Artificial
Intelligence (AI) is the mantra of the current era. The phrase is
intoned by technologists, academicians, journalists and venture
capitalists alike. As with many phrases that cross over from technical
academic fields into general circulation, there is significant
misunderstanding accompanying the use of the phrase. But this is not the
classical case of the public not understanding the scientists — here
the scientists are often as befuddled as the public. The idea that our
era is somehow seeing the emergence of an intelligence in silicon that
rivals our own entertains all of us — enthralling us and frightening us
in equal measure. And, unfortunately, it distracts us.
There
is a different narrative that one can tell about the current era.
Consider the following story, which involves humans, computers, data and
life-or-death decisions, but where the focus is something other than
intelligence-in-silicon fantasies. When my spouse was pregnant 14 years
ago, we had an ultrasound. There was a geneticist in the room, and she
pointed out some white spots around the heart of the fetus. “Those are
markers for Down syndrome,” she noted, “and your risk has now gone up to
1 in 20.” She further let us know that we could learn whether the fetus
in fact had the genetic modification underlying Down syndrome via an
amniocentesis. But amniocentesis was risky — the risk of killing the
fetus during the procedure was roughly 1 in 300. Being a statistician, I
determined to find out where these numbers were coming from. To cut a
long story short, I discovered that a statistical analysis had been done
a decade previously in the UK, where these white spots, which reflect
calcium buildup, were indeed established as a predictor of Down
syndrome. But I also noticed that the imaging machine used in our test
had a few hundred more pixels per square inch than the machine used in
the UK study. I went back to tell the geneticist that I believed that
the white spots were likely false positives — that they were literally
“white noise.” She said “Ah, that explains why we started seeing an
uptick in Down syndrome diagnoses a few years ago; it’s when the new
machine arrived.”
We
didn’t do the amniocentesis, and a healthy girl was born a few months
later. But the episode troubled me, particularly after a
back-of-the-envelope calculation convinced me that many thousands of
people had gotten that diagnosis that same day worldwide, that many of
them had opted for amniocentesis, and that a number of babies had died
needlessly. And this happened day after day until it somehow got fixed.
The problem that this episode revealed wasn’t about my individual
medical care; it was about a medical system that measured variables and
outcomes in various places and times, conducted statistical analyses,
and made use of the results in other places and times. The problem had
to do not just with data analysis per se, but with what database
researchers call “provenance” — broadly, where did data arise, what
inferences were drawn from the data, and how relevant are those
inferences to the present situation? While a trained human might be able
to work all of this out on a case-by-case basis, the issue was that of
designing a planetary-scale medical system that could do this without
the need for such detailed human oversight.
I’m
also a computer scientist, and it occurred to me that the principles
needed to build planetary-scale inference-and-decision-making systems of
this kind, blending computer science with statistics, and taking into
account human utilities, were nowhere to be found in my education. And
it occurred to me that the development of such principles — which will
be needed not only in the medical domain but also in domains such as
commerce, transportation and education — were at least as important as
those of building AI systems that can dazzle us with their game-playing
or sensorimotor skills.
Whether
or not we come to understand “intelligence” any time soon, we do have a
major challenge on our hands in bringing together computers and humans
in ways that enhance human life. While this challenge is viewed by some
as subservient to the creation of “artificial intelligence,” it can also
be viewed more prosaically — but with no less reverence — as the
creation of a new branch of engineering. Much like civil engineering and
chemical engineering in decades past, this new discipline aims to
corral the power of a few key ideas, bringing new resources and
capabilities to people, and doing so safely. Whereas civil engineering
and chemical engineering were built on physics and chemistry, this new
engineering discipline will be built on ideas that the preceding century
gave substance to — ideas such as “information,” “algorithm,” “data,”
“uncertainty,” “computing,” “inference,” and “optimization.” Moreover,
since much of the focus of the new discipline will be on data from and
about humans, its development will require perspectives from the social
sciences and humanities.
While
the building blocks have begun to emerge, the principles for putting
these blocks together have not yet emerged, and so the blocks are
currently being put together in ad-hoc ways.
Thus,
just as humans built buildings and bridges before there was civil
engineering, humans are proceeding with the building of societal-scale,
inference-and-decision-making systems that involve machines, humans and
the environment. Just as early buildings and bridges sometimes fell to
the ground — in unforeseen ways and with tragic consequences — many of
our early societal-scale inference-and-decision-making systems are
already exposing serious conceptual flaws.
And,
unfortunately, we are not very good at anticipating what the next
emerging serious flaw will be. What we’re missing is an engineering
discipline with its principles of analysis and design.
