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RECENT VIDEOS

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03.30.2015
2 mins
MIT Faculty Shorts

Russ Tedrake
Overcoming Real World Challenges through Collaboration

Russ Tedrake
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03.30.2015
5 mins
MIT Faculty Shorts

Russ Tedrake
Responding to the DARPA Robotics Challenge with Innovation

Russ Tedrake
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03.30.2015
3 mins
MIT Faculty Shorts

Russ Tedrake
New Challenges for the Next Generation

Russ Tedrake
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03.26.2015
42 mins
ILP Video

When Machines Stop Calculating and Start Thinking

Vikash Mansinghka
Research Scientist
MIT Computer Science and Artificial Intelligence Laboratory
We would like to build computing machines that can interpret and learn from their sensory experience, act effectively in real time, and ? ultimately ? design and program themselves. We would like to use these machines as models to help us understand our own minds and brains. We would also like to use computers to build and evaluate models of the world that can help us interpret our data and make more rational decisions. Our current computing machines, built for calculation, are not up to these tasks. Vikash Mansinghka will introduce probabilistic computing, a new approach based on probability and randomness instead of step-by-step logic and determinism. He will show how it brings us closer to a world where machines stop calculating and start thinking.
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03.26.2015
28 mins
ILP Video

Artificial-Intelligence Revives Its Old Ambitions: Unraveling the Mystery of Intelligence

Tomaso Poggio
Eugene McDermott Professor in the Brain Sciences and Human Behavior
Investigator, McGovern Institute
In recent years, artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Jeopardy with great success. But according to Tomaso Poggio, ?These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence. We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are.?Poggio thinks that AI needs to revive its early ambitions of building genuinely intelligent machines. ?It?s time to try again,? he says. ?We know much more than we did before about biological brains and how they produce intelligent behavior. We?re now at the point where we can start applying that understanding from neuroscience, cognitive science, and computer science to the design of intelligent machines.?
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