Read Superintelligence: Paths, Dangers, Strategies Online
Authors: Nick Bostrom
Tags: #Science, #Philosophy, #Non-Fiction
Superintelligence: Paths, Dangers, Strategies (4 page)
Artificial intelligence already outperforms human intelligence in many domains.
Table 1
surveys the state of game-playing computers, showing that AIs now beat human champions in a wide range of games.
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These achievements might not seem impressive today. But this is because our standards for what is impressive keep adapting to the advances being made. Expert chess playing, for example, was once thought to epitomize human intellection. In the view of several experts in the late fifties: “If one could devise a successful chess machine, one would seem to have penetrated to the core of human intellectual endeavor.”
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This no longer seems so. One sympathizes with John McCarthy, who lamented: “As soon as it works, no one calls it AI anymore.”
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Table 1
Game-playing AI
Checkers | Superhuman | Arthur Samuel’s checkers program, originally written in 1952 and later improved (the 1955 version incorporating machine learning), becomes the first program to learn to play a game better than its creator. |
Backgammon | Superhuman | 1979: The backgammon program BKG by Hans Berliner defeats the world champion—the first computer program to defeat (in an exhibition match) a world champion in any game—though Berliner later attributes the win to luck with the dice rolls. |
| | 1992: The backgammon program TD-Gammon by Gerry Tesauro reaches championship-level ability, using temporal difference learning (a form of reinforcement learning) and repeated plays against itself to improve. |
| | In the years since, backgammon programs have far surpassed the best human players. |
Traveller TCS | Superhuman in collaboration with human | In both 1981 and 1982, Douglas Lenat’s program Eurisko wins the US championship in Traveller TCS (a futuristic naval war game), prompting rule changes to block its unorthodox strategies. |
Othello | Superhuman | 1997: The program Logistello wins every game in a six-game match against world champion Takeshi Murakami. |
Chess | Superhuman | 1997: Deep Blue beats the world chess champion, Garry Kasparov. Kasparov claims to have seen glimpses of true intelligence and creativity in some of the computer’s moves. |
Crosswords | Expert level | 1999: The crossword-solving program Proverb outperforms the average crossword-solver. |
| | 2012: The program Dr. Fill, created by Matt Ginsberg, scores in the top quartile among the otherwise human contestants in the American Crossword Puzzle Tournament. (Dr. Fill’s performance is uneven. It completes perfectly the puzzle rated most difficult by humans, yet is stumped by a couple of nonstandard puzzles that involved spelling backwards or writing answers diagonally.) |
Scrabble | Superhuman | As of 2002, Scrabble-playing software surpasses the best human players. |
Bridge | Equal to the best | By 2005, contract bridge playing software reaches parity with the best human bridge players. |
Jeopardy! | Superhuman | 2010: IBM’s |
Poker | Varied | Computer poker players remain slightly below the best humans for full-ring Texas hold ‘em but perform at a superhuman level in some poker variants. |
FreeCell | Superhuman | Heuristics evolved using genetic algorithms produce a solver for the solitaire game FreeCell (which in its generalized form is NP-complete) that is able to beat high-ranking human players. |
Go | Very strong amateur level | As of 2012, the Zen series of go-playing programs has reached rank 6 dan in fast games (the level of a very strong amateur player), using Monte Carlo tree search and machine learning techniques. |
There is an important sense, however, in which chess-playing AI turned out to be a lesser triumph than many imagined it would be. It was once supposed,
perhaps not unreasonably, that in order for a computer to play chess at grandmaster level, it would have to be endowed with a high degree of
general
intelligence.
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One might have thought, for example, that great chess playing requires being able to learn abstract concepts, think cleverly about strategy, compose flexible plans, make a wide range of ingenious logical deductions, and maybe even model one’s opponent’s thinking. Not so. It turned out to be possible to build a perfectly fine chess engine around a special-purpose algorithm.
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When implemented on the fast processors that became available towards the end of the twentieth century, it produces very strong play. But an AI built like that is narrow. It plays chess; it can do no other.
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In other domains, solutions have turned out to be
more
complicated than initially expected, and progress slower. The computer scientist Donald Knuth was struck that “AI has by now succeeded in doing essentially everything that requires ‘thinking’ but has failed to do most of what people and animals do ‘without thinking’—that, somehow, is much harder!”
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Analyzing visual scenes, recognizing objects, or controlling a robot’s behavior as it interacts with a natural environment has proved challenging. Nevertheless, a fair amount of progress has been made and continues to be made, aided by steady improvements in hardware.
Common sense and natural language understanding have also turned out to be difficult. It is now often thought that achieving a fully human-level performance on these tasks is an “AI-complete” problem, meaning that the difficulty of solving these problems is essentially equivalent to the difficulty of building generally human-level intelligent machines.
