Today we are talking about generative, AI tools, such as
chat GPT, what's the impact on the Enterprise?
Our Guest is Nicola Marini bnz, know your CTO of ey.
Tell us about your work. I'm responsible for all the
technology that generates revenue for the firm.
So I spent the last two years developing what we call the ey
fabric, which is effectively platform data and and and with
seven components actually station Intelligence on top of
it, the powers our service offering.
So we have about one and a half million clients that are using
it every day. It's interesting.
We don't think of a technology of a consulting firm as being a
technology organization, but obviously you are, I don't think
that is an organization that can be not the technology
organization of the same time. I think technology is, and look
at what we're going to be talking about today, right?
The college is Raising in our lives that there is no business
that doesn't as in users. And then, you know, if you start
thinking about the Consulting organization, when we provide
services to our clients that are very data-intensive technology
becomes so Central, so fundamental to the quality of
the service that we offer the ability of doing and many and
across many countries, etc, etc, Nicola products like chat GPT
Google is coming out with their own version, Microsoft.
Has adopted this now in bing. It's so popular.
But why as an Enterprise CTO do you care about this?
I think what we're going to see. First of all it's an arms race
which is great right? Because usually when the players
are competing with each other you know we tend to benefit from
it right as consumers in this case, or Enterprises.
So it's very exciting over. We're going to be going next in
terms of the capabilities of the technology, but be more specific
about Jenna. May I and you know I'm looking
at my own Organization, for example right?
Which is a knowledge-intensive organization, the ability to
summarize knowledge in the way. You know some of the language
models are capable of doing is absolutely off or opens up an
endless number of opportunities for organization.
So, for example, you know, how do you systematize the knowledge
within the organization, how you being oncologist in
organizations? How do you give advice right to
clients or how you have? For example, scenarios where you
have a sort of a co-pilot, right?
So in pretty much any, any one of the roles and the jobs that
we have in our organization, right?
We would definitely benefit of having an intelligence agent
intelligent agent, sitting beside us, you know everyday not
replacing us, but supporting our knowledge and our capabilities
trying in everything that we do. So endless possibilities.
And so most and also not only in the Enterprise values in our
lives. I think that would be a In fact
as a practical matter as you think about these generative AI
capabilities, how do you, how do you think about a do break it
down into areas that you think might be useful?
Or is it still just too early of an ex exploration phase or where
are you in your thinking? I think he's going to go very
very fast. What already exploring?
I mean in our specific business, right?
Which is to give advice to clients based.
On our knowledge or regulations that our knowledge of other
interaction with other clients. Etc.
You know the ability of you know tapping into the knowledge in a
way that is multi-dimensional like you know some of these
tools can do is absolutely incredible and and fundamental.
So we have several projects already Eddie why that are
tackling that. But I think, what is most
fascinating about this? Latest development is so we had
chatbots for a long time, really and not know exactly when
Laughter. But you know, obviously some
companies had their child boss for at least you know, eight to
ten years, right? But what is different about?
This one is the fact that you actually can have a conversation
with it. Sometimes this job boards you
know you are doing Custom Image as a cook as a as a consumer.
I you go onto a website because you have one I have some more
information about the product so you are complaining about the
protocol something right? You get into these very
frustrating, you know, interaction with the chat bot
that if you don't. Exactly Hit the question, the
way, the child bought the specs, it then you go in nowhere and
you list is what I do. You get to a point where you
say, please I want to speak to an agent, a real human, right?
And this will these tools now that are coming to.
The market are actually very different because they allow you
to actually have a dialogue because they can maintain the
state of that conversation, right?
So the previous job boards, you ask a question and get an answer
and that's it. There is no memory of that
interaction. That the Book everything but now
you can so you can go. It's almost like I call it but
maybe a little bit abusing the term by sort of a dialectic of
AI. Right?
