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you, Eric Kavanaugh. Oh yes, ladies and gentlemen, Welcome is
what the future is here? Already itched yet? That's our line from future
Proof Your host here, Eric Kavanaugh, the only coast to coast radio show
all about the information economy, and boy am I excited for today, folks.
Last week we had some great guests, including Levine Rao, the CEO
of Mosaic mL, which just got acquired by Data Bricks for one point three
billion dollars and one of the fastest deals ever made in our industry. Quite
frankly, really impressive stuff, and Data Bricks is just on a tear.
They also rolled out their own vector database. We'll talk about those on the
show today. They're doing amazing things with their data warehouse and with data orchestration,
and even this whole thing about Iceberg and Delta Lake and Hootie and the
different possibilities. They solved it by just looking at the parquet files. So
look all that stuff up. It's a really impressive array of innovations coming out
of Silicon Value these days, and of course all around the world. But
our guests today knows a lot about all those things and also knows a lot
about LMS, So it's going to joke anyone remember data science. We used
to talk about data science all the time. Then we started talking about data
match and of course data products, and then it's large language models and AI
has just taken the world by storm. Well, data science is still around,
and we're going to talk about today is really automating the hard part of
data science to get to the value quickly, so cost to value, time
to value, get those really into a nice happy place. That's going to
make CEO is happy, it's going to make customers happy. Or talk all
about these large language models and ETL for LMS. So with that, let
me bring in Brian Raymond from Unstructured dot Io. Brian, you folks are
doing some really interesting things out there, and I'd like to help our audience
kind of wrap their heads around the magnitude of what's happening and I think you
had mentioned to me that there's been something like thirty billion dollars of investment in
lms in the last six to eight months or so, which is a pretty
staggering number. That shows you that the VC community sees where the rabbit is
running now and that's where they're going, right. Yeah, it's an incredible
shift, especially from where we were twelve months ago, everyone expecting a recession,
massive tech layoffs, contraction on innovation budgets in the commercial sector, to
the run that we've seen since the beginning of the year on the heels of
diffusion models first last summer and then large language models, which is really sparked
by the introduction of chat GPT last November, but now in dozens and dozens
of lms that are emerging every month. Yeah. And so our audience,
our core audience who understand data warehousing and analytics and business intelligence, well they
all know ETL extract transform load. That's been the mainstay of data warehousing for
arguably almost forty years or so, since the earliest days when we figured out
that you could not run analytics on enterprise systems like ERPs, for example.
They're not designed to do that. They're designed to move stuff around and get
the job done. So we invented this whole data warehouse concept ETLT. You
move things, but you're doing a very special kind of ETL and of unstructured
data. So we talk all the time about eighty percent of the data the
enterprise is unstructured. It's in word documents, emails, PDFs, PowerPoint presentations,
etc. The structured data, well that's the numbers of how much product
we've sold, etc. What the cost was. The context for the structured
data comes from the unstructured data, but historically it's been just of the analysts
or the user to kind of piece that together themselves. But now with these
large language models, in particularly with what unstructured dot io is doing, you
can actually pull out this corpus of context and you also kind of clean it
and refine it and get it to the textual value that's going to be used.