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In other words, if somebody
were
to succeed in creating an AI that could understand natural language as well as a human adult, they would in all likelihood also either already have succeeded in creating an AI that could do everything else that human intelligence can do, or they would be but a very short step from such a general capability.
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Chess-playing expertise turned out to be achievable by means of a surprisingly simple algorithm. It is tempting to speculate that other capabilities—such as general reasoning ability, or some key ability involved in programming—might likewise be achievable through some surprisingly simple algorithm. The fact that the best performance at one time is attained through a complicated mechanism does not mean that no simple mechanism could do the job as well or better. It might simply be that nobody has yet found the simpler alternative. The Ptolemaic system (with the Earth in the center, orbited by the Sun, the Moon, planets, and stars) represented the state of the art in astronomy for over a thousand years, and its predictive accuracy was improved over the centuries by progressively complicating the model: adding epicycles upon epicycles to the postulated celestial motions. Then the entire system was overthrown by the heliocentric theory of Copernicus, which was simpler and—though only after further elaboration by Kepler—more predictively accurate.
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Artificial intelligence methods are now used in more areas than it would make sense to review here, but mentioning a sampling of them will give an idea of the breadth of applications. Aside from the game AIs listed in
Table 1
, there
are hearing aids with algorithms that filter out ambient noise; route-finders that display maps and offer navigation advice to drivers; recommender systems that suggest books and music albums based on a user’s previous purchases and ratings; and medical decision support systems that help doctors diagnose breast cancer, recommend treatment plans, and aid in the interpretation of electrocardiograms. There are robotic pets and cleaning robots, lawn-mowing robots, rescue robots, surgical robots, and over a million industrial robots.
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The world population of robots exceeds 10 million.
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Modern speech recognition, based on statistical techniques such as hidden Markov models, has become sufficiently accurate for practical use (some fragments of this book were drafted with the help of a speech recognition program). Personal digital assistants, such as Apple’s Siri, respond to spoken commands and can answer simple questions and execute commands. Optical character recognition of handwritten and typewritten text is routinely used in applications such as mail sorting and digitization of old documents.
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Machine translation remains imperfect but is good enough for many applications. Early systems used the GOFAI approach of hand-coded grammars that had to be developed by skilled linguists from the ground up for each language. Newer systems use statistical machine learning techniques that automatically build statistical models from observed usage patterns. The machine infers the parameters for these models by analyzing bilingual corpora. This approach dispenses with linguists: the programmers building these systems need not even speak the languages they are working with.
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Face recognition has improved sufficiently in recent years that it is now used at automated border crossings in Europe and Australia. The US Department of State operates a face recognition system with over 75 million photographs for visa processing. Surveillance systems employ increasingly sophisticated AI and data-mining technologies to analyze voice, video, or text, large quantities of which are trawled from the world’s electronic communications media and stored in giant data centers.
Theorem-proving and equation-solving are by now so well established that they are hardly regarded as AI anymore. Equation solvers are included in scientific computing programs such as Mathematica. Formal verification methods, including automated theorem provers, are routinely used by chip manufacturers to verify the behavior of circuit designs prior to production.
The US military and intelligence establishments have been leading the way to the large-scale deployment of bomb-disposing robots, surveillance and attack drones, and other unmanned vehicles. These still depend mainly on remote control by human operators, but work is underway to extend their autonomous capabilities.
Intelligent scheduling is a major area of success. The DART tool for automated logistics planning and scheduling was used in Operation Desert Storm in 1991 to such effect that DARPA (the Defense Advanced Research Projects Agency in the United States) claims that this single application more than paid back their
thirty-year investment in AI.
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Airline reservation systems use sophisticated scheduling and pricing systems. Businesses make wide use of AI techniques in inventory control systems. They also use automatic telephone reservation systems and helplines connected to speech recognition software to usher their hapless customers through labyrinths of interlocking menu options.
AI technologies underlie many Internet services. Software polices the world’s email traffic, and despite continual adaptation by spammers to circumvent the countermeasures being brought against them, Bayesian spam filters have largely managed to hold the spam tide at bay. Software using AI components is responsible for automatically approving or declining credit card transactions, and continuously monitors account activity for signs of fraudulent use. Information retrieval systems also make extensive use of machine learning. The Google search engine is, arguably, the greatest AI system that has yet been built.
Now, it must be stressed that the demarcation between artificial intelligence and software in general is not sharp. Some of the applications listed above might be viewed more as generic software applications rather than AI in particular—though this brings us back to McCarthy’s dictum that when something works it is no longer called AI. A more relevant distinction for our purposes is that between systems that have a narrow range of cognitive capability (whether they be called “AI” or not) and systems that have more generally applicable problem-solving capacities. Essentially all the systems currently in use are of the former type: narrow. However, many of them contain components that might also play a role in future artificial general intelligence or be of service in its development—components such as classifiers, search algorithms, planners, solvers, and representational frameworks.