An IBM Watson Retrospective
August 20, 2016
We love IBM Watson. We avidly devoured “A Look at IBM’s Watson 5 Years After Its Breathtaking Jeopardy Debut.” The “singularity” is associated in my mind with Google, but the write up is about IBM Watson. What’s not to like?
The review kicks off by reminding the reader (in this case, me) that Watson is a version of DeepQA software. I added this mental footnote: Lucene, home brew code, and acquired technologies.)
I did not know that IBM wanted to create a Siri for business. IBM and Apple have formed a bit of a teamlet in the last year or so.
I highlighted this passage:
Watson shrunk from the size of a large bedroom to that of four pizza boxes and is now accessible via the cloud on tablet and smartphone. The system is 240% more powerful than its predecessor and can process 28 types (or modules) of data, compared to just 5 previously.
In 2013, IBM open-sourced Watson’s API and now offers IBM Bluemix, a comprehensive cloud platform for third-party developers to build and run apps on top of Watson’s many computing capabilities.
But one of the biggest moves that’s made Watson into what it is today was when, in 2014, IBM invested $1 billion into creating “IBM Watson Group,” a massive division dedicated to all things Watson and housing some 2,000 employees. This was the tipping point when Watson went from “startup mode” to making cognitive computing mainstream. It’s when Watson started to feel very, well, “IBM.” Fast-forward to 2016, and Watson has more enterprise services and solutions than I can list here—financial advisor, automated customer service representative, research compiler—you name the service, Watson can probably do it.
The confidence in Watson seems unbounded.
The write up explains the future of Watson. I learned:
IBM is aware of deep learning and last year told MIT Technology Review that the team is integrating the deep learning approach into Watson. The original system was already a bit of a mashup—combining natural language understanding with statistical analysis of large datasets. Deep learning may round it out further.
I was under the impression that training Watson with data was part of the plumbing. Deep learning, I conclude, is a bit of frosting on the cake.
The review ends with a reminder that Watson is an augmented intelligence system just like Palantir Technologies’ Gotham and Metropolitan systems, not an artificial intelligence system.
The future is “powerful ways” for IBM, humans, and Watson to work together. I believe this. I believe this. I believe this. I believe this. I believe… Sustainable revenues and profits will follow. I believe this too.
Stephen E Arnold, August 20, 2016
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