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dot comsideanalysis dot com and now here's your host. Eric. A. Yes,
indeed, ladies and gentlemen, welcome to the future. It's careening toward
us all the time, but it seems like it's coming faster these days than
ever before. I'm not sure exactly why that's true. They told me When
I got older, time I go buy faster. So maybe that's what's happening,
But I could swear this Jenai stuff has just put everything at light speed,
certainly for the enterprise but also for small business. For anyone who's played
around with this stuff, it is a leap into the future. And as
the futurist William Gibson once said, the future is here already, it's just
not evenly distributed. And I love that expression because it really speaks to the
pockets of innovation that you see around the world and helps you understand that new
technologies and new methods do take time to percolate through society, to permeate to
various circles in the business world or in the consumer world, for example,
and this jenerative AI stuff affects darn near everything, and it's really open the
eyes of businesses worldwide. I mean, the numbers we're hearing are just off
the charts in terms of the number of people using this stuff already. I
mean, hundreds of millions of people are using this technology already today and it's
more or less brand spanking new. So it was twenty twenty two that it
was released in general chat GBT three I believe are three point five. We're
up to like four point five now. Of course Google has barred now it's
called Gemini, Anthropic has clawed there or other. There's Mistral that has the
interesting approach they're taking with smaller models. But the bottom line is that Jenai
is everywhere, and today we're going to dive in with two awesome experts.
We've got Baoja Bui from FPT. It's a Vietnamese company, amazing company.
I went to Vietnam last year to check out their headquarters and everything they do
and was blown away. Really impressive company, very hard working, very diligent,
very focused. They get stuff done. They're not worried about the flash
and anything silly. They're just focused on getting the work done and building cool
technologies. So we have Bioha to talk about that. And Jim Wilt as
well is a fractional CTO and distinguished engineer. And these folks aren't really sparked,
so in studio of audience, live studio audience, don't be shy,
asked them the hard questions on a first to say my take on what Jenai
is, and I'll throw it over to Boja and over to Jim as well.
Really, these Jenai engines are predictive engines. That's what they really boil
down to. They are predicting either the kind of image they think you want
based on your prompt or the kind of text you want based upon your prompt.
Now, they were trained on massive amounts of data on the interwebs,
not on everything. We learned a couple months ago and one of our first
shows of the year that even the folks at open Ai realized early in the
game. They can't just train this on any data that's out there. They
have to focus on certain data sets to get the end result they want,
which is an engine that will reflect back the kind of text that you want.
And it's very powerful. Folks. If you haven't used this stuff,
you need to start playing around with it. And you first start, then
you understand what it does. Then you can kind of get digged deeper into
the weeds to figure out how to use it. But they're basically predicted vension
is what they really are when you get right down to it. So at
that Boha b tell us a bit about yourself and what you see, What
is Jenny and I to you, and what should people use it for.
Yeah, well, thank you Eric for that comments that we are not flashy.
I want to be flashy, but we just we techy people just can't
be flashy. So it's a skill I have to up myself for. But
that's that though. So boha. I've been in IT and technologies for almost
thirty years now, or even more than that. I stop counting the moment
we hit above twenty. My so been with IT technology. I've been CIOs,
I've been vps, I've been on the application development side, I've been
on products. I've been but the since twenty fifteen, no, wait,
twenty ten. I you know, I got introduced into data management, and
you know, the journey starts there. I got passionate about just making informations
out of data. You know, how do I get that information into the
hand of the users, the executives, the people who's driving trucks, not
needing to know when I have to deliver a certain product to a certain customers
of patients. I was in healthcare for ten years at that point. Sale
my whole the introduction to data, I was with the HIE. You know,
if you still remember those terms, you know, the data for healthcare
was a big thing back in two thousand and five ten. So that's my
introduction to data. Now I've been with FPT, I've been their customers,
I've been working with them, and I really appreciate your confirmations that you know,
they are hardworking, very smart. As a customer, I was constantly
amazed by the level of professionalisms that I got from FPT. So not a
plot, I'm just kind of recounting my journey here. What is jen ai?
You know, Jenei, Like you said, it starts out to be
or aio, all right, It starts out to be predictive. You have
data, you have the ability to use code to really mind that data and
then generate information based on that data or predict based on the patterns that came
from that data. And then it gets into the ability to really now using
pattern to start producing new material. So that's what jen Ai is for me.
