As part of a MOOC I just finished (Northwestern’s Content Strategy course, in which there was an interview with IBM’s SVP of the Watson Group, Mike Rhodin) that did a great job of showcasing the potential and future of machine learning. The Watson Group is probably most famous for developing the computer that won on Jeopardy! and it is now deploying that technology to push the boundaries of machine learning.
During the interview, Rhodin explained how Watson can read and understand text. As it can understand text, Watson can learn by reading or obtaining additional information that either will confirm or question a hypothesis. In the latter case, it can then seek out additional information to reach the most likely answer, including looking at historical research and results. It will then give a recommendation and confidence level based on all available information, with the supporting evidence and why the evidence is important. Watson will then analyze whether its recommendations were correct, learn from its mistakes, and effectively get smarter (e.g., why when it played Jeopardy! it got stronger as it completed a column).
Rhodin discussed how Watson is currently helping doctors make more accurate diagnoses. The doctor will tell Watson a patient’s symptoms (Watson can understand spoken English), then Watson will compare these symptoms with what it has read and what is in its knowledge base. It will then narrow down the possible causes and present that information to the doctor. Keep in mind that there is so much research being published daily (and even more historical research) that no doctor can stay on top of all of it. Once Watson has narrowed it down to a few possible causes, it will present these to the doctor with the evidence it generated. The doctor can either do their own research or it may trigger a memory of an article they read in the past. Watson has thus helped the doctor reach a diagnose faster, which often helps recovery rates and reduces treatment costs.
What is exciting is that medical is only one vertical and that machine learning as exemplified by Watson (and I am sure there are competitive products and in a few years Watson will be as dated as a Cray Supercomputer from 1995) can change virtually any industry, including social games. Wouldn’t it be great if we could ask our computer why retention has dropped or which user acquisition channel will have the strongest ROI (try asking Siri that)? What if your game could not only identify players (or more importantly the 0.5 percent driving revenue) likely to churn but also seamlessly reach out to them and take action that keeps them active. What if it could tailor your messaging so that players are more likely to talk to other people about the game and increasing viral marketing. Most importantly, what if you could use Watson to beat your boss in QuizUp :-)?
Watson, and machine learning in general, is going to change the way companies operate. It will help us take all of the data and research being generated and funnel it to improving our products and businesses.
The Business of Social Games and Casino 