In today's cognitive computing products and techniques, the perception of greater intelligent responsiveness comes not so much from having true explanatory power, but rather just having strong predictive power over increasingly chaotic and larger data sets.
To give us a third point of reference beside linear regression and neural nets, I'll use some other terms to bring the focus to natural language processing. In 2011, the IBM Watson system demonstrated greater intelligence than the best human opponents in the domain of linguistically challenging factual Q&A. This was based on the ability to quickly produce high confidence answers from a large corpus of unstructured information in response to challenging questions.
The linguistic product that is now based on that system is called the IBM Watson Engagement Advisor. As with other cognitive computing techniques, the product must first be trained to be an effective system in the target domain. The corpus of unstructured information often takes the form of documents, such as instruction manuals, technical reports, journal articles, and wiki pages. During training, the most important entities and relationships expressed in the documents are identified and stored in order to expedite later search and retrieval during Q&A interactions with users of the system. The identification process within a document is often called annotation, and the annotation and storage processes together are called ingestion.
The most important concept in understanding the training is to understand what really drives the identification, or annotation, of the documents. It's simple, really. It's a Q&A linguistic product, and the annotation and ingestion expedite the production of the A's in response to the Q's, so it is imperative to have a strong and large representative sampling of the potential questions in order to train and test the efficacy of the system. The questions encode the key concepts (e.g. entities, relationships and so forth) to which users of the system are interested in getting answers. Annotators for these key concepts are developed and, during ingestion, they are executed upon the documents.
This is the very basic level of explanation about how the linguistic product would learn, or be trained, to be an effective cognitive computing system for a domain, and future entries will dive further into this topic. For now, let it suffice to say that ingestion and training results in a system capable of producing answers from the corpus in response to questions like those used in training.
During a run-time Q&A session with the system, the user begins by posing a natural language question. The question is first analyzed to find the key concepts, and then a multiphased approach is used to dig up the best results from the ingested corpus content. As with training, there's a lot more to be said over time about how the run-time Q&A works, so more interesting future entries to come, and in fact, it's intrinsically related to the training anyway. To conclude and tee up these future entries, I'll say the high order bit here is that a trained Q&A linguistic product seems somehow more intelligent than a linear regression or even a typical neural net application. Why is that? To get a bit more background for that explanation, I'd encourage you to visit or revisit a few of my earlier blog entries about cognitive computing. Compare your perceptions of the intelligence of the minimax algorithm in  with the linear regression method in , and compare  with the neural net in . What's changing?