timelines

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Timelines
timelines
Main Question: How long will it be until transformative AI is created? (edit question) (write answer)
Alignment Forum Tag

Description

AI Timelines is the discussion of how long until various major milestones in AI progress are achieved, whether it's the timeline until a human-level AI is developed, the timeline until certain benchmarks are defeated, the timeline until we can simulate a mouse-level intelligence, or something else.

AI Timelines is the discussion of how long until various major milestones in AI progress are achieved, whether it's the timeline until a human-level AI is developed, the timeline until certain benchmarks are defeated, the timeline until we can simulate a mouse-level intelligence, or something else.

This is to be distinguished from the closely related question of AI takeoff speeds, which is about the dynamics of AI progress after human-level AI is developed (e.g. will it be a single project or the whole economy that sees growth, how fast will that growth be, etc).

Canonically answered

Can an AI really be smarter than humans?

Until a thing has happened, it has never happened. We have been consistently improving both the optimization power and generality of our algorithms over that time period, and have little reason to expect it to suddenly stop. We’ve gone from coding systems specifically for a certain game (like Chess), to algorithms like MuZero which learn the rules of the game they’re playing and how to play at vastly superhuman skill levels purely via self-play across a broad range of games (e.g. Go, chess, shogi and various Atari games).

Human brains are a spaghetti tower generated by evolution with zero foresight, it would be surprising if they are the peak of physically possible intelligence. The brain doing things in complex ways is not strong evidence that we need to fully replicate those interactions if we can throw sufficient compute at the problem, as explained in Birds, Brains, Planes, and AI: Against Appeals to the Complexity/Mysteriousness/Efficiency of the Brain.

It is, however, plausible that for an AGI we need a lot more compute than we will get in the near future, or that some key insights are missing which we won’t get for a while. The OpenPhilanthropy report on how much computational power it would take to simulate the brain is the most careful attempt at reasoning out how far we are from being able to do it, and suggests that by some estimates we already have enough computational resources, and by some estimates moore’s law may let us reach it before too long.

It also seems that much of the human brain exists to observe and regulate our biological body, which a body-less computer wouldn't need. If that's true, then a human-level AI might be possible with considerably less compute than the human brain.

When will transformative AI be created?

As is often said, it's difficult to make predictions, especially about the future. This has not stopped many people thinking about when AI will transform the world, but all predictions should come with a warning that it's a hard domain to find anything like certainty.

This report for the Open Philanthropy Project is perhaps the most careful attempt so far (and generates these graphs, which peak at 2042), and there's been much discussion including this reply and analysis which argues that we likely need less compute than the OpenPhil report expects.

There have also been expert surveys, and many people have shared various thoughts. Berkeley AI professor Stuart Russell has given his best guess as “sometime in our children’s lifetimes”, and Ray Kurzweil (Futurist and Google’s director of engineering) predicts human level AI by 2029 and the singularity by 2045. The Metaculus question on publicly known AGI has a median of around 2029 (around 10 years sooner than it was before the GPT-3 AI showed unexpected ability on a broad range of tasks).

The consensus answer, if there was one, might be something like: “highly uncertain, maybe not for over a hundred years, maybe in less than 15, with around the middle of the century looking fairly plausible”.

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.

See also:

AI is already superhuman at some tasks, for example numerical computations, and will clearly surpass humans in others as time goes on. We don’t know when (or even if) machines will reach human-level ability in all cognitive tasks, but most of the AI researchers at FLI’s conference in Puerto Rico put the odds above 50% for this century, and many offered a significantly shorter timeline. Since the impact on humanity will be huge if it happens, it’s worthwhile to start research now on how to ensure that any impact is positive. Many researchers also believe that dealing with superintelligent AI will be qualitatively very different from more narrow AI systems, and will require very significant research effort to get right.

