Thursday, March 03, 2005
Learning common sense from simple Natural Text parsing
Jiri,
>> 1) Could you please give me an example of two words which are used near
>> each other, but do not have cause-effect relations?
> I'll give you 6. I'm in a metro train right now and there is a big
> message right in front of me, saying: "PLEASE DO NOT LEAN ON DOORS"
> What cause(s) and effect(s) do you see within that statement?
Let imagine that strong AI is in reasoning process.
But in order to make general reasoning AI needs to have background knowledge (common sense). That's what CyCorp is trying to achieve.
Now let's consider what kind of background knowledge can be extracted from statement "PLEASE DO NOT LEAN ON DOORS".
(Obviously this knowledge extraction should be made not in the actual decision making time, because huge amount of text should be parsed and our test statement is just one of many millions statement).
Ok, what we know from the test statement:
- If you think about "lean" - think about "doors" as one of the options.
- If you think about "door" - think about "lean" as one of the options.
- If you say "do not" - think about saying "please" to.
- If you say "do" - think about saying "please" to.
- "Doors" is a possible cause for "Not lean"
- "Doors" is a possible cause for "lean"
- You "Lean" "on" something.
- If you think about "on" - think about "doors" as one of the options.
You can extract more useful information from this sentence.
Even "Please" -> "Doors" and "Doors" -> "Please" have some sense. Not much though. :-)
Statistical approach would help to find what relations are more important than other.
Do you see my point now?
When it's time to make actual decision, AI would have some common sense database which will provide large, but not endless amount of choices to consider.
All these choices would be pre-rated. That would help to prioritize consideration of these choices.
Now let's consider if structure of the main memory should be adjusted in order to transform "Limited AI to Strong AI.
I don't see any reason to change memory structure in order to make such transition.
Additional mechanisms of updating cause-effect relations would be introduced such as experiment, advanced reading, and "thought experiment". But all these new mechanisms would still use the same main memory.
>> 1) Could you please give me an example of two words which are used near
>> each other, but do not have cause-effect relations?
> I'll give you 6. I'm in a metro train right now and there is a big
> message right in front of me, saying: "PLEASE DO NOT LEAN ON DOORS"
> What cause(s) and effect(s) do you see within that statement?
Let imagine that strong AI is in reasoning process.
But in order to make general reasoning AI needs to have background knowledge (common sense). That's what CyCorp is trying to achieve.
Now let's consider what kind of background knowledge can be extracted from statement "PLEASE DO NOT LEAN ON DOORS".
(Obviously this knowledge extraction should be made not in the actual decision making time, because huge amount of text should be parsed and our test statement is just one of many millions statement).
Ok, what we know from the test statement:
- If you think about "lean" - think about "doors" as one of the options.
- If you think about "door" - think about "lean" as one of the options.
- If you say "do not" - think about saying "please" to.
- If you say "do" - think about saying "please" to.
- "Doors" is a possible cause for "Not lean"
- "Doors" is a possible cause for "lean"
- You "Lean" "on" something.
- If you think about "on" - think about "doors" as one of the options.
You can extract more useful information from this sentence.
Even "Please" -> "Doors" and "Doors" -> "Please" have some sense. Not much though. :-)
Statistical approach would help to find what relations are more important than other.
Do you see my point now?
When it's time to make actual decision, AI would have some common sense database which will provide large, but not endless amount of choices to consider.
All these choices would be pre-rated. That would help to prioritize consideration of these choices.
Now let's consider if structure of the main memory should be adjusted in order to transform "Limited AI to Strong AI.
I don't see any reason to change memory structure in order to make such transition.
Additional mechanisms of updating cause-effect relations would be introduced such as experiment, advanced reading, and "thought experiment". But all these new mechanisms would still use the same main memory.
