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Cake day: October 4th, 2023

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  • So far, there has been zero or one[1] lab leak that led to a world-wide pandemic. Before COVID, I doubt anyone was even thinking about the probabilities of a lab leak leading to a worldwide pandemic.

    So, actually, many people were thinking about lab leaks, and the potential of a worldwide pandemic, despite Scott’s suggestion that stupid people weren’t. For years now, bioengineering has been concerned with accidental lab leaks because the understanding that risk existed was widespread.

    But the reality is that guessing at probabilities of this sort of thing still doesn’t change anything. It’s up to labs to pursue safety protocols, which happens at the economic edge of of the opportunity vs the material and mental cost of being diligent. Reality is that lab leaks may not change probabilities, but yes the events of them occurring does cause trauma which acts, not as some bayesian correction, but an emotional correction so that people’s motivations for atleast paying more attention increases for a short while.

    Other than that, the greatest rationalist on earth can’t do anything with their statistics about label leaks.

    This is the best paradox. Not only is Scott wrong to suggest people shouldn’t be concerned about major events (the traumatic update to individual’s memory IS valuable), but he’s wrong to suggest that anything he or anyone does after updating their probabilities could possibly help them prepare meaningfully.

    He’s the most hilarious kind of wrong.


  • Ah, if only the world wasn’t so full of “stupid people” updating their bayesians based off things they see on the news, because you should already be worried of and calculating your distributions for… inhales deeply terrorist nuclear attacks, mass shootings, lab leaks, famine, natural disasters, murder, sexual harassment, conmen, decay of society, copyright, taxes, spitting into the wind, your genealogy results, comets hitting the earth, UFOs, politics of any and every kind, and tripping on your shoe laces.

    What… insight did any of this provide? Seriously. Analytical statistics is a mathematically consistent means of being technically not wrong, while using a lot of words, in order to disagree on feelings, and yet saying nothing.

    Risk management is not a statistical question in fact. It’s an economics question of your opportunities. It’s why prepping is better seen as a hobby, a coping mechanism and not as viable means of surviving apocalypse. It’s why even when a EA uses their super powers of bayesian rationality the answer in the magic eight ball is always just “try to make money, stupid”.




  • Adversarial attacks on training data for LLMs is in fact a real issue. You can very very effectively punch up with regards to the proportion of effect on trained system with even small samples of carefully crafter adversarial inputs. There are things that can counter act this, but all of those things increase costs, and LLMs are very sensitive to economics.

    Think of it this way. One, reason why humans don’t just learn everything is because we spend as much time filtering and refocusing our attention in order to preserve our sense of self in the face of adversarial inputs. It’s not perfect, again it changes economics, and at some point being wrong but consistent with our environment is still more important.

    I have no skepticism that LLMs learn or understand. They do. But crucially, like everything else we know of, they are in a critically dependent, asymmetrical relationship with their environment. The environment of their existence being our digital waste, so long as that waste contains the correct shapes.

    Long term I see regulation plus new economic realities wrt to digital data, not just to be nice or ethical, but because it’s the only way future systems can reach reliable and economical online learning. Maybe the right things happen for the wrong reasons.

    It’s funny to me just how much AI ends up demonstrating non equilibrium ecology at scale. Maybe we’ll have that self introspective moment and see our own relationship with our ecosystems reflect back on us. Or maybe we’ll ignore that and focus on reductive world views again.




  • It’s hilarious to me how unnecessarily complicated invoking moore’s law is to say anything…

    With Moore’s Law: “Ok ok ok, so like, imagine that this highly abstract, broad process over huge time period, is actually the same as manufacturing this very specific thing over a small time period. Hmm, it doesn’t fit. ok, let’s normalize the timelines with this number. Why? Uhhh because you know, this metric doubles as well. Ok. Now let’s just put these things together into our machine and LOOK it doesn’t match our empirical observations, obviously I’ve discovered something!”

    Without Moore’s Law: “When you reduce the dimensions of any system in nature, flattening their interactions, you find exponential processes everywhere. QED.”





  • Also meta but while I am big on slamming AI enshitification, I am still bullish on using machine learning tools to actually make products better. There are examples of this. Notice how artists react enthusiastically to the AI features of Procreate Dreams (workflow primarily built around human hand assisted by AI tools, ala what photoshop used to be) vs Midjourney (a slap in the face).

    The future will involve more AI products. It’s worthy to be skeptical. It’s also worthy to vote with your money to send the signal: there is an alternative to enshitification.


  • You can read their blog about the AI-crap, in terms of their approach and philosophy. In general, it is optional and not part of the major experience.

    The main reason I use kagi is immediately obvious from doing seaches. I convinced my wife to switch to it when she ask, “ok but what results does it show when I search sailor moon?” and she saw the first page (fan sites, official merch, fun shit she had forgotten about for years).

    What you need to know is that you pay money, and they have to give you results that you like. It’s a whole different world.