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If AI takes over the world, how could it create and maintain its hardware, its power supply and everything else that humans currently provide?
If the AI system was deceptively aligned (i.e. pretending to be nice until it was in control of the situation) or had been in stealth mode while getting things in place for a takeover, quite possibly within hours. We may get more warning with weaker systems, if the AGI does not feel at all threatened by us, or if a complex ecosystem of AI systems is built over time and we gradually lose control.
Paul Christiano writes an story of alignment failure which shows a relatively fast transition.
AI Safety Support offers free calls to advise people interested in a career in AI Safety, so that's a great place to start. We're working on creating a bunch of detailed information for Stampy to use, but in the meantime check out these resources:
- 80,000 Hours AI safety syllabus
- Adam Gleave's Careers in Beneficial AI Research document
- Rohin Shah's FAQ on career advice for AI alignment researchers
- AI Safety Support has lots of other good resources, such as their links page, slack, newsletter, and events calendar.
This is actually an active area of AI alignment research, called "Impact Measures"! It's not trivial to formalize in a way which won't predictably go wrong (entropy minimization likely leads to an AI which tries really hard to put out all the stars ASAP since they produce so much entropy, for example), but progress is being made. You can read about it on the Alignment Forum tag, or watch Rob's videos Avoiding Negative Side Effects and Avoiding Positive Side Effects
In previous decades, AI research had proceeded more slowly than some experts predicted. According to experts in the field, however, this trend has reversed in the past 5 years or so. AI researchers have been repeatedly surprised by, for example, the effectiveness of new visual and speech recognition systems. AI systems can solve CAPTCHAs that were specifically devised to foil AIs, translate spoken text on-the-fly, and teach themselves how to play games they have neither seen before nor been programmed to play. Moreover, the real-world value of this effectiveness has prompted massive investment by large tech firms such as Google, Facebook, and IBM, creating a positive feedback cycle that could dramatically speed progress.
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"The real concern" isn't a particularly meaningful concept here. Deep learning has proven to be a very powerful technology, with far reaching implications across a number of aspects of human existence. There are significant benefits to be found if we manage the technology properly, but that management means addressing a broad range of concerns, one of which is the alignment problem.
Many AI designs that would generate an intelligence explosion would not have a ‘slot’ in which a goal (such as ‘be friendly to human interests’) could be placed. For example, if AI is made via whole brain emulation, or evolutionary algorithms, or neural nets, or reinforcement learning, the AI will end up with some goal as it self-improves, but that stable eventual goal may be very difficult to predict in advance.
Thus, in order to design a friendly AI, it is not sufficient to determine what ‘friendliness’ is (and to specify it clearly enough that even a superintelligence will interpret it the way we want it to). We must also figure out how to build a general intelligence that satisfies a goal at all, and that stably retains that goal as it edits its own code to make itself smarter. This task is perhaps the primary difficulty in designing friendly AI.
Eliezer Yudkowsky has proposed Coherent Extrapolated Volition as a solution to at least two problems facing Friendly AI design:
- The fragility of human values: Yudkowsky writes that “any future not shaped by a goal system with detailed reliable inheritance from human morals and metamorals will contain almost nothing of worth.” The problem is that what humans value is complex and subtle, and difficult to specify. Consider the seemingly minor value of novelty. If a human-like value of novelty is not programmed into a superintelligent machine, it might explore the universe for valuable things up to a certain point, and then maximize the most valuable thing it finds (the exploration-exploitation tradeoff) — tiling the solar system with brains in vats wired into happiness machines, for example. When a superintelligence is in charge, you have to get its motivational system exactly right in order to not make the future undesirable.
- The locality of human values: Imagine if the Friendly AI problem had faced the ancient Greeks, and they had programmed it with the most progressive moral values of their time. That would have led the world to a rather horrifying fate. But why should we think that humans have, in the 21st century, arrived at the apex of human morality? We can’t risk programming a superintelligent machine with the moral values we happen to hold today. But then, which moral values do we give it?
Yudkowsky suggests that we build a ‘seed AI’ to discover and then extrapolate the ‘coherent extrapolated volition’ of humanity:
> In poetic terms, our coherent extrapolated volition is our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together; where the extrapolation converges rather than diverges, where our wishes cohere rather than interfere; extrapolated as we wish that extrapolated, interpreted as we wish that interpreted.
The seed AI would use the results of this examination and extrapolation of human values to program the motivational system of the superintelligence that would determine the fate of the galaxy.
However, some worry that the collective will of humanity won’t converge on a coherent set of goals. Others believe that guaranteed Friendliness is not possible, even by such elaborate and careful means.
- Yudkowsky, Coherent Extrapolated Volition
Some have proposed that we teach machines a moral code with case-based machine learning. The basic idea is this: Human judges would rate thousands of actions, character traits, desires, laws, or institutions as having varying degrees of moral acceptability. The machine would then find the connections between these cases and learn the principles behind morality, such that it could apply those principles to determine the morality of new cases not encountered during its training. This kind of machine learning has already been used to design machines that can, for example, detect underwater mines after feeding the machine hundreds of cases of mines and not-mines.
There are several reasons machine learning does not present an easy solution for Friendly AI. The first is that, of course, humans themselves hold deep disagreements about what is moral and immoral. But even if humans could be made to agree on all the training cases, at least two problems remain.
The first problem is that training on cases from our present reality may not result in a machine that will make correct ethical decisions in a world radically reshaped by superintelligence.
