And to start with, Leonid, one question:
Is GenAI really new?
What's different
with traditional or predictive AI that we've been talking for a long time?
Well, I think it's very important not
to underestimate, but at the same time not to overhype this technology.
It's clearly a breakthrough
from the consumer point of view and from what it is possible now to do with this.
But at the same time, it's important to remember that it's
actually based on the same statistical principles,
the same statistical learning theory
as the rest of AI and machine learning.
And in fact, when we talk about generation,
it is very similar to the task of prediction.
Let's say if we think about demand forecast,
we're predicting the number that, for example,
we’re going to have next month,
here we’re predicting the next word.
So in this sense, conceptually it is very similar,
but at the same time to achieve that success,
if a really important breakthrough has been made, and,
first of all, it's a new way of learning, which is called self-supervised learning.
It's a way to learn from the data without supervision,
without labelled examples.
Second, it's a new architecture, neutral network architecture.
It’s called Transformers that has been invented in 2017.
And then there is clearly an idea of transfer learning,
the concept that allows you to train on one data on one task
and then transfer that knowledge to other tasks.
And can you give us examples Leonid,
on what this is bringing or changing in industries?
Well, I can give you example from my experience,
you know, I work a lot on predictive maintenance type of problems.
In those type of problems, we use numerical prediction to calculate
the probability of the failure of the equipment,
but what we get is really just a number.
What's important for maintenance screws is
to understand where this number came from and what to do about it.
And so, generative AI will be able to provide
the textual explanation of our model,
plus literally provide us step-by-step instructions on how to fix that problem.
Another example could be within banking or
insurance industry where we try to predict a risk scores for customers.
In that case, of course, we're always hunting for more and more data,
more and more information.
And 80 % of the world information is unstructured.
So this type of algorithm allows us to bring that information towards numerical
algorithms that actually predict risk scores.
IPhone has changed our lives and the lives of many.
Would you say that GenAI will be of the same magnitude and
that we are going to live another type of iPhone moment?
When we're speaking about generative AI, it really depends on
If you are in the industry, for example, in car-manufacturing or
in oil and gas industry, where your main business actually building cars
or extracting oil generators, generative AI will definitely help you,
will help you reduce your cost, will help you improve your productivity,
but it will not change overall your process.
You'll still need to build those cars.
But at the same time, if you are in creative industry or
industry that manages information, that builds on information,
generative AI can be transformative for you.