Despite practical challe nges, we are hopeful that
informeddiscussionsamongpolicy-makersandthe
public about data and the capabilities of machine
learning, will lead to insightful designs of programs
and policies that can balance the goals of protecting
privacy and ensuring fairness with those of reaping
the benefits to scientific research and to individual
and public health. Our commitments to privacy and
fairness are evergreen, but our policy choices must
adapt to advance them, and support new tech-
niques for deepening our knowledge.
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10.1126/science.aac4520
REVIEW
Machine learning: Trends,
persp ectives, and prospects
M. I. Jordan
1
* and T. M. Mitchell
2
*
Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today’s most rapidly growing technical fields,
lying at the intersection of computer science and statistics, and at the core of artificial
intelligence and data science. Recent progress in machine learning has been driven both by
the development of new learning algorithms and theory and by the ongoing explosion in the
availability of online data and low-cost computation. The adoption of data-intensive
machine-learning methods can be found throughout science, technology and commerce,
leading to more evidence-based decision-making across many walks of life, including
health care, manufacturing, education, financial modeling, policing, and marketing.
M
achine learning is a discipline focused
on two interrelated questions: How can
one construct computer systems that auto-
matically improve through experience?
and What are the fundamental statistical-
computational-information-theoretic laws that
govern all learning systems, including computers,
humans, and organizations? The study of machine
learning is important both for addressing these
fundamental scientific and engineering ques-
tions and for the highly practical computer soft-
wareithasproducedandfieldedacrossmany
applications.
Machine learning has progressed dramati-
cally over the past two decades, from laboratory
curiosity to a practical technology in widespread
commercial use. Within artificial intelligence (AI),
machine learning has emerged as the method
of choice for developing practical software for
computer vision, speech recognition, natural lan-
gu a ge processing, robot control, and other ap-
plications. Many developers of AI systems now
recognize that, for many applications, it can be
far easier to train a system by showing it exam-
ples of desired input-output behavior than to
program it manually by anticipating the desired
response for all possible inputs. The effect of ma-
chine learning has also been felt broadly across
co mputer science and across a range of indus-
tries concerned with data-intensive issues, such
as consumer services, the diagnosis of faults in
complex systems, and the control of logistics
chains. There has been a similarly broad range of
effects across empirical sciences, from biology to
cosmology to social science, as machine-learning
methods have been developed to analyze high-
throughput experimental data in novel ways. See
Fig. 1 for a depiction of some recent areas of ap-
plication of machine learning.
A learning problem can be defined as the
problem of improving some measure of perform-
an c e when executing some task, through some
type of training experience. For example, in learn-
ing to detect credit-card fraud, the task is to as-
sign a label of “fraud” or “not fraud” to any given
credit-card transaction. The performance metric
to be improved might be the accuracy of this
fraud classifier , and the training experience might
consist of a collection of historical credit-card
transactions, each labeled in retrospect as fraud-
ulent or not. Alternatively, one might define a
different performance metric that assigns a higher
penalty when “fraud” is labeled “not fraud” than
when “not fraud” is incorrectly labeled “fraud.”
One mig ht also def in e a di f ferent type of training
experience—for example, by including unlab-
eled credit-card transactions along with labeled
examples.
A diverse array of machine-learning algorithms
ha s been developed to cover the wide variety of
data and problem types exhibited across differ-
ent machine-learning problems (1, 2). Conceptual-
ly, machine-learning algorithms can be viewed as
searching through a large space of candidate
programs, guided by training experience, to find
a program that optimizes the performance metric.
Machine-learning algorithms vary greatly, in part
by the way in which they represent candidate
programs (e.g., decision trees, mathematical func-
tions, and general programming languages) and in
partbythewayinwhichtheysearchthroughthis
space of programs (e.g., optimization algorithm s
with well-understood convergence guarantees
and evolutionary search methods that evaluate
successivegenerationsofrandomlymutatedpro-
gr a ms). Here, we focus on approaches that have
been particularly successful to date.
Many algorithms focus on function approxi-
mation problems, where the task is embodied
in a function (e.g., given an input transaction, out-
put a “fraud” or “not fraud” label), and the learn-
ing problem is to improve the accuracy of that
function, with experience consisting of a sample
of known input-output pai rs of the fun ction . In
some cases, the function is represented explicit-
ly as a parameterized functional form; in other
cases, the function is implicit and obtained via a
search process, a factorization, an optimization
SCIENCE sciencemag.org 17 JULY 20 15 • VOL 349 ISSUE 6245 255
1
Department of Electrical Engineering and Computer
Sciences, Department of Statistics, University of California,
Berkeley, CA, USA.
2
Machine Learning Department, Carnegie
Mellon University, Pittsburgh, PA, USA.
*Corresponding author. E-mail: jordan@cs.berkeley.edu (M.I.J.);
tom.mitchell@cs.cmu.edu (T.M.M.)
on January 11, 2018 http://science.sciencemag.org/Downloaded from
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