Entries Tagged ‘Machine Learning’

Will Watson affect embedded systems?

Wednesday, February 23rd, 2011 by Robert Cravotta

IBM’s Watson computer system recently beat two of the strongest Jeopardy players in the world in a real match of Jeopardy. The match was the culmination of four years of work by IBM researchers. This week’s question has a dual purpose – to focus discussion on how the Watson innovations can/will/might affect the techniques and tools available to embedded developers – and to solicit questions from you that I can ask the IBM research team when I meet up with them (after the main media furor dies down a bit).

The Watson computing system is the latest example of innovations in extreme processing problem spaces. The NOVA’s video “Smartest Machine on Earth” provides a nice overview of the project and the challenges that the researchers faced while getting Watson ready to compete against human players in the game Jeopardy. While Watson is able to interpret the natural language wording of Jeopardy answers and tease out appropriate responses for the questions (Jeopardy provides answers and contestants provide the questions), it was not clear from the press material or the video that Watson was performing processing of natural language in audio form or only text form. A segment near the end of the NOVA video casts doubt on whether Watson was able to work with audio inputs.

In order to bump Watson’s performance into the champion “cloud” (a distribution presented in the video of the performance of Jeopardy champions), the team had to rely on machine learning techniques so that the computing system could improve how it recognizes the many different contexts that apply to words.Throughout the video, we see that the team kept adding more pattern recognition engines (rules?) to the Watson software so that it could handle different types of Jeopardy questions. A satisfying segment in the video was when Watson was able to change its weighting engine for a Jeopardy category that it did not understand after receiving the correct answers of four questions in that category – much like a human player would refine their understanding of a category during a match.

Watson uses 2800 processors, and I estimate that the power consumption is on the order of a megawatt or more. This is not a practical energy footprint for most embedded systems, but the technologies that make up this system might be available to distributed embedded systems if they can connect to the main system. Also, consider that the human brain is a blood-cooled 10 to 100 W system – this suggests that we may be able to drastically improve the energy efficiency of a system like Watson in the coming years.

Do you think this achievement is huff and puff? Do you think it will impact the design and capabilities of embedded systems? For what technical questions would you like to hear answers from the IBM research team in a future article?