I found a neat essay discussing the history of Doug Lenat, Eurisko, and cyc here. The essay is pretty cool, Doug Lenat made one of the largest and most systematic efforts to make Good Old Fashioned Symbolic AI reach AGI through sheer volume and detail of expert system entries. It didn’t work (obviously), but what’s interesting (especially in contrast to LLMs), is that Doug made his business, Cycorp actually profitable and actually produce useful products in the form of custom built expert systems to various customers over the decades with a steady level of employees and effort spent (as opposed to LLM companies sucking up massive VC capital to generate crappy products that will probably go bust).
This sparked memories of lesswrong discussion of Eurisko… which leads to some choice sneerable classic lines.
In a sequence classic, Eliezer discusses Eurisko. Having read an essay explaining Eurisko more clearly, a lot of Eliezer’s discussion seems a lot emptier now.
To the best of my inexhaustive knowledge, EURISKO may still be the most sophisticated self-improving AI ever built - in the 1980s, by Douglas Lenat before he started wasting his life on Cyc. EURISKO was applied in domains ranging from the Traveller war game (EURISKO became champion without having ever before fought a human) to VLSI circuit design.
This line is classic Eliezer dunning-kruger arrogance. The lesson from Cyc were used in useful expert systems and effort building the expert systems was used to continue to advance Cyc, so I would call Doug really successful actually, much more successful than many AGI efforts (including Eliezer’s). And it didn’t depend on endless VC funding or hype cycles.
EURISKO used “heuristics” to, for example, design potential space fleets. It also had heuristics for suggesting new heuristics, and metaheuristics could apply to any heuristic, including metaheuristics. E.g. EURISKO started with the heuristic “investigate extreme cases” but moved on to “investigate cases close to extremes”. The heuristics were written in RLL, which stands for Representation Language Language. According to Lenat, it was figuring out how to represent the heuristics in such fashion that they could usefully modify themselves without always just breaking, that consumed most of the conceptual effort in creating EURISKO.
…
EURISKO lacked what I called “insight” - that is, the type of abstract knowledge that lets humans fly through the search space. And so its recursive access to its own heuristics proved to be for nought. Unless, y’know, you’re counting becoming world champion at Traveller without ever previously playing a human, as some sort of accomplishment.
Eliezer simultaneously mocks Doug’s big achievements but exaggerates this one. The detailed essay I linked at the beginning actually explains this properly. Traveller’s rules inadvertently encouraged a narrow degenerate (in the mathematical sense) strategy. The second place person actually found the same broken strategy Doug (using Eurisko) did, Doug just did it slightly better because he had gamed it out more and included a few ship designs that countered the opponent doing the same broken strategy. It was a nice feat of a human leveraging a computer to mathematically explore a game, it wasn’t an AI independently exploring a game.
Another lesswronger brings up Eurisko here. Eliezer is of course worried:
This is a road that does not lead to Friendly AI, only to AGI. I doubt this has anything to do with Lenat’s motives - but I’m glad the source code isn’t published and I don’t think you’d be doing a service to the human species by trying to reimplement it.
And yes, Eliezer actually is worried a 1970s dead end in AI might lead to FOOM and AGI doom. To a comment here:
Are you really afraid that AI is so easy that it’s a very short distance between “ooh, cool” and “oh, shit”?
Eliezer responds:
Depends how cool. I don’t know the space of self-modifying programs very well. Anything cooler than anything that’s been tried before, even marginally cooler, has a noticeable subjective probability of going to shit. I mean, if you kept on making it marginally cooler and cooler, it’d go to “oh, shit” one day after a sequence of “ooh, cools” and I don’t know how long that sequence is.
Fearmongering back in 2008 even before he had given up and gone full doomer.
And this reminds me, Eliezer did not actually predict which paths lead to better AI. In 2008 he was pretty convinced Neural Networks were not a path to AGI.
Not to mention that neural networks have also been “failing” (i.e., not yet succeeding) to produce real AI for 30 years now. I don’t think this particular raw fact licenses any conclusions in particular. But at least don’t tell me it’s still the new revolutionary idea in AI.
Apparently it took all the way until AlphaGo (sometime 2015 to 2017) for Eliezer to start to realize he was wrong. (He never made a major post about changing his mind, I had to reconstruct this process and estimate this date from other lesswronger’s discussing it and noticing small comments from him here and there.) Of course, even as late as 2017, MIRI was still neglecting neural networks to focus on abstract frameworks like “Highly Reliable Agent Design”.
So yeah. Puts things into context, doesn’t it.
Bonus: One of Doug’s last papers, which lists out a lot of lessons LLMs could take from cyc and expert systems. You might recognize the co-author, Gary Marcus, from one of the LLM critical blogs: https://garymarcus.substack.com/
I guess that I’m the resident compiler engineer today. Let’s go.
The process will reach a fixed point after three iterations. In fancier language, Glück 2009 shows that the fourth, fifth, and sixth Futamura projections are equivalent to the third Futamura projection for a fixed choice of (compiler-)compiler and optimizer. This has practical import for cross-compiling; when I used to use Gentoo, I would watch GCC build itself exactly three times, and we still use triples in our targets today.
Oh, it’s his lucky day! Yud, you’ve just been Schmidhuber’d! Starting in 2003, Schmidhuber’s lab has published research on Gödel machines, self-improving machines which prove that their self-modifications will always be better than previous iterations. They are named not just after Gödel, but after his First Incompleteness Theorem; Schmidhuber et al proved easily that there will always be at least one speedup theorem which a Gödel machine can never reach (for a given choice of axioms, etc.)
Once again the literature on metaheuristics exists, and it culminates in the discovery of genetic algorithms. As such, we can immediately apply the concept of gene-oriented evolution (“beanbag” or “gene pool” reasoning) and note that, if goals don’t change and new genes don’t enter the pool, then eventually the population stagnates as the possible range of mutated genes is tested and exhausted. It doesn’t matter that some genes are “meta” genes that act on other genes, nor that such actions are indirect. Genes are genes.
I’m gonna close with a sneer from Jay Bellou, who I hope is not a milkshake duck, in the comments:
One thing I’ve been missing is takedowns of Rationalist ideology about theoretical computer science. The physics, I can do, along with assorted other topics.
Awesome, informative post!
Those are some neat links! I don’t think Eliezer mentions the Godel Machines or the metaheuristic literature anywhere in the sequences, and given his fixation on recursive self improvement he really ought to have. It could be a simple failure to do a proper literature review, or it could be deliberate neglect given that the examples you link show all of these approaches max out (and thus illustrate a major problem with the concept of strong AGI trying to bootstrap to godhood, it is likely to hit diminishing returns).