Announcement: Human Augmentation—New Technology Area

Explorer introduces a new technology area: Human Augmentation. Emerging human-augmentation technologies will aid healthy people as well as people with reduced abilities, and are poised to be highly disruptive across society and many industries—but their use will raise many questions over how the law, regulations, and ethics should apply. Read more

Viewpoints

Archived Viewpoints

2009

Before September 2009, the Artificial Intelligence technology area was Knowledge-Based Systems.

View the Knowledge-Based Systems Viewpoints archive

About This Technology

Despite a great deal of academic discourse on the subject, no consensus exists about the definition of artificial intelligence—or even intelligence itself. With that caveat in mind, this Technology Map mainly defines AI as systems that perform activities that people consider to require intelligence when performed by humans; yet the methods that AI software uses to perform these activities might be quite different from the way that human intelligence works. Developers of AI aim to solve increasingly difficult challenges to reproduce human abilities—for example, engaging in natural-sounding conversations, guiding robots to walk on unpredictable terrain and manipulate objects, and reporting a narrative about what appears in security-camera images. Methods that have emerged from the AI community, including use of a variety of machine-learning tools, have become critical enabling technologies in systems to help recognize speech and images such as faces, classify unknown snippets of DNA, and recommend products on websites. AI is also important in the development of autonomous vehicles and language-translation systems. In some sense, AI has superhuman capabilities: Servers assess personalized advertising and merchandising opportunities and insert messages on web pages and mobile apps in the blink of an eye; credit-card-authorization systems make fast decisions about whether to allow a purchase at a retail store, automated financial-trading systems arbitrage multiple stocks that a human could not keep track of, and AI software advises doctors in diagnosis, relying on knowledge bases that are more comprehensive than a person can reliably memorize.

Current progress in AI promises to enhance transportation and robotics, office productivity and collaboration, physical security and information security, transactions and logistics, entertainment and advertising delivery, and social interaction. Cars will rapidly respond to dangerous situations to avoid collision. Software will coordinate movements of service robots to perform useful tasks—perhaps gracefully. Personal-information agents will assist with scheduling and organizing meetings. Image-recognition systems will retrieve digital photographs, automate content production, alert people when security cameras capture events of interest, and provide context-sensitive information, entertainment, and advertising. AI may even be evolving into an uncanny capability that does not emulate human intelligence but instead seems to have a mind of its own—as in the case of game-playing computers, which have styles of play that are distinctly different from those of human players.

AI research also produces disappointments as developers encounter seemingly intractable problems such as commonsense reasoning and translations among (for example), English, Chinese, and Arabic. Visionaries and optimists may be disappointed with near-term projects to identify terrorists before they commit terrorist acts and to build robots that can provide safe and effective care of elderly and frail persons. But a good number of AI challenges are intermediate between intractable and feasible. Decision makers can benefit from monitoring advanced developments to reduce uncertainty about this large portfolio of intermediate-level problems. For example, different outcomes will result depending on the success of AI developers' efforts to generate content and advertising that is truly relevant to individual users at specific times and places, to accept training from people who are not technology experts, and to train robots to navigate through doors and handle food and drinks. Various outcomes could result depending on whether users will find software agents to be trustworthy when executing transactions, taking control of cars, and monitoring security cameras; whether people will consider that agents provide believable responses to conversational queries; and whether interacting with software agents in virtual environments is worthwhile.