How could a computer acquire knowledge of its environment?

Question

I've quite often seen AI respond to John Searle's Chinese room argument by accepting the systems reply: while the man in the room doesn't understand Chinese, the room (the system) as a whole could - or at least the Chinese room argument doesn't conclude that it could not.

Yet the systems reply doesn't seem to address the main problem the CRA poses for AI. One of Searle's premisses says that symbols are semantically vacant, are intrinsically meaningless, that in themselves they don't indicate what they mean or refer to. And all the computer gets is the symbol (i.e. tokenized shape).

Given that computers do process symbols, the sensory symbols received from digital sensors will not indicate what was sensed. How, then, is it possible for a computer to come to know and understand the world?

Answer 1

Let's look at "Chinese Room". The words traverse the optic nerve as a complicated neural pattern with no semantic significance. Once they hit the brain, the brain can assign meaning to the letters, words, and phrase. If they hit the brain of someone only literate in Chinese, they'd look like meaningless symbols.

Therefore, the room and the brain are alike in receiving meaningless input and assigning meaning. The processes in the room assign meaning to the symbols that Searle doesn't see any meaning in.

It's important to remember the context of the Chinese Room, the Turing Test. In this, a human communicates with two text interfaces (teletypes in the original), one another human and the other a computer, and attempts to determine which of the other is the human and which the computer. This has been widely been considered a test of artificial intelligence. (Turing did not claim that; he predicted, inaccurately, that the computers would be reasonably successfully in 2000.) Therefore, the comparison is between the Chinese room getting Chinese text and a Chinese speaker getting Chinese text.

Answer 2

I guess much here depends on the definition of "knowledge" and "understanding" -- today, self-driving cars already learn about their environments. (I considered putting quotation marks around "learn" but it is common practice not to in this context.) So for a weak sense of knowledge and understanding, where we require only (say) that there is information in the system that correlates with the world and is used to take desirable actions in the world, today's systems already acquire it. Of course one might be interested in a stronger sense of knowledge and understanding, perhaps involving some conscious awareness. This is what Searle's argument is about, and we're not going to settle that discussion here...

Answer 3

Algorithmically calculable answers must be part of Godel-incomplete systems, with true but unprovable statements. But a strange loop system can form a tangled hierarchy, a network of reinforcement and doubt, like language in use, where tentative uses for symbols are used then refined and meaning created relationally and through interplay and interaction.

When our brains cease to get input from the optic nerve, they don't close part of the brain. It (eventually) hijacks and bootstraps the area to continue to try and create a model of the world as isomorphic as possible to it - as demonstrated iteratively, by patterns of interaction, cross-reference, etc - ie by use.

Your premise that symbols are semantically vacant is untenable, in terms of viewing language in practice - they are imbued with it by use. Mathematical systems reveal consequences to statements already made, that is they don't generate meaning but unpack it. Creative mathematical thinking however, works forwards to consequences & backwards to axioms, then around again, in interaction with the world, creatively.

How, then, is it possible for a computer to come to know and understand the world?

Through interaction, trial and error, exploration, heuristics. Just like us. Whatever means it had to interact, they would be senses - including mental models & simulations, which we use for instance in motion prediction for game playing.

The Chinese room is only intelligent like a deck of cards, or an abacus, say. True Artificial General Intelligence would have to be in a strange loop, not such a flat hierarchy with explicit defined rules.

Answer 4

I mean one possible way to attack it would be to point out that, just because you aren't aware of the semantic information doesn't mean that there isn't semantic information intrinsic to the subject.

So you can try to find patterns within the noise and relations between the patterns. So that you can create word clouds without knowing what the words mean. Like if a sentence works with all symbols the same except one you can infer that there is an equality in some sense between those two words. You might be able to infer important ideas by the number of connections or filler words by the combination of being used frequently but the ability to be left out of a sentence and still be valid according to the syntax.

Now a human observer is capable of inferring quite a lot from the simple fact that this person is a human as are the people who invented the language so there are certain quirks of the human condition that you could infer are so universal that you might start trying to map them.

But even if that language was not Chinese but completely alien to any human you might still try to find regularities, make predictions for the output, check whether the predictions and the actual responses match and refine your predictions because of that i.e. do science.

And to some degree that is already happening with machine learning. You give a program an input, they literally guess (generate a random number) and output that. Then the user tells them the real output and they compare the guess and the real value and change their guess. So idk if they are meant to guess a result of an addition like 2+2 then they might guess 3, receive 4 as answer compute 4-3 = 1 which is positive so they change their parameters to create a bigger number let's say 5 where again they compute 4 -5 =-1 which is negative so they adjust the parameters to get a smaller number but bigger than 3 and so on until they got to 4.

Now they technically still don't know how to add, they just know that 2 on one input and 2 on the other input equals 4 as the output. But from the fact that a swap of the input results in the same output you'd already necessitate an intrinsic symmetry. Now you could be wrong assuming those are numbers but in reality those are words, but you'd still have given them some sort of semantic meaning even if it were wrong.

So if you assume that this is not a 1 way communication but that the computer uses it's outputs to make inferences on the environment than it could also progressively improve it's guesses and assign meaning to things that are arbitrary and meaningless.

Answer 5

There is no way a computer could acquire knowledge.

Knowledge can be defined as interpreted and understood information, that has a meaning. Computers can only process information, meaningless data, they cannot interpret or understand it, find any meanings. Computers don't know or understand anything.

A computer can be programmed to change its behaviour depending on the information it receives. This means that the programmer understands what the information means and can decide what the computer should do with it.

Answer 6

Same way we do. By acquiring data (through senses) and analyzing it.

You have to first have a precise definition of knowledge.

Data -> Information -> Knowledge -> Intelligence

Data is raw input. It always come from senses. Whatever senses your organism or machine have. In humans and computers both we always have it in form of electrical signals.

Information is arranged data. Sort it, average it, find the highest or lowest values, find mode and median and so on.

Knowledge is much more involved. Its about finding rules from information. Such as:

1. Amount paid never exceed 1,000,000 in a month.
2. Whenever customers buy jackets from our shops 65 percent of the time they also buy juices to consume while shopping.

We have never been able to make a computer program that can find rules on its own. All our so-called A.I. work is algorithmical deep down, therefore has no intelligence whatsoever. It don't even make a knowledge base on its own.

With or without our assistance, if an information base is built, then we have whats equivalent to symbols for our computers. The data now has meanings.

On basis of those meanings it can make better decisions about its actions and can have better predictions. Once your self-driving car understand how signals work it obviously drive better.

To understand how signals work (an example of knowing a rule) it must have an idea of all traffic stopping (maximum value of location variables of other cars), colours of signal lights etc. The location values of other cars and the colours of signal lights are symbols for computers.

Once data become symbols it has meanings. Those meanings can then be used in finding rules. Once rules are known to an organism / computer it has knowledge of the system and environment it work in.