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-: As one of the biggest players in the field
how are you thinking about responsible AI?
-: I think there's a spectrum of alternatives.
One is I'm gonna do an open experiment
on the human population and see what happens.
There's another that is full head in the sand:
We're just gonna avoid this technology.
We think that there is a thoughtful middle ground
that allows you to use the potential of this technology
but do it in a responsible way.
And part of that is we do think
that the government should play a strong role in regulation.
We have a slightly different perspective.
We don't think that there should be, you know
as some have lobbed out, you know, the IAEA of AI.
We think about it slightly differently.
We think all the agencies that are regulating
different parts of the economy need to build their AI skills
in each of those agencies to effectively regulate it
in a way that allows those industry sectors
to function at full efficiency.
So we're a very strong advocate
of government regulation here, but we want to do it
in a thoughtful way that gets most of the benefit
of this technology in a way that's responsible for society.
I think it's always easier to imagine
the efficiency side of these technologies,
all the jobs that might be changed or removed.
It's very hard to foresee the effectiveness side of it.
So just like, you know, if it was the internet,
if it was mobile, like they've created so many more jobs
and had productivity benefits
on society that have been greater than the downside.
So we'll see the innovator community
find the opportunities we can create with this technology.
So we are betting on the field.
We announced our Watson X platform
which is our foundation model platform.
The other thing that we've bet on is that for enterprises
there'll be a set of use cases that you can use an API
and generate the first draft of something.
But if it's running the core business,
if it's a medical diagnosis,
things where there's real business outcomes or lives
on the line, you're gonna want to have a higher level
of trust and governance certainty on that model.
Yeah. So we've put a lot into everything
from a set of models we build with kind of data provenance
so they begin from clean data so you know
that what you'll get out won't be a bad answer,
but then governance to watch the bias and risk
around it when it's in operations for the enterprise.
So a lot of our bet has been around that at the core.
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