Saturday, February 19, 2011

IBM has a Natural Language Purpose

I wanted to write a blog post about the advancements of Natural Language Processing in light of the performance of IBM's Watson on the Jeopardy challenge last week.  Natural Language Processing is the science of transforming and interpreting human spoken and written language by artificial means.  Generally this type of study has been limited to academic research due to the high computing power demands.  Now there are even open source software implementations, including many R Natural Language Processing packages.  There is a lot to write about the new advances in NLP.

Instead I came across an interesting editorial on the cheap publicity stunt that is IBM's Watson.  At first I thought the article was a comedy that would make fun of Watson's errors on Jeopardy.  Then I realized the author Colby Cosh is not jesting at all.  This should not be news to me.  The field of Operations Research, which was definitely used to help develop Watson, is a widely misunderstood field.  Cosh has a hard time understanding why IBM would want to develop such a stunt to compete against humans.  Cosh seems to think that the only gain is IBM's shareholders.  I can assure you that if IBM wanted to make money on this venture they would have created a computer that would compete on American Idol.  Jeopardy is no ratings juggernaut in the US.

So what purpose would IBM have for competing on Jeopardy.  Perhaps the idea of "competition" is misleading.  In my eyes I was not seeing if a computer can beat humans in a battle of wits.  I was seeing if a device could interpret, process, and return meaningful information on the same level as human interpretation.  Natural Language Processing is like code breaking.  Similarly mathematics, physics, natural science are like codes to mathematicians, scientists, and engineers.  It is the process of trying to decipher and interpret our natural surroundings.  Language is no different.  I can see it easy for Cosh to think that the sole idea of the competition is to beat humans.  The purpose was simply to decipher the natural language code.  In a better understanding of natural language we can then understand our surroundings a little better.

So why the hype with a computer?
"So why, one might ask, are we still throwing computer power at such tightly delimited tasks,..."
The answer can be found already in the field of Operations Research and Management Science.  Perhaps Cash has purchased a plane ticket in the past few years.  He might have noticed that air transportation has become very affordable due to competitive pricing.  A lot of that is due to optimization and revenue management algorithms in the airline industry.  Perhaps he noticed the increase in quality, service, and price of privatized parcel postage.  The science of better decision making and transportation algorithms have greatly improved supply chain and delivery efficiency.  The list can go on and on.  Artificial Intelligence is probably a poor way of describing computer optimization and machine learning science.  Artificial Intelligence is not going to replace human intelligence but only help improve the human based decisions that we make every day.  IBM has already stated that they wish to improve the medical field with Watson.  Medical diagnosis requires vast amounts of information and Watson can help decipher medical journals, texts, and resources within seconds.  Applications of Watson could be used in third world countries where medical resources are scarce.

I will be looking forward to IBM's advancement with Natural Language Processing.  This offers a new venture into better decision sciences.  Perhaps "smacking into the limits" of artificial intelligence will create a better life for those that use human intelligence every day.

1 comment:

Paul Rubin said...

A couple or three years ago, I chanced to hear talks at the annual INFORMS meeting by people from JIEDDO, the DoD unit created expressly to devise ways to defeat the threat of IEDs. One of them said that they face a significant problem, which might be appropriate for natural language processing combined with machine learning. The problem is that field reports of IED detonations lack standardization. I don't recall the details, but my sense is that some reports are fairly specific as to date, time, location, type and size of device, while others are of the "I heard a big boom" variety. Maybe Watson (or a descendant) will help make sense of those reports. (Once Watson figures out where Toronto is, anyway.)