IBM Watson Health cuts back Drug Discovery 'artificial intelligence' after lackluster sales

IBM’s Health division has been crumbling for a while.

Last year, it laid off a large chunk of its employees after it failed to win hospital contracts. The failure was, apparently, caused by using subcontractors and cheap H1B visa holders.

Fixed that for you.

IBM does not understand that parsing and weights are only a small part of AI technology, that although the texts created with the help of AI-parsing seem to be sufficient - it is not so, they are not sufficient though necessary! These structured texts do not explain what the words mean, but merely mechanically explain how they are used.

Look up the definition of almost any word in a dictionary? It says what the word means and usually gives an example of how it is used. Training AI on terabytes you get only an example of how it is used! Only a portion of that dictionary definition!

Patent Search is a litmus test and you can claim that you have AI only if you have it, because it can be done ONLY using the true the words' meanings. Which are in the dictionary. However IBM has only Augmented Intelligence, it uses only the explanatory, second part of dictionary definitions... Thus IBM abandoned pharmacology, it has no Artificial Intelligence.

I suppose that when most of your profit comes from telling people

its really expensive to make drugs then if its cheap to make them then your whole modus operandi goes away.

I mean that your search phrase, extended with synonyms, is filtered through your profile, and a textual array of considerable size is created. That is, you are looking for information not with one or two phrases, but with hundreds and thousands; where a very significant part of the search phrases comes from subtexts, both dictionary definitions and examples.

If you train texts in your profile only by examples and not by dictionary definitions, you will learn how patterns are used without knowing what their words mean. That is, the computer is purely mechanically, without understanding, looking for context-close texts on examples.

For instance Google and Yandex translations. This is not a translation! This is the tragedy! The computer does not understand what and how it does!

It seems to me that IBM Watson also does not understand what it is searching for, for example, in pharmacology. Accordingly, IBM is losing this market, as well as all others.

Apparently I'm the only one who understands AI technology.

I wonder how much of my speculation is true?

There is a sentence:

"After the publication was made, the correspondence was lost."

There are two synonyms in the sentence: "publication" and "correspondence"; and therefore "was made" and "was lost" refer to both. But IBM Watson (probably!) works with only these two whole patterns: "publication was made" and "correspondence was lost", not noticing that there are two synonymous there.

In other words, the sentence produces four patterns - two descriptive parts refer to each of the two synonyms. However, IBM Watson (probably!) only gets two, and therefore loses two possible matches, 50% of the information. IBM (presumably!) loses 50% of information! And only because IBM (presumably!) trains the whole patterns! And doesn't see the synonyms/ thesaurus.

Therefore, it is surprising, if we accept the assumption that IBM trains whole patterns, that IBM Watson finds something at all. But it becomes clear why IBM throws away a very profitable project...

I wonder how much of my speculation is true?

IBM fails to undersrand its new customers and what they want.

I say new as the number of old IBM customers is shrinking due to blues hook and rip practises.

Can you leverage AI for medicine. Unproven. IBM cannot though.

Another hypothesis

When you make your searching profile, you get sets of patterns into their contexts. This method was applied by IBM in Jeopardy!: IBM initiated a context for each question, using dictionaries and encyclopedias as its subtext. Then IBM looked for pieces of the most relevant texts, which have the most of search patterns.

IBM called this technology Annotation Query Language.

A hypothesis: in pharmacology, this did not work: almost all pharmacological information is HISTORICALLY contained in manually structured data, i.e. has no explicit contexts/ very few patterns but only implicit subtexts. IBM, therefore, cannot search for answers, having a lot patterns and almost none in data! IBM knows not how to restore subtexts? But IBM knew at Jeopardy!...

AI can restore because it annotates words by their definitions-subtexts.

This is another idea why IBM dropped pharmacology.