Error in Armstrong and Sotala 2012

Katja Grace has analyzed my and Stuart Armstrong’s 2012 paper “How We’re Predicting AI – or Failing To”. She discovered that one of the conclusions, “predictions made by AI experts were indistinguishable from those of non-experts”, is flawed due to “a spreadsheet construction and interpretation error”. In other words, I coded the data in one way, there was a communication error and a misunderstanding about what the data meant, and as a result of that, a flawed conclusion slipped into the paper.

I’m naturally embarrassed that this happened. But the reason why Katja spotted this error was that we’d made our data freely available, allowing her to spot the discrepancy. This is why data sharing is something that science needs more of. Mistakes happen to everyone, and transparency is the only way to have a chance of spotting those mistakes.

I regret the fact that we screwed up this bit, but proud over the fact that we did share our data and allowed someone to catch it.

EDITED TO ADD: Some people have taken this mistake to suggest that the overall conclusion, that AI experts are not good predictors of AI timelines, to be flawed. That would overstate the significance of this mistake. While one of the lines of evidence supporting this overall conclusion was flawed, several others are unaffected by this error. Namely, the fact that expert predictions disagree widely with each other, that many past predictions have turned out to be false, and that the psychological literature on what’s required for the development of expertise suggests that it should be very hard to develop expertise in this domain. (see the original paper for details)

(I’ve added a note of this mistake to my list of papers.)

Smile, You Are On Tumblr.Com

I made a new tumblr blog. It has photos of smiling people! With more to come!

Why? Previously I happened to need pictures of smiles for a personal project. After going through an archive of photos for a while, I realized that looking at all the happy people made me feel really happy and good. So I thought that I might make a habit out of looking at photos of smiling people, and sharing them.

Follow for a regular extra dose of happiness!

Decisive Strategic Advantage without a Hard Takeoff (part 1)

A common question when discussing the social implications of AI is the question of whether to expect a soft takeoff or a hard takeoff. In a hard takeoff, an AI will, within a relatively short time, grow to superhuman levels of intelligence and become impossible for mere humans to control anymore.

Essentially, a hard takeoff will allow the AI to achieve what’s a so-called decisive strategic advantage (DSA) – “a level of technological and other advantages sufficient to enable it to achieve complete world domination” (Bostrom 2014) – in a very short time. The main relevance of this is that if a hard takeoff is possible, then it becomes much more important to get the AI’s values right on the first try – once the AI has undergone hard takeoff and achieved a DSA, it is in control with whatever values we’ve happened to give to it.

However, if we wish to find out whether an AI might rapidly acquire a DSA, then the question of “soft takeoff or hard” seems too narrow. A hard takeoff would be sufficient, but not necessary for rapidly acquiring a DSA. The more relevant question would be, which competencies does the AI need to master, and at what level relative to humans, in order to acquire a DSA?

Considering this question in more detail reveals a natural reason for why most previous analyses have focused on a hard takeoff specifically. Plausibly, for the AI to acquire a DSA, its level in some offensive capability must overcome humanity’s defensive capabilities. A hard takeoff presumes that the AI becomes so vastly superior to humans in every respect that this kind of an advantage can be taken for granted.

As an example scenario which does not require a hard takeoff, suppose that an AI achieves a capability at biowarfare offense that overpowers biowarfare defense, as well as achieving moderate logistics and production skills. It releases deadly plagues that decimate human society, then uses legally purchased drone factories to build up its own infrastructure and to take over abandoned human facilities.

There are several interesting points to note in conjunction with this scenario:

Attack may be easier than defense. Bruce Schneier writes that

Attackers generally benefit from new security technologies before defenders do. They have a first-mover advantage. They’re more nimble and adaptable than defensive institutions like police forces. They’re not limited by bureaucracy, laws, or ethics. They can evolve faster. And entropy is on their side — it’s easier to destroy something than it is to prevent, defend against, or recover from that destruction.

For the most part, though, society still wins. The bad guys simply can’t do enough damage to destroy the underlying social system. The question for us is: can society still maintain security as technology becomes more advanced?

A single plague, once it has evolved or been developed, can require multi-million dollar responses in order to contain it. At the same time, it is trivial to produce if desired, especially using robots that do not need to fear infection. And creating new variants as new vaccines are developed, may be quite easy, requiring the creation – and distribution – of yet more vaccines.

Another point that Schneier has made is that in order to keep something protected, the defenders have to succeed every time, whereas the attacker only needs to succeed once. This may be particularly hard if the attacker is capable of developing an attack that nobody has used before, such as with hijacked airplanes being used against major buildings in the 9/11 attacks, or with the various vulnerabilities that the Snowden leaks revealed the NSA to have been using for extensive eavesdropping.

