AI timelines and strategies

AI Impacts sometimes invites guest posts from fellow thinkers on the future of AI. These are not intended to relate closely to our current research, nor to necessarily reflect our views. However we think they are worthy contributions to the discussion of AI forecasting and strategy.

This is a guest post by Sarah Constantin, 20 August 2015

One frame of looking at AI risk is the “geopolitical” stance. Who are the major players who might create risky strong AI? How could they be influenced or prevented from producing existential risks? How could safety-minded institutions gain power or influence over the future of AI? What is the correct strategy for reducing AI risk?

The correct strategy depends sharply on the timeline for when strong AI is likely to be developed. Will it be in 10 years, 50 years, 100 years or more? This has implications for AI safety research. If a basic research program on AI safety takes 10-20 years to complete and strong AI is coming in 10 years, then research is relatively pointless. If basic research takes 10-20 years and strong AI is coming more than 100 years from now (if at all), then research can wait. If basic research takes 10-20 years and strong AI is coming in around 50 years, then research is a good idea.

Another relevant issue for AI timelines and strategies is the boom-and-bust cycle in AI. Funding for AI research and progress on AI has historically fluctuated since the 1960s, with roughly 15 years between “booms.” The timeline between booms may change in the future, but fluctuation in investment, research funding, and popular attention seems to be a constant in scientific/technical fields.

Each AI boom has typically focused on a handful of techniques (GOFAI in the 1970’s, neural nets and expert systems in the 1980’s) which promised to deliver strong AI but eventually ran into limits and faced a collapse of funding and investment. The current AI boom is primarily focused on massively parallel processing and machine learning, particularly deep neural nets.

This is relevant because institutional and human capital is lost between booms. While leading universities can survive for centuries, innovative companies are usually only at their peak for a decade or so. It is unlikely that the tech companies doing the most innovation in AI during one boom will be the ones leading subsequent booms. (We don’t usually look to 1980’s expert systems companies for guidance on AI today.) If there were to be a Pax Googleiana lasting 50 years, it might make sense for people concerned with AI safety to just do research and development within Google. But the history of the tech industry suggests that’s not likely. Which means that any attempt to influence long-term AI risk will need to survive the collapse of current companies and the end of the current wave of popularity of AI.

The “extremely short-term AI risk scenario” (of strong AI arising within a decade) is not a popular view among experts; most contemporary surveys of AI researchers predict that strong AI will arise sometime in the mid-to-late 21st century. If we take the view that strong AI in the 2020’s is vanishingly unlikely (which is more “conservative” than the results of most AI surveys, but may be more representative of the mainstream computer science view), then this has various implications for AI risk strategy that seem to be rarely considered explicitly.

In the “long-term AI risk scenario”, there will be at least one “AI winter” before strong AI is developed. We can expect a period (or multiple periods) in the future where AI will be poorly funded and popularly discredited. We can expect that there are one or more jumps in innovation that will need to occur before human-level AI will be possible. And, given the typical life cycle of corporations, we can expect that if strong AI is developed, it will probably be developed by an institution that does not exist yet.

In the “long-term AI risk scenario”, there will probably be time to develop at least some theory of AI safety and the behavior of superintelligent agents. Basic research in computer science (and perhaps neuroscience) may well be beneficial in general from an AI risk perspective. If research on safety can progress during “AI winters” while progress on AI in general halts, then winters are particularly good news for safety. In this long-term scenario, there is no short-term imperative to cease progress on “narrow AI”, because contemporary narrow AI is almost certainly not risky.

In the “long-term AI risk scenario”, another important goal besides basic research is to send a message to the future. Today’s leading tech CEOs will not be facing decisions about strong AI; the critical decisionmakers may be people who haven’t been born yet, or people who are currently young and just starting their careers. Institutional cultures are rarely built to last decades. What can we do today to ensure that AI safety will be a priority decades from now, long after the current wave of interest in AI has come to seem faddish and misguided?

The mid- or late 21st century may be a significantly different place than the early 21st century. Economic and political situations fluctuate. The US may no longer be the world’s largest economy. Corporations and universities may look very different. Imagine someone speculating about artificial intelligence in 1965 and trying to influence the world of 2015. Trying to pass laws or influence policy at leading corporations in 1965 might not have had a lasting effect (this would be a useful historical topic to investigate in more detail.)

And what if the next fifty years looks more like the cataclysmic first half of the 20th century than the comparatively stable second half of the 20th century? How could a speculative thinker of 1895 hope to influence the world of 1945?

Educational and cultural goals, broadly speaking, seem relevant in this scenario. It will be important to have a lasting influence on the intellectual culture of future generations.

For instance: if fields of theoretical computer science relevant for AI risk are developed and included in mainstream textbooks, then the CS majors of 2050 who might grow up to build strong AI will know about the concerns being raised today as more than a forgotten historical curiosity. Of course, they might not be CS majors, and perhaps they won’t even be college students. We have to think about robust transmission of information.

In the “long-term AI risk scenario”, the important task is preparing future generations of AI researchers and developers to avoid dangerous strong AI. This means performing and disseminating and teaching basic research in new theoretical fields necessary for understanding the behavior of superintelligent agents.

A “geopolitical” approach is extremely difficult if we don’t know who the players will be. We’d like the future institutions that will eventually develop strong AI to be run and staffed by people who will incorporate AI safety into their plans. This means that a theory of AI safety needs to be developed and disseminated widely.