The
current public dialog about these issues too often uses “AI” as an
intellectual wildcard, one that makes it difficult to reason about the
scope and consequences of emerging technology. Let us begin by
considering more carefully what “AI” has been used to refer to, both
recently and historically.
Most
of what is being called “AI” today, particularly in the public sphere,
is what has been called “Machine Learning” (ML) for the past several
decades. ML is an algorithmic field that blends ideas from statistics,
computer science and many other disciplines (see below) to design
algorithms that process data, make predictions and help make decisions.
In terms of impact on the real world, ML is the real thing, and not just
recently. Indeed, that ML would grow into massive industrial relevance
was already clear in the early 1990s, and by the turn of the century
forward-looking companies such as Amazon were already using ML
throughout their business, solving mission-critical back-end problems in
fraud detection and supply-chain prediction, and building innovative
consumer-facing services such as recommendation systems. As datasets and
computing resources grew rapidly over the ensuing two decades, it
became clear that ML would soon power not only Amazon but essentially
any company in which decisions could be tied to large-scale data. New
business models would emerge. The phrase “Data Science” began to be used
to refer to this phenomenon, reflecting the need of ML algorithms
experts to partner with database and distributed-systems experts to
build scalable, robust ML systems, and reflecting the larger social and
environmental scope of the resulting systems.
This
confluence of ideas and technology trends has been rebranded as “AI”
over the past few years. This rebranding is worthy of some scrutiny.
Historically,
the phrase “AI” was coined in the late 1950’s to refer to the heady
aspiration of realizing in software and hardware an entity possessing
human-level intelligence. We will use the phrase “human-imitative AI” to
refer to this aspiration, emphasizing the notion that the artificially
intelligent entity should seem to be one of us, if not physically at
least mentally (whatever that might mean). This was largely an academic
enterprise. While related academic fields such as operations research,
statistics, pattern recognition, information theory and control theory
already existed, and were often inspired by human intelligence (and
animal intelligence), these fields were arguably focused on “low-level”
signals and decisions. The ability of, say, a squirrel to perceive the
three-dimensional structure of the forest it lives in, and to leap among
its branches, was inspirational to these fields. “AI” was meant to
focus on something different — the “high-level” or “cognitive”
capability of humans to “reason” and to “think.” Sixty years later,
however, high-level reasoning and thought remain elusive. The
developments which are now being called “AI” arose mostly in the
engineering fields associated with low-level pattern recognition and
movement control, and in the field of statistics — the discipline
focused on finding patterns in data and on making well-founded
predictions, tests of hypotheses and decisions.
Indeed,
the famous “backpropagation” algorithm that was rediscovered by David
Rumelhart in the early 1980s, and which is now viewed as being at the
core of the so-called “AI revolution,” first arose in the field of
control theory in the 1950s and 1960s. One of its early applications was
to optimize the thrusts of the Apollo spaceships as they headed towards
the moon.
Since
the 1960s much progress has been made, but it has arguably not come
about from the pursuit of human-imitative AI. Rather, as in the case of
the Apollo spaceships, these ideas have often been hidden behind the
scenes, and have been the handiwork of researchers focused on specific
engineering challenges. Although not visible to the general public,
research and systems-building in areas such as document retrieval, text
classification, fraud detection, recommendation systems, personalized
search, social network analysis, planning, diagnostics and A/B testing
have been a major success — these are the advances that have powered
companies such as Google, Netflix, Facebook and Amazon.
One
could simply agree to refer to all of this as “AI,” and indeed that is
what appears to have happened. Such labeling may come as a surprise to
optimization or statistics researchers, who wake up to find themselves
suddenly referred to as “AI researchers.” But labeling of researchers
aside, the bigger problem is that the use of this single, ill-defined
acronym prevents a clear understanding of the range of intellectual and
commercial issues at play.
The
past two decades have seen major progress — in industry and
academia — in a complementary aspiration to human-imitative AI that is
often referred to as “Intelligence Augmentation” (IA). Here computation
and data are used to create services that augment human intelligence and
creativity. A search engine can be viewed as an example of IA (it
augments human memory and factual knowledge), as can natural language
translation (it augments the ability of a human to communicate).
Computing-based generation of sounds and images serves as a palette and
creativity enhancer for artists. While services of this kind could
conceivably involve high-level reasoning and thought, currently they
don’t — they mostly perform various kinds of string-matching and
numerical operations that capture patterns that humans can make use of.
