Read Superintelligence: Paths, Dangers, Strategies Online
Authors: Nick Bostrom
Tags: #Science, #Philosophy, #Non-Fiction
Superintelligence: Paths, Dangers, Strategies (5 page)
One high-stakes and extremely competitive environment in which AI systems operate today is the global financial market. Automated stock-trading systems are widely used by major investing houses. While some of these are simply ways of automating the execution of particular buy or sell orders issued by a human fund manager, others pursue complicated trading strategies that adapt to changing market conditions. Analytic systems use an assortment of data-mining techniques and time series analysis to scan for patterns and trends in securities markets or to correlate historical price movements with external variables such as keywords in news tickers. Financial news providers sell newsfeeds that are specially formatted for use by such AI programs. Other systems specialize in finding arbitrage opportunities within or between markets, or in high-frequency trading that seeks to profit from minute price movements that occur over the course of milliseconds (a timescale at which communication latencies even for speed-of-light signals in optical fiber cable become significant, making it advantageous to locate computers near the exchange). Algorithmic high-frequency traders account for more than half of equity shares traded on US markets.
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Algorithmic trading has been implicated in the 2010 Flash Crash (see
Box 2
).
By the afternoon of May, 6, 2010, US equity markets were already down 4% on worries about the European debt crisis. At 2:32 p.m., a large seller (a mutual fund complex) initiated a sell algorithm to dispose of a large number of the E-Mini S&P 500 futures contracts to be sold off at a sell rate linked to a measure of minute-to-minute liquidity on the exchange. These contracts were bought by algorithmic high-frequency traders, which were programmed to quickly eliminate their temporary long positions by selling the contracts on to other traders. With demand from fundamental buyers slacking, the algorithmic traders started to sell the E-Minis primarily to other algorithmic traders, which in turn passed them on to other algorithmic traders, creating a “hot potato” effect driving up trading volume—this being interpreted by the sell algorithm as an indicator of high liquidity, prompting it to increase the rate at which it was putting E-Mini contracts on the market, pushing the downward spiral. At some point, the high-frequency traders started withdrawing from the market, drying up liquidity while prices continued to fall. At 2:45 p.m., trading on the E-Mini was halted by an automatic circuit breaker, the exchange’s stop logic functionality. When trading was restarted, a mere five seconds later, prices stabilized and soon began to recover most of the losses. But for a while, at the trough of the crisis, a trillion dollars had been wiped off the market, and spillover effects had led to a substantial number of trades in individual securities being executed at “absurd” prices, such as one cent or 100,000 dollars. After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to
post facto
cancellation under existing trade rules).
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The retelling here of this episode is a digression because the computer programs involved in the Flash Crash were not particularly intelligent or sophisticated, and the kind of threat they created is fundamentally different from the concerns we shall raise later in this book in relation to the prospect of machine superintelligence. Nevertheless, these events illustrate several useful lessons. One is the reminder that interactions between individually simple components (such as the sell algorithm and the high-frequency algorithmic trading programs) can produce complicated and unexpected effects. Systemic risk can build up in a system as new elements are introduced, risks that are not obvious until after something goes wrong (and sometimes not even then).
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Another lesson is that smart professionals might give an instruction to a program based on a sensible-seeming and normally sound assumption (e.g. that trading volume is a good measure of market liquidity), and that this can produce catastrophic results when the program continues to act on the instruction with iron-clad logical consistency even in the unanticipated situation where the assumption turns out to be invalid. The algorithm just does what it does; and unless
it is a very special kind of algorithm, it does not care that we clasp our heads and gasp in dumbstruck horror at the absurd inappropriateness of its actions. This is a theme that we will encounter again.
A third observation in relation to the Flash Crash is that while automation contributed to the incident, it also contributed to its resolution. The pre-preprogrammed stop order logic, which suspended trading when prices moved too far out of whack, was set to execute automatically because it had been correctly anticipated that the triggering events could happen on a timescale too swift for humans to respond. The need for pre-installed and automatically executing safety functionality—as opposed to reliance on runtime human supervision—again foreshadows a theme that will be important in our discussion of machine superintelligence.
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Progress on two major fronts—towards a more solid statistical and information-theoretic foundation for machine learning on the one hand, and towards the practical and commercial success of various problem-specific or domain-specific applications on the other—has restored to AI research some of its lost prestige. There may, however, be a residual cultural effect on the AI community of its earlier history that makes many mainstream researchers reluctant to align themselves with over-grand ambition. Thus Nils Nilsson, one of the old-timers in the field, complains that his present-day colleagues lack the boldness of spirit that propelled the pioneers of his own generation:
Concern for “respectability” has had, I think, a stultifying effect on some AI researchers. I hear them saying things like, “AI used to be criticized for its flossiness. Now that we have made solid progress, let us not risk losing our respectability.” One result of this conservatism has been increased concentration on “weak AI”—the variety devoted to providing aids to human thought—and away from “strong AI”—the variety that attempts to mechanize human-level intelligence.
