IBM Defines Information Access the Madison Avenue Way
October 7, 2015
Yesterday (October 6, 2015) I wrote a little dialogue about the positioning of IBM as the cognitive computing company. I had a lively discussion at lunch after the story appeared about my suggesting that IBM was making a grand stand play influenced by Madison Avenue thinking, not nuts and bolts realities of making sales and generating revenue.
Well, let’s let IBM rejiggle the line items in its financial statements. That should allow the critics of the company to see that Watson (which is the new IBM) account for IBM revenues. I am okay with that, but for me, the important numbers are the top line revenue and profit. Hey, call me old fashioned.
In the midst of the Gartner talk about IBM, the CNBC exclusive with IBM’s Big Blue dog (maybe just like the Gartner talk and thus not really “exclusive”?), and the wall paper scale ads in the New York Times and Wall Street Journal, there was something important. I don’t think IBM recognizes what it has done for the drifting, financially challenged, and incredibly fragmented search and content processing market. Even the LinkedIn enterprise search discussion group which bristles when I quote Latin phrases to the members of the group will be revivified.
Indexing and groupoiing are useful functions. When applied with judgment, an earthworm of unrelated words and phrases may communicate more effectively.
To wit, this is IBM’s definition of Watson which is search based on Lucene, home brew code, and IBM acquisitions’ software:
Author extraction—Lots of “extraction” functions
Concept expansion
Concept insights—I am not sure I understand the concept functions
Concept tagging—Another concept function
Dialog—Part of NLP maybe
Entity extraction—Extraction
Face detection with the charming acronym F****d—Were the Mad Ave folks having a bit of fun?
Feed detection—Aha, image related
Image Link extraction—Aha, keeping track of urls
Image tagging—Aha, image indexing. I wonder is this is recognition or using information in the file or a caption
Keyword extraction
Language detection
Language translation
Message resonance—No clue here in Harrod’s Creek
Natural language classifier—NLP again
Personality insights—Maybe figuring out what the personality of the author of a processed file means?
Question and answer (I think this is natural language processing which incorporates many other functions in this list)—More NLP
Relationship extraction—IBM has technology from its purchase of i2 which performs this function. How does this work on disparate streams of unstructured content? I have some thoughts
Review and rank—Does this mean relevance ranking?
Sentiment analysis—Yes, is a document with the word F****d in it positive or negative
Speech to text—Seems similar to text to speech
Taxonomy—Ah, ha. A system to generate a list of controlled terms. No humans needed? Nah, humans can be billable and it is an IBM function
Text extraction—Another extraction function
Text to speech
Tone analyzer—So what is the tone of a document containing the string F****d?
Tradeoff analytics—Hmm. Now Watson is doing a type of analytics presumably performed on text? What are the thresholds in the numerical recipe? Do the outputs make sense to a normal human?
Visual recognition—Baffller
Watson news—Is this news about Watson or news presented in Watson via a feed-type mechanism. Phrase does not even sound cool to me.
Now that’s a heck of a list. Notice that the word “search” does not appear in the list. I did not spot the word “semantics” either. Perhaps I was asleep at the switch.
When I was in freshman biology class in 1962, Dr. Daphne Swartz, a very traditional cut ‘em up and study ‘em scientist, lectured for 90 minutes about classification. I remember learning about Aristotle and this dividing organizations into two groups: Plants and animal. I know this is rocket science, but bear with me. There was the charmingly named Carolus Linnaeus, a fan of herring I believe, who cooked up the kingdom, genus, species thing. Then there was, much later, the wild and crazy library crowd which spawned Dewey or, as I named him, Mr. Decimal.
Why is this germane?
It seems to me that IBM’s list of Watson functions needs a bit of organization. In fact, some of the items appear to below to other items; for example: language detection and language translation. More egregious is the broad concept of natural language processing. One could, if one were motivated, argue that entity extraction, text extraction, and keyword extraction might look similar to a non-Watsonian intellect. Dr. Swartz would probably have some constructive criticism to offer.
What’s the purpose of this earthworm list?
Beats me. Makes IBM Watson seem more than Lucene with add ons?
Stephen E Arnold, October 7, 2015
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