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Learn more at inside analysis dot comsideanalysis dot com, and now here's your host,
Eric Kavanaugh. 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 generative 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 Jennai 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 Baoha to talk about that,
and Jim Wilt as well is a fractional CTO and distinguished engineer. And these
folks are 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 Booja 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
predictivens, is what they really are when you get right down to it.
So at that Boha, 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 said, though, so bo. 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,
been on products. I've been but 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 Amputy. 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 patterns to
start producing new material. So that's what Jenei 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 oh, 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 professors trying to prevent their student from generating ai jen ai for papers.
But is that not a bad thing 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 permeate every aspect of our
life nowadays. So we all have to get onto that bandwagon. That's a
great way to put it on. Like the way you said that, where
are we going with Jenny? I 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, it's
interesting. 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. Baha'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 Generator of 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, when 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 it's 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 expect to see that are rallied.
Yeah, that's a great way to put it. And maybe Booja,
I'll throw this one over to you. What I've seen with AI and other
capacities too, and now of course with gen AI is that almost invariably it
surfaces 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 fast we can
generate, oh, how much we can retrieve. So it's not I don't
think that it's a 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 we're saying the old day, but
it's 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 least 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 about 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 huh. 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 vetting the 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 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 bouh 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 a 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 the 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 you said either ambed or vector base 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 to you know,
a much larger resource who can comp 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 Loo 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 SEQL to struct 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. Take us 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 uh discord. I
guess it's a discord channel, and oh my goodness, it's crazy. The
amount of artwork and the sophistication of the art that you can create with this
stuff. It's just wild. So things are changing rapidly. I remember hearing
who is saying, whoever wins this war wins the world. Now again,
there are use cases for this stuff. There are dangerous sides of it,
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 readiness? As we said we 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 Cobo 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 Wilde throw it over to you. In terms of
organizational readiness, well, I mean that the 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, uncertainty, deception sometimes and organizations.
When you're looking at the adoption of jenator AI, where we're excited about it
and public in general is excited about it, a lot of organizations have,
when 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, you know,
a time 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. What 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 in 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 to 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 frold 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 them 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 blind 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 grabbing that. And I'll kind of pass a lot
about how because I think she can probably talk a little bit 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 Jem 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 others, uh use
where we have products already out there using visions too to detect issues on say,
you know, winterbine, 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 developers nowadays really need to leverash
ai jen ai to help them just make you know, as a double person,
Jim was, 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 fast. 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 chain 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
documents, feed it in and it's almost like a dynamically generated cliffs notes.
When you're in high school and college of these cliffs notes. Right, It's
like Boom's like for you. It check out all the filler words and it
come dons for you and get 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 TELP 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 result, 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. Just and some people were
skeptical, like, just give it a try, and so they did,
and their minds were blown 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 HAA, 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 have been in an organization for fifteen twenty years,
the amount of organic growth and customizations and that governance on this platform are 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 Matillian we're 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. 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, that's interesting because the discovery process can be enhanced by asking the
generative a engine give me ten questions. Ask me ten questions as I'm going
to discover something, and then we'll 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 for 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 it 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. We're 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 incredibly knowledgeable intern who will tell you whatever you
ask it may not be right all the time. It 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 put that to help folks, will be right back. You're listening
to a very cool episode of Inside and Out. 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 w from FPT and distinguished
engineer and fractional CTO Jim Wilts. 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 already these anecdotes that get thrown out. It's amazing how
fastest stuff happens. Everyone's probably heard about the lawyer who went out and threw
citations in and 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 at 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 that's customer facing.
There's several issues already, but those are probably the biggest ones. Jim Will,
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 explained 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, buy
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 none 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 could ask good 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 and 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 that 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 chat bots, this is going to
be probably one of the most common use cases is fueling a chat bot and
feeding it enough information from past conversations to know where to go. As 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 chatbot 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 out 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 throw us over
at the gym. 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 say. 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
aster. 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, and generative a. I 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, 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 generator 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 we're augmenting, and I think
one of the cool okays 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 it's a give and take when
you think about it, well, it's go ahead, Eric, Yeah you
made you brought up such a it's 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 GENAI engines can,
in a moment, like as you're getting to the customer service rep,
whip up a whole bunch of stuff that's accurate and relevant 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 not to
use. That's why it's so powerful. Folks. Podcast bone a second,
I'm 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 aale 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. You know, 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 log file. You know, you can monitor your
network all day long, but if you don't analyze that lock file and read
the log file and figuring out what it's telling you. It's quite less and
useless, right, So that's one of the applications that had been produced,
and it basically general use gen AI or a large language model to read the
log file and then just detect the differences the abnormality and call out and abnormality
and then do adjuscency analysis and to understand what kind of 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,
right, can you those things? Because it's brutal, I mean very
brutal. I'll throw it over to Jim. 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 had 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 system 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 is 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 Gennai stuff 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 you're going to be able to start
doing with AI is pitting multiple logs against each other. It could be through
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 it's really
interesting is it can read back to you what your code is doing. If
you sometimes are trying to understand this code that you don't understand what it's doing,
comments don't make sense. Who comments to their code anyway? Then you
can make a correction to the code, or it can it 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 to look
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'll need one
experienced engineer versus you need a roomful of engineer well 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 that line because that's brilliant.
The future is here and it's exciting what folks will be talking about. Ha.
We of FPT America's look up this company FPT. They're here. Huge.
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 been
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Chris Karragio. House Intelligence Chair Mike Turner says CIA Director at William Burns told
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