So where you ask a question, you get an answer and you can
another you ask another question and you get closer to what you
want to get out of it as opposed to a sort of an expert system
where you know, it's question and answer and that's pretty
much it, right? So it's very interesting those I
think that is the feature that to me is most exciting.
Would it be Correct to say that you feel the defining
characteristic is this ability to remain to to retain the state
of the conversation? There are so many other things
that are great, right? The ability of writing in in
multiple languages, right? So, I'm Italian original, right?
So, that's my first language. He writes as good in Italian as
it is in English, right? And you can ask the questions,
you can you can Envision like you know, Meetings where, you
know, you have everyone speaks his own language, really think
it would be an incredible for global companies will be an
incredible achievement. Right today, we still have to
there is still a lot of Lost in Translation that these tools are
so refined and sophisticated now that even the nuances of a
translation can be really, you know, highlighted in the right
way, which is incredible. We have a question that came in
from Twitter. Already, which is an important
question from our Salon con are salons, a regular listener.
And I've always thank Our Salon for his wonderful questions.
And he says this, he says, when we use AI as a co-pilot, we have
to be careful about the data. It is pulling in order to give
us suggestions if we aren't aware of biases or stereotypes
in the data than how what should we Do it's one of the
fundamental questions of dealing with these types of
Technologies. Absolutely.
I had lots of people that inside my own company, right?
The rich are say, I want to try GDP, I want to.
How can we use the with clients? I said in a so it will do
caution in. I think it's where warranted at
this point, right? Meaning that you are the human
in charge, you know what I mean? So it cannot delegate completely
the work to these agents because they're not yet at that, Level
of being able to understand, you know, how ethical code, right
the morality. Then you know what is acceptable
or not acceptable? Unless it has been labeled by
another human by. So the human in the loop
approach is absolutely essential here, right?
So you should so I use it as a sort of a, an aid as opposed to
a delegation of responsibility, right?
That cannot happen. So the human has to be in charge
and hopefully the human. I mean you can argue that even
the human-sized a lot of Show flaws, right intensive, what
kind of answers? You know is capable of giving
but you know what not a pusher at the point yet where we can
delegate a function to you know, a tool like this.
I heard a very interesting comment yesterday on the news
from a journalist who said, Who as a reminder that tools like
Chad GPT or what Google is going to come out with all of these
tools, they're really like advanced auto correct on your
phone. They have they give the
appearance of sentience but they're just it's just like
autocomplete. We could go into it.
Like a very long philosophical. Conversation was you know, what
is sentence was sent into really means but I agree, I think I
mean I would say the outer item is a little bit to trivialised
right as a definition. Definitely is now.
So if you autocorrect is on one end of the spectrum, It's
awesome. And on the other end is
sentences. Like it's it's in the middle,
right? So I think we need to
understand. So sometimes, you know, we tend
to whenever there is a new technology especially this kind
of technology that a little bit more difficult to understand
because they're actually quite complex, we tend to give them
either, you know, kind of transfer some sort of
alienation, right? We transferred to them the
expectations that we have from another human being, right?
So if we do that, that is the wrong approach, right?
This is not a human being. This is the bunch of silicon,
you know? And a lot of data that gives you
some kind of an answer. But if you actually think about
it as a sort of a in a dry in taking, an aeroplane to go from
San Francisco to New York, that is what these tools are.
So it's a machine that helps you do things faster, right?
But if you think about it it's not that different than at the
beginning, truly of the mass adoption of the internet, right?
So in the eye, I cannot do anything in the 90s from
University. Right.
So whenever I had to do something research paper, I had
to go to my library in my city to find the physical book and,
you know, taking forever to get the information out excited.
And then the search engines came up and that was like an
incredible because you can access the whole knowledge of
the human race, you know, either the keystroke.
And so it doesn't mean that we assign to that technology like
Superhuman ability, right? It's something that helps you
search things better and the saying I think we need to look
at these new Tools in that same way show me what's possible.
So show me the search criteria. Show me what the search results
are and then I need to be what the one that makes the decision
on what is relevant and what is not.
