TEACHING A MACHINE TO FORGET
By Adrienne LaFrance The Atlantic
Humans have a tendency to rely on machines as a way of understanding
ourselves. The mechanical world has long provided metaphors for how the
human body works.
“We’ve always had technological analogies to try to explain
biology,” said Chris Atkeson, a roboticist at Carnegie Mellon
University. “One idea of how the brain worked was it’s hydraulic. People
described hydraulic clocks and the heart pumping blood. Then we had
steam engines as a metaphor for how [our bodies] worked. Then we got
electricity.”
Expanding upon this tradition, in 1948, the mathematician and
philosopher Norbert Wiener published his book Cybernetics, which used
computer-brain analogies to lay the foundation for how people now think
about the Information Age.
Today, of course, computers figure prominently in explanations of
living systems. People routinely describe the brain as computer-like, as
though our memories are stored on a hard drive made of gray matter.
Under scrutiny, that analogy is no less clunky than the figurative
comparisons that preceded it. And the limitations of these metaphors go
both ways. Machine learning, which involves training a computer to
recognize patterns by showing it large data sets of images or other
information, is often described as teaching a computer brain to “see”
the world a certain way. Which makes some sense: Both machines and
humans amass knowledge based on what they’ve seen in the past.
“Everything a computer ‘sees’ is based on what it ‘knows’...
depending on what you mean by ‘sees,’ ” Emily Pittore, a software
engineer at iRobot, wrote to me in an email. “I use scare quotes because
I hesitate to apply the language of human cognition to computers too
liberally.”
“If you mean ‘sees’ as ‘optical input,’ then computers always see
the same thing,” she said. In other words, machines ignore minor
aesthetic blips and sensor noise, while “humans have a much more
complicated sensor — eyeballs and a brain,” she said.
Visual processing among humans is also heavily influenced by what
they already know — but what they actually see or perceive can vary
dramatically, even when the input is the same. That’s according to a
study, published last month in the Journal of Experimental Psychology:
Human Perception and Performance, based on findings from researchers at
Johns Hopkins. The researchers conducted a series of experiments to
figure out the extent to which prior knowledge of the Arabic alphabet
would affect how different people perceived Arabic letters.
They found that the same letters look different to people, depending
on whether they can read Arabic. And though they focused on letters for
their assessment, the researchers said their findings would apply to
anything — objects, photographs, illustrations and so on. The
overarching takeaway was this: What you already know profoundly affects
how you see. Which sounds intuitive, right? But these findings are more
nuanced than they may seem.
“We’re not just saying, ‘Oh, you’re an expert, so you see things
differently,’ ” said Robert Wiley, a graduate student in cognitive
science at Johns Hopkins and the study’s lead author. “The subtle point
is that it goes beyond your explicit knowledge to actually change your
visual system. These are things we don’t have conscious access to.”
Which is why humans can’t really unlearn things neatly. Because we
don’t know how to untangle what we see and how we see it in the first
place. You might forget a fact or lose a skill you once had, but there’s
no way to map — and therefore no way to deliberately refine — the ways
in which exposure to certain inputs has altered your perceptions.
Machines, however, can unlearn.
In fact, some computer scientists say it’s increasingly important
that they’re designed for this purpose. Part of the promise of machine
learning systems is that computers will be able to process tremendous
data streams — for purposes like facial recognition, for example. Entire
industries are transforming as a result of these computing powers. With
the proliferation of sensitive data flowing through vast networks,
humans need to be able to tell computers when and precisely how to
forget huge swaths of what’s called data lineage — the complex
information, computations and derived data that propagate brain-like
computer networks.
“Such forgetting systems must carefully track data lineage even
across statistical processing or machine learning, and make this lineage
visible to users,” wrote Yinzhi Cao and Junfeng Yang, computer science
professors at Lehigh University and Columbia University, respectively.
“They let users specify the data to forget with different levels of
granularity … These systems then remove the data and revert its effects
so that all future operations run as if the data had never existed.”
Cao and Yang outlined their idea for such a system in a paper for
Security & Privacy, a journal of the Institute of Electrical and
Electronics Engineers, in 2015. The ability to wipe a single thread of
data from a much larger set has multiple potential benefits, they say.
Someone could remove their own sensitive personal data from a machine.
Academics could use unlearning to clean up or otherwise correct
analytics data, thereby making a predictive algorithm more accurate.
The power to manipulate data this way could be seen as its own
security threat — if data were altered maliciously, for example — but
Cao told me protective measures would be possible. For example: “Before
removing research results related to a person in the EU, Google needs a
scan of the requester’s photo ID,” he said in an email. “This is just
one method of authentication, and other methods involve
username/password, two-factor authentication, fingerprints and so on.”
The idea has generated excitement among computer scientists. Cao and
Yang are the recipients of a $1.2 million National Science Foundation
grant to further develop the concept. If they’re successful, and if
machine unlearning becomes as crucial and ubiquitous a computing feature
as Cao and Yang suggest it should, what will forgetting systems mean for
the way people think about the processing functionalities of the human
brain? Not much, probably, until the next technology comes along and
offers a more compelling analogy.
“There is much that we don’t know about brains. But we do know that
they aren’t magical,” Gary Marcus, a professor of psychology and neural
science at New York University, wrote in The New York Times last year.
“They are just exceptionally complex arrangements of matter. Airplanes
may not fly like birds, but they are subject to the same forces of lift
and drag. Likewise, there is no reason to think that brains are exempt
from the laws of computation.”
Human-machine metaphors have never been perfect, but they can be
useful, even as computers learn and unlearn in ways that humans cannot.
“We want to do more with the conceptual model provided by these giant
calculating machines,” the cultural anthropologist Margaret Mead said in
1948, referring to computers, according to Ronald Kline’s book, “The
Cybernetics Moment.” “There is no trap of saying the human body is a
machine, but merely that the methods, especially the the mathematics
used in these machine problems, may be available tools for thinking more
precisely about human behavior.” +
Distributed by Tribune Content Agency
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