General Intelligence Does Not Exist

There is a specific case of the use of the word general that is entirely inapprpriate – intelligenece. The problem is that we don’t know and possible it may be impossible to know what constitutes as general intelligence. We do not the significance of what we don’t know.

To assert that an entity has general intelligence would require you to know everything. It’s sort of like how you need to know the entire Earth’s surface area before you can say that someone has explored most of it.

Artificial Intelligence has many people in it working together towards a “General Intelligence”. What they are really doing is moving towards human-like intelligence. This term is a much more accurate description of what they are working towards.

The strange thing about intelligence is that you can not understand all the thoughts of an entity smarter than yourself. This means that a human could never intentionally create an AI greater than themselves. It would require some kind of randomness to create the AI. If you keep generating AI by means of simply producing random code, eventually, you will produce something more intelligent than yourself.

Ants are smarter than humans when it comes to working in large crowds. Humans often crush themselves to death in large crowds. It’s very surprising to say the least that ants behave more intelligently than humans. They have almost no brain compared to us. We can build fusion reactors and smash particles into each other at the speed of light but the tasks that ants accomplish every day are too much for us.

Swarm intelligence is an intelligence we lack. When we’re being crushed to death by people running away from a fire in a crowded building, people scream. This makes it hard. I’ve never really understood this response, screaming achieves nothing other than making it more difficult for those around you to communicate. If you were in isolation screaming does make sense. Anyway, the simple rules that ants have triumph over our neocortex. It reminds me of cellular automata in which simple rules can create complex beautiful behavior.

I strongly advise against using the phrase “general intelligence” again. Use human-like intelligence instead. Perhaps it will turn out that there’s a huge space of problems that we cannot comprehend that are parts of everyday life. That we’re not even aware of. I sometimes get the idea that, perhaps just as we can only view a small part of the visible spectrum (and the non-visible spectrum does have profound consequences on our body) perhaps our mind can only ‘see’ a very small space of problems.

 

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The Nature of Computation

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If you were building an AI for a game of chess it would be strange if you did anything other than modelling the game. But take a look at the universe we live in and the best equations to explain the reality. Some physicists will tell you that time does not exist. That time and space are really made of the same thing. It is my conception that if we put all the greatest minds together working on a chess AI, they would come to the conclusion that there is something more fundamental that the pieces and the squares. There is some more fundamental substance that they are both constructed from that would serve us better when creating an AI. Just as space-time  is named, we’ll call this fundamental matter square-piece.

This square-piece would allow a much more effective AI. An argument against this might be that we don’t know what reality is defined as, but chess *is* defined as pieces on a board obeying various rules therefore it can’t be anything else. This is wrong however. Just because a problem can be defined in a certain way does not mean it cannot be reduced to a simpler problem. Reducing the game so that the AI can make computations on the fundamental matter would be more effective.

I enjoy making wild statements that are difficult to verify (this seems to ruffle some feathers at times but I don’t care), so here’s another: our brains are actually operating on this fundamental square-piece when playing chess. This is how we can still compete current day chess AI that just iterates through potential scenarios on a weak processor that has a ‘mere’ 100 million transistors on it.

I think to exploit this square-piece a cellular automaton is needed. The space of algorithms that can be designed by humans is infinite in size but that does not mean it explores the entirety of the space of algorithms. There are many algorithms our brains could never comprehend that are essential to building intelligent systems. Instead of designing a CA, we need to search for one that does what we need. Play chess well.

Chess Is Dead

Chess AIs are now far better the top GMs (Grandmasters). This has dire consequences for the realm of chess players. It allows cheaters to use AIs to help them play. This devastates the game as it. Previously, cheating would have been impossible at the top level since it would be impractical for a GM to assist you playing. It is now possible to run a program on your smartphone that plays better than the best player in the world. If a person goes to the toilet in a chess match, how do you know that they aren’t putting the board onto their phone for analysis?