Hoping
that the reader will tolerate one last acronym, let us conceive broadly
of a discipline of “Intelligent Infrastructure” (II), whereby a web of
computation, data and physical entities exists that makes human
environments more supportive, interesting and safe. Such infrastructure
is beginning to make its appearance in domains such as transportation,
medicine, commerce and finance, with vast implications for individual
humans and societies. This emergence sometimes arises in conversations
about an “Internet of Things,” but that effort generally refers to the
mere problem of getting “things” onto the Internet — not to the far
grander set of challenges associated with these “things” capable of
analyzing those data streams to discover facts about the world, and
interacting with humans and other “things” at a far higher level of
abstraction than mere bits.
For
example, returning to my personal anecdote, we might imagine living our
lives in a “societal-scale medical system” that sets up data flows, and
data-analysis flows, between doctors and devices positioned in and
around human bodies, thereby able to aid human intelligence in making
diagnoses and providing care. The system would incorporate information
from cells in the body, DNA, blood tests, environment, population
genetics and the vast scientific literature on drugs and treatments. It
would not just focus on a single patient and a doctor, but on
relationships among all humans — just as current medical testing allows
experiments done on one set of humans (or animals) to be brought to bear
in the care of other humans. It would help maintain notions of
relevance, provenance and reliability, in the way that the current
banking system focuses on such challenges in the domain of finance and
payment. And, while one can foresee many problems arising in such a
system — involving privacy issues, liability issues, security issues,
etc — these problems should properly be viewed as challenges, not
show-stoppers.
We
now come to a critical issue: Is working on classical human-imitative
AI the best or only way to focus on these larger challenges? Some of the
most heralded recent success stories of ML have in fact been in areas
associated with human-imitative AI — areas such as computer vision,
speech recognition, game-playing and robotics. So perhaps we should
simply await further progress in domains such as these. There are two
points to make here. First, although one would not know it from reading
the newspapers, success in human-imitative AI has in fact been
limited — we are very far from realizing human-imitative AI aspirations.
Unfortunately the thrill (and fear) of making even limited progress on
human-imitative AI gives rise to levels of over-exuberance and media
attention that is not present in other areas of engineering.
Second,
and more importantly, success in these domains is neither sufficient
nor necessary to solve important IA and II problems. On the sufficiency
side, consider self-driving cars. For such technology to be realized, a
range of engineering problems will need to be solved that may have
little relationship to human competencies (or human
lack-of-competencies). The overall transportation system (an II system)
will likely more closely resemble the current air-traffic control system
than the current collection of loosely-coupled, forward-facing,
inattentive human drivers. It will be vastly more complex than the
current air-traffic control system, specifically in its use of massive
amounts of data and adaptive statistical modeling to inform fine-grained
decisions. It is those challenges that need to be in the forefront, and
in such an effort a focus on human-imitative AI may be a distraction.
As
for the necessity argument, it is sometimes argued that the
human-imitative AI aspiration subsumes IA and II aspirations, because a
human-imitative AI system would not only be able to solve the classical
problems of AI (as embodied, e.g., in the Turing test), but it would
also be our best bet for solving IA and II problems. Such an argument
has little historical precedent. Did civil engineering develop by
envisaging the creation of an artificial carpenter or bricklayer? Should
chemical engineering have been framed in terms of creating an
artificial chemist? Even more polemically: if our goal was to build
chemical factories, should we have first created an artificial chemist
who would have then worked out how to build a chemical factory?
A
related argument is that human intelligence is the only kind of
intelligence that we know, and that we should aim to mimic it as a first
step. But humans are in fact not very good at some kinds of
reasoning — we have our lapses, biases and limitations. Moreover,
critically, we did not evolve to perform the kinds of large-scale
decision-making that modern II systems must face, nor to cope with the
kinds of uncertainty that arise in II contexts. One could argue
that an AI system would not only imitate human intelligence, but also “correct” it, and would also scale to arbitrarily large problems. But we are now in the realm of science fiction — such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda.
that an AI system would not only imitate human intelligence, but also “correct” it, and would also scale to arbitrarily large problems. But we are now in the realm of science fiction — such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda.
It
is not hard to pinpoint algorithmic and infrastructure challenges in II
systems that are not central themes in human-imitative AI research. II
systems require the ability to manage distributed repositories of
knowledge that are rapidly changing and are likely to be globally
incoherent. Such systems must cope with cloud-edge interactions in
making timely, distributed decisions and they must deal with long-tail
phenomena whereby there is lots of data on some individuals and little
data on most individuals. They must address the difficulties of sharing
data across administrative and competitive boundaries. Finally, and of
particular importance, II systems must bring economic ideas such as
incentives and pricing into the realm of the statistical and
computational infrastructures that link humans to each other and to
valued goods. Such II systems can be viewed as not merely providing a
service, but as creating markets.
There are domains such as music, literature and journalism that are
crying out for the emergence of such markets, where data analysis links
producers and consumers. And this must all be done within the context of
evolving societal, ethical and legal norms.
