Overview of Artificial Intelligence

Overview of Artificial Intelligence

Updated October 24, 2017
Overview of Artificial Intelligence
The use of artificial intelligence (AI) is growing across a
Currently, AI technologies are highly tailored to particular
wide range of sectors. Stakeholders and policymakers have
applications or tasks, known as narrow (or weak) AI. In
called for careful consideration of strategies to influence its
contrast, general (or strong) AI refers to intelligent behavior
growth, reap predicted benefits, and mitigate potential risks.
across a range of cognitive tasks, a capability which is
This document provides an overview of AI technologies
unlikely to occur for decades, according to most analysts.
and applications, recent private and public sector activities,
Some researchers also use the term “augmented
and selected issues of interest to Congress.
intelligence” to capture AI’s various applications in
physical and connected systems, such as robotics and the
AI Technologies and Applications
Internet of Things, and to focus on using AI technologies to
Though definitions vary, AI can generally be thought of as
enhance human activities rather than to replace them.
computerized systems that work and react in ways
commonly thought to require intelligence, such as solving
In describing the course of AI development, the Defense
complex problems in real-world situations. According to
Advanced Research Projects Agency (DARPA) has
the Association for the Advancement of Artificial
identified three “waves.” The first wave focused on
Intelligence (AAAI), researchers broadly seek to
handcrafted knowledge that allowed for system-enabled
understand “the mechanisms underlying thought and
reasoning in limited situations, though lacking the ability to
intelligent behavior and their embodiment in machines.”
learn or address uncertainty, as with many rule-based
systems from the 1980s. The second wave, from
The field of AI encompasses many methodologies and areas
approximately the 2000s to the present, has focused on
of emphasis, such as machine learning (ML), deep learning,
advances in neural networks and machine learning (e.g.,
neural networks, robotics, machine/computer vision (image
image recognition, language translation) using statistical
processing), and natural language processing. Advances in
models and big data sets. The third wave will focus on
these areas, in information processing and hardware
contextual adaptation—learning and reasoning as the
technology generally, and in the availability of large-scale
system encounters new tasks—moving towards general AI.
data sets, have all contributed to recent progress in AI.
Among researchers and developers, the outlook on AI
Applications of AI are found in everyday technologies,
development and application across sectors is widely
such as video games, web searching, spam filtering, and
optimistic, though challenges exist. Such challenges are
voice recognition (e.g., Apple’s Siri). Notable AI systems
both technical (e.g., availability of datasets to train AI
have beaten human champions of games like chess, Go, and
systems, and understanding and removing biases from AI-
Jeopardy!. More broadly, AI has applications across a
based decisions) and societal (e.g., addressing potential
variety of sectors, including the following examples:
workforce shifts, privacy, security, and ethical use).
 Transportation—self-driving cars, adaptive traffic
Private Sector Activity
management to reduce wait times and emissions;
In recent years, the private sector has been increasing
 Health care—diagnostics and targeted treatments;
research and development (R&D) investments and hiring in
 Education—digital tutors;
AI, particularly at large technology companies such as
 Agriculture—soil moisture monitoring and targeted crop Amazon, Facebook, Google, IBM, and Microsoft. Large
watering;
companies are also acquiring AI startups and launching
 Finance—early detection of unusual market
venture funds to support startups. These and other
manipulation and anomalous trading;
technology companies, along with the AAAI—a nonprofit
 Law—machine analysis of law case history;
scientific society—have formed the Partnership on AI,
 Manufacturing—automated delivery, improved worker
which aims to create best practices, educate the public, and
safety and productivity via machine-human teaming;
serve as a platform for discussing AI technologies and
 Cybersecurity—autonomous detection and
societal impacts.
decisionmaking to improve reaction times to threats;
 Defense—autonomous and semi-autonomous weapons
Automotive and ride-sharing companies, such as Toyota
systems;
and Uber, have also announced large investments in AI
 Space exploration—spacecraft and rover autonomy; and
research, as well as partnerships with university scientists
 “AI for the social good”—using AI to address
and engineers. For example, the Toyota Research Institute
includes experts from Stanford’s AI Laboratory and
widespread societal challenges, e.g., to monitor wildlife
Massachusetts Institute of Technology’s Computer Science
populations, target anti-poaching efforts, and identify
and AI Laboratory.
intervention zones for poverty reduction efforts.
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Overview of Artificial Intelligence
Federal Activity
Those who support wider development and use of AI
In 2016, the National Science and Technology Council
technologies assert that increased federal and private sector
(NSTC) formed the Subcommittee on Machine Learning
research, investments, and collaboration can provide
and Artificial Intelligence (MLAI) to help coordinate
economic and societal benefits widely if well considered.
federal efforts and the development of federal R&D
Concerns about AI advances include questions of reliability
priorities in AI. The MLAI and Networking and
and accountability for autonomous decisionmaking; ethical
Information Technology Research and Development
use (particularly in defense and law enforcement settings);
(NITRD) subcommittees, and a team from the Executive
privacy; and equitable sharing of AI benefits.
Office of the President, released three reports on AI:
Research and Development. Should Congress wish to