Chess online is also ruined. It’s impossible to know if you’re playing against a human or an AI. AI isn’t a plague but it’s more thrilling as a game knowing that you’re up against a human. I am in favour of AI.

It’s time for the GMs to find a new game to play. We have been exceeded by computers. There is no need to keep playing a game that computers have become better than us at. It’s a waste of your time. I don’t mind if you genuinely enjoy chess and continue playing. But as a form of art chess is no more. It is now solved by the brute force of computers.

I am learning to play Go because there is no AI in sight that could come anywhere near the top players on an 19×19 board. It’s exciting to know that the realizations you have cannot be beaten by a computer enumerating all possible moves.

Computers and Optimizing Schedules

When will we begin to use computers for useful things such as scheduling? There are many ways we could vastly improve our life by taking advantage of the computational power we have available. One big area in in scheduling. Schools in particular could take advantage of this. Right now the system is:

Lump people born in a 365 day period together.

Put them in a fixed schedule.

Put them through the school at the same rate regardless of their ability.

In a strong system which took advantage of computation, the following properties would be true:

No groups of people would be  fixed – everyone would be treated as an individual

There would be no fixed schedule. The computer would determine on the fly what your schedule is (for the next x days).

Smarter students would progress through the system faster. This is the whole reason we use it. If all students have to take the same classes then this whole algorithm is pointless.

I’m not an expect on scheduling algorithms but this is a feasible problem. Here’s how the solution would look:

It would need to be run on a very powerful computer. Actually, the more power the computer had the better the scheduling algorithm would work. This is because the bottleneck to how good of a schedule you can design is the amount of computing you can do (as it should be – a sign that you’ve found the solution to a problem is when the bottleneck is computational power and not designing better algorithms).

The only real problem with system is that at the start and end of the scheduling there would be bunching just like in the normal way we do it. Classes can’t run if there’s just one student. It has to wait until there are enough students to make it worthwhile. A student in the middle of his school lifetime would have the most flexibility whereas those at the end would have the least. It’s like how a binomial distribution is. The intervals at the start are quite jerky and jumpy, but the middle is smooth.

The strength of the algorithm would improve as the number of students improved because it allows for greater flexibility. This is why it is ideal to have a larger group.

I think some people would sturggle to imagine what an optimized timetable would look like. It’s pretty simple. The better students progress faster through the system because they have to take fewer classes. The computer would figure out who the best students were by looking at their tests results and so on. There’s actually an episode of Star Trek where a super computer decides everything for a civilization including all moral decisions, and I’m reminded some what of that but this is an ideal calculation for computers. Humans are terrible at optimizing schedules.

Why I believe P = NP

The mainstream belief is that P != NP. Of course nobody knows the answer with certainty otherwise I wouldn’t be making this post. I’m aware that people are going to be angry that I believe P = NP. However I do think it is a losing strategy to try to get everyone to have the same beliefs when it is not certainty but I’ll talk more about that in a later post.

Go.

This is a game played on a 19×19 board. No AI has ever come close to beating a top player. Even with a huge advantage given to the AI it is still crushed. The reason I believe is that our brains are using P = NP algorithms unconsciously. We are not fully aware of the computations our unconscious mind is making.

Most people are uncomfortable with not knowing why they did something so they claim it was “free will” (whatever that might be). “I did x because I chose to” rather than “I don’t know why I did x”. So when I suggest that there may be processes in their brain that they are not fully aware of they take offense.

No AI has ever beaten a competent Go player on a 19×19 board. And this will continue to be the case until we find an algorithm proving P = NP.

The arguments I’ve heard are that the brain is the ultimate parallel processor. If that’s the case, why can AIs beat humans at small boards? The important thing to note is that as the board size increases, the humans get better and better against AI. My intuition says that this is because the number of potential games increases by 2^n where n is the number of intersections (squares) on the board. Our primitive AIs are exploring all of this tree whereas our unconscious mind is doing some sort of mysterious computation (which if we had in writing would prove P = NP) that allows us to not be very affected by the size of the board.