Right, tell us about that and how that works. Yeah, I
mean we use the phrase ETL for ELBMS. That's that's a metaphor that breakly
it's probably something closer to connect transformed stage and what that means in practice interests
that we're building enterprise grade connectors kind of like what five trend has done an
airbyte, but that are built from the ground up for files containing natural language
dayler major repositories where those live. So think amazon As three as your blob
SharePoint. Wherever these these files contain natural language, they are are generated and
then save down so we're able to grab those on the transform stage. Instead
of mapping relational databases to one another, what we're doing is we're doing a
couple of things. We're doing file transformation first, so those pds, pptxs,
xls, whichever the file extension might be getting, extracting the natural enguage
data out of it and rendering it into a common JSON format typically and then
on the staging side, getting it ready for the analytics downstream. And so
that might be tokenization, vectorization, chunking scheme of mapping to the particular Jason
scheme that's required given the task downstream. Either way, what's it has in
common with ETL is that this is data engineering, and this is a data
engineering in service of data science of machine learning and making this data available to
models. And then we'll talk more about how it's being made available through training
or through vector databases. But that critical step of how do you connect to
that knowledge where it's being stored, transform into a common format, and then
get it ready is a very slow and manual process still today in twenty twenty
three. Yeah, and you had mentioned something that I think is quite compelling
that when you're extracting, let's say, from a word document or from a
PowerPoint presentation, your technology has the capacity to identify headers versus subheads versus body
text, etc. And folks had probably seen this now. And there are
some great algorithms in the engines that we use, like on Google and YouTube
and other places where it'll automatically put tables of contents together, or you look
at something like otter ai taking notes on your meetings and it'll say who said
what when, and even distill that for you and do some analysis dynamically for
you, which is incredibly powerful. I mean, you think about all the
ways we used to take notes and just try to keep track of what we
had heard and go back to your notes and explain, well, now it's
all recorded and it's all translated, and it's organized for you. And it
sounds like that's kind of what you're doing with the Sunstructured data is you're able
to pull out the value the signal, which is once you want anyway,
and then plug that signal into the model to stage it somewhere to be able
to leverage it for some analytical purpose. Rights. That's that's exactly right,
and in practice we think about it as both cleaning the data and so getting
rid of unwanted artifacts off the OCR something use a operable character recognition or undergo
that file transformation process. There's almost always just nasty artifacts that data engineers and
data scientsts who are doing this data engineering, you have to confine white space,
unicode characters, sentence fragments, these sorts of things, so that you
got to clean it up. But then you need to curate your data.
And this is something that folks have been thinking about a long time. When
you have columns and rows but with respect and natural anage processing. Suppose you
have an analyst research report. Everyone's kind of seen one of these states on
Ford Motor Company over the last quarter. That analyst research report might GM.
It may have a section, have another section talking about supply chain issues,
but just like a meandering body paragraph, that's actually or the security and that's
what you want to push through your machine learning model, your pipeline, or
make available to your large language model. And so in that example, how
do you take thousands of these that all have different lived and you know,
a dozen different file formats and curate that natural English data and so you can
park get somewhere to make it available right now? The answer is, and
historically the answer has been custom regular expressions, Python scripts for every single document
layout. So it's a slow, painful process, and that's where we're Yeah,
that's really remarkable. And you know, when I think about topics like
information life cycle management, right like information comes in, it gets used,
it gets stored. I mean just even the basics of storage, like when
I win. Over the weekend, I've been thinking about our conversation last week
and thinking to myself, this is big in so many ways. I mean
just d duping for example, just the number of duplicate documents that people have
in large organizations. What's the average like seven they say per documents, I
mean, depending upon which study you look at. So that's a whole lot
of wasted space and wasted time and effort, etc. And it's also just
chaos there. I mean, basically unstructured data is chaos right now in most
organizations, and you are providing a mechanism to extract a meaning from those environments
and then feed them into these foundational models or large language models or whatever.
That is a huge deal. I mean, it's almost the kind of thing
and I joked about this when I was writing an article about knowledge graphs that
you know, so many organizations have had to choose do we actually make sense
of all the stuff that we have, or do we just focus on the
now and do what's needed at the moment. And of course they all do
the latter and just sweep the rest under the SharePoint, as I joked about
it, but now you'll be able to extract that value, and then you
could do even interesting things about the storage. Right once you have it out,
if you're not required by law to keep these things, you can drop
all that's or at least put it in super cold storage somewhere where it's not
costing a lot of money. Point being this is it's a revolution of information
management. What do you think. I think we've seen kind of three stages
fold since last November and so, especially with respect to large language models.
The first was, hey, let's just use what's in memory let's just ask
chat GPT questions and see see how far we get. And that was incredible.
