One of the most common objections we hear when talking about artificial general intelligence (AGI) is that “AGI is ill-defined, so you can’t really say much about it.”
In an earlier post, I pointed out that we often don’t have precise definitions for things while doing useful work on them, as was the case with the concepts of “number” and “self-driving car.”
Still, we must have some idea of what we’re talking about. Earlier I gave a rough working definition for “intelligence.” In this post, I explain the concept of AGI and also provide several possible operational definitions for the idea.
The idea of AGI
As discussed earlier, the concept of “general intelligence” refers to the capacity for efficient cross-domain optimization. Or as Ben Goertzel likes to say, “the ability to achieve complex goals in complex environments using limited computational resources.” Another idea often associated with general intelligence is the ability to transfer learning from one domain to other domains.
To illustrate this idea, let’s consider something that would not count as a general intelligence.
Computers show vastly superhuman performance at some tasks, roughly human-level performance at other tasks, and subhuman performance at still other tasks. If a team of researchers was able to combine many of the top-performing “narrow AI” algorithms into one system, as Google may be trying to do,1 they’d have a massive “Kludge AI” that was terrible at most tasks, mediocre at some tasks, and superhuman at a few tasks.
Like the Kludge AI, particular humans are terrible or mediocre at most tasks, and far better than average at just a few tasks.2 Another similarity is that the Kludge AI would probably show measured correlations between many different narrow cognitive abilities, just as humans do (hence the concepts of g and IQ3): if we gave the Kludge AI lots more hardware, it could use that hardware to improve its performance in many different narrow domains simultaneously.4
On the other hand, the Kludge AI would not (yet) have general intelligence, because it wouldn’t necessarily have the capacity to solve somewhat-arbitrary problems in somewhat-arbitrary environments, wouldn’t necessarily be able to transfer learning in one domain to another, and so on.
- In an interview with The Register, Google head of research Alfred Spector said, “We have the knowledge graph, [the] ability to parse natural language, neural network tech [and] enormous opportunities to gain feedback from users… If we combine all these things together with humans in the loop continually providing feedback our systems become … intelligent.” Spector calls this the “combination hypothesis.” ↩
- Though, there are probably many disadvantaged humans for which this is not true, because they do not show far-above-average performance on any tasks. ↩
- Psychologists now generally agree that there is a general intelligence factor in addition to more specific mental abilities. For an introduction to the modern synthesis, see Gottfredson (2011). For more detail, see the first few chapters of Sternberg & Kaufman (2011). If you’ve read Cosma Shalizi’s popular article “g, a Statistical Myth, please also read its refutation here and here. ↩
- In psychology, the factor analysis is done between humans. Here, I’m suggesting that a similar factor analysis could hypothetically be done between different Kludge AIs, with different Kludge AIs running basically the same software but having access to different amounts of computation. The analogy should not be taken too far, however. For example, it isn’t the case that higher-IQ humans have much larger brains than other humans. ↩
Before that, she graduated from University of Vienna with a BSc in Mathematics. In her spare time, Benja studies questions relevant to AI impacts and Friendly AI, including: AI forecasting, intelligence explosion microeconomics, reflection in logic, and decision algorithms.
Benja has attended two of MIRI’s research workshops, and is scheduled to attend another in December.
Luke Muehlhauser: Since you’ve attended two MIRI research workshops on “Friendly AI math,” I’m hoping you can explain to our audience what that work is all about. To provide a concrete example, I’d like to talk about the Löbian obstacle to self-modifying artificial intelligence, which is one of the topics that MIRI’s recent workshops have focused on. To start with, could you explain to our readers what this problem is and why you think it is important?
Today we released a new technical report by visiting researcher Katja Grace called “Algorithmic Progress in Six Domains.” The report summarizes data on algorithmic progress – that is, better performance per fixed amount of computing hardware – in six domains:
- SAT solvers,
- Chess and Go programs,
- Physics simulations,
- Mixed integer programming, and
- Some forms of machine learning.
Our purpose for collecting these data was to shed light on the question of intelligence explosion microeconomics, though we suspect the report will be of broad interest within the software industry and computer science academia.
The preferred page for discussing the report in general is here.
In recent boolean satisfiability (SAT) competitions, SAT solver performance has increased 5–15% per year, depending on the type of problem. However, these gains have been driven by widely varying improvements on particular problems. Retrospective surveys of SAT performance (on problems chosen after the fact) display significantly faster progress.
Chess programs have improved by around 50 Elo points per year over the last four decades. Estimates for the significance of hardware improvements are very noisy, but are consistent with hardware improvements being responsible for approximately half of progress. Progress has been smooth on the scale of years since the 1960s, except for the past five. Go programs have improved about one stone per year for the last three decades. Hardware doublings produce diminishing Elo gains, on a scale consistent with accounting for around half of progress.
Improvements in a variety of physics simulations (selected after the fact to exhibit performance increases due to software) appear to be roughly half due to hardware progress.
