System and Method of Grading AI Assets
A method is provided for grading an artificial intelligence (AI) asset. After an AI asset is received for transaction, its performance is evaluated on a specialized task and a baseline of performance is established based on an evaluated state of the AI asset. The AI asset is then graded based on the evaluated performance in a task-environment. A value is ascribed to the AI asset. The AI asset is made available for transaction on an AI asset exchange. A related method is also provided where a second evaluation and grading are performed after the AI asset is trained.
1 . A method for grading an artificial intelligence (AI) asset, comprising the steps of:
receiving an AI asset for transaction;
evaluating performance of the AI asset on a specialized task and establishing a baseline of performance based on an evaluated state of the AI asset;
grading the AI asset based on the evaluated performance in a task-environment; and
ascribing a value to the AI asset; and
making the AI asset available for transaction on an AI asset exchange.
2 . The method of claim 1 , wherein the AI asset is an AI model and the evaluation step comprises evaluation on a set of test data for which true values are known.
3 . The method of claim 2 , wherein the test data is an MNIST data set.
4 . The method of claim 2 , wherein the baseline is a baseline measurement of accuracy.
5 . The method of claim 2 , wherein the baseline is a baseline measurement of precision.
6 . The method of claim 2 , wherein the baseline is a baseline measurement of recall.
7 . The method of claim 2 , wherein the baseline is a weighted average of precision and recall.
8 . The method of claim 1 , wherein the evaluation is an intrinsic evaluation.
9 . The method of claim 1 , wherein the evaluation is an extrinsic evaluation.
10 . The method of claim 1 , wherein the evaluation is a formative evaluation.
11 . The method of claim 1 , wherein the evaluation is a summative evaluation.
12 . The method of claim 1 , wherein the AI asset is a classification model and the evaluation step includes evaluation in a confusion matrix.
13 . The method of claim 1 , wherein the evaluation is for reliability in a core area of expertise.
14 . The method of claim 1 , wherein the evaluation is for predictability.
15 . The method of claim 1 , wherein the evaluation is for learning/adaptation ability.
16 . The method of claim 1 , wherein the evaluation is for adaptivity.
17 . The method of claim 1 , wherein the evaluation is for ability to recursively self-improve.
18 . The method of claim 1 , wherein the evaluation is for resource or time requirements.
19 . The method of claim 1 , wherein the AI asset is a chatbot or dialogue model and the evaluation incorporates a recurrent neural network (RNN) architecture.
20 . A method for grading an artificial intelligence (AI) asset, comprising the steps of:
receiving an AI asset for transaction;
performing a first evaluation of performance of the AI asset on a specialized task and establishing a baseline of performance based on an evaluated state of the AI asset;
performing a first grading of the AI asset based on the evaluated performance in a task-environment; and
ascribing a first valuation to the AI asset;
following a transaction to a party of the AI asset for training the AI asset, receiving the AI asset back from the party;
performing a second evaluation of performance of the AI asset on the same specialized task and comparing the performance to the baseline;
performing a second grading of the AI asset based on the comparison to the baseline; and
ascribing a second valuation to the AI asset.
21 . The method of claim 20 , further comprising making the AI asset available at the second value.