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The Summer of AI: Watson, Google & more


Written by: Carole-Ann BerliozPublished on: Mar 21, 20114 comments

Do you remember how Artificial Intelligence (AI) became taboo?

Star Wars - C3POMany of us were fascinated by Artificial Intelligence a few decades ago.  This discipline carried an extraordinary potential.  We started dreaming about expert systems that would outperform humans, systems that would write themselves and other fantastic progress outpacing our own capabilities.  We certainly did a great job exciting our imagination leading to a long list of sci-fi movies and other fantasies.

Then winter came

The technology ended up disappointing the masses.  We wanted to believe in the incredible potential but realized that the current approach was much more limited than we had hoped.  I personally believe that the industry mostly failed in managing expectations.  The technological progress was nothing to be ashamed of.  Lots of techniques, algorithms and new approaches came out of this period but living up to those over-inflated expectations was unrealistic.

I was personally involved in Expert Systems back then.  Neuron Data had a great product: Nexpert Object.  In other posts I might describe some of the projects I did back in the 90’s.  It was very exciting… although, admittedly, the systems did not write themselves.  The skills required to transcode the business expertise were not common.  The learning curve was steep.

It was harsh, certainly, to bury all those efforts and banish the terminology altogether.  The dream did not vanish completely though.  AI remained alive in the collective imagination.  More movies and books kept satisfying our hunger for that dream of fabricated intelligence that could help humanity — or destroy it as we enjoy fearing in those movies –, that could approach it to the point we could confuse an android with a real person, or that could perform amazing tasks.

AI technology also survived the winter in hibernation.  Those passionate AI researchers looked for more realistic objectives for the technology.  I read once in a book — back in 1997 — a great expression that I have shared more than once with some of you in the past: “From Artificial Intelligence to Intelligent Agents”.  If nothing else, this book helped me realize back then that we needed to change our approach to AI.  Instead of the monolithic expert system, there was an opportunity to add intelligence in small specific tasks distributed over the network.  The budding concept of Decision Services was born.  It may or may not be a coincidence that Blaze Advisor and ILOG JRules were conceived around that time.  The BRMS movement was another perspective on AI, still focused on adding intelligence to our systems but intelligence that came straight from the Business Experts, intelligence that was under their control the whole time.

AI spanned much more than one technology in reality and many other fields of research kept investing and refining the technology for the same purpose of making systems smarter.  It is no wonder that, after a long and rigorous winter, AI is finally able to bubble up to the public once again, this time as a more mature discipline, less ambitious in many ways and more accomplished too.

And now we are at the dawn of the Summer of AI

BRMS and Decision Management is certainly a topic I am passionate about but, looking around me, I realize the phenomenal progress and applicability of other techniques (that I am also very interested in although not as dedicated to).

As a proof that AI may have finally become an accepted term for the public (again), I have collected a few pieces of evidence.  The list is long.  I decided to focus on a couple of recent articles.

How could I ignore the Watson phenomenon?  This is most definitely the triggering event for this flooding of AI publicity.  Granted IBM had Deep Blue playing chess in the 90’s, but we may have considered the game too structured to recognize the talent.  Now, beating the Jeopardy champions is a greater challenge since it requires more than brute force.  Winning the game requires an amazing ability to deal with a general lack of precision that does require “intelligence”.  For the first time in a very long time, the Press was impressed by the performance of the machine, by its intelligence.  Not that the world is desperately looking for greater Jeopardy champions, but the idea that such technology could be used in other contexts where precision is approximate at best, where data is partially know, is extremely appealing.  Think about call centers or emergency situations where humans are pressed to make decisions when data-points are lacking.

Factoid: Joseph Bigus, who wrote the book I quoted earlier, is a Senior Technical Staff Member at the IBM T.J. Watson Research Center.  AI is a small world I keep realizing.

After the wave of press coverage — I was going to say tsunami but decided not to out of respect for our Japanese friends — for the Watson project at IBM, more thoughts converged on the usability and potential usefulness of AI in other areas.  Peter Norvig elaborated on the progress made by AI in this great article.  I like in particular his analysis on the limitation of Expert Systems: reliance on Experts interviews.

Learning turned out to be more important than knowing. In the 1960s and 1970s, many A.I. programs were known as “Expert Systems,” meaning that they were built by interviewing experts in the field (for example, expert physicians for a medical A.I. system) and encoding their knowledge into logical rules that the computer could follow. This approach turned out to be fragile, for several reasons. First, the supply of experts is sparse, and interviewing them is time-consuming. Second, sometimes they are expert at their craft but not expert at explaining how they do it. Third, the resulting systems were often unable to handle situations that went beyond what was anticipated at the time of the interviews.

From a Decision Management perspective, we do face a similar challenge but that would be the topic for another post.

The third proof of the Summer of AI is the recent Turing Award going to Leslie Valient.  AI is popular again.  Although Leslie’s work is not very recent, he is being recognized now for his contribution to machine learning.

I could go on and on.  You have probably seen articles on AI in general newspapers.  Summer is here.

Man Versus Machine

The main difference between the old days of AI and the new Summer that may be starting now is the role of the Machine.  We dreamt of Machines that would be able to replace Humans.

The possibility that one day Machines will replace humans has of course been at the center of long debates, raising deep issues, going as far as making us question what being human really means. Kurtzweil has famously argued that the Singularity is near and will have profound implications on human evolution (we will transcend biology, he claims). On a more negative note, Bill Joy wrote a famous article in Wired in 2000, in which he worried that we will in effect lose control of our technology and run the risk of becoming an endangered species. Recently, the Atlantic published a long article on Mind vs Machine, in which a more nuanced approach is taken – yes, Machines may well pass the Turing test but that does not signify a path towards irrelevance for humans.

Star Trek Voyager DoctorThe reality in my mind is that we need Machines that can augment Humans.  We need better processing power to supplement our human limitations but we are not ready yet to let a Machine make the final decision.  Think about Healthcare for example, it is appealing to think that a virtual doctor could have access to the latest and greatest research on every possible topic and would be able to compare and analyze all possible treatments including the side effects and possible risks.  Hasn’t Star Trek (Voyager) already painted that vision with its android doctor?  But in the end, we like that a real person is making those life and death decisions with ethical safeguards we would be hard-pressed to implement completely and accurately for a machine.

John Seely Brown, from the Deloitte Center for the Edge and author of “The Power of Pull”, commented in a recent article in the NY Times that machines that are facile at answering questions only serve to obscure what remains fundamentally human.  My take is that the success of AI resides in the ability to combine both.  If we could, the Machine’s incredible power with the unique intuition of Humans, we could get the best of both worlds.

 

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