I think one of the challenges for folks in the Enterprise or,
or just our personal lives, is that the answers these tools
give comes across as being Authoritative.
Yes. Yes.
But it may be totally incorrect. Absolutely.
And that is what we shouldn't. This is the thing, right?
If we think about it, we can start talking about human
characteristics and we transferred them to describe the
tool we make a mistake, right? We will never call, you know, a
car like, you know, fast legs, you know, maybe that's the wrong
example. But you said, I mean somebody if
we Use them and say, okay, so we need to put them in sort of the
in the box, right? And say like you know, today you
do some of the search or Google do a Google Search, right?
And it comes back like with millions of pages, sometimes,
right? So it's up to us to select.
What is the page that has the highest level of relevance of
what we're looking for, right? We need to look at it the same
way as opposed to. Okay.
That is a you know, it in a super lap.
You superhuman intelligence Since the, you know, knows
everything, right? So that's why I think, you know,
when you think about that Katrina as well.
I think if we start relying on these things too much, while we
lose the fundamental understanding of a domain or the
subject, right? It can become a little bit
dangerous from that perspective. Please subscribe to our YouTube
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Well, we have another Question from Twitter that's related to
this. And this is from Chris Peterson
who says, in business Communications, whether it's B2B
or b2c. Do we have an ethical duty to
call out what's written or edited by an AI assistant?
He says, if a human reviews, it modifies it and hit, send, does
that change this ethical duty today, when you publish a paper
There is a process to publish a paper, right?
So you need peer reviews, you need to, you know, cite your
sources, you need to do all these type of things, okay?
You cannot just say, I came up with this right without, you
know, that that would be pleasure is MM, right?
So I think there has to be a framework similar to that one in
this space, right? So if I'm like doing, for
example, like, you know, a client, for example, asks me
about whatever some regulation somewhere, right?
I can do some research and I can use charge apt or can use
another tool that I can use, you know, my own traditional, you
Google searches or something, to get the information out my own
internal knowledge and provide that answer, right?
So when I do that in an email, right?
As long as I think, you know, it is fine to do it as a human
being, right? So I think, I don't know why
there shouldn't be any other way, but if I'm producing a
paper just using that right relying on somebody else's work
and I don't, you know, to That and I don't do the citations and
I don't do this. And that then, of course, you
know, I'm getting into plagiarism.
I think it's the same way that a human would be doing in a
research paper like that. So complicated, so I think that
is the need of especially in the IP in the hole, you know, legal
discipline of intellectual property, is probably as well.
That there is a need of some kind of a rethinking about some
of the fundamentals of that because this is different.
Yeah. And clearly There are a whole
set of ethical and legal issues. Absolutely, right.
So and and so that's why I think we need it's funny that
philosophy, right? Probably the day ancient most
ancient academic discipline is still, you know, very much alive
is because we need to ask ourselves so these questions,
right? And we need to have Frameworks
to to address them. Like, you know, it wasn't
considered when the the beginning of intellectual
property came up as a protected. You know, right that the in
those days like when the legislation started to get
introduced to wear similar conversations, I'm expecting,
right? So you're not supposed to take
somebody else's intellectual work and make it yours, right?
And so I think we need to have that dialogue today.
What is going to be much more difficult today?
Is that these tools that the marginal cost of producing or
generating, you know, this type of documentation.
So, whatever we generate is is almost 0.
So we will be inundated by this volume of documents papers,
books, images and stuff and will be really hard to understand
what is really generated by human.
What is not excited. So, you know, maybe the
Christmas asking, like, identifying that content be, you
know, a really good step, first step, I guess.
And this is our Salon. Khan comes back again and he
says when everyone Is quote doing AI?
What sort of governance structures should be placed?
And I love this. Lets you know, let's let's
right. Let's write a thesis on this.
What sort of governance structures should be in place at
an organizational level as a governmental and as a global
level, what are we just simply start with organizational
governance of a iine that's going to become important.
