ARTIFICIAL INTELLIGENCE
In computer science, artificial
intelligence (AI), sometimes called machine intelligence,
is intelligence demonstrated by machines, in contrast to
the natural intelligence displayed by humans and animals. Leading AI textbooks define the
field as the study of "intelligent agents": any device that perceives
its environment and takes actions that maximize its chance of successfully
achieving its goals. Colloquially, the term "artificial
intelligence" is often used to describe machines (or computers) that mimic
"cognitive" functions that humans associate with the human mind, such
as "learning" and "problem solving".
As machines become increasingly
capable, tasks considered to require "intelligence" are often removed
from the definition of AI, a phenomenon known as the AI effect. A
quip in Tesler's Theorem says "AI is whatever hasn't been done
yet." For instance, optical character recognition is
frequently excluded from things considered to be AI , having become a
routine technology. Modern machine capabilities generally classified as AI
include successfully understanding human speech, competing at the
highest level in strategic game systems
(such as chess and Go), autonomously operating cars,
intelligent routing in content delivery networks, and military
simulations.
Artificial intelligence was founded
as an academic discipline in 1955, and in the years since has experienced
several waves of optimism, followed by disappointment and the loss of
funding (known as an "AI winter"), followed by new approaches,
success and renewed funding. For most of its history, AI research has been
divided into subfields that often fail to communicate with each
other. These sub-fields are based on technical considerations, such as
particular goals (e.g. "robotics" or "machine
learning"), the use of particular tools ("logic"
or artificial neural networks), or deep philosophical differences. Subfields
have also been based on social factors (particular institutions or the work of
particular researchers).
The major limitation in
defining AI as simply "building machines that are intelligent" is
that it doesn't actually explain what artificial intelligence is? What
makes a machine intelligent?
In their groundbreaking
textbook Artificial Intelligence: A Modern Approach, authors Stuart
Russell and Peter Norvig approach the question by unifying their work around
the theme of intelligent agents in machines. With this in mind, AI is "the
study of agents that receive percepts from the environment and perform
actions." (Russel and Norvig viii)
Norvig and Russell go on
to explore four different approaches that have historically defined the field
of AI:
1.
Thinking humanly
2.
Thinking rationally
3.
Acting humanly
4.
Acting rationally
The first two ideas
concern thought processes and reasoning, while the others deal with behavior.
Norvig and Russell focus particularly on rational agents that act to achieve
the best outcome, noting "all the skills needed for the Turing Test also
allow an agent to act rationally." (Russel and Norvig 4).
Patrick Winston, the
Ford professor of artificial intelligence and computer science at MIT, defines AI as "algorithms enabled by constraints, exposed by
representations that support models targeted at loops that tie thinking,
perception and action together."
In the near term,
the goal of keeping AI’s impact on society beneficial motivates research
in many areas, from economics and law to technical topics such
as verification, validity, security and control. Whereas it
may be little more than a minor nuisance if your laptop crashes or gets
hacked, it becomes all the more important that an AI system does what
you want it to do if it controls your car, your airplane, your pacemaker,
your automated trading system or your power grid. Another short-term challenge
is preventing a devastating arms race in
lethal autonomous weapons.
In the long term,
an important question is what will happen if the quest for strong AI succeeds
and an AI system becomes better than humans at all cognitive tasks. As pointed
out by I.J. Good in 1965, designing smarter AI systems is itself a
cognitive task. Such a system could potentially undergo recursive
self-improvement, triggering an intelligence explosion leaving
human intellect far behind. By inventing revolutionary new technologies,
such a superintelligence might help us eradicate war,
disease, and poverty, and so the creation of strong AI might be the
biggest event in human history. Some experts have expressed concern,
though, that it might also be the last, unless we learn to align the goals of
the AI with ours before it becomes superintelligent.
There are some who question whether strong AI will
ever be achieved, and others who insist that the creation of superintelligent
AI is guaranteed to be beneficial. At FLI we recognize both of these
possibilities, but also recognize the potential for an artificial
intelligence system to intentionally or unintentionally cause great harm.
We believe research today will help us better prepare for and prevent such
potentially negative consequences in the future, thus enjoying the benefits
of AI while avoiding pitfalls.
HOW CAN AI
BE DANGEROUS?
Most
researchers agree that a superintelligent AI is unlikely to exhibit human
emotions like love or hate, and that there is no reason to expect AI to become
intentionally benevolent or malevolent. Instead, when considering how AI might become a
risk, experts think two scenarios most likely:
1. The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence
systems that are programmed to kill. In the hands of the wrong person, these
weapons could easily cause mass casualties. Moreover, an AI arms race
could inadvertently lead to an AI war that also results in mass casualties. To
avoid being thwarted by the enemy, these weapons would be designed to
be extremely difficult to simply “turn off,” so humans could
plausibly lose control of such a situation. This risk is one that’s
present even with narrow AI, but grows as levels of AI intelligence and
autonomy increase.
2.
The AI is programmed to do something beneficial, but it develops a
destructive method for achieving its goal: This can happen whenever
we fail to fully align the AI’s goals with ours, which is strikingly difficult.
If you ask an obedient intelligent car to take you to the airport as fast as
possible, it might get you there chased by helicopters and covered in
vomit, doing not what you wanted but literally what you asked for. If a
superintelligent system is tasked with a ambitious geoengineering project, it
might wreak havoc with our ecosystem as a side effect, and view human
attempts to stop it as a threat to be met.
As these
examples illustrate, the concern about advanced AI isn’t malevolence but
competence. A
super-intelligent AI will be extremely good at accomplishing its goals, and if
those goals aren’t aligned with ours, we have a problem. You’re probably
not an evil ant-hater who steps on ants out of malice, but if you’re in charge
of a hydroelectric green energy project and there’s an anthill in the region to
be flooded, too bad for the ants. A key goal of AI safety research is to never
place humanity in the position of those ants.
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