Three months ago I spoke at a conference affectionately titled “Datapalooza” sponsored by IBM. My talk covered how modern AI can infer the features and transformations that make raw data predictive. I’m not sure exactly how many IBM people were in the crowd, but two IBM database and analytics leads grabbed me after the talk:
“We love what you’re doing. The Watson team is attempting to do things like this internally but is nowhere near this yet.” – [names withheld]
What’s interesting is that Watson has been coming up more and more recently when I speak to customers. The billions of dollars IBM has invested to market Watson has created an air of mystery and hype around what artificial intelligence can do. In fact, IBM is expecting Watson to grow to over $1B per year in revenue in the next 18 months. Yet we haven’t seen any prospect choose Watson over Eureqa to date. So what’s going on?
Speaking at IBM’s Datapalooza (2016) in Seattle, WA.
I remember the excitement in the AI lab (CCSL) at Cornell University when IBM’s Watson computer competed in the game show Jeopardy in 2011. A group of us watched live as the computer beat the show’s top player, Ken Jennings.
IBM had pioneered one of the most interactive AI systems in history. Instead of simulating moves in chess more than before (as it’s predecessor Deep Blue had done), Watson appeared to actually “think.” It interpreted speech and searched data sources for a relevant response. It inspired similar technology, like Apple’s Siri and Microsoft’s Cortana, which came out over the next few years.
Unlike Apple, Google, Facebook, and others, however, IBM recognized an enormous opportunity in the market. Every business in the world today stockpiles data faster than can be analyzed. Literally hundreds of billions of dollars in value lies in the applications of this data. Perhaps the technology that could win quiz competitions like Jeopardy could unlock some of this value as well. IBM decided to step out of the safe confines of a specific application, and attempted to work with business data and real-world problems with commercial deployments of Watson.
The interest in IBM Watson tied to Jeopardy over 10 years.
Coincidentally, I began working on the preliminary technology behind Eureqa around the same time. Eureqa was focused on a broader challenge; instead of trying to interpret sentences and look up responses, Eureqa was tasked with deducing how any arbitrary system behaved – just provide the data/observations. It became the first AI that could think like a scientist and produce new explanations for how any system worked.
The similarity, and the power, of both Eureqa and Watson is that they are examples of Machine Intelligence – meaning the answers they output can be meaningfully interpreted and understood, as opposed to some statistical prediction or data visualization. But this is where the similarities end.
Watson’s great challenge has been adapting its technology for answering trivia questions to real business problems. Despite the prevalence of unstructured text data, very few new business problems appear to be blocked by the ability to look up relevant information in text. From the WSJ: “According to a review of internal IBM documents and interviews with Watson's first customers, Watson is having more trouble solving real-life problems.”
The data that most businesses have today consists of log data, event data, sensor data, sales data, or other numeric data (data where Watson’s core technology doesn’t apply). The major questions they need answered relate to what causes other things to happen, what triggers or blocks certain outcomes, or simply what’s possible with the data I have and how do I even begin? To me, the key interest and success behind Eureqa has come from its applicability to real business data and problems. It finds physical relationships, and interpretable models, which answer these types of questions directly.
Earlier this year, IBM announced they are splitting the different components inside Watson into individual services instead of trying to map a complete solution for customers. They may no longer be a pioneer in the space, but perhaps they're starting to acknowledge what businesses need most today.