Um, and then around the December January February timeframe, folks started saying,
hey, what if we connected these with external data right and made it
available either by retraining models like bloom which was one of the first open source
elms, or putting in a vector database. And so they grabbed data mostly
off the Internet that was already in a fairly clean format that you can get
over API and made it available and built some really cool applications on top of
that. And then as we progress throughout the spring, folks were like,
you know, what if we're actually going to deploy these things. If we're
gonna you know, the my T study, Goldman Sack studies showing the productivity
promise of these, the productivity that, you know, the promise, the
productivity gains with these, we're going to have to take all of our private
data and utilize the conjunction of these models. Then it said, okay,
we can't. It's not just what's in memory. It's not just the data
that's easy that's already out there. It's the hard data that we're continuing to
produce tens of thousands of files every day. How do we do that?
And that's the reality that the industry as a whole is confronting kind of head
on right now as we cool prototypes and start to transition them into production.
Right that's such a big deal. And you had told me before the show
too that you know, really there are two paths that organizations can take,
and one is to embed your data that you chose to use or put it
into these vector databases, and then you've got a layer of abstraction. Basically,
you've got the model and then it reaches out to the vector database as
it needs to. That strikes me as the way to go. Frankly,
it's sort of loosely coupled, if you will, as opposed to tightly coupled.
Tell us about that and how your part to the left of either option.
Sure, So, as you mentioned kind of past, Number one is
to either pre train or fine tune a model with your data. And so
this is where mosaic mL comes in. They're fantastic where customers of mosaic mL
we've pre trained our own model using them. And the objective there is how
do you take your data and then encode it into that into the memory of
that model, and so it knows it's seen and is familiar with and answer
questions about your data. There's two problems with that, or shortcomanies I should
say why that's not sufficient in and of itself. The first is recency latency
of the data and so it's frozen in time. And so what that requires
is for you to periodically curate large batches of new data, label it and
then re encode it in. So you're paying for label and class. You're
paying for the compute costs and a time and energy to run those projects that
we've been in call it over the last ten years. And it's still it's
still required for a lot of the cap like you know, the large lement
based projects that we're doing. However, a new kind of path has emerged,
and it's on the back of a technique called retrieval augmented generation RAG or
databases. And here what you do is you park a bunch of your data
in a vector database with vectorized embedding representation. So think of it like a
seventy digit string of numbers. It's a multidimensional representation of that text. And
as you prompt a model. What it does. It looks at what it
has in memory, but then also reaches over and grabs relevant data from the
vector database, considers it all together, and and produce a response that solves
the second problem, which is hallucined hallucinations. It's not a silver bullet.
But what you can do is you can anchor it. You can make it
show homework and so you have chain of thought reasoning. A buch great way
to describe it. Yeah, I'm right in this town. You can anchor
it. That's exactly right. So you're you're optimizing your chances of getting a
real, governed, trusted answer out of this thing, as opposed to hallucination.
And you know, I'd mentioned to you that when I was covering the
Data Breaks conference to go to market, guy Adam something from chat GPT was
engaging with me on Twitter, which of course is now called xs. Elon
took a page out of Mark Zuckerberg's book to Arenas, but he was pointing
out that hallucinations are a feature. Note with these large language models, they
are predictive engines, and their job is to predict what text they think you
want to hear based upon your prompts. Right, So they're really fusing vectors
of information together dynamically in pros formats to create content, either marketing content or
business proposals or whatever. So it's not going to go away completely, but
to your point, to anchor it like that's to give it tremendous weight and
to basically gravitated toward the truth, right, Yeah, and for it to
be able to show that somewhere. So say it produces h you know,
a TPS memo, a page line smml. Right, you should be able
to drill down and on this paragraph, Um, you know what documents underlie
the inference. You know that that that generated that, and that's achievable using
techniques like RAG and you know some of the iterations of that. Yeah,
I mean that's very very powerful. And so you you do wind up getting
into a conversation with this engine and you walk through it and you help it
understand things, and it helps you understand things. It's a very bidirectional thing
in that sense, right, because you're trying to get to the bottom of
something and you're trying to generate. That's what they do. These are generative
models. Generative AI generate stuff. It creates new things, right, It
absolutely does, and so, like the it is a feature, not a
bug. But at the same time, if you're depending on this for business
purposes, you don't want a five year old that's completely fabricating information. You
want something something more like Encyclopedia Britannica where it's written, but that you can
trill down on sources inside and so. And there's a wide spectrum there in
between what and what you can produce using these models. Yeah, there is
an absolutely wide spectrum, and it's a learning process. But that's the other
thing that gets me really excited about this is that, especially when you train
it using your own data, you now have this very robust window pane into
your organizations text and imagery and language and messaging, so you can look for
these things, and then that's a whole discovery process, right And to me,
discovery has always been one of the most important parts of any sort of
analytical process because you know, first of course, you gather data, you
organize data. In the old world, you had to come up with your
data model early, your schema, basically your start your snowflake or whatever you're
gonna do Kimble versus in mind, that kind of stuff, But you had
to decide ahead of time that plumbing was going to look like. Well,
that's very fragile, and that's very difficult to use in a fast changing world.