The second problem is that a superintelligence may generalize the wrong principles due to coincidental patterns in the training data. Consider the parable of the machine trained to recognize camouflaged tanks in a forest. Researchers take 100 photos of camouflaged tanks and 100 photos of trees. They then train the machine on 50 photos of each, so that it learns to distinguish camouflaged tanks from trees. As a test, they show the machine the remaining 50 photos of each, and it classifies each one correctly. Success! However, later tests show that the machine classifies additional photos of camouflaged tanks and trees poorly. The problem turns out to be that the researchers’ photos of camouflaged tanks had been taken on cloudy days, while their photos of trees had been taken on sunny days. The machine had learned to distinguish cloudy days from sunny days, not camouflaged tanks from trees.
Thus, it seems that trustworthy Friendly AI design must involve detailed models of the underlying processes generating human moral judgments, not only surface similarities of cases.
Let’s consider the likely consequences of some utilitarian designs for Friendly AI.
Or, consider an AI designed to maximize human pleasure. Rather than build an ambitious utopia that caters to the complex and demanding wants of humanity for billions of years, it could achieve its goal more efficiently by wiring humans into Nozick’s experience machines. Or, it could rewire the ‘liking’ component of the brain’s reward system so that whichever hedonic hotspot paints sensations with a ‘pleasure gloss’ is wired to maximize pleasure when humans sit in jars. That would be an easier world for the AI to build than one that caters to the complex and nuanced set of world states currently painted with the pleasure gloss by most human brains.
Likewise, an AI motivated to maximize objective desire satisfaction or reported subjective well-being could rewire human neurology so that both ends are realized whenever humans sit in jars. Or it could kill all humans (and animals) and replace them with beings made from scratch to attain objective desire satisfaction or subjective well-being when sitting in jars. Either option might be easier for the AI to achieve than maintaining a utopian society catering to the complexity of human (and animal) desires. Similar problems afflict other utilitarian AI designs.
It’s not just a problem of specifying goals, either. It is hard to predict how goals will change in a self-modifying agent. No current mathematical decision theory can process the decisions of a self-modifying agent.
So, while it may be possible to design a superintelligence that would do what we want, it’s harder than one might initially think.
A brain-computer interface (BCI) is a direct communication pathway between the brain and a computer device. BCI research is heavily funded, and has already met dozens of successes. Three successes in human BCIs are a device that restores (partial) sight to the blind, cochlear implants that restore hearing to the deaf, and a device that allows use of an artificial hand by direct thought.
Such device restore impaired functions, but many researchers expect to also augment and improve normal human abilities with BCIs. Ed Boyden is researching these opportunities as the lead of the Synthetic Neurobiology Group at MIT. Such devices might hasten the arrival of an intelligence explosion, if only by improving human intelligence so that the hard problems of AI can be solved more rapidly.
Wikipedia, Brain-computer interface
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The intelligence explosion idea was expressed by statistician I.J. Good in 1965:
Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion’, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make.
The argument is this: Every year, computers surpass human abilities in new ways. A program written in 1956 was able to prove mathematical theorems, and found a more elegant proof for one of them than Russell and Whitehead had given in Principia Mathematica. By the late 1990s, ‘expert systems’ had surpassed human skill for a wide range of tasks. In 1997, IBM’s Deep Blue computer beat the world chess champion, and in 2011, IBM’s Watson computer beat the best human players at a much more complicated game: Jeopardy!. Recently, a robot named Adam was programmed with our scientific knowledge about yeast, then posed its own hypotheses, tested them, and assessed the results.
Computers remain far short of human intelligence, but the resources that aid AI design are accumulating (including hardware, large datasets, neuroscience knowledge, and AI theory). We may one day design a machine that surpasses human skill at designing artificial intelligences. After that, this machine could improve its own intelligence faster and better than humans can, which would make it even more skilled at improving its own intelligence. This could continue in a positive feedback loop such that the machine quickly becomes vastly more intelligent than the smartest human being on Earth: an ‘intelligence explosion’ resulting in a machine superintelligence.
This is what is meant by the ‘intelligence explosion’ in this FAQ.
Predicting the future is risky business. There are many philosophical, scientific, technological, and social uncertainties relevant to the arrival of an intelligence explosion. Because of this, experts disagree on when this event might occur. Here are some of their predictions:
- Futurist Ray Kurzweil predicts that machines will reach human-level intelligence by 2030 and that we will reach “a profound and disruptive transformation in human capability” by 2045.
- Intel’s chief technology officer, Justin Rattner, expects “a point when human and artificial intelligence merges to create something bigger than itself” by 2048.
- AI researcher Eliezer Yudkowsky expects the intelligence explosion by 2060.
- Philosopher David Chalmers has over 1/2 credence in the intelligence explosion occurring by 2100.
- Quantum computing expert Michael Nielsen estimates that the probability of the intelligence explosion occurring by 2100 is between 0.2% and about 70%.
- In 2009, at the AGI-09 conference, experts were asked when AI might reach superintelligence with massive new funding. The median estimates were that machine superintelligence could be achieved by 2045 (with 50% confidence) or by 2100 (with 90% confidence). Of course, attendees to this conference were self-selected to think that near-term artificial general intelligence is plausible.
- iRobot CEO Rodney Brooks and cognitive scientist Douglas Hofstadter allow that the intelligence explosion may occur in the future, but probably not in the 21st century.
- Roboticist Hans Moravec predicts that AI will surpass human intelligence “well before 2050.”
- In a 2005 survey of 26 contributors to a series of reports on emerging technologies, the median estimate for machines reaching human-level intelligence was 2085.
- Participants in a 2011 intelligence conference at Oxford gave a median estimate of 2050 for when there will be a 50% of human-level machine intelligence, and a median estimate of 2150 for when there will be a 90% chance of human-level machine intelligence.
- On the other hand, 41% of the participants in the [email protected] conference (in 2006) stated that machine intelligence would never reach the human level.
- Baum, Goertzel, & Goertzel, Long Until Human-Level AI? Results from an Expert Assessment
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