You’ve probably heard of machine learning.
That’s when a computer learns
everything it needs to know from a giant dataset,
using trial and error.
But that’s not what babies do.
They aren’t a clean slate upon entering the world.
Babies have innate knowledge that helps them
to voraciously learn and rapidly adapt.
There are just some things you can’t learn
from trial and error.
But many computer scientists argue that
most human skills are learned
and AI could learn them too,
without the need for pre-loaded rules.
Still, a growing number of researchers
are attempting to encode AI
with a bit of common sense.
The current craze in AI are neural nets,
collections of simple computing elements,
loosely modeled on neurons in the brain,
that adjust their connections
as they encounter more data.
They’ve produced incredible achievements
in the past few years,
from facial recognition to beating humans
at poker and go.
But neural nets require thousands of training examples
to reliably form associations.
And even then,
they can produce some embarrassing blunders.
Compare this to a child who can see an image just once
and after that instantly recognize it in other contexts.
Some AI’s can play classic Atari games
with super human skill,
but when you remove all the aliens but one,
the player inexplicably becomes a sitting duck.
Different labs are categorizing human instincts
and then trying to encode them into AI.
These systems sit somewhere between
pure machine learning and completely programmed.
One team developed an AI called
They’ve embedded the rule that:
such a thing as objects and relationships
between those objects exist.
This is like a baby’s innate parsing of the world
into objects.
In tests, once the AI learns the specific properties
and relationships, it is able to predict the behavior
of falling strings and bouncing balls in a box.
Another group’s “neural physics engine”
beat less structured neural nets
at predicting ball collisions in containers.
And a lab created an AI which has an embedded rule
to treat letters as objects
and separate them from their background.
This allowed it to solved CAPTCHAs
better than other neural nets
that were trained with 50,000 times more data.
We’re far away from AIs
that can truly thinks like humans,
But with these latest attempts
to reproduce common sense artificially,
researchers believe they will get closer
to creating robots that can fully interact
with the world the way we do.
Machines that start like a baby and learn like a child.