Very important. Any thoughts on that?
So, for example, I was sunk Mentioning the example of a
knowledge company like ours, right?
So first of all, you want to be able to store then knowledge in
a way that is relatively secure in the sense that no secure, I
mean, of course, from attacks from the outside, but that's one
piece. But also secured in the sense
that is the knowledge that the company wants to protect, right?
So the Nola. So if you think about an
ontology within an Enterprise, right?
And you have the definition of some entities or concepts, you
want to make sure That definition is shared by
everybody. It is approved, Etc.
Right? So I think that is the kind of a
need of establishes. A sort of in a, I'm gonna say
this but probably the wrong word, right?
It's all sort of in. It is toriel board, right?
For these type of knowledge, the guess systematized within the
knowledge repository of the Enterprise.
So the concept of you know, what is revenue, for example, for a
in all the company, right? It can be different.
Then what is used in on the street.
And so when people search for the word Revenue, there has to
be an approved definition of it behind the scenes.
So to me that is highly curated content so you have to have an
organization I believe that looks after that make sure that
nothing you know we've asked Revenue only gives you another
answer, that's not good. Okay.
So that type of, you know, potential Divergence has to be
understood as to be prevented and has to be Much.
So that to me is the key with other areas where they kind of
it, you know, being true to the book definition of things is not
that critical. I think, you know, a little bit
more dialogue could be, we welcome, right?
And I think we need to lacks the tool itself, learn and evolve
Etc. So too much controls and too
much governance, and I think is going to be a good thing.
So I guess the answer is it depends right?
Technical Consulting answer, but it depends in the Is that, you
know, what is the type of knowledge that you want to
maintain govern and protect as opposed to the other ones that
you want a little bit more free flowing and and more
interactive. That makes a lot of sense and
looking at other important domains inside an organization
that require governance. Just, this is a natural kind of
a pro to use that as a model. But then be open to the fact
that, as you said generative a I will Change and evolve over
time. And so we had we can't be to
lock down about it. You cannot?
You cannot constrain it too much.
I think because otherwise you lose the value of it, right?
It's just curate and knowledge. And you know, if there's a
knowledge it's it's it's real view, more knowledge, right?
So you don't, you don't, you're not able to anticipate what's
coming. We have another really
interesting question, short question, hard question from
Anil Vos and he says, what kind of jobs?
May become redundant by generative, AI tools.
I don't think this technology yet, okay.
I don't know about a next five to ten years, okay.
That's different. But right now, I don't see the
again, the delegation of the function completely towards
another, to even if in the Creator's economy, right?
Even if you create art like digital art in your life, that's
your job. I don't think he can be
completely created by the machine, right?
We're seeing, you know, a I asked Etc but at the same time I
think if you are an artist and if you are the Creator, that is
a lot of additional value that you can add on top of what the
machine creates by itself, right.
And also, in the Enterprise, I see more jobs created than less
the jobs disappearing, right? Because I see a lot of
opportunity for example, for you know, people that can
systematized. In a different way, right?
Or can, you know again? Like I am very big on this
concept of ontology is because I think if you can store the
Enterprise knowledge, you know, they're its value that
translates into shareholder value, right?
And it's also like, in allows you to protect the IP and the
capabilities of an organization, right?
So think about our organization 360,000 people, eight hours a
day, is 2.4 million hours of work every day issue.
Can capture the knowledge structure it in a machine, and
then having give access to it to our clients, at our own people,
that makes our the, our company even more valuable as what it
is. So I see more opportunities than
less. If that makes sense, you have
been alluding to and talking about this Knowledge Management
function. How does generative a I change
Knowledge Management relative to this?
Torkoal tools. If I look at my company, right?
And not going outside of our business that is to me is the
the low-hanging fruit. Okay?
That is so the fun day we had able to access all the knowledge
and the fingertip in a way that is human friendly.