And where are we going with jen Ai? It's everywhere, It's you
know, Jim with Jim and I talked a lot about jen Ai and the
growth of jen Ai and Ai overall. But Jim, it took fifteen years
for cloud to be adopted. I still, you know, a few years
ago there was a company that's still not looking at cloud, but jen Ai
in two years we got widespread adoptions. I talked to my son. My
son is doing a co op at a farmer company, and I asked him,
so, what are you doing over you know, your bread time or
he's using jen Ai to do some optimizations in their lapwork RNA recognitions RNA pattern
So you know, it goes from a corporate level to students. You know
how many the professor is trying to prevent that student from generating ai jen ai
for papers. But is that not a bad thing? You know, because
at the end of the day, is why reciting information that you can get
from Google just put it in your own words? Is that you know,
so again is permeate every aspect of our life nowadays. So we all have
to get onto that bandwagon. That's a that's a great way to put it
on. Like the way you said that, where are we going with Jennai
everywhere? I'll throw it over to Jim to comment on that. I have
to say, I mean, I watched the whole big data craze take off
fifteen seventeen years ago. Let's say that was the biggest thing i'd ever seen.
This is like five to ten times bigger than that in terms of the
number of people getting in in terms of the impact, in terms of the
number of people and impacts, it's just off the charts. But what do
you think, Jim, Yeah, you both have really covered a lot of
the basics that we wanted to, you know, get out there in terms
of the history and where it's come Bau, how it's correct. And looking
at adoption of the PC was you know, about ten years fifteen years to
get to fifty million users. That took you know, about ten years to
fifteen years for cloud adoption to really land. And when we look at machine
learning and generator of AI, machine learning has been around for ten years plus,
it's been around. There are people that have been working in the space
for over twenty thirty years with neural networks and so forth. But the big
difference with generative AI is is it's really applicable to the common person. Anybody
can get into a prompt situation to some learn some prompt engineering, if you
will, and start getting results that are just massively interesting. And I think
that you know, the statistic that hit me that Baja mentioned is actually it
was two months, Generative AI has reached one hundred million users. That's twice
what it took these other technologies what ten, you know, fifteen years to
achieve. You know, the Internet took five years to achieve fifty million users.
It's already surpassed the airnet's adoption in just two months. What's also exciting
about generative AI is that we're traditional AI is using what's traditionally called supervised learning.
Baja mentioned it's and you did too, Eric. It's mentioned that it's
based on a lot more data, and that's called unsupervised learning. What that
allows it to do is to create what I'm going to say, patterned recognitions
and results that they're out of our view. So it looks like it's thinking,
if you will, on its own, but it's working with such massive
data. It's just really really exciting to see that it can find patterns that
we normally wouldn't find ourselves. And I think that's one of the things that
makes it that aha moment that oh this is different. It's accessible, and
it produces results that are pretty transformational because it's putting together patterns that we wouldn't
expect to see that are valid. Yeah, that's a great way to put
it. And maybe Baoja, I'll throw this one over to you. What
I've seen with AI and other capacities too, and now of course with GENAI
is that almost invariably it surface is something that I did not expect. And
that's very interesting because typically any AI algorithm is just a recipe that is designed
to address a particular type of situation. Let's say a particular function, a
particular array, or something that happens in the world. You're trying to predict
what happens or optimize what happens. And you would think that we know ourselves
well enough to not be surprised by our own behavior, but that's just not
the case. Like and so every time I use one of these engines,
it throws up something I did not expect, and that's good news because that's
it triggers the creative process and it makes us think more. What do you
think? I think I absolutely agree with you. I think that we,
as great as our human brains are, we have limited capacity or you know,
how how fast we can generate or how much we can retrieve. So
it's not I don't think that it's uh, it's it's a. I think
it's more of a retrieval, you know ability, whereas the machines it's gonna
take back. It's gonna take everything that it knows and give it back to
you, and it reminds you of things that hey, I knew this,
but I didn't remember. And that's where the benefits or the efficiency gains in
using generative AI is so great. It's because it really takes away your the
need for us to remember everything that you have to you know. That's why
in we're saying the old day, but it really is still there. You
know, you have document template because people cannot remember everything every single time,
so you need template to repeat to save time. You don't have to recreate
things all the time. Generative AI is able to really do that retrieval of
your template and give it all back to you very timely. But you know,
retrieval and knowing what to retrieve and when to retrieve is also important because
yeah, as much as we like to get everything back, you don't always
want everything that JENEI give back to you at that's quite yet. Well,
and that's a comment I had made before our show. I was talking with
Mark Palmer, who was a real smart executive in the tech space, and
he mentioned that he read some research recently that showed something like eighty percent of
people who are using jen Ai right now are just taking whole hog whatever it
comes out of that engine and using it. That has a very bad idea,
right, bo, How you want to always vet that stuff? Oh?
Absolutely, it will give you good ideas, but you have to be
careful because number one, it does hallucinate, so it does make stuff up.