And so what we have now is a much more robust way to understand
what the data is telling us. Tell me what the model should look like,
tell me what the processes should look like. Still vet it, still
make your own decisions, but it's a much different and much more dynamic and
flexible world. Folks, don't touch that. I'll be right back. If
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terms. Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Oh yes, folks, take us to the future. Indeed, that's exactly
what large language models are doing. They're taking us to the future right now.
We're going there right now, folks. It's very exciting stuff. I've
played around with these. They are very powerful and they're good to lots of
different things. Like one of the things they're very good at is summarizing,
like you can give it one hundred page document and tell it give me a
two page summary of this, and then dive into this, diving of that
very very powerful research tool and content creation tool as well. And of course,
underpinning all this is an LP natural language processing, which has been around
for a very long time, decades, quite frankly, and it really didn't
get anywhere for a long time, and it was kind of stumbling. And
then of course Siria came out and Bixby and all these others, and we
got better at that stuff. But one of the things I've learned talking with
unstructured here is that the dirty secret of natural language processing these days is that
many data science teams are still building one off artisanal models and it takes a
tremendous amount of time. It takes a tremendous amount of effort, and by
the time you're done, the world has probably changed a little bit. So
that's what's really changing, and that affects the cost, that affects the viability,
that affects what people try. Because thinking about it in large organizations,
man, you better have a good feeling about the bet that you're going to
put a million bucks on that it's going to come to pass and generate value,
and that's been a very difficult thing. So tell us a bit about
that, Brian. It's good thing. So I would say that with any
NLP project industry wide, you're looking at about a twenty percent success rate,
historically with enterprise about eighty percent failure rate. And there's a number of challenges
here. This isn't because the folks were working on it didn't try hard enough,
smart enough. You're challenged in two different areas on the NLP side itself
right before it, right at the at the point of inference, and one
is on the pre processing side. So starting with like the NLP side itself,
it's huge lead forward with the introduction to transform based models over their predecessors
in terms of the power of the models. However, let me give you
an example. He wanted to train a model to extract people, like a
named nity recognition model. You can do that. Then if you wanted to
have it identify folkses you know where they work or where they live, or
you know where they went to college, you need some relation extraction model for
each one of those relations person and college or person and towns and maintenance.
So you end up with these Byzantine model pipelines in order to produce the structured
data that they labor intensive, and so in terms of economics, it had
to drive a tremendous amount of value to make sense. However, if you
introduced any complexity in terms of the data feeding into that pipeline, compounding the
economic challenges, and this is the data engineering challenge that never received any BC
money. It didn't receive from any attention. This is where we're really focused.
Which was all right, if you're grabbing Twitter's API and you're feeding that
in, Okay, you understand the schema, you can map that schema,
you can clean it, curate it, and send it in. It's a
one time investment. That's good to go. However, if you're wanting to
do that on your data and taking your emails and your power points and your
memos and your call transcripts or any other type of natural ers data and whatever
formatity, any time the document layout changed, anytime the file extension change,
you needed to go and rebuild the preprocessing pipeline completely costom every time. And
so this meant that the amount the cost of value and the time to value
to get these up and running was extremely expensive and also meant if anything changed
at all, you have this in this huge professional services bill that came your
way in order to keep it running. Right, So LMS changed that first
problem where you don't need these byzantine model pipelines, but you still face that
economic challenge on the preprocessing side, and that's why we're focused there. That
makes a lot of sense. And you know, these transformer models. One
of the things they allow you to do is leverage this attention component, right,
which is basically how much attention the computer spends examining this versus examining that.