First, even “narrow” AI systems, which approach or surpass human intelligence in a small set of capabilities (such as image or voice recognition) already raise important questions regarding their impact on society. Making autonomous vehicles safe, analyzing the strategic and ethical dimensions of autonomous weapons, and the effect of AI on the global employment and economic systems are three examples. Second, the longer-term implications of human or super-human artificial intelligence are dramatic, and there is no consensus on how quickly such capabilities will be developed. Many experts believe there is a chance it could happen rather soon, making it imperative to begin investigating long-term safety issues now, if only to get a better sense of how much early progress is actually possible.

Non-canonical answers

Isn't it too soon to work on AGI safety?

It's true that AGI may be really many years ahead. But what worries a lot of people, is that it may be much harder to make powerful AND safe AI, than just a powerful AI, and then, the first powerful AIs we create will be dangerous.

If that's the case, the sooner we start working on AI safety, the smaller the chances of humans going extinct, or ending up in some Black Mirror episode.

Also Rob Miles talks about this concern in this video.

How long will it be until AGI is created?

Very hard to say. This draft report for the Open Philanthropy Project is perhaps the most careful attempt so far (and generates these graphs), but there have also been expert surveys, and many people have shared various thoughts. Berkeley AI professor Stuart Russell has given his best guess as “sometime in our children’s lifetimes”, and Ray Kurzweil (Google’s director of engineering) predicts human level AI by 2029 and the singularity by 2045. The Metaculus question on publicly known AGI has a median of around 2029 (around 10 years sooner than it was before the GPT-3 AI showed unexpected ability on a broad range of tasks).

The consensus answer is something like: “highly uncertain, maybe not for over a hundred years, maybe in less than 15, with around the middle of the century looking fairly plausible”.

How long will it be until AGI is created?

Nobody knows for sure when we will have ASI or if it is even possible. Predictions on AI timelines are notoriously variable, but recent surveys about the arrival of human-level AGI have median dates between 2040 and 2050 although the median for (optimistic) AGI researchers and futurists is in the early 2030s (source). What will happen if/when we are able to build human-level AGI is a point of major contention among experts. One survey asked (mostly) experts to estimate the likelihood that it would take less than 2 or 30 years for a human-level AI to improve to greatly surpass all humans in most professions. Median answers were 10% for "within 2 years" and 75% for "within 30 years". We know little about the limits of intelligence and whether increasing it will follow the law of accelerating or diminishing returns. Of particular interest to the control problem is the fast or hard takeoff scenario. It has been argued that the increase from a relatively harmless level of intelligence to a dangerous vastly superhuman level might be possible in a matter of seconds, minutes or hours: too fast for human controllers to stop it before they know what's happening. Moving from human to superhuman level might be as simple as adding computational resources, and depending on the implementation the AI might be able to quickly absorb large amounts of internet knowledge. Once we have an AI that is better at AGI design than the team that made it, the system could improve itself or create the next generation of even more intelligent AIs (which could then self-improve further or create an even more intelligent generation, and so on). If each generation can improve upon itself by a fixed or increasing percentage per time unit, we would see an exponential increase in intelligence: an intelligence explosion.

Why work on AI safety early?

MIRI prioritizes early safety work because we believe such work is important, time-sensitive, tractable, and informative.

The importance of AI safety work is outlined in Why is safety important for smarter-than-human AI?. We see the problem as time-sensitive as a result of:

  • neglectedness — Only a handful of people are currently working on the open problems outlined in the MIRI technical agenda.
  • apparent difficulty — Solving the alignment problem may demand a large number of researcher hours, and may also be harder to parallelize than capabilities research.
  • risk asymmetry — Working on safety too late has larger risks than working on it too early.
  • AI timeline uncertainty — AI could progress faster than we expect, making it prudent to err on the side of caution.
  • discontinuous progress in AI — Progress in AI is likely to speed up as we approach general AI. This means that even if AI is many decades away, it would be hazardous to wait for clear signs that general AI is near: clear signs may only arise when it’s too late to begin safety work.

