i3 - November/December 2018 - 13

By Murray Slovick

Tech

A T EC H TO WATC H

Making Machines Think Like Humans

ight at the start, I want to invoke a temporary restraining order: Nowhere in this column will I use the phrase
"machines will never think like humans." Once upon a
time no one thought humans would ever fly in a machine, so I
will be careful about saying what machines can do.

With that disclaimer, even with the
advent of artificial intelligence (AI),
computer programs that think like
humans are far beyond where we are
now. Today's AIs are powerful and
skilled machines (or chips) that can
"learn" in new ways. The obvious evidence of the improved powers of AI are
gadgets like smart speakers with virtual
assistants, being able to unlock your
iPhone via face recognition, online apps
that anticipate your next purchase and
upcoming self-driving vehicles.
We've also watched IBM's Watson
defeat human contestants on the game
show "Jeopardy," and Google's AlphaGo
beat the world's best players of the complex Chinese board game Go.

AI Breakthroughs

Machine learning involves training computers to perform tasks based on examples, rather than solely on programming.
Deep learning has made this approach
more powerful by using artificial neural
networks that loosely mimic how our
brain cells work, forming adjustable
connections between different network
parts. The machine can then learn from
its experience and build up an ability to
interpret similar data in the future.
But even after deep learning, an AI
computer still must gather facts about a
situation through sensors, compare this
information to its stored data, run
through possible actions, choose the
action likely to be successful, and finally
"remember" to replicate this action the
next time it encounters the situation.
The difficulty is that all the impressive
progress we've made to date is mostly due
to learning systems that take advantage of
C TA . t e c h / i 3

large quantities of human-provided data.
The main obstacle we face in duplicating
human learning is how to get machines to
learn in an unsupervised manner. They
need to become more adaptable. We eventually want machines that can learn a new
skill from just one or two examples, duplicating the human ability to work successfully in entirely new situations.
Supervised learning is not how humans
learn. Children can learn just by observation. Teachers don't tell a child "this is
water and water is wet." They experience
it for themselves and know it with certainty after just one trial.
In 1950, Alan Turing, a computing pioneer, conceived a test to measure the progress of a computer exhibiting intelligent
behavior equivalent to, or indistinguishable

from, that of a human. The Turing test
works like this: One of two partners in a
conversation is a machine. A person communicates with both the machine and
human via text on a screen and must guess
whether the typed responses are being
written by the human or the computer. The
more often the AI is mistaken for a human,
the better it is. When the human cannot
reliably tell the machine from the other
human, the machine has passed the test.
Part of the difficulty of developing truly
intelligent machines is that we don't
understand how natural intelligence
works. Our brain contains billions of neurons and we learn by establishing electrical connections between different
neurons. But we don't know exactly how
these connections add up to achieve
higher reasoning, not to mention inference, instinct and self-awareness.
Machine reasoning will improve as we
develop systems that continuously sense,
interact and learn from the world. But
building a machine that can replicate key
facets of human thinking will take time.

Machine learning
involves training
computers to perform
tasks based on
examples, rather than
solely on programming.
2019

CES 2019 See the latest in AI, robotics
and the smart home.

NOVEMBER/DECEMBER 2018

https://www.ces.tech/ https://www.cta.tech/

i3 - November/December 2018

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