And it doesn't take too much thought to realize how important that is,
right, because your brain has this you you're constantly modifying what gets more attention.
Someone just barked outside or barked dog, just bark, Oh, what's
that? You can shift your focus very very quickly. And these models,
I mean, it used to be that you just said go and it had
to go through and do the whole thing, no matter what. Now it's
more like go, but I want you to find this, and it starts
down one path and goes I'm not finding this, and it switches. So
it has the capacity to change its focus and attention. That's a really really
big deal. Can you talk about how that happens and what's actually happening on
the cover. Yeah, So where we were five years ago up to about
a year ago is it would look to the left and I look to the
right, about two hundred to two hundred and fifty tokens typically like a five
to twelve token window token about two thirds of a word. You can think
about it that way, right, and so it like it had blinders on,
Think a horse with blinders on, and it's just looking at these kind
of chunks of text, and it's looking left and looking right and hunting for
answers and looking and evaluating that multidimensional text space within there. With the introduction
of large language models, what's been really exciting is that that five twelve token
window went to one thousand, went to two thousand, and went to four
thousand, and recently has gone to one hundred thousand and beyond. And so
what that means is that you can, as you mentioned earlier, you can
give it one hundred pages attacks and say please summarize this, because it can
look at one hundred pages from thirty thousand feet read everything at once and then
take it all in together. Wow. One of the challenges that's emerged over
the last three months, and some really important research that's been done by folks
at Stanford and elsewhere has been looking at okay, we have these large attention
windows, but is the first quarter of a document or of that corpus considered
the same as the second quarter as a third quarter. And what it was
finding was that there's this big donut hole right in the middle that are looking
at the beginning, the first ten thousand or the last ten thousand tokens and
would ignore most of the meddal thing. And so this is where that relationship
with these vector databases comes in, which is like, okay, let's not
try and feed one hundred thousand tokens at a time. Let's give it a
more reasonable amount, but then give it access to all of this data that's
a vector database. It's cheaper, so the inference cost is cheaper, and
you're going to get better performance. That's interesting too, and I'm guessing that
you know the way the data is persisted on the vector databases, there is
some element of metadata. In other words, this data is about the color
of cars, This data is about the speed of cars. This stata is
about engines or etc. I mean, I even think about on parquet files.
Right on a parque file, that first bit of the parque file gives
you like the men and the max and some basic information. There was a
company infobright that at a database which did that kind of thing, and I
think they unfortunately went away. But you know, the parque file format was
designed to facilitate analytics, and you know data bricks solve that Iceberg hooty Delta
Lake thing by just going to the parquet files. So I'm just guessing there
that in the mechanism that that to persist the data in the vector database and
then of course thee leves accessing it, there is some component where it can
go, Oh he wants to know more about colors of cars, I'll grab
it from here. Is that about right? Well, it's right in a
roundabout sort of way, Eric, and it's about it's a back to the
future story. And Verry over at Lama Index and Harrison Chase and others and
of Metal are doing some really cool work here. So call it this past
February and before you were doing something called nearest neighbor search so co signed similarity,
So have a vectorized representation of your prompt, Hey tell me how to
make spaghetti right, and it would victorize that, and it would look for
similar vectorizing bettings in that vector database and bring it back together. Where the
tech is going now, especially in the last quarter to two quarters, has
been Hey, there's this whole world over here of of data engineering in these
parquet files and all of this meta data that could be valuable. How do
we do cosign similarity and all this super you know, complicated math around nearest
DA research and then also leverage that metadata to produce even better inference. And
so those those worlds are just now coming together, you know, the spring
and end of the summer. That's crazy. Yeah, it's really it's really
fascinating too to see how quickly things are changing. So we had another really
really cool two three months snowflake for a while to Wild is now doing something