It is generative by nature, so it generates new things based upon old
things. Right about what it's seen. All it's doing is reflecting back patterns
of what it's been trained on. But you have to be very careful about
vetting that stuff, right, Oh, absolutely, you have to because it's
trained on informations out there, you know, especially some of the open source
gen ai open Ai for example, it's being trained on the internet, and
not everything on the internet is usable. Just be careful. Not everything is
accurate, right, Not everything is accurate, and even when so that's kind
of going into some of the rest that when we well, you know,
what we have to do in order for us to be ready for gen Ai
is some of those betting under the training of your resources to make sure they
know how to use it safely and effectively. Yeah, and that's the curation
process, right. So we keep hearing about RAG models, retrieval, augmented
generation, which, if I understand it, really what you're doing is you
are trying to use your own trusted governed data as an anchor, and then
you want to be able to spin up text using the LM as a text
generative capacity, if you will. But you wanted to use your facts that
you give it, and those facts are posted as embeddings, either in the
model itself, which some people do, or in a vector database which is
then adjacent to the model. And that people understand the whole concept of persisting
something in the database and then recalling something from the database. But here we've
added a third element, which is the language model to generate stuff. But
still the same concept applies. Right, We're persisting something in a data store,
and then we're leveraging that along with the generative capability of the large language
model to create fresh new text that is ideally accurate with what our corporate data
says. Right Baha in a way, yes, so you know, we
you know, if you're using your data alone and retrieval and then to supplement
it with the large language to to make the interactions easier, or to generate.
It's in text and it's limited, but the ability of gen AI to
you know, so large language model gives you the ability to read the data
that in the database just not possible. You know, pdf my. You
know some of the older companies that I have worked with, the Banks Insurance
Company, they have one hundred years of documents that's probably still in some kind
of fish or images. Large language model nowadays give us that capability to really
retrieve that information. And like you said, either AB or vector bates it
so that now you can retrieve it. So that's the advantage of some of
the newer capability. And then based on that then it gives the ability for
us to interact with it using natural language. So that kind of expand the
resource pool of who can rechieve that kind of information versus before all the tech
people have to write some kind of query. Right now you have the you
know, a much larger pool resource who can come for those right and see
this is I mean, you just hit on one of the biggest transformations that
we're seeing right now. And I heard it from my buddy Lose Simon with
a company called up Tom here in Pittsburgh. He's the one who first said
it to me. And this is so big and we'll talk about this after
the break, but this is a seriously big deal. We used to always
have to learn how to speak in computer language in order to talk to computers.
So we had to learn to speak SQL, the structured query language to
talk to databases. We had to learn Python to talk to various Python apps
or that's used for risk management, lots of different stuff. Honestly, you
had to learn cobol in order to talk to a mainframe for example. Now
you can talk in English words to the computer systems. That is a huge
deal. We'll be right back here listening to Inside Analysis. Respect. Welcome
back to Inside Analysis. Here's your host, Eric Tabanac. All right,
folks, take us to the future and see if we're talking about jen Ai.
It is futuristic. It's amazing, folks. Let's be honest, play
around with this stuff. It's going to blow your mind. A friend of
mine turned me on to mid Journey, which is through Discord. I guess
it's a discord channel, and oh my goodness, it's crazy. The amount
of artwork and the sophistication of the artwork that you can create with this stuff
is just wild. So things are changing rapidly. I remember hearing who is
saying, whoever wins this war wins the world. Now you know again there
are use cases for this stuff. There are dangerous sides of a deep fakes,
things of this nature. We're going to talk about some of that,
But this segment I wanted to get deeper into are you ready and how do
you know? So? How do you know if you're ready to leverage this
kind of technology organizational writiness? As we said, we've picked up last segment.
We're talking about how you can now talk to computers in English words,
in prompts right as supposed to having to learn SQL or COB all these other
languages. And you can also use it to tell you what code does.
You can feed it code and say what does this code do, and it'll
tell you. It won't be always completely accurate, but it'll be pretty darn
close. And I've heard from enough people now to know that it gets pretty
close. It gets to about eighty percent in terms of coding of writing what
you want, and then you just fine tuned to get your last twenty percent.