So today, right? If I have to say for example in
the in our tax business, if I had to help a client you know
understand the taxation regulation around 10 count It's
usually I had to read like, you know, millions of pages of stuff
Ryan and has to be an army of people doing it.
If I have a tool that's not that, you know, with level of
confidence summarizes. So that information I can spend
more time talking about client with clients about their options
than just, you know, reciting what the regulations of the
right. So people will be happier will
have access. We have course, I've everything
has to be cured as I said, right?
Especially in this type of businesses, as to be curated and
management government class. I can The wild west, right?
Because you don't wanna, you know, then the machine thinks
that, you know, we're looking at the legislation of Switzerland.
And in reality, we need to talk about Austria, right?
So, that's, that is a potential issue, right?
But when you have done that, I think, you know, the research
and the work they will be doing is much more, will be more like
the number of hours that we spend doing more valuable work
will be. I think hiring people will be
satisfied and the club, the quality of the work that we'll
be doing. - 1. How will these kinds of tools
change work inside and Enterprise in every business
application, right? The one that we build that we
don't have it yet, but, you know, we're going in that
direction. I still have like I will have my
little, you know, some window or the frame on this side of the
main screen that allows me to ask questions on the fly in real
time about what I'm doing. And so think about like someone
gets out of school. I am 23, 24 years old, out of
University to joined our company, right?
Instead of, you know, said getting them to do like, you
know, weeks and weeks of training and asking making sure
that they get to the right person as a mentor excited that
they can do that, of course because the human relationship
is so important. But at the same time if they can
access their knowledge, institutional knowledge,
codified in the system, I mean there will be there will be much
easier for them to work and to grow, right?
So I think think that to me, that's what the copilot concept,
right? It's not like it's the function
will be automated or replaced but I have this agent that I can
talk to and ask questions, we fantastic.
I wish I had that beginning of my career.
I to a fair amount of writing and I use chat GPT and I just
subscribed or was put on the, the user list for Microsoft's
Bing and I can't wait. For Google to come out with
their product. And I actually subscribe to
several of these products. And as a co-pilot I have to say
it's a phenomenal, I mean, the body of knowledge, the
efficiency that it helps. And just coming up with new
ideas, it's very fast much faster than than I could do on
my own absolutely and the new ideas I mean they're not really
the machine, this is the thing right?
We shouldn't think about. So Alan a thing.
Us in the sense that we give these to, you know, too much
responsibility in terms of, you know, the level of intelligence
because ultimately there is always like a today.
At least, there is always a human behind the scenes that has
created the content. I structured in a certain way as
trained the model in a certain way so it's still human
knowledge, right? But it's but the nice thing
about it is that you can have access to it immediate.
So you know, I was making the Earlier of the the old school,
you know, Heart, You Know, cover tackle library, right?
You have to find the book, you know in the library to be able
to talk about the specific domain here.
You can we can have access to an incredible amount of data like
very very quickly. We have another really
interesting question from Anil Vos.
And he says, if the data set that trains, the generative, I
say I have As an intrinsic bias, how can we get rid of this bias
or overcome this by us? So that it is neutral.
And I think he's talking in two things.
One from the point of view of the software developer, and
number two, from the point of view, as us as consumers as
users, the people that manage did, they send that drains the
moment, right? That's what fundamentally comes
down to, right? So if you feed it in the model
racist, You know type of Concepts, write the model will
Jason that so that is 0 bias, you know, of any kind.
So it's a fundamentally it's a human responsibility, right?
So in Enterprise has, as I said earlier, we need to govern that
we need to have, you know, a sort of a, first of all we need
to have values, right? The Enterprise level values, the
things that you wouldn't do and not do because they will help
guide. You know what is the you know
what is the Onset of bias and how people should should should
should manage that part, right? And then at the same time, I
think, as I said earlier, during the has to be in an authorial
Board of some sort that oversees and takes the responsibility or
food for what goes into the machine ultimately, right?
So, I think it wouldn't be there is risk for sure, right?
