Leonid, there are concerns
from business leaders who are deploying GenAI in companies.
One of them is privacy and security of data.
Any guidance on your side?
First of all, definitely privacy and security are a very important problem,
but it is much more of an engineering problem
than a scientific problem.
So it has to be dealt with in many ways
similar to how we deal with other information systems when we use data.
From the point of view of modelling,
there are really two options that businesses have:
either use models that has been pre-trained for them
as a service from model providers,
or build on-premise,
depending on their goals and on the level of security
and privacy they want for their data.
Leonid, are there ways to control the risk of hallucination?
There are several ways.
One way is when you're training the model,
and if you're training the model yourself,
you can actually limit the amount of data
you use and the quality of the data.
Of course, the model will be smaller than
what the model provided to you by, say, OpenAI.
But then there are also ways to control hallucination
during the execution time, during inference time.
For example, you can provide very specific prompts to the model.
You can actually ask the model to self-reflect on the results,
or you can, for example, run an ensemble of models
where you take several models,
ask them the same question, and then literally
sort of vote for the right solutions.
So in some sense, from the engineering perspective,
we're trying to find a way to build a reliable system
from unreliable parts. And that problem we can solve.