But Jim Wildell throw it over to you. In terms of organizational readiness,
well, I mean that most clever thing I heard in our pre call
last week was to start your first use case internally. So nothing that faces
your customers or your prospects, your partners. But do some project that faces
just your internal staff to learn about these things. Because it's like a new
pair of skis or something with jet engines on them. You have to figure
out how to ride them. But what do you think about all that,
Jim, Yeah, that's a good way of putting an analogy. It's like
seize jet engines. You wouldn't go out on the slopes with other people around
if you have jet engines and your skis. I think one of the interesting
aspects is there's fear and certainty deception sometimes and organizations. When you're looking at
the adoption of Jenator of AI, where we're excited about it, publican general's
excited about it. A lot of organizations have what I'm going to say a
healthy fear, which is in most situations pretty healthy. Right when it came
to PCs or the internet or cloud, it was good to come in it
kind of slow. It's not going to work this time because you really are
not prepared for the adoption that it has gotten in the general public. And
so if you have a policy in your organization not to allow people to use
generative AI, be aware they're using generative AI no matter what it's on their
phone. And I know that we want to protect corporate assets, corporate secrets.
Find that's great. But I think if you go about the adoption we
talked about, I think you're going to find you can ease your way into
it and get what I'm going to say, a lot of those fears dismantled
and a lot of the excitement generated because you're doing it with internal uses,
internal purposes. You mentioned about code analysis. I think it's a code companion.
Every developer should have a tied to their hip because it will help them
find errors that they're putting in their code so fast. You don't let it
write the code for you outright when you do is it's your companion. It's
going to find your blind spots, it's going to find where you made a
mistake. I wrote an example of this using multi threadic code, and I
put an air on it purpose and it was amazing. Not only could the
generative AI find the air, which none of my colleagues could, by the
way, it also fixed the air, which all of my colleagues could do,
but it fixed it in a very concise and correct way. Now I'm
in charge of the code, therefore I'm the one responsible accountable. I should
never just let it fix the code and put it into production. That's just
silliness. But if you do think about it, there's a lot of value
it can be used internal organizations that makes the organization execute much more operationally efficient.
And that's something Bohan I have been doing with other organizations is coaching them
on ways to enhance their internal efficiencies using generative AI. Yes, everybody wants
to be the first one out there, and there are some amazing products that
are coming out as products from organizations, but if you're new to it,
you really need just to kind of froll it inside. One of the retail
organizations I highly regard as highly innovative has taken and create a degenerative AI tool
and given it to fifty thousand of its employees and they don't know what they're
going to do with it. They just want to learn what they're going to
learn from it and what they're going to do from it, And that's that's
a great attitude. You give it a safe tool that's not going to put,
if you will, your corporate data in danger, and you see how
they're using it. You monitor, you see how they're using it, and
you find out what they insights. You know, what spots they're finding,
what got you moments they're getting those those things you said, you you know,
oh my gosh, how did it come up with this? Yes,
exactly. Everybody should be grabbing that. And I'll kind of pass a liver
about how because I think she can probably talk about to the concept of encouraging
operational efficiency in organizations first in terms of learning what you can do with the
tool before you go crazy putting it out there for everybody to see. Well,
there's just so many things that we can gain efficiency, right, you
know, So Jim and I been working together on a few of them.
One is to lavish ai for sound analysis, you know, to look at
what can we do to use sound to detect issues in machines and predictive maintenance
or just assist in the diagnosis of it. There's other use where we have
products already out there using visions two to detect issues on say, you know,
win turbine, If that's a crack, how do you know? You
know? So monitoring using vision AI. We're also working on code assists.
Like Jim was talking about, every develop person nowadays really need to leverish AI
jen AI to help them just make you know, as a double person,
Jim was a double I don't know, Eric, have you ever worked with
any kind of coding, But at the same time, you know how tedious
it was to sit there and type all these commands. If there's something that
really accelerates that coding for you so that you can focus on the big concept,
that's where optimizing the efficiency of every aspect of the organizations will be wonderful.
So that's that's where we are seeing, you know, organization really should
adopt Look at what are the tedious things that nobody really want to do or
they want to do but they wish it could be faster. That's where we
apply AI to help. But how do you get the organizations ready as well?
Right? What are the things that let's just say, if you create
the tool to set I don't know, predict something within your organizations or to
generate some content within the organization. First of all, you need to make
sure that your model is chained on information that you want your employees to use,
and don't just train it onto the internet because who knows what's out there.