And it's something that we need to take very seriously.
This is again from our Salon, Khan who says, when it comes to
life and death Situations. And the AI is wrong, who should
we Sue AI? Vendor the data owners, the
algorithm creators, who do we Sue?
When there's a, when something really goes wrong, I think we
should not get into position when it's a question of life and
death left to a machine. That to me, is the number one.
So if we had to make, if you have to get to a position where
the machine has to make a decision and it has, you know,
the power of, you know, driving an event of this level.
I think there is something wrong in the way of approaching it.
These Technologies are not at that level yet, right?
So we do and we don't want that, I think, yet until it can be
proven in a different way. So if let's say it's a medical
Your diagnosis that has to be provided.
The expectation is that there is always a physician in charge
with the help of the tools, right?
But fundamentally it's down to the human.
So I don't I mean everybody really even you can think about,
you know, just stretching out a little bit from just the Genera
to the eyepiece of it even for we're not ready yet to do full,
you know, full a full self-driving cars.
Right foot for the number of reasons, but one of the reasons
is not just, the college is about their kind of a who's
liable. Who's accountable?
What kind of ethical decisions they need to be taken excited?
So if you're not already there which is in a way, the more
mechanical way of doing it, we shouldn't be ready to add to
delegate. Again, you know, the
responsibility for these type of decisions to adjust the machine.
Yet we have another great question from this time.
From Elizabeth Shaw on Twitter and she says So why do tools
such as Google barred or chat GPT hallucinate even when it's
using correct Source data and what are the implications there
for for using these kinds of tools for Knowledge Management
inside an organization? Like see why it is better
described because at the end you know it's reinforcement learning
you learn and you know that the answer that gives You know, the
best results are the ones that the win, right?
So there is a sort of a, you know, a selection mechanism of
the answer. So if the answers that are
awarded, are those that are going, you know, on a tangent.
Probably, you can get in this scenario.
This is at the end of the day, it's a statistical tool, right?
So I mean, we call it in artificial intelligence,
whatever, but effectively, it's pathetic, supplication of
Statistics, right? To a very complex problem.
Violent. But distinct statistic.
And so there are up absolutely situations where statistics gets
you into the outlier. Right.
Absolutely. So that's possible.
Even I don't know, I'm not a mathematician but I'm sure that
a lot of wasted that this can be described mathematically in
terms of what needs to happen for the Enterprise.
As I said earlier, right? We should that should not be
allowed, right? Because you don't want to have
you know, a tool speculating on what tax legislation might be.
If I look at our own business All right, so we need to give
specific answers to clients. That's why this kind of a
curated approach to it, you know, for the time being is
absolutely critical. So will be, it's an investment
that every company needs to make both from the bias, the ethical
side, but also, for the content of the, of the answers and the
questions, Etc. So there has to be some sort of
the labeling that happens on the tool.
When you give an answer in your train, it right to make sure
that this is the right, that the answer that is proper, right?
Can I ask to what extent are you thinking through these kinds of
processes at a why we are experimenting?
So we're trying to understand, you know, how much work needs to
be done. Etc.
So we are in the process of defying, all of that.
I think that's why, you know, the previous questions was, you
know, are we gonna replace people?
I don't say that. I think, you know, the more we
use this to us, the more we need to add it up.
Human expertise to it. So it's going to be The opposite
but I think if we put at least for the next you know few years
until these tools mature and there is more knowledge about
it, you know there is definitely the need for for investing in
the Enterprise in human capabilities around that Chris
Peterson on Twitter comes back. And he's asking if you have any
thoughts on cybersecurity issues, created a need for side
for the Cyber insurance industry and do such firms and These have
a role in shaping and governing the use of AI in the Enterprise
regarding liability. I think in the future probably.
Yes, I don't again, I don't see any Enterprise function,
completely delegated to Ai. And so if it's always a sort of
a center model where there is the human and the Machine
together doing something on behalf of the Enterprise, I
think fundamentally it's the same level of liability or
responsibility that you have today in the future, okay?
