CAML is a concept in the field of artificial intelligence and machine learning that refers to the use of contextual information to improve the performance of machine learning models. The specific contextual information used, and the way it is incorporated into the model can vary depending on the application.

In the past decade, progressive business leaders have been advocating for reasoning from first principles to solve the unsolvable: basic assumptions deconstructed to the most fundamental truths. To understand the benefits of first principle thinking we must explore the alternative: reasoning by analogy. Two or more things have perceived similarities and are typically used to infer some further similarity which has yet to be observed. However, this does not drive innovation and likely, in the world of machine learning, will lead to false positives.

In the data realm, to overcome this shortcoming, we require voluminous data sets to build towards a more generalized solution in our problem space. Complicating the situation further, this requires massive computational power, long training cycles, and will likely not eradicate false positives…more

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