Wow. I already had high expectations for this discussion, but reading this definitely made my day. I love how both plex and Audrey Tang were taking each other seriously. Gives me glimpses of hope that we as humanity will actually be able to coordinate.
I agree that cheap labour isn't great for the majority. Another thing I kept thinking to myself is how labor is too cheap in India. For example when I saw people farming without a tractor using a cow instead. Why revolt instead of asking for a higher salary or quit? Conditions in India seem to be improving pretty rapidly.
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?
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.
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!
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.
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.
Embarrassingly enough I don't think past me ever tried to defrost it in the oven. Tried to put it in the fridge the day before and then microwave, which was not very effective. Just too excited to use microwave even for things where I know they don't work well because my parents didn't use to have one that I did consider the oven for this. Currently I am sharing a tiny freezer shelf with 7 people, but I might try this in the future.