How specifically does being open-source
not lead us to our end?
>> Dr. Yann LeCun, Turing Award Laureate,
Vice President and Chief AI Scientist
[music] of Meta.
>> We need free and diverse AI platforms
for the same reason that we need free
and diverse press, so we don't have a
single opinion coming to us from AI.
We're still very far from matching human
and animal [music] intelligence and
learning capacities with AI systems.
It's not just around the corner. It's
not going to happen in the next 2 years.
>> Can you talk to us, Yann, about
open-source AI? Why is open-source the
way forward from a security and
innovation perspective?
>> So,
there's going to be a a future, not very
far in the future,
where
all of our interaction with the digital
world
are going to be mediated by AI systems.
We already see this with
uh
uh companies putting out
basically replacement for search engines
uh that are backed by AI systems.
And so, in this in this future, if
all of our digital diet is mediated by
AI systems,
um it's going to be there's going to be
a need for a a a very wide diversity of
AI systems that cater to different
languages, different cultures, different
value systems, different centers of
interest.
And
it it can't be produced by a small
number of companies on the West Coast of
the US.
Um um or any single country.
Uh so, essentially AI
tools, assistants, will become kind of a
repository of all human knowledge.
And
um
it's going to be a a kind of
infrastructure.
And the history shows that
infrastructure software always end ends
up being open-source
because it's more secure, it's more
customizable.
Uh it enables the creation of an
ecosystem on top of it.
And so the the entire infrastructure of
our communication systems, the internet,
um
even our cell phones, uh
software stack for the, you know,
communication communicating with cell
phone towers, it's all open source.
For very good reasons. It's not by
design, it's really the the market
forces that push it. So there's a
similar phenomenon which
is definitely happening now in the last
uh
uh few months that Llama 2 has uh has
been available and more open source uh
uh and then it's been available that
people are using those open source
models and and customizing them for
vertical applications in businesses, but
also uh training them for, you know, a
particular types of uh
uh
languages or or cultures. Uh and so it's
a necessity. We we need it. We need
free and diverse AI platforms for the
same reason that we need free and
diverse press.
Um so we don't have a single opinion
coming to us from from AI. Getting
towards the the title, will AI lead us
to our end? How specifically does being
open source
not lead us to our end? Is there
something fundamental that makes it
easier to secure?
Yes. Uh so it's it's certainly been the
case historically that uh open source
software
has been more more secure simply because
there's more eyeballs on it, right? So
the entire world today runs on Linux.
Um
maybe not your desktop,
uh but probably more more than half of
your phones run on Linux. Whatever is
not an iPhone runs on Linux.
Uh the web servers around the world run
on Linux. All the cloud services run on
Linux. Your car has several
microcontrollers that probably run
Linux.
The uh helicopter on Mars runs Linux. I
mean, Linux runs the world. And the
reason is because
it's open source, so you can port it to
anything you want, and you can make it
more secure. And and so, you know,
there's kind of a similar story uh to be
uh to be made for uh for for AI uh
business models. More eyeballs, more
ways to uh make it safe,
um uh etc. Now,
the only reason why you might think that
it's not a good idea to open source is
if you have this belief that somehow AI
is so powerful
that it is going to take over the world
at some point. Okay. And
uh
and
this is this is not happening.
Um so, first of all, we're still very
far
despite all the wonderful things that AI
can do today,
uh we're still very far from matching
human and animal intelligence and
learning capacities with AI systems.
We're very far.
It's not just around the corner. It's
not going to happen in the next 2 years.
Um we still some need some scientific
breakthroughs to get to the type of
learning abilities that we observe in uh
not just humans, but even your your cat.
Um your cat has a much better
understanding of the physical world than
uh the most powerful AI systems that we
have.
>> I've seen my dog run into the glass door
in my house. Y- Y- I I I I I debate
that, but yeah.
>> Sure, but you know, I've run into glass
doors. Um
so, the the uh
H-
Here is an interesting uh statistic. Um
so, first of all, before we get to
human-level AI, uh we're going to need
some breakthroughs that allow machines
to understand how the world works, which
they can't do at the moment,
uh can remember facts, which they can't
really do at the moment. They don't
really have persistent memory, at least
the LLMs don't.
Um they can reason, and they can plan.
And those are characteristics that are
essential to intelligent behavior.
Current large language models cannot do
any of those things, at least not to the
level that
uh we'd like them to to do.
And the reason is is relatively simple.
Um
if you
if you look at how much data is used to
train those LLMs, it sounds staggering.
It's uh typically
10 trillion tokens, right? A token is
like a word, so it's 10 to the 13,
right? A one with 13 zeros. Uh
words or
word equivalents.
Each word or each of those tokens is
stored in two bytes, so that's 2 * 10 to
the 13 bytes.
Seems staggering. If you evaluate how
long it would take for humans to read
this, it would take, you know, anywhere
between 150,000 and 200,000 years for a
single person to read this reading 8
hours a day. Uh
enormous. So,
the conclusion is those systems are
incredibly smart, right? Because they've
they're capable of digesting this
enormous amount of knowledge.