To most in the know, Watson has long been considered more hype and marketing than technical reality. Presented as infinitely capable, bleeding edge technology, you might think the well-known Watson brand would be delivering explosive growth to IBM.
Reality is far different. IBM’s stock is down in a roaring market. The company is, in effect, laying off thousands of workers by ending it’s work-from-home policy. More than $60M has perhaps been wasted by MD Anderson on a failed Watson project. All of this is happening against the backdrop of a rapidly expanding market for Machine Learning solutions.
But why? I saw Watson dominate on Jeopardy.
And dominate it did, soundly beating Ken Jennings and Brad Reuter. So think for a moment about what Watson was built to do. Watson, as was proven then, is a strong Q&A engine. It does a fine job in this realm and was truly state of the art…in 2011. In this rapidly-expanding corner of the tech universe, that’s an eternity ago. The world has changed exponentially, and Watson hasn’t kept pace.
So what’s wrong with Watson?
- It’s not the all-encompassing answer to all businesses. It offers some core competencies in Natural Language and other domains, but Watson, like any Machine Learning tech, and perhaps more than most, requires a high degree of customization to do anything useful. As such, it’s a brand around which Big Blue sells services. Expensive services.
- The tech is now old. The bleeding edge of Machine Learning is Deep Learning, leveraging architectures Watson isn’t built to support.
- The best talent is going elsewhere. With the next generation of tech leaders competing for talent, IBM is now outgunned.
- …and much more discussed here.
The Machine Learning market is strong and growing. IBM has been lapped by Google, Facebook, and other big name companies, and these leaders are open sourcing much of their work.
Will Watson survive? Time will tell.