So, you know, organization readiness, making sure that the organizations the
employees are ready and understand what are some safe AI practice should be. And
then also from a technology standpoint, setting up the tool that can deliver that
safe products. You know, I came from data data governance is my preach
of the day these days, you know, every single time, get your
data governance in place, get your data clans, get your data in a
place where your model can get to so that it can be trained. Because
if it takes your data scientists pages to get to the data, it really
doesn't get your organization ahead of the game. That's and that's a really good
point that you just made. And so I'm going to throw out a thought
here and maybe a throw it over to Jim first and then baoha, you
can ask it what am I doing wrong? You can ask it all sorts
of questions and feed it a document. So one of the I think relatively
underappreciated and incredibly powerful use cases or functions of these tools is summarization. So
you can take a very long document, let's say a thousand page document,
feed it in and it's almost like a dynamically generated cliffs notes when you're in
high school and these cliffs notes, right, it's like boom, that's like
for you. It take out all the filler words and it condensed it for
you, and it gets you going so fast. You know, Jim and
I were just experimenting what last week we point a model to some users document
user guide that we found on the internet, and then feed the engine that
we create a bunch of code and tell to interpret it for us. It
did it just like that. Jim got the engine all set up in what
fifteen minutes fifty minutes to get the first setback, and then you know it
takes a little bit more training to get to some more eligible results, but
less than a day. Yeah, that's that's crazy. And I was talking
to some folks from Matillion, a software company MDM style company. They had
a use case where they said, look, let's just try to throw it
at chat GPT and just see what it says. And some people were skeptical,
like, just give it a try, and so they did, and
their minds were blown by how accurately it came back. And so the point
being, think about the whole side of discovery. When you're doing data analysis,
the discovery side is so important because you're trying to find the unknown,
right. I mean, it's one thing if you're trying to find a known
like with a query to the database, give me what have already persisted,
But if you're trying to look for patterns that have not yet been identified,
this kind of technology is incredibly powerful because it will on a moment's notice,
absorb tremendous amounts of data and then throw back seemingly random reflections that are very
accurate and very compelling. So on the discovery side, I mean, I
kind of view these engines as being a new front end for almost everything in
the enterprise. Eventually, what do you think about haha? I agree?
I agree the discovery. That's exactly what Jem and I were trying to figure
out last week is how we have an obscure platform that not that many people
know about anymore and very hard to find resource for even if you had resource.
Some of these platforms that had been in an organization for fifteen twenty years.
The amount of organic growth and customizations and that governance on this platform not
just mind blown. But you know, how do you really accelerate that discovery
process so that you really get the engineers to where they produce results versus doing
these tedious, unvalue added activities. Honestly, so AI, that ability is
just it amazed me still and get me very excited. Well, and you
do. So. The guys from Matilian were mentioning they did then need to
go, so they figured something out about how to do it, and then
they actually had to go do the hard work with their existing information systems to
get the design work and the engineering done. But at least they knew which
way to go. At least they knew what's possible. Jim, I'll throw
it to you. Being able to suss out what is genuinely possible in any
given situation is half the battle. Then you have options that you can choose
to go in this direction or that direction. But if you haven't effectively discerned
the nature of the challenge or the range of possibilities, you're still in the
dark and you're shooting in the dark. So this discovery component of these large
language models, I think it's very compelling for being able to understand what's happening
and then have a really good idea of where to go next. What do
you think, Jim, It's interesting because the discovery process can be enhanced by
asking the generative engine give me ten questions. Ask me ten questions as I'm
going to discover something, and it will prompt you, with best practices of
discovery to feed itself information so it can start offering you better analysis. The
example how I was talking about was so much fun because it's an obscure platform
that really hasn't been leveraged, let's say in tens of years, and we
are able to find an old syntax manual with two thousand pages. So doing
the pag approach or grounding or language chaining, you feed that two thousand page
manual in and now it's making what I'm going to say, very precise outputs
that are specific to the rules of the language and the syntax of the language
that it was built in. And so you can take things that are ancient
in the computer world and make very modern to the answers that can give with
the very accuracies that you're seeing and you know it could be one hundred percent
accurate eighty percent of the time, and the times that it's not accurate,
you're going to have to have enough knowledge to call that out. The hallucinations
of the biases, the blind spots. However, what we found is that
as you're going through a discovery process, it's those those gotcha's were basically things
I wasn't thinking about. It will think you know, have you thought about
and it will give you some things. Have you thought about this? Have
you thought about that? And it's like, no, actually I haven't.
So it's a companion, right, It's kind of that person that that can
can have a conversation with you and throw things out at you, but you're
not going to reject them because you're asking them to do that for you.
If that's a really good way to put it. And we'll talk about this
in the next segment coming up here in just a moment, but using it
as a companion, like an incredible, knowledgeable intern who will tell you whatever
you ask it may not be right all the time. You kind of reminded
me of the old Yogi bearra quote. Right, ninety percent of the game
is half mental or something like that that can be completely correct eighty percent of
design the clock the broken clock is correct two times a day, so well,
don't touch that. He folks will be right back. You're listening to
a very cool episode of Inside and Mills. Welcome back to Inside Analysis.