Ultimately, long-term AI strategy bifurcates, depending on whether the future of AI is more “centralized” or “decentralized.”

In a “centralized” future, a small number of individuals, perhaps researchers themselves, contribute most innovation in AI, and the important mission is to influence them to pursue research in helpful rather than harmful directions.

In a “decentralized” future, progress in AI is spread over a broad population of institutions, and the important mission is to develop something like “industry best practices” — identifying which engineering practices are dangerous and instituting broadly shared standards that avoid them. This may involve producing new institutions focused on safety.

Basic research is an important prerequisite for both the “centralized” and “decentralized” strategies, because currently we do not know what kinds of progress in AI (if any) are dangerous.

The “centralized” strategy means promoting something like an intellectual culture, or philosophy, among the strongest researchers of the future; it is something like an educational mission. We would like future generations of AI researchers to have certain habits of mind: in particular, the ability to reason about the dramatic practical consequences of abstract concepts. The discoverers of quantum mechanics were able to understand that the development of the atomic bomb would have serious consequences for humanity, and to make decisions accordingly. We would like the future discoverers of major advances in AI to understand the same. This means that today, we will need to communicate (through books, schools, and other cultural institutions, traditional and new) certain intellectual and moral virtues, particularly to the brightest young people.

The “decentralized” strategy will involve taking the theoretical insights from basic AI research and making them broadly implementable. Are some types of “narrow AI” particularly likely to lead to strong AI? Are there some precautions which, on the margin, make harmful strong AI less likely? Which kinds of precautions are least costly in immediate terms and most compatible with the profit and performance needs of the tech industry? To the extent that AI progress is decentralized and incremental, the goal is to ensure that it is difficult to go very far in the wrong direction. Once we know what we mean by a “wrong direction”, this is a matter of building long-term institutions and incentives that shape AI progress towards beneficial directions.

The assumption that strong AI is a long-term rather than a short-term risk affects strategy significantly. Influencing current leading players is not particularly important; promoting basic research is very important; disseminating information and transmitting culture to future generations, as well as building new institutions, is the most effective way to prepare for AI advances decades from now.

In the event that AI never becomes a serious risk, developing institutions and intellectual cultures that can successfully reason about AI is still societally valuable. The skill (in institutions and individuals) of taking theoretical considerations seriously and translating them into practical actions for the benefit of humanity is useful for civilizational stability in general. What’s important is recognizing that this is a long-term strategy — i.e. thinking more than ten years ahead. Planning for future decades looks different from taking advantage of the current boom in funding and attention for AI and locally hill-climbing.

Sarah Constantin blogs at Otium. She recently graduated from Yale with a PhD in mathematics.

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  1. Great and insightful post! The following is a relatively long and speculative chain of reasoning, which supports influencing companies and key players earlier. I’m not sure how much weight to give it:

    If we take climate change as a role model for AI safety it seems likely that the biggest bottleneck will be getting policy makers on board, not AI researchers and developers. Another factor in favor of this would be the fact that even when the vast majority of scientists is on board (like with climate change), a minority may still develop unsafe AI in the absence of strong regulations. (This is more likely to happen in a decentralized future). At the same time AI safety research may be carried out by some even when scientists are not in complete consensus. This outcome seems only slightly worse than when there is complete consensus (this is complicated by the fact that scientific consensus would influence policy makers as well).

    This post argues that influencing today’s AI companies may have little effect on the institutions that develop AGI in the future. If we were to optimize for influencing policy makers instead, does the same conclusion follow? As we see with climate change and international development, the policy arena can take a long time to turn insights into actions and actions into results. Commitment devices such as the 1990 UN development goals have to be set way in advance. What’s more, AI could be so strategically interesting for countries (or other powerful actors) that they will not push for international regulations when see themselves as the likely winner of an AI arms race. The further off the development of AGI looks, the less likely such an unfavorable situation would be.

    This would imply that if we’re trying to set something like the UN development goals or the Kyoto Protocol happen for AI, we need to think about how policy makers can be influenced long before the likely development of AI.

    Here’s one way this could make influencing AI companies now important: If we want to influence policy makers in say 2025, AI companies and their lobbyists will be included in the process. Google/Alphabet may still be a major player. What’s more, the AI industry is likely to grow rapidly in the short-medium term (I haven’t researched this, it’s just my impressions from the general enthusiasm). They may have a stronger lobby and more political interests soon and be generally bigger. So it seems easier to influence them while they’re relatively small which would mean influencing them sooner rather than later.

    In short: Influence, say, Deep Mind now ==> They’re still on board in 10 years when the AI field is bigger ==> Influence policy makers without a big lobby group against you ==> Set something like the UN development goals early / bring the topic on the political agenda ==> Slowly translate the goals into policies

  2. I really liked this post. I think it does a great job of exploring which strategies are best conditional on which futures. This seems important both for choosing what to do, and identifying which forecasting questions are most decision-relevant.

    I think it’s important to keep in mind our uncertainty about which futures we’re likely to face. The importance of focusing on different futures depends not just on their likelihood, but on the amount of influence we may have over the outcome. This pushes us in the direction of wanting to hedge against scenarios where strong AI comes soon, even when we think these are pretty unlikely, because we’re the only people who can. I wrote about this consideration (with some very crude models) here:

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