http://www.wsj.com/articles/whats-next-for-artificial-intelligence-1465827619
The best minds in the
business—Yann LeCun of Facebook, Luke Nosek of the Founders Fund, Nick
Bostrom of Oxford University and Andrew Ng of Baidu—on what life will
look like in the age of the machines
HOW DO YOU TEACH A MACHINE?
Yann LeCun, director of artificial-intelligence research at Facebook,
on a curriculum for software
The traditional
definition of artificial intelligence is the ability of machines to
execute tasks and solve problems in ways normally attributed to humans.
Some tasks that we consider simple—recognizing an object in a photo,
driving a car—are incredibly complex for AI. Machines can surpass us
when it comes to things like playing chess, but those machines are
limited by the manual nature of their programming; a $30 gadget can beat
us at a board game, but it can’t do—or learn to do—anything else.
This
is where machine learning comes in. Show millions of cat photos to a
machine, and it will hone its algorithms to improve at recognizing
pictures of cats. Machine learning is the basis on which all large
Internet companies are built, enabling them to rank responses to a
search query, give suggestions and select the most relevant content for a
given user.
Deep learning, modeled on the human brain, is infinitely more
complex. Unlike machine learning, deep learning can teach machines to
ignore all but the important characteristics of a sound or image—a
hierarchical view of the world that accounts for infinite variety. It’s
deep learning that opened the door to driverless cars,
speech-recognition engines and medical-analysis systems that are
sometimes better than expert radiologists at identifying tumors.
Despite
these astonishing advances, we are a long way from machines that are as
intelligent as humans—or even rats. So far, we’ve seen only 5% of what
AI can do.
IS IT TIME TO RETHINK YOUR CAREER?
Andrew Ng, chief scientist at Chinese Internet giant Baidu, on how AI will impact what we do for a living
Truck
driving is one of the most common occupations in America today:
Millions of men and women make their living moving freight from coast to
coast. Very soon, however, all those jobs could disappear. Autonomous
vehicles will cover those same routes in a faster, safer and more
efficient manner. What company, faced with that choice, would choose
expensive, error-prone human drivers?
There’s a historical
precedent for this kind of labor upheaval. Before the Industrial
Revolution, 90% of Americans worked on farms. The rise of steam power
and manufacturing left many out of work, but also created new jobs—and
entirely new fields that no one at the time could have imagined. This
sea change took place over the course of two centuries; America had time
to adjust. Farmers tilled their fields until retirement, while their
children went off to school and became electricians, factory foremen,
real-estate agents and food chemists.
Truck drivers won’t be so lucky. Their jobs, along with millions of
others, could soon be obsolete. The age of intelligent machines will see
huge numbers of individuals unable to work, unable to earn, unable to
pay taxes. Those workers will need to be retrained—or risk being left
out in the cold. We could face labor displacement of a magnitude we
haven’t seen since the 1930s.
In 1933, Franklin Roosevelt’s New
Deal provided relief for massive unemployment and helped kick-start the
economy. More important, it helped us transition from an agrarian
society to an industrial one. Programs like the Public Works
Administration improved our transportation infrastructure by hiring the
unemployed to build bridges and new highways. These improvements paved
the way for broad adoption of what was then exciting new technology: the
car.
We need to update the New Deal for the 21st century and
establish a trainee program for the new jobs artificial intelligence
will create. We need to retrain truck drivers and office assistants to
create data analysts, trip optimizers and other professionals we don’t
yet know we need. It would have been impossible for an antebellum farmer
to imagine his son becoming an electrician, and it’s impossible to say
what new jobs AI will create. But it’s clear that drastic measures are
necessary if we want to transition from an industrial society to an age
of intelligent machines.
AI: JUST LIKE US?
How intelligent machines could resemble their makers
The
next step in achieving human-level ai is creating intelligent—but not
autonomous—machines. The AI system in your car will get you safely home,
but won’t choose another destination once you’ve gone inside. From
there, we’ll add basic drives, along with emotions and moral values. If
we create machines that learn as well as our brains do, it’s easy to
imagine them inheriting human-like qualities—and flaws. But a
“Terminator”-style scenario is, in my view, immensely improbable. It
would require a discrete, malevolent entity to specifically hard-wire
malicious intent into intelligent machines, and no organization, let
alone a single group or a person, will achieve human-level AI alone.
Building intelligent machines is one of
The greatest scientific
challenges of our times, and it will require the sharing of ideas across
countries, companies, labs and academia. Progress in AI is likely to be
gradual—and open.
—Yann LeCun
HOW TO MASTER THE MACHINES
Nick Bostrom, founding
director of the Future of Humanity Institute at Oxford University, on
the existential risk of AI. Interviewed by Daniela Hernandez.
Can you tell me about the work you’re doing?
We
are interested in the technical challenges related to the “control
problem.” How can you ensure that [AI] will do what the programmers
intended? We’re also interested in studying the economic, political and
social issues that arise when you have these superintelligent AIs. What
kinds of political institutions would be most helpful to deal with this
transition to the machine- intelligence era? How can we ensure that
different stakeholders come together and do something that can lead to a
good outcome?
Much of your work has focused on existential risk. How would you explain that to a 5-year-old?
I
would say it’s technology that could permanently destroy the entire
future for all of humanity. For a slightly older audience, I would say
there’s the possibility of human extinction or the permanent destruction
of our potential to achieve value in the future.
What are some of the strategies you think will help mitigate the potential existential risks of artificial intelligence?
Work
on the control problem could be helpful. By the time we figure out how
to make machines really smart, we should have some ideas about how to
control such a thing, how to engineer it so that it will be on our side,
aligned with human values and not destructive. That involves a bunch of
technical challenges, some of which we can start to work on today.
