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Taking AI to the next level

At the heart of Artificial Intelligence lies the question of whether we might be able to create artificial systems that behave and compute in the same manner than human beings do.  This would obviously be a mind-blowing breakthrough were it ever accomplished - it would give us a number of new applications for computers, would change the nature of work in our society, and would force us to redefine the very nature of being human.

Perhaps it is no surprise, then, that such a feat has proven to be incredibly difficult to achieve.  Artificial Intelligence, while it has grown in complexity and scope, is still quite far from achieving any kind of accurate human resemblance.

However, this may change very soon.

Back in 2008, the world of science was abuzz with excitement over a new invention in electronics - the "memristor."  This is an electrical component that behaves very similarly to "resistors" in an electrical circuit, but with one key difference.  Memristors impede the flow of electricity - however, the amount that they do so is dependent on the current that has passed through the memristor in the past.

Now, this might not seem like such a big deal...essentially, this just means that how much resistance a memristor has right now changes over time and as a result of its previous inputs.  But think about the implications of this - essentially, such a piece of hardware has the ability to store some information about its previous input.  It has the electrical equivalent of memory.  With that in mind, let's venture back into the realm of cognitive science.

The problem with traditional artificial intelligence is that it is based on a computer architecture that is inherently different from biological brains.  Computers have a specific place where computations are carried out (CPU), a specific place for short-term memory (RAM), and a specific place for long-term memory (the hard disk) .  What this means is that any time a particular bit of information needs to be altered, it has to pass through a number of bottlenecks that drastically reduce the efficiency and speed of the system.

For those of you who are familiar with brains, you know that they don't work in this fashion - there is no central processing unit embedded within your skulls, there is no "hard drive" area that stores all of your memories.  Instead, there are only millions upon millions of neurons in an interconnected and never-ending chorus of electrical activity.

Such a system does not need to separate its various functions into discrete locations because, broadly speaking, every location in the brain carries out every function that a normal computer would.  The neurons (and possibly their neighboring cells, Glia) both carry out computation as well as store information about the past.

And so all of our efforts to simulate brains have hit this fundamental roadblock - it is incredibly difficult to create machines that act like brains without being built like them.  This is where memristors come in.

By allowing memory to be embedded directly within artificial networks, we are one giant step closer to mimicking the way that biological neural networks compute and store information.  Such a revolution in information technology will allow us to create systems that behave very differently from those currently in use, allowing us to perform tasks that most computers have a lot of difficulty with.

There are a number of different research programs that have realized this and are in the process of doing some really interesting research into artificial intelligence, and I'll keep an eye out for any interesting information about what people are doing with this fascinating technology.  For now, check out this article out of Boston University.  It's written by a team working with HP labs to create one of the first "neural" artificial computers.

And so with these new tools at hand, we can begin to create systems that not only behave, but are built very similarly to human brains.  We're just at the tip of the iceberg when it comes to understanding what these powerful networks are capable of, and the future is a bright one indeed.

via IEEE Spectrum

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