Of
course, classical human-imitative AI problems remain of great interest
as well. However, the current focus on doing AI research via the
gathering of data, the deployment of “deep learning” infrastructure, and
the demonstration of systems that mimic certain narrowly-defined human
skills — with little in the way of emerging explanatory
principles — tends to deflect attention from major open problems in
classical AI. These problems include the need to bring meaning and
reasoning into systems that perform natural language processing, the
need to infer and represent causality, the need to develop
computationally-tractable representations of uncertainty and the need to
develop systems that formulate and pursue long-term goals. These are
classical goals in human-imitative AI, but in the current hubbub over
the “AI revolution,” it is easy to forget that they are not yet solved.
IA
will also remain quite essential, because for the foreseeable future,
computers will not be able to match humans in their ability to reason
abstractly about real-world situations. We will need well-thought-out
interactions of humans and computers to solve our most pressing
problems. And we will want computers to trigger new levels of human
creativity, not replace human creativity (whatever that might mean).
It was John McCarthy (while a professor at Dartmouth, and soon to take a
position at MIT) who coined the term “AI,” apparently to distinguish his
budding research agenda from that of Norbert Wiener (then an older professor at MIT). Wiener had coined “cybernetics” to refer to his own vision of intelligent systems — a vision that was closely tied to operations research, statistics, pattern recognition, information theory and control theory. McCarthy, on the other hand, emphasized the ties to logic. In an interesting reversal, it is Wiener’s intellectual agenda that has come to dominate in the current era, under the banner of McCarthy’s terminology. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than
in most fields.)
position at MIT) who coined the term “AI,” apparently to distinguish his
budding research agenda from that of Norbert Wiener (then an older professor at MIT). Wiener had coined “cybernetics” to refer to his own vision of intelligent systems — a vision that was closely tied to operations research, statistics, pattern recognition, information theory and control theory. McCarthy, on the other hand, emphasized the ties to logic. In an interesting reversal, it is Wiener’s intellectual agenda that has come to dominate in the current era, under the banner of McCarthy’s terminology. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than
in most fields.)
But we need to move beyond the particular historical perspectives of McCarthy and Wiener.
We
need to realize that the current public dialog on AI — which focuses on
a narrow subset of industry and a narrow subset of academia — risks
blinding us to the challenges and opportunities that are presented by
the full scope of AI, IA and II.
This
scope is less about the realization of science-fiction dreams or
nightmares of super-human machines, and more about the need for humans
to understand and shape technology as it becomes ever more present and
influential in their daily lives. Moreover, in this understanding and
shaping there is a need for a diverse set of voices from all walks of
life, not merely a dialog among the technologically attuned. Focusing
narrowly on human-imitative AI prevents an appropriately wide range of
voices from being heard.
While
industry will continue to drive many developments, academia will also
continue to play an essential role, not only in providing some of the
most innovative technical ideas, but also in bringing researchers from
the computational and statistical disciplines together with researchers
from other
disciplines whose contributions and perspectives are sorely needed — notably
the social sciences, the cognitive sciences and the humanities.
disciplines whose contributions and perspectives are sorely needed — notably
the social sciences, the cognitive sciences and the humanities.
On
the other hand, while the humanities and the sciences are essential as
we go forward, we should also not pretend that we are talking about
something other than an engineering effort of unprecedented scale and
scope — society is aiming to build new kinds of artifacts. These
artifacts should be built to work as claimed. We do not want to build
systems that help us with medical treatments, transportation options and
commercial opportunities to find out after the fact that these systems
don’t really work — that they make errors that take their toll in terms
of human lives and happiness. In this regard, as I have emphasized,
there is an engineering discipline yet to emerge for the data-focused
and learning-focused fields. As exciting as these latter fields appear
to be, they cannot yet be viewed as constituting an engineering
discipline.
Moreover,
we should embrace the fact that what we are witnessing is the creation
of a new branch of engineering. The term “engineering” is often
invoked in a narrow sense — in academia and beyond — with overtones of cold, affectless machinery, and negative connotations of loss of control by humans. But an engineering discipline can be what we want it to be.
invoked in a narrow sense — in academia and beyond — with overtones of cold, affectless machinery, and negative connotations of loss of control by humans. But an engineering discipline can be what we want it to be.
In
the current era, we have a real opportunity to conceive of something
historically new — a human-centric engineering discipline.
I
will resist giving this emerging discipline a name, but if the acronym
“AI” continues to be used as placeholder nomenclature going forward,
let’s be aware of the very real limitations of this placeholder. Let’s
broaden our scope, tone down the hype and recognize the serious
challenges ahead.