Here's your host, Eric Kavanaugh. All right, folks, back here on
Inside Analysis, Rocket and Rolling today with Baalja Whet from FPT and distinguished engineer
and fractional CTO Jim Wilt. This guy knows a lot and a lot of
people too. You know, there's a lot of cool people. He does
a lot of stuff which is always fun. And we want to talk about
pitfalls. There are always pitfalls. I think I'll just throw it out there.
There are all these anecdotes that get thrown out. It's amazing how fast
this stuff happens. Everyone's probably heard about the lawyer who went out and threw
citations in It turned out to be nonsense citations. Well, shame on that
fool for not just taking the extra step to make sure. And this is
where you get into the rag model concept right. In other words, you
want to use a trusted data source like whatever sources they have for cases,
for historical cases. And I'm telling you, folks, lawyering as an industry
is in the crosshairs. Accounting as an industry is in the crosshairs of AI.
All these things that we've done traditionally are absolutely positively in the crosshairs of
AI. And as Jim was kind of suggesting, eighty percent of the time
it'll be one hundred percent accurate, you do have to know when it's the
twenty percent to take a hard look at stuff. So that's the biggest pitfall
I think is don't just rush in and start throwing this stuff it generates out
into the world. Be careful about what you make this customer facing. There's
several issues already, but those are probably the biggest ones. Jim Wilt,
I'll throw it over to you, what are any other major pitfalls you've seen
that people should watch out for with jen AI. I think one of the
things I run into and you almost have to take a pitfall as comical and
enjoy the entertainment aspect of it. When I was writing an article, I
was tweaking hyper parameters just to see what happens when you break it right,
and I had a test explain quantum entanglement. And I did it with different
temperatures, and I chose one that was out of the parameter range and it
created gibberish. I mean, my, you know, one year old granddaughter
could have done better. But it was just random characters group together. So
it wasn't even English language. It wasn't any language at all. It was
kind of thing. So it will break and you and part of the learning
process, you need to know when it breaks, how to break it by
it breaks, you know, and you need to be on guard for these
kinds of things. But as I was finding things, question the answers.
That's probably one of the things I would say in every situation. What it's
going to be critical. You talked about adoption in another earlier segment. You
want to start with non mission critical and work your way towards mission critical.
You don't start with mission critical. That's the lawyer example you gave and others
that are out there in the last week. You don't want that. Start
with non mission critical and work your way with confidence and learning and maturity.
I've asked times, when it gives me a quote, can you give me
the source of the quote, And it will say, oh, I'm sorry
that I made that up. I mean literally, that's the words that we'll
use. I'm sorry I made that quote up. That didn't come from that
Forrester Gardner article, or if it does refer to an article, I'll say,
can you give me the number enumeration of the articles so I can look
it up and it will give me a false number. So you have to
drill down into, you know, the material before you publicly go somewhere with
it. If it's just helping you make a decision, it can be somewhat
wrong and you're not going to hurt anybody with it. But if it's anything
you're going to be then promoting forward. You have to be accurate about the
data and the results, and you can ask the questions, can you give
me the source for that answer? Can you give me references to where I
can refer others to learn more about the answer. If it can't do that,
you're probably in the hallucination zone and you want to get out of there
right. Well, you know so, Jim, you talked about making sure
that you check the words right. And part of the pitfall of adoptions also
is moving too fast for the organizations. I think that we really need to
watch for is the organization ready and if not doesn't mean that you shouldn't adopt,
but you really have to build almost like a safety net, an on
ramp for the organizations to adopt. So meeting your organization, your user where
they are, making sure we understand where people are with the ability to use
what we're going to put out there, and whether or not we have provided
the appropriate level of handholding for this kind of work is very important too.
You're reminding me of something. I'm so glad you brought this up because you
put this concept of an on ramp, So you want to be careful at
this pace at which you go. You want an on ramp and you also
want an off ramp, right, I think you want an off ramp,
And what I'm referring to is that, And this is going to be an
ongoing question. Where do you actually insert the generative capability in your workflow?
There's going to be some point at which it comes in and you're going to
want to know, of course where that is, and you're going to want
to have a toggle to turn it off or on. So if you use
it, for example, for customer service, for chatbots. This is going
to be probably one of the most common use cases is fueling a chatbot and
feeding it enough information from past conversations to know where to go. That's the
beauty of having an existing system is that you can just take the database of
everything your chatbot has already said, do some analysis on when the chat bot
did a good job and when the customer was satisfied. Okay, that's the
behavior we want to model and mimic. But then at some point, if
it starts going walky, you want to have the off ramp. Oh,
turn it off like this, shut it down. It's just like an employee.
You maybe drank too much before going into work that night, and like,
okay, you better go home, buddy, it looks like you're both
Well. That come up and comes down in any chatbot is so important.