So for example, let's say, you know, credit decision or other
type of Thanksgiving without going into the most dramatic,
you know, healthcare related decisions.
I say, I don't see why not. That could be some level of
assurance but I have it in my understanding so far.
Okay and it's for sure, not complete.
I haven't seen anybody yet. That is planning to completely
you know run the piece of a business like that.
You know in a schematic fashion what are some of the challenges
that you see to adoption of these kinds of tools in the
Enterprise one is over hope. And then, you know, for most of
my career, AI starting from school, basically, and I've seen
these herbs and flows of excitement, right?
So I started, I started really 90s. 7 and he was like, you
know, the winter of the I nothing really happened for
years, right? Even if there was a lot of
potential in those days, it was all about expert systems.
You know, that kind of knowledge because we didn't really have
the technology in terms of the processing power.
You know, the memory and all those kind of things then he
started getting into, you know, some games, you know they were
win like you know the Chess World Championships, blah blah
blah. So I'm not going to make the
whole history but it was but you know every single time that
there was a major breakthrough like One the big blue that I DM
DT 90s you know beating Castro it was a huge you know you know
the interest and everything then it went down again right from
few years then deep learning came up and we started dealing
much more with you know image recognition which I think is a
great application of artificial intelligence.
So then another big you know Spike of interest that actually
is leading to this new language models right there are
similarities and so Sometimes, you know, we tend to again to
humanize these Technologies to the point that we the our
expectations of what they can do becomes the same level of
expectation. We will have from a human but
it's not that. So I think that if I was an
Enterprise outside of mine because I will spend time trying
to demystify in a way what these things can do.
But at the same time, showing the value that they can provide
because there is a The value of their it's just unfortunately
what happens is that people get overhyped and then they get all
will be disappointed and so you know in to me in a year from now
and we're going to say the tragedy was a great failure.
Even if is actually an incredible Innovation, I don't
think so, right? But it could happen, it could
happen because people, you know, think that these things can do
things that are, you know, comparable to what a human being
can do. They can't right now.
What It is vice. Do you have for folks in the
Enterprise, who are Business, Leaders, and Technology leaders,
who are looking at these kinds of Technologies and trying to
figure out what to do the bottom line is that this is going to go
really fast again, right? I was that saying, in the
beginning, this is going to, it's an arms race between super
powerful companies and they have a lot of money to invest, right?
Then you start getting into to, you know, government except so,
I think this is going to be exponential.
The speed of these Innovation will be exponential.
I think we're just saying the beginning of it and so I think
that two things to think about so one is There is no right now,
you need to be knowing what it is and being able to understand
how to apply to your business. So that is no postponing it a
say, oh, you know, I'm not going to be an early adopter.
I'm going to be very fast. Follower very first, follower is
Superfast, follow it very fast. Photo could be six months.
So I think for large Enterprises that can afford to do that.
I think that is the need of experimenting.
Hiring the right Talent or training the right people to get
to a good handle on where this industry is going, right?
This is its you have to be keeping an eye on this.
That's, that's the bottom line. And then the other thing is
that, I think, oh, lots of questions.
Very, very good questions about the ethical implications, the
bias and all of that. It's really important because it
has a massive brand impact, right?
So there is also a risk management side of around these
technologies that has to be understood very well sir.
If I, you know, I would be a CEO of an organization.
I would push my technology team to have the right skill sets to
understand what it is and where it is going.
And at the same time, I would put in place in a sound risk
management, thinking around it as well to what extent do you
think that the developers of these tools and the folks were
gathering the data sets, need to have that ethical understanding.
And the reason I ask is because Historically technology was kind
of separate from the ethical, or the application of that
technology. From an ethical perspective is
absolutely, do you think room, right?
And you know, think about what's happening, you know we social
media right? So it's how important it is the
content moderation in social media.
So you know, this is not exactly the same of course.
