This Article IsCreated at 2024-03-02Last Modified at 2024-03-02Referenced as ia.www.f20

Learning to Adapt and Interfere

I had the following thoughts when thinking about the essence of learning while half-awake.

to adapt to circumstances

河流改道 River channel migration

You know how rivers change which way they flow depending on many factors. Can we say that the river has learned about the terrain, or it understands the terrain? If anything, the content of some memory (in which way the river flows, in this case) changed because of the environment has changed. A channel migration is made up by many locallized decisions of water (which way they will flow).

I wonder what other things in nature has memory?

What constitutes as “learning” is rather a philosophical question, not an empirical one. after all, “learning” just means “trying to do something better and successful at that”, not describing how the subject can do that thing better. After all, rivers do “learn” which way they flow and the knowledge is used afterwards (until next time they change course).

Compression Algorithms

Compression algorithm adjust the dictionary to fit the compressed text. They can recognize patterns and beat you at rock-paper-scissors. In a sense, they well-deserved the name of artificial intelligence.

rock-paper-scissors bot with LZ state transition

Text Classification with Gzip:

I wonder what will if lossy compression will perform better here.

to interfere with something’s fate

Let’s say you want to change a thingthing‘s current state SS to a state you want, SS', and you want to interfere it by FF with least effort minimize (effort(F)effort(F)).

efforteffort here is defined as however you want, but is usually “energy spent”.

Other than go straight to the thingthing and fiddle with it, some tasks might have more civilized energy-efficient ways to solve them.

insert your will into the thingthing

entangle with it and wills it. be its leader. give incentive for it to do the bidding for you. give others incentive for them to change the thingthing for you.

Oh no, I’m already seeing AI contractor hire AI contractor hire AI contractor hire AI contractor hire a human to do tasks.

observing the present more closely

other than studying history, observing the present closely helps with decision making.

say no to oneshot image recognition. feed the model more visual input from different angles, and let it look at the images at its own pace. energy cost is why nothing in nature can do image recognition in constant time.

let time past and wait for a better opportunity

sometimes, just let things play out and interfere at the right moment saves energy. if you can wait, wait. yes, that means making models be able to signal “sleepiness”.