• 5 Posts
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Joined 2 years ago
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Cake day: August 29th, 2023

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  • Given the libertarian fixation, probably a solid percentage of them. And even the ones that didn’t vote for Trump often push or at least support various mixes of “grey-tribe”, “politics is spiders”, “center left”, etc. kind of libertarian centrist thinking where they either avoided “political” discussion on lesswrong or the EA forums (and implicitly accepted libertarian assumptions without argument) or they encouraged “reaching across the aisle” or “avoiding polarized discourse” or otherwise normalized Trump and the alt-right.

    Like looking at Scott’s recent posts on ACX, he is absolutely refusing responsibility for his role in the alt-right pipeline with every excuse he can pull out of his ass.

    Of course, the heretics who have gone full e/acc absolutely love these sorts of “policy” choices, so this actually makes them more in favor of Trump.


  • In terms of writing bots to play Pokemon specifically (which given the prompting and custom tools written I think is the most fair comparison)… not very well… according to this reddit comment a bot from 11 years ago can beat the game in 2 hours and was written with about 7.5K lines of LUA, while an open source LLM scaffold for playing Pokemon relatively similar to claude’s or gemini’s is 4.8k lines (and still missing many of the tools Gemini had by the end, and Gemini took weeks of constant play instead of 2 hours).

    So basically it takes about the same number of lines written to do a much much worse job. Pokebot probably required relatively more skill to implement… but OTOH, Gemini’s scaffold took thousands of dollars in API calls to trial and error develop and run. So you can write bots from scratch that substantially outperform LLM agent for moderately more programming effort and substantially less overall cost.

    In terms of gameplay with reinforcement learning… still not very well. I’ve watched this video before on using RL directly on pixel output (with just a touch of memory hacking to set the rewards), it uses substantially less compute than LLMs playing pokemon and the resulting trained NN benefits from all previous training. The developer hadn’t gotten it to play through the whole game… probably a few more tweaks to the reward function might manage a lot more progress? OTOH, LLMs playing pokemon benefit from being able to more directly use NPC dialog (even if their CoT “reasoning” often goes on erroneous tangents or completely batshit leaps of logic), while the RL approach is almost outright blind… a big problem the RL approach might run into is backtracking in the later stages since they use reward of exploration to drive the model forward. OTOH, the LLMs also had a lot of problems with backtracking.

    My (wildly optimistic by sneerclubbing standards) expectations for “LLM agents” is that people figure out how to use them as a “creative” component in more conventional bots and AI approaches, where a more conventional bot prompts the LLM for “plans” which it uses when it gets stuck. AlphaGeometry2 is a good demonstration of this, it solved 42/50 problems with a hybrid neurosymbolic and LLM approach, but it is notable it could solve 16 problems with just the symbolic portion without the LLM portion, so the LLM is contributing some, but the actual rigorous verification is handled by the symbolic AI.

    (edit: Looking at more discussion of AlphaGeometry, the addition of an LLM is even less impressive than that, it’s doing something you could do without an LLM at all, on a set of 30 problems discussed, the full AlphaGeometry can do 25/30, without the LLM at all 14/30,* but* using alternative methods to an LLM it can do 18/30 or even 21/30 (depending on the exact method). So… the LLM is doing something, which is more than my most cynical sneering would suspect, but not much, and not necessarily that much better than alternative non-LLM methods.)








  • Yeah they are normally all over anything with the word “market” in it, with an almost religious like belief in market’s ability to solve things.

    My suspicion is that the writer has picked up some anti-Ukrainian sentiment from the US right wing (which in order to rationalize and justify Trump’s constant sucking up to Putin has looked for any and every angle to tear Ukraine down). And this anti-Ukrainian sentiment has somehow trumped their worship of markets… Checking back through their posting history to try to discern their exact political alignment… it’s hard to say, they’ve got the Scott Alexander thing going on where they use disconnected historical examples crossed with a bad analogies crossed with misappropriated terms from philosophy to make points that you can’t follow unless you already know their real intended context. So idk.






  • Yeah, he thinks Cyc was a switch from the brilliant meta-heuristic soup of Eurisko to the dead end of expert systems, but according to the article I linked, Cycorp was still programming in extensive heuristics and meta-heuristics with the expert system entries they were making as part of it’s general resolution-based inference engine, it’s just that Cyc wasn’t able to do anything useful with these heuristics and in fact they were slowing it down extensively, so they started turning them off in 2007 and completely turned off the general inference system in 2010!

    To be fair far too charitable to Eliezer, this little factoid has cites from 2022 and 2023 when Lenat wrote more about lessons from Cyc, so it’s not like Eliezer could have known this back in 2008. To sneer be actually fair to Eliezer, he should have figured they guy that actually wrote and used Eurisko and talked about how Cyc was an extension of it and repeatedly refers back to lessons of Eurisko would in fact try to include a system of heuristics and meta-heuristics in Cyc! To properly sneer at Eliezer… it probably wouldn’t have helped even if Lenat kept the public up to date on the latest lessons from Cyc through academic articles, Eliezer doesn’t actually keep up with the literature as it’s published.







  • You need to translate them into lesswrongese before you try interpreting them together.

    probability: he made up a number to go with his feelings about a topic

    subjective: the number is even more made up and feelings based than is normal for lesswrong

    noticeable: the number is really tiny, but big enough for Eliezer to fearmonger about!

    No, you don’t get to actually know what the number is, then you could penalize Eliezer for predicting it wrongly or question why that number specifically. Just trust that the bayesianified language shows Eliezer thought really hard about it.