Yep, I thought about this. More in-person/video interaction with people who had already learned this lesson would have helped. Especially, watching them work or study on things they consider hard while they think out loud would have helped.
Morpheus
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Cheap Labour Everywhere
My Software Setup
Patience and Willingness to Be Slow
What are high-level ways to formalize the dataset-assembly subproblem? What are some heuristics for solving this subproblem? How should we think about/model the problem of solving all three subproblems jointly?I have read the first summary post and this one. I have only skimmed your abstraction posts etc., so maybe I am missing something here. But if you are ultimately aiming for some particular pivotal act routed through human understanding, I think you should spend at least 1-6 months trying to just solve that pivotal act (or trying multiple if it turns out that particular one seems less tractable). Look at what type of knowledge and training data are useful for your brain and go for understanding directly rather than trying to route it indirectly through an autoencoder that you don’t know how to train yet. I am reasonably confident your plan is harder than just trying to go for say adult human intelligence enhancement directly. But even if that is false, I am very confident you will ultimately save a bunch of time by investing enough time in this step. You will save time when training that autoencoder, when debugging and validating any algorithms you run on top of your autoencoder and when trying to learn concepts from your autoencoder. For example, if you go for adult genetic intelligence enhancement, you are going to run into peculiarities of genetics and I think it’s just easier to learn about them directly from a textbook optimized for pedagogy and not just short description length. This should be your first step, not your last step. Listen to Andrej Karpathy and become one with the data!
Notes on “Homology, Genes and Evolutionary Innovation”
Consider Small Walks at Work
Thanks for spotting! Fixed!
help of course, but I was thinking of
Topoisomerase, which untangles DNA. My understanding is if you pull separate strands, Topoisomerase finds the local the crossing points that are under pressure and unties them by cutting and gluing. After being cut and before being glued, the DNA stays attached to the Topoisomerase, so the double strand doesn’t just fall apart.
Some Biology Related Things I Found Interesting
I am interested in participating in some type of commitment like this.
I am not too surprised by this, but I wonder if he still stands by what he said in the interview with Dwarkesh:
Some years ago, in the 2010s, I did some analysis with other people of — if this kind of picture happens then which are the firms and parts of the economy that would benefit. There’s the makers of chip equipment companies like ASML, there’s the fabs like TSMC, there’s chip designers like NVIDIA or the component of google that does things like design the TPU and then there’s companies working on the software so the big tech giants and also companies like OpenAI and DeepMind. In general the portfolio picking at those has done well. It’s done better than the market because as everyone can see there’s been an AI boom but it’s obviously far short of what you would get if you predicted this is going to go to be like on the scale of the global economy and the global economy is going to be skyrocketing into the stratosphere within 10 years. If that were the case then collectively, these AI companies should be worth a large fraction of the global portfolio. So I embrace the criticism that this is indeed contrary to the efficient market hypothesis. I think it’s a true hypothesis that the market is in the course of updating on in the same way that coming into the topic in the 2000s that yes, they’re the strong case even an old case the AI will eventually be biggest thing in the world it’s kind of crazy that the investment in it is so small. Over the last 10 years we’ve seen the tech industry and academia realize that they were wildly under investing in just throwing compute and effort into these AI models. Particularly like letting the neural network connectionist paradigm languish in an AI winter. I expect that process to continue as it’s done over several orders of magnitude of scale up and I expect at the later end of that scale which the market is partially already pricing in it’s going to go further than the market expects.
> Dwarkesh Patel 02:32:28
Has your portfolio changed since the analysis you did many years ago? Are the companies you identified then still the ones that seem most likely to benefit from the AI boom?
> Carl Shulman 02:32:44A general issue with tracking that kind of thing is that new companies come in. Open AI did not exist, Anthropic did not exist. I do not invest in any AI labs for conflict of interest reasons. I have invested in the broader industry. I don’t think that the conflict issues are very significant because they are enormous companies and their cost of capital is not particularly affected by marginal investment and I have less concern that I might find myself in a conflict of interest situation there.
And chemistry??? Its mostly brought into the picture to talk about stoichiometry, the study of the rate and equilibria of chemical reactions. Still, what?
For what it’s worth, deconfusing my patchy chemistry understanding from secondary school by reading proper university chemistry and biochemistry books paid off. For example, my chemistry class hadn’t given me deep enough appreciation for the fact that sometimes a reaction might be thermodynamically favorable, but has horrible kinetics. The same is true for evolution in a lot of places, but I have not seen a lot of people using that intuition. Like in some sense that is obvious, but in another sense I think in a lot of subjects including evolution, econ and learning I was applying that intuition inconsistently, and I definitely didn’t make the distinction that clearly. Since chemistry has explicit energies there that you can calculate, it’s easier to not commit type errors.
I noticed the following paragraphs go into more detail about how the sources relate to the claim. So my example wasn’t well chosen.
I stopped using google as my default search engine and use brave search instead now. Googles AI summary is worse than useless. The first example I tried perfectly illustrates my point. The first paragraph of their AI summary links to 8 different sources. How do those 8 sources relate to the claim? I have no way of knowing without reading all 8 sources. Also, the AI summary takes a longer time to load than the main search results, and it’s lazy loading animation is distracting. I could not find any way to turn it off.
Meanwhile, with brave search I can turn off the AI summary, although I didn’t feel like I had to because the summary was adding value by making it easy to see how the claim was related to the source (sorry no image included, because I don’t know how to take screenshots of mouse-hover-over features like that, which tend to close when you screenshot them). Meanwhile, I haven’t noticed large quality differences between brave search and google. I also tried Kagi, but I could not find any quality differences compared to brave search (although I also didn’t explicitly create a benchmark for myself). If I try to find something so obscure it isn’t indexed on brave search, I mostly use GPT-5 with search or deep research enabled.
For all Arbital content, there is the Arbital scrape index. Most (all?) of that material has been incorporated into Lesswrong’s concept pages.
It is hard to do as a prefix in German, I think. It sounds a bit antiquated to me, but you could try “Jung war X”. But yes, in general, I think you are going to run into problems here because German inflects a lot of words based on the gender.
Your German also gives away the gender. Probably use some language model to double check your sentences.
I queried my brain (I am German) and noticed my claim doesn’t predict the result. Then checked online and I had male and female backwards from what I read in a dictionary once
After checking random words I noticed the bias is the other way around and female is more likely. Google gave me the same. Now I am confused.
Yes, I vaguely remember using AutoHotKey on Windows as well. If you have a system that is working well for you, I would not recommend switching because switching costs. I think I mostly used AutoHotKey to type special characters more easily on my keyboard. That problem was solved by switching to the neoqwertz which was designed by nerds like me who had already found better solutions to all the problems I had. If I have a functionality I use frequently enough that I want a Keybind for it, I can usually implement it as a shell one-liner or get Claude to do it for me. Then I can assign a keybind to it in i3 (It allows different modes like in vim, which allows for a lot of different Keybinds). I do not know a really good tool for text-expansion on linux though. Espanso works but it’s completions are not reliable enough for me to make use of them a lot (I don’t know if AutoHotKey did a really reliable job here either). I don’t know Everything, but from a first look it looks similar to what you might get out of fzf?