Small notes on the Rapture of the Nerds. If AI keeps on improving, will explosive intelligence eventually cut humans out of the loop and go on without us? Will we be pensioned in that case?
The internet has opinions about this.
- Matthe Hutson, Computers ace IQ tests but still make dumb mistakes. Can different tests help?
- François Chollet, The implausibility of intelligence explosion
- Is Science Stagnant?
- Ground zero, perhaps, Vernor Vinge’s The Coming Technological Singularity
- Stuart Russell on Making Artificial Intelligence Compatible with Humans, and interview on various themes in his book (Russell 2019)
- Attempted Gears Analysis of AGI Intervention Discussion With Eliezier
- Superintelligence: The Idea That Eats Smart People
- Kevin SCott argues for trying to find a unifying notion of what knowledge work is to unify what humans and machines can do. (Scott 2022)
- Hildebrandt (2020) argues for talking about smart tech instead of AI tech.
- Everyone love’s Bart Selman’s AAAI Presidential Address: The State of AI
- Asymptotic burnout and homeostatic awakening: a possible solution to the Fermi paradox?
A fruitful application of these ideas is in producing interesting science fiction and contemporary horror.
It is a shibboleth for the Rationalist community to express the opinion that the risks of a possible AI explosion are under-managed compared to the risks of more literal explosions. Also to wonder if an AI singularity happened and we are merely simulated by it.
I contend that managing e.g. climate crisis is on the critical path to even getting to hard AI takeoff and we are not managing that risk well enough to get to the more exciting hard AI risks, so whether which one we are failing to manage worse seems to me to be not so interesting.
Incoming
References
Acemoglu, Daron, David Autor, Jonathon Hazell, and Pascual Restrepo. 2020. “AI and Jobs: Evidence from Online Vacancies.” Working Paper 28257. National Bureau of Economic Research.
Acemoglu, Daron, and Pascual Restrepo. 2018. “Artificial Intelligence, Automation and Work.” Working Paper 24196. National Bureau of Economic Research.
———. 2020. “The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand.” Cambridge Journal of Regions, Economy and Society 13 (1): 25–35.
Birhane, Abeba, and David J. T. Sumpter. 2022. “The Games We Play: Critical Complexity Improves Machine Learning.” arXiv.
Bubeck, Sébastien, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, et al. 2023. “Sparks of Artificial General Intelligence: Early Experiments with GPT-4.” arXiv.
Chollet, François. 2019. “On the Measure of Intelligence.” arXiv:1911.01547 [Cs], November.
Collison, Patrick, and Michael Nielsen. 2018. “Science Is Getting Less Bang for Its Buck.” The Atlantic, November 16, 2018.
Everitt, Tom, and Marcus Hutter. 2018. “Universal Artificial Intelligence: Practical Agents and Fundamental Challenges.” In Foundations of Trusted Autonomy, edited by Hussein A. Abbass, Jason Scholz, and Darryn J. Reid, 117:15–46. Cham: Springer International Publishing.
Hildebrandt, Mireille. 2020. “Smart Technologies.” Internet Policy Review 9 (4).
Hutson, Matthew. 2022. “Taught to the Test.” Science 376 (6593): 570–73.
Hutter, Marcus. 2000. “A Theory of Universal Artificial Intelligence Based on Algorithmic Complexity.” arXiv.
Manheim, David, and Scott Garrabrant. 2019. “Categorizing Variants of Goodhart’s Law.” arXiv.
Mitchell, Melanie. 2021. “Why AI Is Harder Than We Think.” arXiv:2104.12871 [Cs], April.
Nathan, Christopher, and Keith Hyams. 2021. “Global Policymakers and Catastrophic Risk.” Policy Sciences, December.
Philippon, Thomas. 2022. “Additive Growth.” Working Paper. Working Paper Series. National Bureau of Economic Research.
Russell, Stuart. 2019. Human Compatible: Artificial Intelligence and the Problem of Control. Penguin Books.
Scott, Kevin. 2022. “I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale.” American Academy of Arts & Sciences, 2022.
Silver, David, Satinder Singh, Doina Precup, and Richard S. Sutton. 2021. “Reward Is Enough.” Artificial Intelligence 299 (October): 103535.
Sunehag, Peter, and Marcus Hutter. 2013. “Principles of Solomonoff Induction and AIXI.” In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011, edited by David L. Dowe, 386–98. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer.
Wong, Michael L., and Stuart Bartlett. 2022. “Asymptotic Burnout and Homeostatic Awakening: A Possible Solution to the Fermi Paradox?” Journal of The Royal Society Interface 19 (190): 20220029.
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