Lawrence Krubner 🇺🇸🇺🇦

Put me on a desert island with nothing but some clothes, some fishing nets, a few weeks of food, a good knife, a good axe, and a good whetstone, and I could eventually rebuild a reasonably good AI Large Language Model. This might take me 20 years, but I could do it. The island would need to have at least one large river with a strong current, since I would not have electricity, so I would need the current of the river to power the AI. Obviously I would need to spend the first few weeks building a home and setting up the nets to give me a reliable supply of food that I could get without too much effort, but then I could get to work. The "Large Language Model" would be restricted to what I have in my head, but that is still a reasonable amount. I would need to encode what I remember of John Doe, Satan's speech in Milton's Paradise Lost, the parts I remember of Shakespeare and Hemmingway and Fitzgerald and Doris Lessing. I have some of my own essays memorized, and I could write some more. All that matters is that I encode enough text that I can train the model. This might take me 7 or 8 years, but I could eventually encode a reasonably large amount of text. I would end up with a very small Large Language Model. Some people think you need electricity to build a computer but that is not correct. Back in 1936 Alan Turing described an "infinite state machine" which became our computers, but there is no electricity in his description. You only need something that goes around and around and around, so if you build an old fashioned waterwheel, like you see on any old mill, then you can build a computer. This is why I said the island would need to have at least one big river with a strong current. I would spend the first two years setting up the infrastructure: a waterwheel powered by the river, a drive shaft, some building to house it all, multiple "state machine" wheels that would hold a large number of uniform sticks, which could be pushed left (0) or right (1) which would set me up to start binary programming. I would probably use different wheels to encode different "software libraries", such as one wheel for math operations, one wheel for text operations. I would need some kind of clutch to move the "reader" from one wheel to another, as I engage different "libraries." Figuring out the clutch would take some careful thought, but if I hardcode the number of wheels and what they do then the work becomes easier (that is, if I hardcode "wheel 4 is for math operations, wheel 5 is for text operations" then this becomes easier). I would have to get good at wood working, but presumably, if it is the only thing I do for 20 years, I will get good at it. The most difficult trick would be figuring out the knots that, on the one hand, are firm enough to keep the sticks in place but, on the other hand, are flexible enough that the sticks can be pushed to the other position, when necessary. I would have to get the knots right so they could imitate the yes/no behavior of semiconductors. Once I had the machinery working, I would have to develop the "software," though the "software" would be patterns in the sticks that can be either left or right. I would need standard patterns to handle strings of text, and other standard patterns to handle numbers. This would take me 6 or 7 years, but I could do it. I've read the original LLM paper from 2017 (Attention is all you need) so I get the basic idea, and (in real life) I'm working on specialized n-gram models that are informed by the NLP advances of recent years. I would eventually have working AI. This might be a stupid way to spend 20 years on a desert island but at least it would keep me busy. But here is the important point: there are now tens of thousands of engineers who can all do the same thing. The knowledge is widespread. The idea that AI will go away is ludicrous. Now that we know how to do it, we realize how easy it is. I've told this story before, but a few years ago I was on the subway and I was sitting next to a Hispanic woman who was traveling with her daughter, who I judged to be 6 or 7 years old, and the mom was drilling the daughter on some of the math the daughter had recently learned: Mom: "What is 3 plus 4? Daughter: "7" Mom: "What is 9 plus 0?" Daughter: "9" Mom: "What is 7 minus 0?" Daughter: "7" The daughter clearly knew what zero was, and how to use it. By contrast, many of the most brilliant people who ever lived didn't know about zero: Plato, Aristotle, Ptolemy. They lived and died with no concept of zero. Some ideas are like that: before they are invented the most brilliant people alive don't know about it, but after they idea is invented then a 6 year old girl can easily learn it. AI is like that. The 2017 paper (Attention is all you need) described the "self-attention dot-product" technique, which is a branch of Linear Algebra. And now that we know it, we realize it is relatively easy. Put me on a desert island and I can re-invent the whole thing. And many other engineers can do the same. Therefore, AI is not going away, it is with us permanently, like the concept of zero. People need to get used to this.

America and Europe are asleep. A deep sleep. A coma. How is this allowed? Why don't we take action to stop this? And the EU in particular. There is on their doorstep. Why is the EU unable to do anything?

Achen and Bartels cover this in their book "Democracy For Realists." The average voter knows very little. And yet we have an abundance of research showing that people are better off in democracies. The reason democracies work so well does not seem to have anything to do with voters being informed. Rather, the benefits seem to be more mechanical: the parties rotate in power, which limits corruption, since each party worries about going to jail if the other parties win the next election.

This is the New York Times! President Trump admires the backside of Senator Jon Husted.