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from
www.dailybeast.com
"HELLO DAVE"
If I Only Had a Brain: How AI ‘Thinks’
AI can beat humans in chess, Go, poker and Jeopardy. But
what about emotional intelligence or street smarts?
02.19.17 12:01
AM ET
Artificial intelligence has gotten
pretty darn smart—at least, at certain tasks. AI has defeated world champions
in chess, Go, and now poker. But can
artificial intelligence actually think?
The
answer is complicated, largely because intelligence is complicated. One can be
book-smart, street-smart, emotionally gifted, wise, rational, or experienced;
it’s rare and difficult to be intelligent in all of these ways. Intelligence
has many sources and our brains don’t respond to them all the same way. Thus,
the quest to develop artificial intelligence begets numerous challenges, not
the least of which is what we don’t understand about human intelligence.
Still,
the human brain is our best lead when it comes to creating AI. Human brains
consist of billions of connected neurons that transmit information to one
another and areas designated to functions such as memory, language, and
thought. The human brain is dynamic, and just as we build muscle, we can
enhance our cognitive abilities—we can learn. So can AI, thanks to the
development of artificial neural networks
(ANN), a type of machine learning algorithm in which nodes simulate neurons
that compute and distribute information. AI such as AlphaGo, the program that beat the
world champion at Go last year, uses ANNs not only to compute statistical
probabilities and outcomes of various moves, but to adjust strategy based on
what the other player does.
Facebook,
Amazon, Netflix, Microsoft, and Google all employ deep learning, which expands
on traditional ANNs by adding layers to the information input/output. More
layers allow for more representations of and links between data. This resembles
human thinking—when we process input, we do so in something akin to layers. For
example, when we watch a football game on television, we take in the basic
information about what’s happening in a given moment, but we also take in a lot
more: who’s on the field (and who’s not), what plays are being run and why,
individual match-ups, how the game fits into existing data or history (does one
team frequently beat the other? Is the quarterback passing for as many yards as
usual?), how the refs are calling the game, and other details. In processing
this information we employ memory, pattern recognition, statistical and
strategic analysis, comparison, prediction, and other cognitive capabilities.
Deep learning attempts to capture those layers.
You’re
probably already familiar with deep learning algorithms. Have you ever wondered
how Facebook knows to place on your page an ad for rain boots after you got
caught in a downpour? Or how it manages to recommend a page immediately after
you’ve liked a related page? Facebook’s DeepText
algorithm can process thousands of posts, in dozens of different languages,
each second. It can also distinguish between Purple Rain and the reason you
need galoshes.
Deep
learning can be used with faces, identifying family members who attended an
anniversary or employees who thought they attended that rave on the down-low.
These algorithms can also recognize objects in context—such a program that
could identify the alphabet blocks on the living room floor, as well as the
pile of kids’ books and the bouncy seat. Think about the conclusions that could
be drawn from that snapshot, and then used for targeted advertising, among
other things.
Google
uses Recurrent Neural Networks
(RNNs) to facilitate image recognition
and language translation. This enables Google Translate to go beyond a typical
one-to-one conversion by allowing the program to make connections between
languages it wasn’t specifically programmed to understand. Even if Google
Translate isn’t specifically coded for translating Icelandic into Vietnamese,
it can do so by finding commonalities in the two tongues and then developing its own language
which functions as an interlingua, enabling the translation.
Machine
thinking has been tied to language ever since Alan Turing’s seminal 1950
publication “Computing Machinery and
Intelligence.” This paper described the Turing Test—a measure of
whether a machine can think. In the Turing Test, a human engages in a
text-based chat with an entity it can’t see. If that entity is a computer
program and it can make the human believe he’s talking to another human, it has
passed the test. Iterations of the Turing Test, such as the Loebner Prize, still exist, though it’s
become clear that just because a program can communicate like a human (complete
with typos, an abundance of exclamation points, swear words, and slang) doesn’t
mean it’s actually thinking. A 1960s Rogerian computer therapist program called
ELIZA duped participants into believing they were chatting with an actual
therapist, perhaps because it asked questions and unlike some human
conversation partners, appeared as though it’s listening. ELIZA harvests key words
from a user’s response and turns them into question, or simply says, “tell me
more.” While some argue that ELIZA passed the Turing Test, it’s evident from
talking with ELIZA (you can try it yourself here) and similar chatbots that
language processing and thinking are two entirely different abilities.
But
what about IBM’s Watson, which thrashed the top
two human contestants in Jeopardy? Watson’s dominance relies on access to
massive and instantly accessible amounts of information, as well as its
computation of answers’ probable correctness. In the game, Watson received this
clue: “Maurice LaMarche found his inner Orson Welles to voice this rodent whose
simple goal was to take over the world.” Watson’s possible answers and
probabilities were as follows:
Pinky
and the Brain: 63 percent
Ed
Wood: 10 percent
capybara:
10 percent
Googling
“Maurice LaMarche” quickly confirms that he voiced Pinky. But the clue is
tricky because it contains a number of key terms: LaMarche, voiceover, rodent,
and world domination. “Orson Welles” functions as a red herring—yes, LaMarche
supplied his trademark Orson Welles voice for Vincent D’Onofrio’s character in Ed
Wood, but that line of thought has nothing to do with a rodent. Similarly,
a capybara is a South American rodent (the largest in the world, which perhaps
Watson connected with the “take over the world” part of the clue), but the
animal has no connection to LaMarche or to voiceovers unless LaMarche does a
mean capybara impression. A human brain probably wouldn’t conflate concepts as
Watson does here; indeed, Ken Jennings buzzed in with the right answer.
Still,
Watson’s capabilities and applications continue to grow—it’s now working on cancer.
By uploading case histories, diagnostic information, treatment protocols, and
other data, Watson can work alongside human doctors to help identify cancer and
determine personalized treatment plans. “Project Lucy”
focuses Watson’s supercomputing powers on helping Africa meet farming,
economic, and social challenges. Watson can prove itself intelligent in
discrete realms of knowledge, but not across the board.
Perhaps
the major limitation of AI can be captured by a single letter: G. While we have
AI, we don’t have AGI—artificial general intelligence (sometimes
referred to as “strong” or “full” AI). The difference is that AI can excel at a
single task or game, but it can’t extrapolate strategies or techniques and
apply them to other scenarios or domains—you could probably beat AlphaGo at Tic
Tac Toe. This limitation parallels human skills of critical thinking or
synthesis—we can apply knowledge about a specific historical movement to a new
fashion trend or use effective marketing techniques in a conversation with a
boss about a raise because we can see the overlaps. AI can’t, for now.
Some
believe we’ll never truly have AGI; others believe it’s simply a matter of time
(and money). Last year, Kimera unveiled Nigel, a program it
bills as the first AGI. Since the beta hasn’t been released to the public, it’s
impossible to assess those claims, but we’ll be watching closely. In the
meantime, AI will keep learning just as we do: by watching YouTube videos
and by reading books.
Whether that’s comforting or frightening is another question.
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