with a relational AI, I think, And he was even telling me,
he said, three months ago, I would have told you a very different
thing that I'm going to tell you now, because that's how much advancement we've
made in that short period of time. And that's what really is exciting,
and there are lots of factors to that, right. The open source movement
is a big factor, big contributing factor. Just in the mindset of committing
code to public and then leveraging it and being able to share so that we
all collectively build these foundations and different vendors can focus on their part speaks to
tremendous success in the near future with these technologies. What do you think,
Well, it's funny you mentioned him. His most recent book, DA Data
Preneurs just bought for everyone in the company and gave it a copy to everyone
at our offsite last week. Nice folks. This is kind of a forty
year view on the world that we're at today, the importance of data for
driving business outcomes, and that it's the architecture that's evolving. Yeah, and
the architecture is evolving. Mean, that's kind of where I was going earlier
in the show about how just having an information strategy. So my European partner
Eve Mulkers and I were going to be working on this, and I've had
this theory for a long time now and we're going to try to crystallize this
a bit. But every organization must have and that can be focused on any
number of different thing be one area of focus understanding what are us? What
kind of a team do we need to put together to be able to let
these things? And that's the job of the Information Strategy Group, and you
hash out the use cases. You know, when we had Navine round the
show last week, he said, yes, I mean marketing, content creation,
business proposals, the sales and marketing in general is a big area of
focus for us. It's going to be the next couple of years, he
thinks. And it's great because it turns every writer into a ten X writer,
just in the same way that rules will turn a good engineer into a
ten X engineer. And who doesn't want a bunch of ten X writers and
creators out there? It helps you by giving you eighty percent of what you
need, and then if you get good at the prompt you can get to
eighty five to ninety percent, and you've got to fine tune it on top.
But still, if every marketer walked in every morning and had eighty percent
of their traditional job done for them, that's a pretty good change in workflow
and in productivity increase. Right, It's not only that it's triggered a foot
race, and it's a foot race in terms of product within this economy that
we're all operating again. And so what you're seeing now is shareholder pressure and
competitive pressure to adopt and integrate this technology as quickly as possible because it's producing
true quality and improvements and the work that's done, but also the efficiency with
which it's accomplished. And so I think one of the most surprising things over
the last year has been that a lot of the you know, in the
past decade that low skilled jobs we're going to be the first impacted by AI,
right, But we're seeing in practice is that's actually high school that are
impacted, but that's on everything sideways. And so goldman sacts are both extremely
bullish, both extremely bullish on you know, the effect that these models are
going to have on growing the economy, but it's going to reshuffle it at
the same time. Yeah, well, and we're also going to see some
organizational changes too. I was talking about this last week on the show in
hierarchies in teams and how you build teams and who's responsible for what in certain
teams, you know, I mean, I think back to my old days
in the print press world where you had this whole linear process of laying out
the paper and getting all the articles that you're not going to be updating that
document unless you're going to print another eighty thousand and so as a very different
world. And that's kind of like what we are in this world today where
the Web comes along. Now I can publish something that I can change it
instantly. There's a mistake, boom changed, we got it out of there,
right. Even if you think about databases, how it used to be
overwriting records and databases. Now we're appending records so all. And I think
we're going to see some real changes in organizational structures that reflect that. Meaning
you're gonna you know, people who were just copywriting now they're doing more than
copywriting. They're actually engaging more with the salespeople to learn from them, or
whatever the case may be. You're going to see the traditional flow of work
managing that I'm seeing Companies like Drift for examp Ample are out what used to
take three or four different people using what used to take three or four different
people. They're all doing it in real time by scoring someone who comes to
your website, creating some copy to send them and all this stuff. It's
like, wow, that's how that's how day to day life changes. Right.