Can you give me an example?
There
are different ideas on how to approach this control problem. One line
of attack is to study value learning. We would want the AI we build to
ultimately share our values, so that it can work as an extension of our
will. It does not look promising to write down a long list of everything
we care about. It looks more promising to leverage the AI’s own
intelligence to learn about our values and what our preferences are.
Values differ from person to person. How do we decide what values a machine should learn?
Well,
this is a big and complicated question: the possibility of profound
differences between values and conflicting interests. And this is in a
sense the biggest remaining problem. If you’re optimistic about
technological progress, you’ll think that eventually we’ll figure out
how to do more and more.
We will conquer nature to an
ever-greater degree. But the one thing that technology does not
automatically solve is the problem of conflict, of war. At the darkest
macroscale, you have the possibility of people using this advance, this
power over nature, this knowledge, in ways designed to harm and destroy
others. That problem is not automatically solved.
How might we be able to deal with that tension?
I don’t have a simple answer to that. I don’t think there’s an easy technofix.
Wouldn’t
a self-programming agent be able to free itself from the shackles of
the control systems under which we place them? Humans do this all the
time already, to some extent, when we act selfishly.
The
conservative assumption would be that the superintelligent AI would be
able to reprogram itself, would be able to change its values, and would
be able to break out of any box that we put it in. The goal, then, would
be to design it in such a way that it would choose not to use those
capabilities in ways that would be harmful to us. If an AI wants to
serve humans, it would assign a very low expected utility to an action
that would lead it to start killing humans. There are fundamental
reasons to think that if you set up the goal system in a proper way,
these ultimate decision criteria would be preserved.
LET’S IMPROVE THE MINDS WE HAVE
Luke Nosek, co-founder of PayPal and the Founders Fund, on the need to train our brains before the artificial ones arrive
Earlier
this year, the korean Go champion Lee Sedol played a historic five-game
match against Google’s AlphaGo, an artificially intelligent computer
program. Sedol has 18 world championships to his name. On March 19,
2016, he lost to software.
High-performance computing today is
unprecedentedly powerful. Still, we remain stages away from creating an
artificial general intelligence with anywhere near the capabilities of
the human mind. We don’t yet understand how general, human-level AI
(sometimes referred to as AGI, or strong AI) will work or what influence
it will have on our lives and economy. The scale of impact is often
compared to the advent of nuclear technology, and everyone from
Stephen Hawking to
Elon Musk to the creator of AlphaGo has advised that we proceed with caution.
The
nuclear comparison is charged but apt. As with nuclear technology, the
worst-case scenario for strong AI—malevolent superintelligence turns on
humanity and tries to kill it—would be globally devastating. Conversely,
the optimistic predictions are so blindingly positive (universal
economic prosperity, elimination of disease) that we may be biased by
both undue fear and optimism.
Strong AI could help billions of
people lead safer, healthier, happier lives. But to design this machine,
engineers will need a better understanding—greater than that of anyone
alive today—of the complex social, neurological and economic realities
faced by a society of intelligent humans and machines. And if we upgrade
the minds we already have, we’ll be better equipped to conceptualize,
build and coexist with strong AI.
We can divide the enhancement
of human intelligence into three stages. The first, using technology
like Google Search to augment and supplement the human mind, is well
under way Compare a fifth-grader with a library card in 1996 to a
fifth-grader on the Google home page in 2016—just keystrokes from much
of human knowledge.
If stage one involves supplementing the mind
with technology, then stage two is about amplifying the mind directly.
Adaptive learning software personalizes education and makes adjustments
to lessons in real time. If a student is excelling, the pace will
increase. If he or she is struggling, the program might slow down,
switch teaching styles or signal to the instructor that assistance is
needed. Adaptive learning and online education could mean the end of
one-size-fits-all education. Integration with virtual and augmented
reality could also amplify intelligence in unexpected ways.
Stage
three of intelligence enhancement involves a fundamental transformation
of the mind. Transcranial magnetic stimulation, or TMS, is a
noninvasive, FDA-approved treatment in which an electromagnetic coil is
applied to the head. TMS is currently being used to treat post-traumatic
stress disorder, autism and drug-resistant major depression. Sample
sizes at such facilities as the Brain Treatment Center in Newport Beach,
Calif., and the University of Louisville in Kentucky are small and the
duration of impact unknown, but high percentages of individuals—up to
90% for a trial with 200 higher-functioning autistic patients—have shown
improvement. Initial signs indicate that TMS could be effective for a
wide, seemingly unrelated range of neurological conditions. If we can
positively affect injured or non-neurotypical brains, we may not be far
from improving connections in healthy brains and enhancing intelligence
in a generalized way.
Strong AI appears to be on the horizon, but
for now the human mind is the only one we have. Enhancing our own
intelligence is the first step toward creating—and successfully
coexisting with—the intelligent machines of the future.
YOU CAN’T TEACH (MACHINES) COMMON SENSE
At least not yet. And it’s the biggest barrier to true artificial intelligence.
Predictive
learning, also called unsupervised learning, is the principal mode by
which animals and humans come to understand the world. Take the sentence
“John picks up his phone and leaves the room.” Experience tells you
that the phone is probably a mobile model and that John made his exit
through a door. A machine, lacking a good representation of the world
and its constraints, could never have inferred that information.
Predictive learning in machines—an essential but still undeveloped
feature—will allow AI to learn without human supervision, as children
do. But teaching common sense to software is more than just a technical
question—it’s a fundamental scientific and mathematical challenge that
could take decades to solve. And until then, our machines can never be
truly intelligent.
—Yann LeCun
