Gen AI, can it think like human?
Can it reason?
The golden test for intelligence is a Turing test.
It's an idea that has been introduced
more than 70 years ago, which is quite simple.
Can a machine mislead the investigator
into believing that he is talking to a human and not to a machine?
Well, today's large language models can easily beat that test.
Now, the question is if that makes them intelligent or not.
There are very many other benchmarks out there,
and some of them those machine easily beat.
For example, it can take USMLE test,
which is a test for becoming a doctor in the U.S.
It can score very highly on GRE test.
At the same time, it can make very, very simple and silly mistakes.
And we all have seen that if we tried ChatGPT.
So the jury’s still out there. But there is a common belief that
these are really strong statistical models that have a lot of information embedded.
In fact, famously, Yann LeCun said that the amount of memory
compensates for the weak logic abilities of these models.
Some critics of Gen AI just say it's a stochastic parrot.
What do they mean exactly?
It came from a paper published
several years ago by one of the Google researchers. This term is kind of a stick.
But what they're trying to say is it's stochastic, and it is true.
The model, in fact,
is just trying to predict the next word using for that distribution of the words
that it learned from the large corpora of data.
And so it's a random process,
sampling from the distribution. And so it is stochastic.
Why they call it parrot is to just emphasize
that the model is missing any logical abilities, and, again,
just generating tokens one at a time and out of a regressive manner.
So it is, in some sense, the stochastic parrot.
But at the same time, we are observing a lot of very interesting
abilities of these models, quite unexpected.
I think it's a bit of misleading to call it just a stochastic parrot.
It's an interesting debate among the experts,
but stochastic parrot or not, what's the impact for business?
For business, it’s really doesn’t matter
as long as algorithms can do their job, as long as we can,
from somewhat unreliable parts, unreliable algorithms,
build reliable systems that solve business problems,
it really doesn’t matter if those algorithms can actually think or not.
Now, of course, it's important for the future,
important for the futurists,
important for the future applications,
but as of today, if they work, they work.