The largest number factored to date has grown by about 5.5 digits per year for the last two decades; computing power increased 10,000-fold over this period, and it is unclear how much of the increase is due to hardware progress.
Some mixed integer programming (MIP) algorithms, run on modern MIP instances with modern hardware, have roughly doubled in speed each year. MIP is an important optimization problem, but one which has been called to attention after the fact due to performance improvements. Other optimization problems have had more inconsistent (and harder to determine) improvements.
Various forms of machine learning have had steeply diminishing progress in percentage accuracy over recent decades. Some vision tasks have recently seen faster progress.
In 2008, security expert Bruce Schneier wrote about the security mindset:
Security requires a particular mindset. Security professionals… see the world differently. They can’t walk into a store without noticing how they might shoplift. They can’t use a computer without wondering about the security vulnerabilities. They can’t vote without trying to figure out how to vote twice…
SmartWater is a liquid with a unique identifier linked to a particular owner. “The idea is for me to paint this stuff on my valuables as proof of ownership,” I wrote when I first learned about the idea. “I think a better idea would be for me to paint it on your valuables, and then call the police.”
…This kind of thinking is not natural for most people. It’s not natural for engineers. Good engineering involves thinking about how things can be made to work; the security mindset involves thinking about how things can be made to fail. It involves thinking like an attacker, an adversary or a criminal. You don’t have to exploit the vulnerabilities you find, but if you don’t see the world that way, you’ll never notice most security problems.
A recurring problem in much of the literature on “machine ethics” or “AGI ethics” or “AGI safety” is that researchers and commenters often appear to be asking the question “How will this solution work?” rather than “How will this solution fail?”
Here’s an example of the security mindset at work when thinking about AI risk. When presented with the suggestion that an AI would be safe if it “merely” (1) was very good at prediction and (2) gave humans text-only answers that it predicted would result in each stated goal being achieved, Viliam Bur pointed out a possible failure mode (which was later simplified):
Example question: “How should I get rid of my disease most cheaply?” Example answer: “You won’t. You will die soon, unavoidably. This report is 99.999% reliable”. Predicted human reaction: Decides to kill self and get it over with. Success rate: 100%, the disease is gone. Costs of cure: zero. Mission completed.
This security mindset is one of the traits we look for in researchers we might hire or collaborate with. Such researchers show a tendency to ask “How will this fail?” and “Why might this formalism not quite capture what we really care about?” and “Can I find a way to break this result?”
That said, there’s no sense in being infinitely skeptical of results that may help with AI security, safety, reliability, or “friendliness.” As always, we must think with probabilities.
Volunteers at MIRI Volunteers and elsewhere have helpfully transcribed several audio/video recordings related to MIRI’s work. This post is a continuously updated index of those transcripts.
All transcripts of Singularity Summit talks are available here.
Other available transcripts include:
- Philosophy Talk: Turbo-Charging the Mind (with Anna Salamon)
- BloggingHeads.tv: Eliezer Yudkowsky and Scott Aaronson on superintelligence and many-worlds
- BloggingHeads.tv: Eliezer Yudkowsky and Massimo Pigliucci on consciousness and uploading
- AGI 2011: Whole Brain Emulation, as a platform for creating safe AGI (with Anna Salamon)
- AGI 2011: Risk-averse preferences as an AGI safety technique (with Anna Salamon)
- Dawn or Doom: Why ain’t you rich? (with Nate Soares)
From December 14-20, MIRI will host another Workshop on Logic, Probability, and Reflection. This workshop will focus on the Löbian obstacle, probabilistic logic, and the intersection of logic and probability more generally.
Participants confirmed so far include:
- Nate Ackerman (Harvard)
- John Baez (UC Riverside)
- Paul Christiano (UC Berkeley)
- Benja Fallenstein (U Bristol)
- Cameron Freer (MIT)
- Jeremy Hahn (Harvard)
- Wojtek Moczydlowski (Google)
- Michele Reilly (independent)
- Will Sawin (Princeton)
- Nate Soares (Google)
- Nisan Stiennon (Stanford)
- Greg Wheeler (LMU Munich)
- Eliezer Yudkowsky (MIRI)
If you have a strong mathematics background and might like to attend this workshop, it’s not too late to apply! And even if this workshop doesn’t fit your schedule, please do apply, so that we can notify you of other workshops (long before they are announced publicly).
Nick Beckstead recently finished a Ph.D in philosophy at Rutgers University, where he focused on practical and theoretical ethical issues involving future generations. He is particularly interested in the practical implications of taking full account of how actions taken today affect people who may live in the very distant future. His research focuses on how big picture questions in normative philosophy (especially population ethics and decision theory) and various big picture empirical questions (especially about existential risk, moral and economic progress, and the future of technology) feed into this issue.
Apart from his academic work, Nick has been closely involved with the effective altruism movement. He has been the director of research for Giving What We Can, he has worked as a summer research analyst at GiveWell, and he is currently on the board of trustees for the Centre for Effective Altruism, and he recently became a research fellow at the Future of Humanity Institute.