By Christopher Mims
If you want to understand the limitations of the algorithms that
control what we see and hear -- and base many of our decisions upon
-- take a look at Facebook Inc.'s experimental remedy for revenge
porn.
To stop an ex from sharing nude pictures of you, you have to
share nudes with Facebook itself. Not uncomfortable enough?
Facebook also says a real live human will have to check them
out.
Without that human review, it would be too easy to exploit
Facebook's antirevenge-porn service to take down legitimate images.
Artificial intelligence, it turns out, has a hard time telling the
difference between your naked body and a nude by Titian.
The internet giants that tout their AI bona fides have tried to
make their algorithms as human-free as possible, and that's been a
problem. It has become increasingly apparent over the past year
that building systems without humans "in the loop" -- especially in
the case of Facebook and the ads it linked to 470 "inauthentic"
Russian-backed accounts -- can lead to disastrous outcomes, as
actual human brains figure out how to exploit them.
Whether it's winning at games like Go or keeping watch for
Russian influence operations, the best AI-powered systems require
humans to play an active role in their creation, tending and
operation. Far from displacing workers, this combination is
spawning new nonengineer jobs every day, and the preponderance of
evidence suggests the boom will continue for the foreseeable
future.
Facebook, of course, is now a prime example of this trend. The
company recently announced it would add 10,000 content moderators
to the 10,000 it already employs -- a hiring surge that will impact
its future profitability, said Chief Executive Mark Zuckerberg.
And Facebook is hardly alone. Alphabet Inc.'s Google has long
employed humans alongside AI to eliminate ads that violate its
terms of service, ferret out fake news and take down extremist
YouTube videos. Google doesn't disclose how many people are looped
into its content moderation, search optimization and other
algorithms, but a company spokeswoman says the figure is in the
thousands -- and growing.
Twitter has its own teams to moderate content, though the
company is largely silent about how it accomplishes this, other
than touting its system's ability to automatically delete 95% of
terrorists' accounts.
Almost every big company using AI to automate processes has a
need for humans as a part of that AI, says Panos Ipeirotis, a
professor at New York University's Stern School of Business.
America's five largest financial institutions employ teams of
nonengineers as part of their AI systems, says Dr. Ipeirotis, who
consults with banks.
AI's constant hunger for human brains is based on our increasing
demand for services. The more we ask for, the less likely a
computer algorithm can go it alone -- while the combination can be
more effective and efficient. For example, bank workers who
previously read every email in search of fraud now make better use
of their time investigating emails the AI flags as suspicious, says
Dr. Ipeirotis.
What AI Can (and Can't) Do
A machine-learning-based AI system is a piece of software that
learns, almost like a primitive insect. That means that it can't be
programmed -- it must be taught.
To teach them, humans feed these systems examples, and they need
truckloads. To build an AI filter to identify extremist content on
YouTube, humans at Google manually reviewed over a million videos
to flag qualifying examples, says a Google spokeswoman.
An algorithm can only be as good as "the quantity and quality of
the training data to get [it] going," says Robin Bordoli, CEO of
CrowdFlower Inc., which provides human labor to companies that need
people to train and maintain AI algorithms, from auto makers to
internet giants to financial institutions.
Even when an AI has been trained, its judgment is never perfect.
Human oversight is still needed, especially with material in which
context matters, such as those extremist YouTube posts. While AI
can take down 83% before a single human flags them, says Google,
the remaining 17% needs humans. But this serves as further
training: This data can then be fed back into the algorithm to
improve it.
Relying on AI can lead to false positives, as when the company
pulls down legitimate content that its algorithms think might be
offensive.
There are many cases when AI can barely perform a task at all,
as in the case of Facebook's nude pic filter. Transcribing receipts
and business cards, tagging videos and moderating adult content are
all tasks that "should be easy for machine learning, but in
practice are too unstructured," says Vili Lehdonvirta, a senior
research fellow at the Oxford Internet Institute in the United
Kingdom.
Dr. Lehdonvirta maintains the Online Labor Index, a real-time
estimate of the number of people employed for these sorts of tasks.
By his calculations, the number of tasks posted to crowdsourced
online labor platforms, which includes this kind of work, is up 40%
in the past year alone.
Systems at risk of being gamed by fraudsters also require
constant human attention, says Dr. Ipeirotis. AIs, once trained,
are inexhaustible, but this is a curse as much as a blessing:
People who outsmart the algorithm can multiply their results a
millionfold.
Humans, on the other hand, are slower than AI, but can identify
patterns based on very little information. Any time a system must
deal with bad actors -- like when an entity posing as an American
on Twitter is actually a Russian agent -- there is no replacement
for live staffers.
A Growing Workforce
Some of this labor happens through outsourced systems like
CrowdFlower and Mechanical Turk, Amazon.com Inc.'s system for
outsourcing individual computer microtasks to a global workforce of
more than 500,000 people.
Across the globe, between 10,000 and 20,000 people a week pick
up online piecework, flagging porn in online forums, teaching
self-driving systems to identify pedestrians and training
facial-recognition algorithms, Dr. Lehdonvirta estimates. When you
include companies' own internal teams, there are probably hundreds
of thousands of humans, world-wide, whose work is sold as AI, he
says.
Dr. Lehdonvirta's estimates don't include the world's biggest
human-AI workforce: China's censors. Estimates for the Chinese
government's operation alone range from 100,000 to one million. In
addition, every Chinese internet company that distributes or hosts
content must have its own censors, typically one per 100,000
users.
Facebook isn't in China. One China internet executive told The
Wall Street Journal that if it were, it would need 20,000 content
moderators -- for video alone.
(END) Dow Jones Newswires
November 12, 2017 07:14 ET (12:14 GMT)
Copyright (c) 2017 Dow Jones & Company, Inc.
Meta Platforms (NASDAQ:META)
Historical Stock Chart
From Mar 2024 to Apr 2024
Meta Platforms (NASDAQ:META)
Historical Stock Chart
From Apr 2023 to Apr 2024