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Why We Can't Yet Build True Artificial Intelligence, Explained In One Sentence

Dylan Love
jaron lanier
jaron lanier


Jaron Lanier.

Buried in Maureen Dowd's latest column for The New York Times is a perfect one-sentence explanation of why true artificial intelligence for machines isn't coming any time soon.

Jaron Lanier, prominent author/thinker/cybersociologist-type lays out why it is currently impossible to build machines with the type of "true" artificial intelligence that sci-fi is nearly founded on:

We’re still pretending that we’re inventing a brain when all we’ve come up with is a giant mash-up of real brains. We don’t yet understand how brains work, so we can’t build one.

We bolded that last sentence because it pretty much explains the predicament for AI. Until we more fundamentally understand that which we're trying to clone, everything else is an impressive attempt up Everest that never totally summits.

This jibes with a sentiment that renowned author and cognitive scientist Douglas Hofstadter posed earlier this year. He calls current prominent pursuits in the artificial intelligence arena "vacuous":

[IBM's "Jeopardy!"-winning supercomputer] Watson is basically a text search algorithm connected to a database just like Google search. It doesn't understand what it's reading. In fact, "read" is the wrong word. It's not reading anything because it's not comprehending anything. Watson is finding text without having a clue as to what the text means. In that sense, there's no intelligence there. It's clever, it's impressive, but it's absolutely vacuous.

We've got a ways to go before machines are truly smart.

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