Murray Shanahan, DeepMind senior scientist and cognitive robotics professor, discusses human perceptual biases and the tendency to anthropomorphize machines.
“We see someone there when there isn't, and of course that, for me, that was very interesting because it made me think about when we do that with machines, where we often, we do Maybe, you know, we think that there's someone at home when there isn't.”
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More from Murray Shanahan
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Shanahan: Supercomputers will reach human brain scale computing within two years
“Well, depending on how you calculate it, we're pretty close to human brain scale computing already in the world's fastest supercomputers. And we will get there within the next couple of years.”
Shanahan: Physical embodiment is the origin of human intelligence
“No doubt when it comes to human intelligence and human consciousness Our physical embodiment is a huge part of that. It's in, it's where our intelligence originates from because what we, what our brains are really here to do is to help us to navigate and manip…”
DeepMind's DQN is arguably one of the first artificial general intelligences
“To my mind, DQN is in a sense, one of the very first general intelligences because it learns completely from scratch. You can throw A whole variety of problems at it, and it, you know, it doesn't always do that well, but in many cases it does pretty well.”
Shanahan: Symbolic AI ideas can be rehabilitated into deep reinforcement learning
“It made me realize that there were various ideas from symbolic AI that could be rehabilitated and put into deep reinforcement learning systems in a more modern guise. And so that's the kind of thing that I'm most interested in.”
Shanahan: Raw speech recognition has essentially been solved
“So speech recognition has more or less been cracked. The, they're just the process of turning the way raw waveform into text into, so that that's been cracked.”
The AI frame problem is determining relevance without being computationally overwhelmed
“The frame problem in its largest guise is all about how how a thinking mechanism or thinking creature or a thinking machine, if you like can work out what's relevant and what's not relevant to its To its ongoing cognitive processes and how it isn't overwhelmed…”
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