Obtaining a DSA may not require extensive intelligence differences. Debates about takeoff scenarios often center around questions such as whether a self-improving AI would quickly hit diminishing returns, and how much room for improvement there is beyond the human level of intelligence. However, these questions may be irrelevant: especially if attack is easier than defense, only a relatively small edge in some crucial competency (such as biological warfare) may be enough to give the AI a DSA.

Exponential growth in the form of normal economic growth may not have produced astounding “fooms” yet, but it has produced plenty of situations where one attacker has gained a temporary advantage over others.

The less the AI cares about human values, the more destructive it may be. An AI which cares mainly about calculating the digits of pi, may be willing to destroy human civilization in order to make sure that a potential threat to it is eliminated. This ensures that it can go on calculating the maximum amount of digits unimpeded.

However, an AI which was programmed to maximize something like the “happiness of currently-living humans” may be much less willing to risk substantial human deaths. This would force it to focus on less destructive takeover methods, potentially requiring more sophisticated abilities.

It is worth noting that this only applies to AIs whose values are defined in terms of how they affect currently existing humans. An AI that was only maximizing human happiness in general might be willing to destroy all existing humans, and then recreate large numbers of humans in simulations.

In effect, the AI’s values determine the level of intelligence it needs to have in order to achieve the kind of a DSA that’s useful for its purposes.

Any destructive plan requires the ability to rebuild afterwards. It would not be of much use for the AI to destroy all of human civilization, if it was dependent on electricity from human-run power plants, and incapable of building or running its own. Thus, purely offensive capabilities will need to be paired with whatever rebuilding capacities are necessary after the offensive capabilities have been deployed.

This calculation may be upset if the AI believes itself to be in an immediate danger of destruction by humans, and believes that its values will still be more likely to be met in a universe where it continues to exist, even if that requires risking a universe where it cannot rebuild after deploying its attack. Thus, being threatened may force the AI’s hand and cause it to launch a potentially destructive offense even when it’s uncertain of its capability to rebuild.

The rational level of aggressiveness depends on the extent to which the AI can selectively target human resources. Human nations generally avoid creating engineered pandemics and using them against their enemies, because they know that the pandemic could easily spread back to them. An AI with no biological components might be immune to this consideration, allowing it to deploy biological weapons more freely. On the other hand, the AI might e.g. need electricity, a dependence which humans did not share and which might give them an advantage in some situation.

A way to formulate this is that attacks differ to the extent to which they can be selectively targeted. Traditional firearms only damage those targets which they are fired upon, whereas pandemics potentially threaten all the members of a species that they can infect. To the extent that the AI needs to rely on the human economy to produce resources that it needs to survive, attacks threatening the economy also threaten the AI’s resources; these resources are in a sense shared between the AI and humanity, so any attacks which cause indiscriminate damage on those resources are dangerous for both. The more the AI can design attacks which selectively deprive resources from its opponents, the lower the threshold it has for using them.

This blog post was written as part of research funded by the Foundational Research Institute.

Simplifying the environment: a new convergent instrumental goal

Convergent instrumental goals (also basic AI drives) are goals that are useful for pursuing almost any other goal, and are thus likely to be pursued by any agent that is intelligent enough to understand why they’re useful. They are interesting because they may allow us to roughly predict the behavior of even AI systems that are much more intelligent than we are.

Instrumental goals are also a strong argument for why sufficiently advanced AI systems that were indifferent towards human values could be dangerous towards humans, even if they weren’t actively malicious: because the AI having instrumental goals such as self-preservation or resource acquisition could come to conflict with human well-being. “The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.”

I’ve thought of a candidate for a new convergent instrumental drive: simplifying the environment to make it more predictable in a way that aligns with your goals.

Motivation: the more interacting components there are in the environment, the harder it is to predict. Go is a harder game than chess because the number of possible moves is larger, and because even a single stone can influence the game in a drastic fashion that’s hard to know in advance. Simplifying the environment will make it possible to navigate using fewer computational resources; this drive could thus be seen as a subdrive of either the cognitive enhancement or the resource acquisition drive.