We also think it is possible to do useful work in AI safety today, even if smarter-than-human AI is 50 or 100 years away. We think this for a few reasons:

  • lack of basic theory — If we had simple idealized models of what we mean by correct behavior in autonomous agents, but didn’t know how to design practical implementations, this might suggest a need for more hands-on work with developed systems. Instead, however, simple models are what we’re missing. Basic theory doesn’t necessarily require that we have experience with a software system’s implementation details, and the same theory can apply to many different implementations.
  • precedents — Theoretical computer scientists have had repeated success in developing basic theory in the relative absence of practical implementations. (Well-known examples include Claude Shannon, Alan Turing, Andrey Kolmogorov, and Judea Pearl.)
  • early results — We’ve made significant advances since prioritizing some of the theoretical questions we’re looking at, especially in decision theory and logical uncertainty. This suggests that there’s low-hanging theoretical fruit to be picked.

Finally, we expect progress in AI safety theory to be useful for improving our understanding of robust AI systems, of the available technical options, and of the broader strategic landscape. In particular, we expect transparency to be necessary for reliable behavior, and we think there are basic theoretical prerequisites to making autonomous AI systems transparent to human designers and users.

Having the relevant theory in hand may not be strictly necessary for designing smarter-than-human AI systems — highly reliable agents may need to employ very different architectures or cognitive algorithms than the most easily constructed smarter-than-human systems that exhibit unreliable behavior. For that reason, some fairly general theoretical questions may be more relevant to AI safety work than to mainline AI capabilities work. Key advantages to AI safety work’s informativeness, then, include:

  • general value of information — Making AI safety questions clearer and more precise is likely to give insights into what kinds of formal tools will be useful in answering them. Thus we’re less likely to spend our time on entirely the wrong lines of research. Investigating technical problems in this area may also help us develop a better sense for how difficult the AI problem is, and how difficult the AI alignment problem is.
  • requirements for informative testing — If the system is opaque, then online testing may not give us most of the information that we need to design safer systems. Humans are opaque general reasoners, and studying the brain has been quite useful for designing more effective AI algorithms, but it has been less useful for building systems for verification and validation.
  • requirements for safe testing — Extracting information from an opaque system may not be safe, since any sandbox we build may have flaws that are obvious to a superintelligence but not to a human.

How long will it be until AGI is created?

In early 2013, Bostrom and Müller surveyed the one hundred top-cited living authors in AI, as ranked by Microsoft Academic Search. Conditional on “no global catastrophe halt[ing] progress,” the twenty-nine experts who responded assigned a median 10% probability to our developing a machine “that can carry out most human professions at least as well as a typical human” by the year 2023, a 50% probability by 2048, and a 90% probability by 2080.

Most researchers at MIRI approximately agree with the 10% and 50% dates, but think that AI could arrive significantly later than 2080. This is in line with Bostrom’s analysis in Superintelligence:

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 [human-level machine intelligence] 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.

Given experts’ (and non-experts’) poor track record at predicting progress in AI, we are relatively agnostic about when full AI will be invented. It could come sooner than expected, or later than expected.

Experts also reported a 10% median confidence that superintelligence would be developed within 2 years of human equivalence, and a 75% confidence that superintelligence would be developed within 30 years of human equivalence. Here MIRI researchers’ views differ significantly from AI experts’ median view; we expect AI systems to surpass humans relatively quickly once they near human equivalence.

It is possible that it's too early to work on the problem. It’s true that although AI has had some recent successes – like DeepMind’s newest creation AlphaGo defeating the human Go champion in April – it still has nothing like humans’ flexible, cross-domain intelligence. No AI in the world can pass a first-grade reading comprehension test. Facebook’s Andrew Ng compares worrying about superintelligence to “worrying about overpopulation on Mars” – a problem for the far future, if at all.

But this apparent safety might be illusory. A survey of leading AI scientists show that on average they expect human-level AI as early as 2040, with above-human-level AI following shortly after. And many researchers warn of a possible “fast takeoff” – a point around human-level AI where progress reaches a critical mass and then accelerates rapidly and unpredictably.

Unanswered canonical questions