Related: Even When Contrarians Win, They Lose
I had long thought that Jeff Hawkins (and the Redwood Center, and Numentia) were pursuing an idea that didn't work, and were continuing to fail to give up for a prolonged period of time. I formed this belief because I had not heard of any impressive results or endorsements of their research. However, I recently read an interview with Andrew Ng, a leading machine learning researcher, in which he credits Jeff Hawkins with publicizing the "one learning algorithm" hypothesis - the idea that most of the cognitive work of the brain is done by one algorithm. Ng says that, as a young researcher, this pushed him into areas that could lead to general AI. He still believes that AGI is far though.
I found out about Hawkins' influence on Ng after reading an old SL4 post by Eliezer and looking for further information about Jeff Hawkins. It seems that the "one learning algorithm" hypothesis was widely known in neuroscience, but not within AI until Hawkins' work. Based on Eliezer's citation of Mountcastle and his known familiarity with cognitive science, it seems that he learned of this hypothesis independently of Hawkins. The "one learning algorithm" hypothesis is important in the context of intelligence explosion forecasting, since hard takeoff is vastly more likely if it is true. I have been told that further evidence for this hypothesis has been found recently, but I don't know the details.
This all fits well with Robin Hanson's model. Hawkins had good evidence that better machine learning should be possible, but the particular approaches that he took didn't perform as well as less biologically-inspired ones, so he's not really recognized today. Deep learning would definitely have happened without him; there were already many people working in the field, and they started to attract attention because of improved performance due to a few tricks and better hardware. At least Ng's career though can be credited to Hawkins.
I've been thinking about Robin's hypothesis a lot recently, since many researchers in AI are starting to think about the impacts of their work (most still only think about the near-term societal impacts rather than thinking about superintelligence though). They recognize that this shift towards thinking about societal impacts is recent, but they have no idea why it is occurring. They know that many people, such as Elon Musk, have been outspoken about AI safety in the media recently, but few have heard of Superintelligence, or attribute the recent change to FHI or MIRI.
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Yes I am.
Step 1: Learn Bayes
Step 2: Learn reference class
Step 3: Read 0 to 1
Step 4: Read The Cook and the Chef
Step 5: Reason why are the billionaires saying the people who do it wrong are basically reasoning probabilistically
Step 6: Find the connection between that and reasoning from first principles, or the gear hypothesis, or whichever other term you have for when you use the inside view, and actually think technically about a problem, from scratch, without looking at how anyone else did it.
Step 7: Talk to Michael Valentine about it, who has been reasoning about this recently and how to impart it at CFAR workshops.
Step 8: Find someone who can give you a recording of Geoff Anders' presentation at EAGlobal.
Step 9: Notice how all those steps above were connected, become a Chef, set out to save the world. Good luck!
I model probabilistic thinking as something you build on top of all this. First you learn to model the world at all (your steps 3-8), then you learn the mathematical description of part of what your brain is doing when it does all this. There are many aspects of normative cognition that Bayes doesn't have anything to say about, but there are also places where you come to understand what your thinking is aiming at. It's a gears model of cognition rather than the object-level phenomenon.
If you don't have gears models at all, then yes, it's just another way to spout nonsense. This isn't because it's useless, it's because people cargo-cult it. Why do people cargo-cult Bayesianism so much? It's not the only thing in the sequences. The first post, The Simple Truth, big parts of Mysterious Answers to Mysterious Questions, and basically all of Reductionism are about the gears-model skill. Even the name rationalism evokes Descartes and Leibniz, who were all about this skill. My own guess is that Eliezer argued more forcefully for Bayesianism than for gears models in the sequences because, of the two, it is the skill that came less naturally to him, and that stuck.
What would cargo-cult gears models look like? Presumably, scientism, physics envy, building big complicated models with no grounding in reality. This too is a failure mode visible in our community.