SYSTEM AND METHOD FOR VIGOROUS ARTIFICIAL INTELLIGENCE
A system and method for predicting a characteristic of an object in an artificial intelligence system. The method includes evaluating the object using a first model to produce a first prediction of a characteristic of the object. The object is evaluated using a second model to produce a second prediction of the characteristic of the object, the second model being dissimilar to the first model. A final prediction of the characteristic of the object is generated as a function of dynamic weightings of the first prediction and the second prediction.
1 . A method of characterizing an object input to an artificial intelligence (AI) system, comprising:
evaluating said object using a first model to produce a first prediction of a characteristic of said object;
evaluating said object using a second model to produce a second prediction of said characteristic of said object, said second model being dissimilar to said first model; and
generating a final prediction of said characteristic of said object as a function of dynamic weightings of said first prediction and said second prediction.
2 . The method recited in claim 1 , wherein evaluating said object using said first or second model further comprises determining a quality of said first or second prediction, respectively.
3 . The method recited in claim 2 , wherein said quality comprises a measure of the confidence in said prediction.
4 . The method recited in claim 2 , wherein said dynamic weightings are a function of said quality.
5 . The method recited in claim 1 , wherein said dynamic weightings are a function of at least one external input.
6 . The method recited in claim 1 , wherein said dynamic weightings are a function of at least one predefined rule.
7 . The method recited in claim 1 , wherein said evaluating said object using first and second models are executed in parallel.
8 . The method recited in claim 1 , wherein said first model comprises a neural network.
9 . The method recited in claim 8 , further comprising training said first model using a corpus of data.
10 . The method recited in claim 1 , wherein one of said first and second models comprises a Fast Fourier Transform.
11 . An artificial intelligence (AI) system for characterizing an input object, comprising:
at least one processor; and,
at least one memory, said at least one memory containing instructions which, when executed by said at least one processor, are operative to:
evaluate said object using a first model to produce a first prediction of a characteristic of said object;
evaluate said object using a second model to produce a second prediction of said characteristic of said object, said second model being dissimilar to said first model; and,
generate a final prediction of said characteristic of said object as a function of dynamic weightings of said first prediction and said second prediction.
12 . The AI system recited in claim 11 , wherein evaluating said object using said first or second model further comprises determining a quality of said first or second prediction, respectively.
13 . The AI system recited in claim 12 , wherein said quality comprises a measure of the confidence in said prediction.
14 . The AI system recited in claim 12 , wherein said dynamic weightings are a function of said quality.
15 . The AI system recited in claim 11 , wherein said dynamic weightings are a function of at least one external input.
16 . The AI system recited in claim 11 , wherein said dynamic weightings are a function of at least one predefined rule.
17 . The AI system recited in claim 11 , wherein the operations of evaluating said object using said first model and evaluating said object using said second model are executed in parallel.
18 . The AI system recited in claim 11 , wherein said first model comprises a neural network.
19 . The AI system recited in claim 18 , further comprising the operation of training said first model using a corpus of data.
20 . The AI system recited in claim 11 , wherein one of said first and second models comprises a Fast Fourier Transform.
21 . An artificial intelligence (AI) modulator for characterizing an object, comprising:
a processor; and,
a memory, said memory containing instructions which, when executed by said processor, are operative to cause said AI modulator to:
receive a first evaluation of said object from a first model, said first evaluation comprising a first prediction of a characteristic of said object;
receive a second evaluation of said object from a second model, said second evaluation comprising a second prediction of said characteristic of said object, said second model being dissimilar to said first model; and,
generate a final prediction of said characteristic of said object as a function of dynamic weightings of said first prediction and said second prediction.
22 . The AI modulator recited in claim 21 , wherein said first or second evalutions of said object further comprises a quality of said first or second prediction, respectively.
23 . The AI modulator recited in claim 22 , wherein said quality comprises a measure of the confidence in said prediction.
24 . The AI modulator recited in claim 22 , wherein said dynamic weightings are a function of said quality.
25 . The AI modulator recited in claim 21 , wherein said dynamic weightings are a function of at least one external input.
26 . The AI modulator recited in claim 21 , wherein said dynamic weightings are a function of at least one predefined rule.
27 . The AI modulator recited in claim 21 , wherein one of said first and second models comprises a neural network.
28 . The AI modulator recited in claim 21 , wherein one of said first and second models comprises a Fast Fourier Transform.