One of the most forward leading organizations I know folks probably won't believe me
on this is actually the Pentagon and the Intelligence organizations. And you know I
have a background and intelligence and we've been doing a lot of work with them
from the very beginning. Analytic engines in the form of lms that don't require
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Craig market a chief day to day office, and they envisioned a future where
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That's eight hundred two five four thirty two eighteen. Welcome back to Inside
Analysis. Here's your host Eric Kabinat all right, folks, back here on
Inside Analysis. Automating the hard part of data science, and folks, that
is what AI does. It automates a lot of the hard stuff, the
difficult things, but also the tedious tasks, for example, gathering information,
optimizing pipelines. Just been writing about observability. We have our top twenty list
of observability vendors out there. That's all testiful because Google had a great vision
about container orchestration about really the next generation of the foundation of enterprise software is
what we're talking about. But right there at the end of the last segment,
Brian, this next generation of really fast and fast developed apps. That
which is very exciting stuff because now, and it's interesting, I'm seeing this
as a trend in the marketplace of AI infused apps or analytics infused apps,
or you're not just hitting my sequel database or you know, a Maria DP
or whatever. Instead you're hitting some of these models and thus can can leverage
the power of analysis, the power of AI very very quickly. That's like
a Cambrian explosion right now, right It's one of the biggest stories of the
last six months has been the fact that this is not just a data science
and machine learning engineer world anymore. Directly on top of these LEM architectures very
easily, and we've first started in March with a huge demand for markdown and
other file types in the length chain ecosystem, one of the key kind of
orchestration tools in the LM space, and since then it's unlocked thousands and thousands
of new users space for thousands thousands of new users, entities that hadn't talked
to each other. Um. You know, you had the data scientists that
we're working on everything under the hood, and they'd hand it often. Then
you'd have some React developers build on top it. And now you're having build
directly on these element engines, and it's really exciting to see what they're coming
up with. That's very cool too. And we were amusing in the break
about my observation over the last five to seven years as data science has really
taken route and gotten a lot of investment, a lot of activity. I
told you that my experiences those teams never talk to the data management teams,
which is like, what, like, why would you have your data warehousing
people not talking to your data science teams? And that's the answer. The
short answer as well, it's different projects that they're working on, but from
an organizational cohesion perspective, you want these people talking to each other and working
on the same things because you could be That's the beauty of open source,
right is you don't have to always rebuild and reinvent wheels because that's what companies
were doing is reinventing wheels every day. And I think the excitement and the
power of these models is really going to force new conversations to get people to
talk to each other and get a much more cohesive approach to what we're doing,
which is going to save money, save time, and do my favorite
thing, which is improved morale of your workforce. What do you think,
Well, a lot of the you know, aiml in issues with thein large
organizations have been computer versions and looking into syboy lines. You're looking at crops
IoT data right and doing large ternal analysis on that time serious analysis on that
or kind of you know, really in depth and LP projects and if you
introduce new data to any of those. What you have to do you have
to go get new label data to retrain and rebuild a stack. You have
to monitor it. So what you have is you had these I don't want
to call them stovepipe, but you almost had these kind of experiments in an
incubator, right, or these capabilities that could just look at this little window
of data and now what the model is. Becoming as powerful as they are,
you're able to bring to bear a lot more data in different types of
data to this analytic foundation, and meaning that us as people right within organization,
we're talking to folks that we haven't talked to before. And so to
your point, exact theory. And this gets very interesting too because I think
about the amount of heavy lifting that has to be done to get certain projects
afloat and get the analysis that you want, get the insights that you need
to make your decisions. And I think what we're going to see here is
a sparking out of all these different ideas from different sources. We're gonna be
able to get signal from places we never thought we could get signal because people
are going to be able to see it in the models and the results and
what they get, you know, seeing is believing. One of my favorite
philosophers, Wittgenstein, he wrote this book, Tractatus Logico Philosophicus, which was
really a big joke almost on himself and on the industry, because you get
to the end of the book and he said, yeah, everything I said,
forget about it. He says. In fact, he said, use
it as a ladder to climb up into the clouds, and then kick the
ladder away, and then you have reached enlightenment. Now you understand things.
And one of his big points was some things cannot be said, they must
be shown right and it's like, wow, that's pretty interesting. And that's
what is happening when people use these models and start seeing what they can see
into the details of things. My buddy Steve Lucas from Booming was joking they
connected an ALM to some of their data and he said, show me all
the different versions of order to cash that we have right now, and it
did and he was just like, how did you do that? So this
learning experience is going to be I think very inspiring, yes, daunting,
yes, a bit challenging or you know, causing a little bit of fear.
