Watson and QAMs in general have not received nearly as much press as the other two capabilities which really fails to recognize the sophistication of what’s going on here. Knowledge Retrieval QAMs are Quite Complex Still there is a crossover area between simple search (what year was George Washington born) versus the much more complex capabilities of QAMs (given these symptoms, identify the best course of medical treatment). For the most part these don’t qualify for what we think of as AI but they’re included here since they are a major form of output from our virtual AI assistants like SIRI. Search can rely on a variety of older techniques associated with much more simple routines. Watson at its historical core is a QAM and this is still its strong suit. A QAM is required to return the single most likely answer while interpreting the context and nuance of the speech or text input. It’s almost not fair to put these in the same category since the standards for search are only to return a list of possible sources in which the answer may be found. Knowledge Retrieval: This field is a combination of search and Question Answering Machines (QAMs like Watson). This field has been largely driven by Recurrent Neural Nets (RNNs). Siri, Alexa, and Cortana represent examples of this field that used to be the butt of jokes but are now more than commercially acceptable. Text and speech processing: This is generally known as Natural Language Processing (NLP), the capability to not only intake speech and text but to understand the nuanced context of strings of words, to perform search or translation on them, and output words and blocks of text and speech. Image and video processing: Largely driven by Convolutional Neural Nets (CNNs) this field has been getting most of the press with capabilities like facial and object recognition. There are three broad capabilities in today’s AI and they are: Three Capabilities of AI (Cognitive Computing)įirst let’s review. It’s time we got in tune with the modern Watson, or more correctly IBM’s Watson Group and its Watson platform and took a look at all there is to offer. But that’s the Watson of 2011, a virtual eon ago in DS time, especially with the leaps-and-bounds progress in AI. Recently we wrote about how the ‘popular’ Watson of Jeopardy fame still lingers in the memories of our non-data scientist colleagues and perhaps misleads them about the capabilities of AI. If you want to exploit the advances we’ve made in AI you need to understand where Watson is today and where it’s heading. It contains all the capabilities for image and video, natural language speech and text input and output, and the most comprehensive knowledge recovery module yet combined together. Summary: IBM’s Watson as it exists today is as close as we’ve come to a single integrated platform for AI.
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