Sure, I specifically meant the marketing though. I kind of get why they do it but I'm not sold on the idea that it's helpful to present AI as an infallible silver bullet.

In terms of systems I'm not sure why IBM would stand out, Microsoft, Amazon and Google all offer relatively coherent pipelines for the engineering side of NLP and all of those are backed up by accomplished research teams. I'm sure you can find everything from total horror stories to beautiful testimonials for all of these platforms.

Unfortunately it's difficult to tell what Google etc are doing with their AI systems, but I get the feeling that Google and Facebook in particular have a culture of focusing on end-to-end statistical machine learning to the exclusion of everything else. I base this intuition on the way google translate is trained end-to-end and without any attempt to integrate grammatical or other background knowledge (which causes it to make mistakes that a rule-based system would easily avoid, e.g. getting the gender of nouns wrong when translating between two languages that have gendered nouns via English, that doesn't, etc).

I think these two companies in particular would be very unwilling to design and build a system like Watson, integrating symbolic techniques alongside statistical ones. They probably recruit so much for statistical machine learning skills that they don't have the know-how to do it anyway.

The funny thing is that, like many large corporations, they probably have ad-hoc expert systems except they don't call them that. Pretty much any sufficiently large and complex system that encodes "business rules" is essentially an expert system. But, because "expert systems failed" companies and engineers will not use the knowledge that came out of expert system research to make their systems better.

That AI winder really got us good.