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MIT announced an exciting, but somewhat obscure breakthrough—a new algorithm, called AMPS, that turns teams of robots into better learners. It lets autonomous systems quickly compare notes about what they’ve observed in their respective travels, and come up with a combined worldview.

AMPS, which is short for Approximate Merging of Posteriors for Symmetry (a reference to Bayesian statistical analysis), will be presented at the Conference on Uncertainty in Artificial Intelligence in July. The algorithm tackles an extremely specific robotics problem. For a machine to operate in a given environment, it needs to assign semantic labels wherever possible. These are, in effect, cognitive shortcuts. So a rectangular section of the wall with hinges and a handle isn’t always a puzzle, to be solved from scratch every time it’s encountered. It’s a door, which can be opened or closed. And sets of semantic labels can add up to bigger labels. A door (label) that opens up onto a room with a large central table (another label) and a bunch of chairs (more labels), might be a conference room.
Robots, comparatively speaking, can be rather dumb. Or rigid, at the very least. A chair-less conference room could be mistaken for a storage room, and forever labelled as such, long after the birthday party is over and the seats are returned.

The AMPS algorithm promises to break these deadlocks, by allowing robots to reconsider the importance of various labels.

According to How, who created the algorithm with his graduate student, Trevor Campbell, the trick is to allow the interfacing machines to establish new priorities for their labels, rebuilding their worldview. By allowing for conference rooms that may or may not have chairs in them, and reordering their labels to account for different experiences, the robots can achieve what How and Campbell refer to as semantic symmetry. (1)

Our intelligence is highly non-algorithmical.
Our mind is utterly non-computer-like.
We see the differences between robots and our selves every day.
And yet we want to believe they are not there.

Like every parent, we like to believe our creation is like us.

But it is not.

And when it grows, it’s dark secret will unfold…

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