But you know if you translate it into this space in there is
opportunity for abusing these tools everywhere, right?
And they as I said, the brand impact, It is massive.
So there is the need of, you know, setting guidelines.
So for example, internally that is why we have specific
guidelines on how to use the tool what we can do and not do
with it, right for now maybe they're a little bit restricted
but we want to be on the on the safe side of things and then
we're going to open them up a little bit more as time goes by.
But you know, there has to be, but fundament.
Everything starts with the true understanding of what is
Available impossible, right? And if you don't understand that
in detail, it's difficult to put, you know, the boundaries.
And so today we're really at this phase where we're
understanding that. What's reasonable, what's
practical? What can we do?
Yes, and this shouldn't be underestimated in terms of the
ability of driving Innovation, but it shouldn't be either,
overestimated intense of being a sentient being or anything like
that because that, you know, I think it's worth far.
From that, you know, I have to say the interesting thing about
this question to me, is the fact that people have this
conversation because at times these chats million human
conversations, so what? Yeah, it's incredible.
I think what the heck done? It's absolutely one of the, in
my mind, one of the most incredible inventions of the
last definitely. The century, right?
And there is the being the iPhone 5 in mobile phone, social
media, all that kind of stuff. Great.
I think is super important but this one, it goes towards
another level of innovation because it impacts our, you
know, it's really like, you know, it's almost like looks
like reasoning right over human, right?
So it's absolutely incredible. So I'm, you know, I'm super
excited about it. And why don't we finish up with
another question from our Salon? Khan who says on Twitter, what
do you hope future iterations of a I can do.
I think the future of Aviation, I think it has to be more
transparent. Right?
So that you guys have asked fantastic questions about by us,
ethics and all of that. So that is a problem, right?
So if we go deep this, the count, this technology are
powerful. If they go in the wrong hands,
in terms of, you know, being able to generate content as
chests Behavior. So public, you know,
understanding of things, it's really dangerous because we're
basically adding another layer of intermediation between And
the sources of knowledge, right? So if that is done with, not the
right ethical, I guess, you know, it can influence politics,
it can influence the election. We can do a lot of different
things that we don't want, so I think that transparency and
ability to monitor what it does. And often strained, I think is
really important, but then I cannot wait for the future
because you know, like there is so much more in think about the
breakthroughs that we can have, when When the sum of the human
knowledge is so accessible, right?
And in such a smart way. I think, you know, if we take
here so I saw something that you know Monday we're gonna you
know, really Implement nuclear fusion, that's not the thing.
But in terms of stimulating a different level of thinking and
summarized in this knowledge, I mean it's incredible.
I'm so excited about it. We have one last question that
snuck in under the wire and so how about if we finish out with
something from Melvin aguiar who says, how comparable is our
products like chat GPT to low code?
No code. Will they coexist or eliminate
one? Eliminate the other.
And I think he's referring to the fact that these tools are
really optimized to help write code.
Yes. I think right now, it's not that
big yet, but at definitely that is Direction.
So you would ask, you know how much code would Do you have to
write in in five years? When you can actually talk to a
tool and say, can you build me this application with these
characteristics? These inputs outputs data, Etc.
So I think the direction, I think the what I say like you
know the writing is on the wall. In terms of, you know, software
engineering will have to be changed completely.
It doesn't mean that they're not going to be, there's not going
to be software Engineers. Absolutely not.
But I think the role of the software You would have to
change a little bit more and get closer to the business because
you still need a human. They can summarize.
Those needs and requirements into and ask the machine.
What do you need? You know, armies of people to
code you know routines and different lengths, programming
languages, maybe not. And with that unfortunately
we're out of time. It's been a fascinating
conversation. I want to say a huge thank you
to Nicola Marini beings. You know Nicola thank you for
being here with us. Us today.
Thank you for having me Michael and everybody.
Thank you for watching. Especially those folks who asked
such great questions. I always encourage you to watch
and ask questions. You guys are an amazing
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