But I think on balance, people are going to be like, oh
my goodness, this is really powerful. Let's get the ball rolling. What
do you think, Oh, ten years ago we were talking a bit about
big data. What we didn't have to go as big data was big analytics.
And you had the data, but you didn't have a means for actually
harness leveraging that data to answer the questions that you really wanted answers to.
Rather, you're just curious on that you wanted to noodle on and pursue your
curiosity. Do the things that make you human, right, that are uniquely
human. And now that we have big analytics, like with LMS, you're
able to do that. And to see what folks were able to uncover is
incredibly exciting and is unlocking entirely new job job descriptions like prompt engineers who don't
need to have any coding ability and are getting paid three hundred thousand dollars at
some places right out of the gate, right Well, and you know,
just think about because I tell you, when I first started to playing with
these things, I'm like, hmm, if you can write in French and
German and English and different languages, I bet I can write in coalball in
JavaScript, and the short answer is yes, yes it can. And now
I think is in data bricks coming out with their encoder or something or their
decodes kind of fire. That's calling it something that basically you can pump code
into it and say what does this code do? Right? Which was the
big miss, the big issue with coball developers because a lot of these old
systems were written in coball and if you don't have someone who knows Coball hitherto,
you were just at the end of the line folks, and you'd have
to just work around it. But now you can actually go in And this
is what really excites me is somewhere down the future being able to examine entire
information architectures and understand the flow of data and systems and what's doing what in
which case and optimize that because that's great for sustainability, for efficiency, for
data quality, for process quality, all these things. We're going to get
really good at not wasting time. Final thoughts from you. I think that
what we're finding is that language really is the native what you just described as
code to text. We have image attacks, we have speech to text,
and we have tax to text, right and I think the trick is going
to be how do we actually make this Like the last ten percent is going
to be the hardest, and so how do you make this production bread where
you can trust it enough to hand over the work that you don't want to
do so that you can work on the things that really are most rewarding for
you and they are the most impactful to the organization. We're at that moment
right now where enterprises are just beginning to engage engage with this, and that's
on us as an industry to help solve that problem. That's interesting, Yeah,
and I think that you know, companies do need to be practical and
understand what are these tools supposed to be used for. Hammers are used to
hammer nails, screw drivers were used to screw things in. You have different
tools that have different purposes, and it's going to be important for people to
really understand what lms should do right now and what they should not do.
Like there's the fun case of the guy who asked it to give him case
law that it just made up and it was all hallucinations. Well, don't
do that. I think that is not a proper use of the technology.
It will get you in trouble. But to your point, you've got to
get someone look at it this stuff, folks, I promise you know,
marketing people, you're curiosity people, right that, the people who are naturally
curious and want to know things. That's who you want playing around with this
stuff. That's who you want playing around with these engines. To figure out
the use cases and then get your use cases and just you know, be
methodical about it, but don't not go down the road. Right, this
is the kind of thing where everyone is going to be leveraging these technologies in
some way, shape or form, and they're they're advancing so quickly. I'll
give you one minute left. That's one of the other really interesting things is
how fast they're evolving and improving. So the problem that was a big issue
last month may not be an issue anymore. Right, Yeah, that's exactly
right. And so what we've seen is, you know, folks were really
worried about this becoming an oligopoly over the winter months and just a couple of
companies owning everything. Right, Well, you've seen you know, the teams
and the capabilities that Navine and others have built a mosaic and the good work
on the architecture side have pulled the cost down to building these dramatically to several
hundred thousand dollars or several million dollars, and so from a capital investment standpoint,
is more tractable. From an inference side, that's that's an area where
it's still really expensive and we need to work on. But but it's it's
far more accessible than it was twelve months ago, and infinitely more accessible than
it was when GPT three was first introduced in June at twenty twenty. Yeah,
that's a pretty fast innovation curve. I would call that logarithmic. Just
go walk, it's going like that. Will folks look these guys up online?
Brian Raymond from Unstructured dot io Just like it sounds unstructured dot io ETL
for LMS. You're gonna be hearing a lot more from these folks. You're
gonna be hearing a lot more about LM's folks sending email info and Inside analysis
dot com. I want to know what you want to know. We'll talk
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