You really need to be able to collect the information when your chatbot start making
no sense that you know, be able to really train that because it's a
continuous training as well. Right, Well, that's the thing too. So
my buddy Usama Fayad, who runs the Institute for Experiential AI I heard this
a bit, Jim. He said, the whole thing about experiential means human
in the loop. And he makes a really good point. And there is
this narrative in the media that, oh, AI, it's going to just
take over the business and run everything. No, no, no, no,
you sure don't ever want to do that. You want to have someone
watching it all the time to make sure that it does what you want it
to do, because it will move it blinding speeds, and so if you
don't course correct in time, it's going to be with blinding speeds when careen
off the tracks, right, Jim, Yeah, I think the chatbot example
is a good segue into what I'm going to say augmenting. I'm going to
say services and everything that's augmented means as a human it's in that loop.
They have the accountability and responsibility to have the final stay. So maybe what
we'll see I would love to see in the future is opposed to just chatbots
doing it randomly and anonymously, that it connects you directly to a person with
information that's augmented by the conversation, so the person on the other end can
understand your situation much faster, and then through the generative AI get you to
your resolution much faster. But that human is going to if you will facilitate
that process or you know, in the sense that it's not completely blund But
you brought up a good point too. There's an article I wrote where I
started it with the tractor didn't replace the farmer. You know, a generative
AI won't replace your job, right, However, I am the article with
the tractor didn't replace the farmer, but farmers with tractors did replace those without.
And I think that's a big distinction to what you said, Eric,
because it's very specific. The human in the loop is a necessity. The
power of the human and the loop, if you will, in terms of
accuracy and impact, is going to increase astronomically, right, And that's going
to now create what I'm going to say what Baja was saying earlier. Am
I going to ban papers from being written by jenator of AI? Or am
I going to allow it to augment people who are writing papers? Different question,
But that's kind of the thing you have to get through. Well,
Jim was a T shirt. I have never been a T shirt. So
I'm advocating for allow Jenny I to write paper. I think for augmenting,
and I think one of the cool. Okay, that is augmenting. Yeah,
you've got I wrote something. Read this and tell me what I wrote.
Okay, Okay, that is not what I was trying to say.
All right, correct it for me a little bit. Let it correct it,
and I can say, Okay, that's good. Now I understand where
I made a mistake. Now I'm going to continue writing from that, not
take it. I'm going to take what it corrected and make it my voice
and my intention and then have it read it back to me again. So
it's a give and take when you think about it, Well, it's yeah,
go ahead, Eric, Yeah you made you brought up such a such
a great use case, and I'll give an example of why. And then
we've got a podcast ponus segment coming up here next. But think about in
customer service, and this is what really fascinates me about these engines is how
it decides to choose what it chooses, right, And that's why you have
to try it, to learn and to kind of figure it out, because
there is a method to the madness. Obviously, it's a computer system,
right, I mean, it's got rules and instructions and things that's supposed to
do. But you think about customer service and historically you have a customer service
center, it's going to bring on bringing up your system, bringing up your
record, et cetera. It's a very slow process and it's typically from one
system where there are lots of other systems where customers have touched the organization touch
points as they call them, on the call center, on the website,
in a store, maybe I wrote a letter or something. You never know.
But these Jenai engines can in a moment, like as you're as you're
getting to the customer service rep, whip up a whole bunch of stuff that's
accurate in religt it and just give suggestions to the call center person. So
now it's not this slow, painful thing. It's going real fast and the
human can decide what to use and what not to use. That's why it's
so powerful. Folks, Podcast bone a second coming up next. You're listening
to Inside Analysis. All right, folks, time for the podcast bonus segment
here and a fantastic inside Analysis. We've been talking to Boo Ho We of
FPT America's and Jim Wilt fractional CTO and distinguished engineer, and we're talking all
about jen AI use cases, what to do, how to get started,
And as we were getting excited about that last use case, maybe about how
I'll throw this over to you first. You know, one thing I figured
out early is well, wait a minute, if this thing can write in
German and French and Spanish, I bet it can write in HTML and JavaScript
and C plus plus. And short answer is yes, it's your can and
it can also absorb that kind of information too. So think about systems and
information systems talking to each other in bits and bytes, and you know,
you get log files basically, which to a human if you know what the
log file means, okay, now it's meaningful. But the scale of log
files you get, like thousands and thousands of log files you're trying to scan
through, well ninety eight percent of them are the same, and then the
ones that are different, Ah, that's when something probably went wrong. You
can even throw that stuff at these engines and say hey, I got all
these log files, what the heck does that mean? And get back some
interesting answers. What do you think, boja? Oh absolutely, actually that
was cybersecurity. I think leverage AI at least a few years ago. Yeah,
there's been systems that knew exactly what you said. If you in a
network management at all, one of the dreaded activity is read the lock file.