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Nilsson’s sentiment has been echoed by several others of the founders, including Marvin Minsky, John McCarthy, and Patrick Winston.
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The last few years have seen a resurgence of interest in AI, which might yet spill over into renewed efforts towards artificial
general
intelligence (what Nilsson calls “strong AI”). In addition to faster hardware, a contemporary project would benefit from the great strides that have been made in the many subfields of AI, in software engineering more generally, and in neighboring fields such as computational neuroscience. One indication of pent-up demand for quality information and education is shown in the response to the free online
offering of an introductory course in artificial intelligence at Stanford University in the fall of 2011, organized by Sebastian Thrun and Peter Norvig. Some 160,000 students from around the world signed up to take it (and 23,000 completed it).
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Expert opinions about the future of AI vary wildly. There is disagreement about timescales as well as about what forms AI might eventually take. Predictions about the future development of artificial intelligence, one recent study noted, “are as confident as they are diverse.”
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Although the contemporary distribution of belief has not been very carefully measured, we can get a rough impression from various smaller surveys and informal observations. In particular, a series of recent surveys have polled members of several relevant expert communities on the question of when they expect “human-level machine intelligence” (HLMI) to be developed, defined as “one that can carry out most human professions at least as well as a typical human.”
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Results are shown in
Table 2
. The combined sample gave the following (median) estimate: 10% probability of HLMI by 2022, 50% probability by 2040, and 90% probability by 2075. (Respondents were asked to premiss their estimates on the assumption that “human scientific activity continues without major negative disruption.”)
These numbers should be taken with some grains of salt: sample sizes are quite small and not necessarily representative of the general expert population. They are, however, in concordance with results from other surveys.
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The survey results are also in line with some recently published interviews with about two-dozen researchers in AI-related fields. For example, Nils Nilsson has spent a long and productive career working on problems in search, planning, knowledge representation, and robotics; he has authored textbooks in artificial intelligence; and he recently completed the most comprehensive history of the field written to date.
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When asked about arrival dates for HLMI, he offered the following opinion:
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10% chance: 2030
50% chance: 2050
90% chance: 2100
Table 2
When will human-level machine intelligence be attained?
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Judging from the published interview transcripts, Professor Nilsson’s probability distribution appears to be quite representative of many experts in the area—though again it must be emphasized that there is a wide spread of opinion: there are practitioners who are substantially more boosterish, confidently expecting HLMI in the 2020–40 range, and others who are confident either that it will never happen or that it is indefinitely far off.
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In addition, some interviewees feel that the notion of a “human level” of artificial intelligence is ill-defined or misleading, or are for other reasons reluctant to go on record with a quantitative prediction.
My own view is that the median numbers reported in the expert survey do not have enough probability mass on later arrival dates. A 10% probability of HLMI not having been developed by 2075 or even 2100 (after conditionalizing on “human scientific activity continuing without major negative disruption”) seems too low.
Historically, AI researchers have not had a strong record of being able to predict the rate of advances in their own field or the shape that such advances would take. On the one hand, some tasks, like chess playing, turned out to be achievable by means of surprisingly simple programs; and naysayers who claimed that machines would “never” be able to do this or that have repeatedly been proven wrong. On the other hand, the more typical errors among practitioners have been to underestimate the difficulties of getting a system to perform robustly on real-world tasks, and to overestimate the advantages of their own particular pet project or technique.
The survey also asked two other questions of relevance to our inquiry. One inquired of respondents about how much longer they thought it would take to reach superintelligence assuming human-level machine is first achieved. The results are in
Table 3
.
Another question inquired what they thought would be the overall long-term impact for humanity of achieving human-level machine intelligence. The answers are summarized in
Figure 2
.
My own views again differ somewhat from the opinions expressed in the survey. I assign a higher probability to superintelligence being created relatively soon after human-level machine intelligence. I also have a more polarized outlook on the consequences, thinking an extremely good or an extremely bad outcome to be somewhat more likely than a more balanced outcome. The reasons for this will become clear later in the book.
Table 3
How long from human level to superintelligence?
| | |
|---|---|---|
| Within 2 years after HLMI | Within 30 years after HLMI |
TOP100 | 5% | 50% |
Combined | 10% | 75% |