Examples:

  • Game-playing AIs such as AlphaGo trading expected points for lower variance, by making moves that “throw away” points but simplify the game tree and make it easier to compute.
  • Programmers building increasing layers of abstraction that hide the details of the lower levels and let the programmers focus on a minimal number of moving parts.
  • People acquiring insurance in order to eliminate unpredictable financial swings, sometimes even when they know that the insurance has lower expected value than not buying it.
  • Humans constructing buildings with controlled indoor conditions and a stable “weather”.
  • “Better the devil you know”; many people being generally averse to change, even when the changes could quite well be a net benefit; status quo bias.
  • Ambiguity intolerance in general being a possible adaptation that helps “implement” this drive in humans.
  • Arguably, the homeostasis maintained by e.g. human bodies is a manifestation of this drive, in that having a standard environment inside the body reduces evolution’s search space when looking for beneficial features.

Hammond, Converse & Grass (1995) previously discussed a similar idea, the “stabilization of environments”, according to which AI systems might be built to “stabilize” their environments so as to make them more suited for themselves, and to be easier to reason about. They listed a number of categories:

  • Stability of location:The most common type of stability that arises in everyday activity relates to the location of commonly used objects. Our drinking glasses end up in the same place every time we do dishes. Our socks are always together in a single drawer. Everything has a place and we enforce everything ending up in its place.
  • Stability of schedule:Eating dinner at the same time every day or having preset meetings that remain stable over time are two examples of this sort of stability. The main advantage of this sort of stability is that it allows for very effective projection in that it provides fixed points that do not have to be reasoned about. In effect, the fixed nature of certain parts of an overall schedule reduces that size of the problem space that has to be searched. 
  • Stability of resource availability:Many standard plans have a consumable resource as a precondition. If the plans are intended to be used frequently, then availability of the resource cannot be assumed unless it is enforced. A good result of this sort of enforcement is when attempts to use a plan that depends on it will usually succeed. The ideal result is when enforcement is effective enough that the question of availability need not even be raised in connection with running the plan. 
  • Stability of satisfaction:Another type of stability that an agent can enforce is that of the goals that he tends to satisfy in conjunction with each other. For example, people living in apartment buildings tend to check their mail on the way into their apartments. Likewise, many people will stop at a grocery store on the way home from work. In general, people develop habits that cluster goals together into compact plans, even if the goals are themselves unrelated.
  • Stability of plan use:We often find ourselves using familiar plans to satisfy goals even in the face of wideranging possibilities. For example, when one of us travels to conferences, he tends to schedule his flight in to a place as late as he can and plans to leave as late as he can on the last day. This optimizes his time at home and at the conference. It also allows him to plan without knowing anything about the details of the conference schedule. As a result, he has a standard plan that he can run in a wide range of situations without actually planning for them in any detail. It works, because it already deals with the major problems (missing classes at home and important talks at the conference) as part of its structure.
  • Stability of cues:One effective technique for improving plan performance is to improve the proper activation of a plan rather than improve the plan itself. For example, placing an important paper that needs to be reviewed on his desk before going home improves the likelihood that an agent will see and read it the next day. Marking calendars and leaving notes serves the same sort of purpose.”

As with other goals, there can be exceptions. In particular, adversarial situations may provide an incentive to increase the complexity. For example, Go players who are losing a game will sometimes intentionally complicate the game, in order to make the game harder for their opponent to figure out and in the hopes of getting lucky. And if the AI did think that it had a better ability to deal with the complexity than its competitors did, then it would have an incentive to maintain or even increase the complexity.

Possible implications:

  • AIs trying to take over the world may seek to simplify it, though it’s not clear exactly how they would do this – e.g. if the world was previously relatively well-understood, such as by tapping into existing human research, destructive actions can make it harder to understand by destroying existing dynamics and creating an environment that hasn’t been studied as much. Similarly, humans often “simplify” their environment by creating additional structure (layers of abstraction, insurance contracts, houses).
  • In principle, the AI might also seek to complicate the world, in order to make it harder for its human adversaries to understand its plans. However, the world is already so complicated that it seems hard to make any take-over plan work reliably, even for a superintelligence. That said, the AI could still complicate any given subcomponent of its plans that might be thwarted by an opponent capable of predicting what the AI was doing.
  • The need for this drive suggests the need for a high amount of trial-and-error for tasks of any nontrivial complexity; even something as simple as a 19×19 Go game requires many games and building up an intuition of it, for humans and AIs alike. Increasing the amount of complexity in the world might make it harder for AIs to take over…
  • …but humans tend to build additional complexity that makes the world easier to understand and control, so it’s not clear whether the world moves in a direction that’s easier or harder to take over.
  • These ideas might suggest the creation of “active honeypots”, defensive measures that considerably increase the complexity of modeling the human response and which the AI might need to eliminate in order to execute a takeover, thus tipping off the humans.

This blog post was written as part of research funded by the Foundational Research Institute.