You know, you can monitor your network all day long, but if
you don't analyze that log file and read the log file and figuring out what
it's telling you is quite less and useless. Right, So that's one of
the applications that had been produced, and it basically general use jen Ai or
a large language model to read the log file and then just detect that differences
the abnormality and call out the abnormality and then do adjuscency analysis and to understand
what kind of abnormality it is and where the threat is coming from. So
absolutely, well that was specific for cybersecurity, but you know applications application debugging,
right, Oh my gosh, can you those things? Because it's brutal,
I mean very brutal. I'll throw it over the gym. Debugging has
historically been one of the hardest things to do, and even with sophisticated technology.
And I remember fifteen sixteen years ago interviewing a guy I always mentioned this.
He it's such a cool name, Zohar Gilt. That was his name.
He was with the company called Precise, and they got bought by Idea.
But what they would do is help you this is a long time ago.
They would help you do monitoring of systems and application dynamics and why did
this break? Why did this go down? Even back then, you would
have interesting heuristics. You'd see CPU usage went down on a timeframe, and
the application speed went up, and all this stuff happened. But the human
beings still had to parse all that together in his or her head to make
sense of it, and then you would learn patterns of things. Oh okay,
if this goes up and that goes down, it's usually because there's a
disk failure or something. Well. Now, in the world of development,
in part because of Kubernetes, but in part because of just the complexity of
the cloud, we have containers and containerization, and we have all kinds of
observability. I mean, there are so many tools out there. I joke
about this. There's a company Mesmo, which just sits on top of your
observability data and helps you marshal it and go in the right direction. So
there's so much activity that there's a meta tool now that sits on top of
it. That's how much observability we have, but it's still up to the
developer to piece it all together. So this GENAI stock can be tremendously powerful
at absorbing massive amounts of these log files, finding some correlation between them,
and at least getting you on the right track. What do you think,
Jim, Yeah, there's definitely the ability to scan and look for anomalies or
anything along the non what I'm going to say traditional paths, because logs can
be mundane and you're looking for that needle in the haystack can be very frustrating
and to broker it across multiple logs. What's really going to be able to
start doing with AI is pitting multiple logs against each other. It could be
see the time stamps, there could be through events. Whatever you can do.
You need to train it enough to understand that this log is connected to
this log this way. Then you're going to be looking at multiple facets of
the same problem from multiple viewpoints, so that you can have better ways of
understanding what's going on. That. The other thing is, you know,
with generative AI, you can write your test cases. It can look at
your code, look for anomalies in your code. I think one of the
things. That's really interesting is it can read back to you what your code
is doing. If you sometimes are trying to understand there's code that you don't
understand what it's doing. Comments don't make sense. Who comes to their code
anyway? Then you can make a correction to the code, or it can
augment you to making a correction to the code. Then you get another model
to look at that change that you made and explain what it's doing. Is
it still doing what it was said before or is it doing something different?
Is that what you wanted for an outcome? So we're going to get to
where I'm going to say, we're going to leverage these tools increasingly more complex,
you know, around the way that we can solve problems that we never
would have attacked the problems before. Before I would look at one log at
a time, or I'd get a room. I've done this and telcom where
you put people, you know, a dozen people in a room for six
weeks at all the logs to find out where did the error occur? You
know, because the CEO was contacted by a text and you can't find it.
With Generator AI, we can start training the models to look at things
multifaceted so that we can actually start finding errors that we never would be able
to find on our own before because it just has more eyes. If you
will on the screen, then we can put go ahead baha. Well,
and you know, just on top of that, you need experience engineer to
be able to read the lock and understand the lock and really recognize the issues
when you come across it. Now with jen Ai, you really can take
all of that and abstract it and make it so that the people you need
one experienced engineer versus you need a room full of engineer to do it.
That's such as Yeah, that's such a great way to put it, and
it's exciting that I think we have to close on the line that was brilliant.
The future is here and it's exciting. Well, folks will be talking
about ha, we of FPT America's look up this company FPT. They're huge
there. I mean they're a massive company. I have a wonderful origin story
about where they came from. Very interesting stuff. And of course Jim Wilt
distinguished engineer and fractional CTO. We do archive all these events for later listening
and viewing. Send me an Nemei if you want to be on the show,
info at Inside analysis dot com. We'll talk to you later. You've
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NBC News Radio, I'm Chris Garraggio. California Congressman Adam Schiff is defending
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