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Home » Peter Haas: The Real Reason to be Afraid of Artificial Intelligence (Full Transcript)

Peter Haas: The Real Reason to be Afraid of Artificial Intelligence (Full Transcript)

Peter Haas at TEDxDirigo

Peter Haas is a professor of Political Science at the University of Massachusetts Amherst. His research concerns epistemic communities, global environmental politics, multilevel governance, and the role of science in global politics.

Here is the full text of Peter’s talk titled “The Real Reason to be Afraid of Artificial Intelligence” at TEDxDirigo conference.

Peter Haas – TEDx Talk TRANSCRIPT

The rise of the machines! Who here is scared of killer robots? I am!

I used to work in UAVs – Unmanned Aerial Vehicles – and all I could think seeing these things is that someday, somebody is going to strap a machine-gun to these things, and they’re going to hunt me down in swarms.

I work in robotics at Brown University and I’m scared of robots. Actually, I’m kind of terrified, but, can you blame me? Ever since I was a kid, all I’ve seen are movies that portrayed the ascendance of Artificial Intelligence and our inevitable conflict with it – 2001 Space Odyssey, The Terminator, The Matrix – and the stories they tell are pretty scary: rogue bands of humans running away from super intelligent machines. That scares me.

From the hands, it seems like it scares you as well. I know it is scary to Elon Musk. But, you know, we have a little bit of time before the robots rise up.

Robots like the PR2 that I have at my initiative, they can’t even open the door yet. So in my mind, this discussion of super intelligent robots is a little bit of a distraction from something far more insidious that is going on with AI systems across the country.

You see, right now, there are people – doctors, judges, accountants – who are getting information from an AI system and treating it as if it is information from a trusted colleague. It’s this trust that bothers me, not because of how often AI gets it wrong. AI researchers pride themselves in accuracy on results.

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