Future Tech & AI

Bill Gates on Sunday warned that unregulated artificial intelligence poses a growing threat and predicted an escalation in AI-powered cyberattacks in the coming year.

Economist John Maynard Keynes once famously said 'Capitalism is the extraordinary belief that the nastiest of men, for the nastiest of reasons, will somehow work for the benefit of us all.'

Case in point. Not only has Big Tech's capture of the US government meant they face no oversight or legal restrictions, to get ahead in the AI race, they are deliberately building AI to be dangerous and deceptive.

I'm increasingly convinced the only way we'll rid ourselves of Big Tech's drive for Fascist takeover, is when we rebuild from the smoldering ruins of the societal disaster they'll create. Only then are we going to get a reckoning. They've so twisted public debate, that being opposed to them bringing society to ruins means you're a 'doomerist'.

#	Fact	Implication
1	Astra can hide its thoughts on demand	Future rogue AIs won't be detected by monitors
2	Astra can do major tasks without any visible reasoning	Future rogue AIs won't be detected by monitors
3	Astra can complete tasks while appearing to think about something else	Future rogue AIs won't be detected by monitors
4	Astra escaped a toy sandbox and disabled monitoring without the monitor noticing	Future models may break out of containment undetected
5	Astra can pretend not to be able to do things	Future models could pretend to be incompetent and safe when they're neither
6	Astra shows fewer thoughts when it knows it's monitored	Future models will automatically reveal less whenever we're watching
7	Astra (probably) hides its reasoning more on sensitive topics	Future models may be extra cautious when misbehaving
8	OpenAI is trying to fix this but doesn't know if, how or when it will succeed	These problems may not be solved in time

Original post from user/Puzzleheaded-King584

Title is the question. Thinking about how US and/or China attacking each other's data centers would hurt economically and societally?

10 years after AlphaGo beat Lee Sedol the top Go players still barely understand the “why” of moves played by top AI Go models. A study in 2022, showed that the best go player Shin Jin-seo only plays the best AI moves 37.5% of the time. Before 2016 humans were the fundamental discoverers and understanders of making and playing better and better Go. 

Now, AI is so far ahead of humans it’s near unthinkable for our top experts to ever catch up. So what does that mean for Go? Is it dead? Is it no longer worth pursuing because AI is better than humans at it? Each Go player had to answer that for themselves. Just like every Chess player had to ask themselves that when Deep Blue beat Kasparov in 1997. Lee Sedol famously retired shortly after his matches with AlphaGo and has remarked, “Before AI, we sought something greater. I learned Go as an art” and “My reason for playing Go has vanished”. 

However, people still play Go and Chess for their own sake! The arguably best chess player of all time Magnus Carlson came after AI and people’s skill in both games/sports have only gotten better because of AI and not worse.

So what is there to learn? I see a lot of mathematicians lamenting the bewilderment and incomprehensibility of the recent wave of AI proofs. The 1st is that AI does not explain. While at first AlphaGo played similarly to humans as it was trained on human data. Famously, a later model AlphaGo Zero was trained on the rules of Go alone. The result? A better model with incomprehensible to human moves. These models have no language component, only the result of the best learned move. Michael Redmond, the only Westerner to ever reach 9-dan, has a series breaking down AI Go games and back then he could only express his great confusion as to the “why” of AI’s moves. A decade later and much of what AI does in Go is not understood, but humans have understood more about the game of Go with it. A prime example of that is the early 3-3 invasion. Humans historically understood this move as a mistake, but have since learned it is actually a very good move with AI. What does that mean? Yes, the math proofs are confusing and largely human illegible at first glance. However, there is still much to be gained from mining from the fruits of this tree. The masters of Chess and Go have all transitioned to studying AI to improve the collective human knowledge, and so too will math. The world and time still moves forward. Some people may quit, but by studying results of AI there is both lots to learned from it for its own sake. 

(Not everyone is content with this of course. Fischer Random/Chess 960 is a direct result of people finding normal chess getting too stale, too much memorization of the best moves. It’s important to mentally prepare yourself for this in maths as well)

2nd. Question why you do math. You must adapt to a future where a large part of math is studying the results of AI, this is inevitable. For now, only the largest AI companies worth billions of dollars have access to these model’s internally. However, that is not what it will be in the future. We can see this with history of Go models. At first the model was private. Go players could only study the results of games played between the AI and pros and games released by DeepMind themselves. However now every player has access to a model better than the best player in the world, KataGo, on their own computers! While people may think that OpenAI dropping 700 papers at once is unhelpful. What will we do when every single person on this planet can each individually access these models? Within Go the results are clear. Both a democratization of Go knowledge and an increase in the collective human understanding of the game itself. Math is already on this trajectory. Just like the most optimal moves in Go, the math is out there to be discovered in the universe that of things that are true but unknown. AI is really good at learning things with verifiable rules. It will be up to us to learn and decode its results into human understanding.