To most in the know, IBM’s 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 its 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.