• By
  • Michael Hickins

IBM’s Watson supercomputer has proven that its speed-reading and data-processing skills can trump all comers on Jeopardy. But the next generation supercomputer will go a step further: use its database of knowledge to come up with reasoned responses.

So says Eduard Hovy, director of the Information Sciences Institute at the University of Southern California, whose work contributed to the development of Watson.

According to Mr. Hovy, Watson’s prowess (and progress), demonstrated by the supercomputer’s successful stint on the game show Jeopardy, wasn’t really news to the computer science community; however, the demonstration “shows the world that language technology, and artificial intelligence in general, is advancing.”

Source: IBM
IBM’s Watson supercomputer competes on the game show Jeopardy!

Mr. Hovy told Digits the next, more important step, is on the horizon: RACR (which stands for either Reading and Contextual Reasoning or Reading and Contextual Reasoner — Mr. Hovy said the research teams haven’t settled on which name to use).

That next generation, perhaps only five years away, will be able to do something Watson can’t, which is to learn background knowledge on given topics and then “do reasoning about that.”

The difference is subtle but important. Watson uses its size and parallel processing capabilities to speedily match parts of speech with reams of information it finds on the Internet, and then pick the most likely answer to a given question. But it can’t think contextually, bringing interrelated facts to bear on a particular problem. It’s the difference between speed-reading to find an answer in a textbook,versus being able to use experience with similar situations to solve a problem.

RACR, the successor generation to Watson, will be able to find answers to questions by understanding context, and reasoning based on background knowledge that’s been fed into its memory. It will also be able to make more sophisticated decisions about which pieces of information are trustworthy by using qualitative “indirect measures,” such as whether a given piece of information is praised by third parties.

Mr. Hovy’s team is currently adding “billions of pieces of background knowledge, creating extract rules for the computer to follow, and then testing those rules.”

Further down the line, said Mr. Hovy, his team, and others also working with IBM on this project, will begin developing machines that can begin to teach themselves.

If Watson isn’t already sparking fears of a world dominated by evil automatons — “baggage that is not warranted,” said Mr. Hovy — then maybe this new generation will.


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