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/
To elaborate on the other answers about alphaevolve. the LLM portion is only a component of alphaevolve, the LLM is the generator of random mutations in the evolutionary process. The LLM promoters like to emphasize the involvement of LLMs, but separate from the evolutionary algorithm guiding the process through repeated generations, LLM is as likely to write good code as a dose of radiation is likely to spontaneously mutate you to be able to breathe underwater.
And the evolutionary aspect requires a lot of compute, they don't specify in their whitepaper how big their population is or the number of generations, but it might be hundreds or thousands of attempted solutions repeated for dozens or hundreds of generations, so that means you are running the LLM for thousands or tens of thousands of attempted solutions and testing that code against the evaluation function everytime to generate one piece of optimized code. This isn't an approach that is remotely affordable or even feasible for software development, even if you reworked your entire software development process to something like test driven development on steroids in order to try to write enough tests to use them in the evaluation function (and you would probably get stuck on this step, because it outright isn't possible for most practical real world software).
Alphaevolve's successes are all very specific very well defined and constrained problems, finding specific algorithms as opposed to general software development