In his beautiful 2011 book The Most Human
Human: What Talking to Computers
Teaches Us About What It Means to Be
Alive, Brian Christian shares his experience as a “human confederate” in a quirky
contest known as the Loebner Prize. It’s
inspired by Alan Turing’s proposal for how
to gauge computer intelligence. In each
round of the Loebner event, human judges
sit at a computer and carry out text chats
with two unseen interlocutors. One is a
human and one is a software program—
a chatbot. The judges must guess which
is which, and the chatbot that most often
gets mistaken for a human wins money for
its programmer. As an aside, a second title
is awarded as well, and that’s the one that
intrigued Christian. It goes to the person
who was least likely to be mistaken for a
computer: the most human human.
You can imagine the task facing a chatbot creator: combine language-processing
code with rules of thumb about how one
behaves in a conversation, and add just
enough weirdness to keep the program
from seeming robotic. But how should a
human participant demonstrate the fact
of being human through typed words
alone? It’s not obvious what aspects of
your soul you ought to bare, what ineffable
qualities you could signal that no machine
could convincingly mimic.
The ideas Christian wrestled with—
what cognition is, and what aspects of it
are exclusively human—are ancient subjects in philosophy. But advances in artificial intelligence are about to force all of us
to confront these questions in one way or
another. What are the technological limits
constraining how much of our economy
and our daily lives might be automated?
What’s the best way to design computers
so they augment human capabilities, making people and machines better together
than either could be on their own?
That’s the context for this special issue
of MIT Technology Review.
AI is one of the most widely hyped
technologies, but it’s also easily misun-
Brian Bergstein is
MIT Technology Review’s
editor at large.
derstood. To sort things out, we visit a
pioneer of the much-heralded technique
known as deep learning (see “Is AI Riding a One-Trick Pony?” on page 28). We
explore the effects automation is having
on labor (“India Warily Eyes AI,” page 38).
We introduce you to an entrepreneur who
dreams of using AI to keep people healthy
(page 46), and we explore how China’s
investments in the technology could alter
the global economic order (page 66).
We offer no doomsday scenarios of
out-of-control AI or extreme joblessness.
Those outcomes seem unlikely for rea-
sons Rodney Brooks describes on page
82. Besides, given the existential threats
we actually face (environmental catastro-
phe, international conflict), it’s unproduc-
tive to put AI on the list as another thing
to fear. It’s much more likely that people
will use advanced computing to solve big
problems, whether it’s by finding medical
cures, developing greener materials, or
doing other things we can’t yet envision.
However, if AI truly is to benefit all of
humanity and not just exacerbate inequality, we need to be thoughtful about how it’s
built and who’s building it, as Fei-Fei Li,
Tabitha Goldstaub, and Cynthia Dwork
discuss on pages 26, 45, and 53. And we
ought to think carefully about how our
new tools could change us, as Rana el
Kaliouby (page 8) and Louisa Hall (page
74) explain. Imagination is required,
because AI is still in its early days. Very
human humans can still shape it.