augment federal AI R&D support, options could include
Preparing for the Future of Artificial Intelligence

direct funding for basic research at federal agencies and in
National Artificial Intelligence R&D Strategic Plan

the private sector, especially for research unlikely to be
Artificial Intelligence, Automation, and the Economy
supported by industry; increased sharing of federal datasets
to private sector developers for AI training and
These reports built on four workshops held across the
development; and prize competitions or tax incentives for
country by the Office of Science and Technology Policy.
commercial R&D efforts. For example, grand challenges—
The workshops focused on aspects of AI including social
such as those held by DARPA to build autonomous
good; safety and control; legal and governance
vehicles capable of navigating complex courses—could
implications; and near-term social and economic
help set ambitious goals for R&D teams to advance
implications of related technologies.
foundational technologies.
According to the National AI R&D Strategic Plan report,
Public-private partnerships could also help leverage private
U.S. government investments in unclassified R&D in AI-
sector data and funds to support R&D of AI technologies.
related technologies in 2015 totaled approximately $1.1
To cite one example, the Smart Manufacturing Innovation
billion. Some federal agencies have long-standing programs
Institute—a partnership between the nonprofit Smart
conducting and supporting AI-related R&D, such as
Manufacturing Leadership Coalition and the Department of
DARPA, the National Science Foundation, and U.S. Navy.
Energy—plans to invest over $140 million to develop
advanced manufacturing technologies such as smart sensors
The Senate Subcommittee on Space, Science, and
and to support workforce and education activities.
Competitiveness held a broad overview hearing on the state
of AI, entitled “The Dawn of Artificial Intelligence,” on
Workforce and the Economy. Federal access to expertise
November 30, 2016. Additional Senate and House
has been a concern in AI and related fields such as
committee hearings have included aspects or applications of
cybersecurity. Public and private stakeholders have noted a
AI, including the following examples:
need for more technical expertise in government. Should
Congress wish to expand an AI-knowledgeable federal
 “The Promises and Perils of Emerging Technologies for
workforce, options could include direct hiring authorities or
Cybersecurity,” Senate Committee on Commerce,
contractual agreements with the private sector. Further,
Science, and Transportation, March 22, 2017
Congress may consider efforts to grow the talent pool in AI
 “Self-Driving Cars: Levels of Automation,” House
through education programs focused on AI, or the
Committee on Energy and Commerce, March 21, 2017
expansion of scholarship-for-service programs to both
 “The Transformative Impact of Robots and
support education and bring new talent into federal service.
Automation,” Joint Economic Committee, May 25, 2016
In the private sector, some analysts have predicted that AI
 “Worldwide Threat Assessment of the US Intelligence
advances may have disproportionate impacts on low-wage
Community,” Senate Select Committee on Intelligence,
workers, causing job losses from AI-enhanced automation.
February 9, 2016.
On the other hand, AI is predicted to create new types of
jobs. One way to help address workforce shifts could be
Additionally, the Congressional Artificial Intelligence
through retraining displaced workers, either through private
Caucus, founded in 2015, has been active in the 115th
sector initiatives or federal programs or incentives.
Congress, including organizing congressional briefings.
Coordination. Given the range of applications across
Legislation from the 115th Congress has mentioned AI or
sectors, interagency coordination will likely be an important
ML as part of a focus on multiple topics, such as computer
mechanism for ongoing federal efforts in AI, including
science education, workforce training, data sharing between
consideration of adaptive regulatory approaches to allow
public and private sectors, defense and national security.
for rapid technology advancements. Coordination could be
assigned to the NITRD and MLAI subcommittees, or
Issues and Policy Considerations
Congress or the Administration could create a new
Though debates over AI may vary by sector, overarching
interagency mechanism, possibly with nonfederal members.
considerations for ongoing federal engagement include
crafting a balance in policies and regulations that mitigate
Laurie A. Harris, Analyst in Science and Technology
social and ethical risks of new technologies while providing
Policy
an environment that allows for, and potentially actively
supports, AI without hindering innovation.
IF10608
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Overview of Artificial Intelligence

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