY and sure enough they did it. House past 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 haddoob and with the Kafka And of course you have
NIME. 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 is so NIME is a bit atypical in its open source
models. I mean fundamentally, there are really three different ways 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 aiding 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 core, 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 in production,
you have to buy some commercial bits and pieces around it. And then
you have, of course also the Mango debs that are essentially databases that type
of stuff. Also maybe even a data bax with Spark. They have really
cool open source technology and they make it accessible to you in the cloud.
For 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 rest 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 that they science 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 nine 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 rolls, 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 lot of open source contributors that are contributing additional functionality into the Linemen
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
year 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 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 am a probably about half of our development team, like eighty developers
at nine are focused exclusively on the Luditic 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
assurance 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 throw 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.
That makes a lot of sense, and you do end to end and
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 under different data sources.
We can access databases, strange file formats. 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 enderlytics functionality,
and a lot of that, as I said before, is of course
based on each arts for the visualization, a lot of Pithen 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 piece is 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 into others, managing
a 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 spirit 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 wet application, and then people consume it, and you
can constantly monitor what's happening in production and then potentially replace retrain or just alert
the data science team and just say, hey, this is so out of
backwth 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, 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, 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 DOPFOW
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. I 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 foundations models, 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 a 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 cared 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 and so 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 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, and 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 fascinated to
watch. But don't shut out. That will be right back. You're listening
to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric
Tabanac. Folks back here on Inside Analysis talking to Michael Berthol. 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 want 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 aren't bad actors involved somehow.
I mean, certainly for regulated industries like mantile 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 to actually apply it, I 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, for example, 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 thought for a second.
It said elections are complex and fast moving. We recommend you use Google.
It is 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 one. I mean, that's what I called 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 at MIME 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 this and this, and Excel out does that
look in a nine doctim and then it gives you 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, and that doesn't help the pro use. All right, that's a
very very simple way of code around the KAY 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, all sorts
of old fashioned aif you well, it's still very powerful and still works,
but the new stuff is sucking all the action out of the room, isn't
that about? Right? Yeah? We see that as well. And sometimes
I mean, I'm an old guy by now, I've seen this in the
past. Right when back propagation came along, everybody was suddenly using grade into
cent for every problem. We just thought, hey, you can solve this
directly. You don't need to do grade into set. Then there was support
vector machines, and there was somebody else, and now then with deep learning,
and now it's AI. So sometimes we see 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 description. 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 library underneath it, it's not that
important, but you still need to understand what the method actually does underneath 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 that's pretty 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 ticket it's 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 Coughka
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 I fire back you so I don't know. I don't know
if 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 it 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 pree 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 therea 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 it's not that much published. And I guess that's kind of your
point about having enough data to train the model. So 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 wants summarized, since it's 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 in Saudi 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 compute 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. One way of looking at what this consensus really does is
I don't know if you watch these YouTube videas about JGBT 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 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 to say 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, in open source analytics
platform, end to end, but there are lots of point tools, whether
it's visualization or number crunching, olapp 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 data, 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 of 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 my about 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 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 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 is 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 continue the normal path. And that's what
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 it's 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 chat GPT and trying to like tease out of it whether it's sentient
or something. I'm like, dude, that is a miss use 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 been 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 passpect. 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 GENII 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, just where Jennai is really strong. And then instead of
using outdated antique libraries for sentiment analysis or text segmentation, you're just handing over
to an AI model and say, hey, segment this, or extract the
key components or create a summary for 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 jen Ai 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 as a tool to help you build workflows, build datazations,
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 your examples
center a lot around firing people, by the way, But I think I
tend to say, and people ask me if Jenny 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 now
have a component on the hubs 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 pull 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. I am 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, when
somebody is creating nine work clos 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 channel 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 money 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 Namealytics 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 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 ndlydrics platform
and they get to choose from one of the approved AIS that it central light
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 US are you also able to do some fin apps
on that, In other words, see how much it's 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 hm.
Yeah, it's all in the workflows 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 you 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 also seriously think about it. It's like, what can HR do,
but can legal really do with it. It's a huge time saver and
it's largely untipped. I totally give with you check and they're interesting times ahead,
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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