This is the fifth entry in my ongoing series of journal entries concerning stuff I find interesting in AI.  This entry will consider a thought I had about a possible final project.

More often than not, I find that programming geeks are somewhat obsessed with optimization even before they have any interest in programming.  When I was younger, I used to play with Lego before all other toys.  After I built my contraptions, I always wondered if I could do it with fewer pieces.  The point of this little aside is that programmers are often thinking about the fewest pieces, the lowest time, and especially the shortest path.  

While wandering around campus with my friend Brad (usually just heading from the MUB to Kingsbury, or vice versa), we often consider whether the path we are taking is the fastest.  This is not just idle speculative chatter; we are both usually pretty cold and wish to get to our destination as soon as possible.  Suddenly at the end of class on Thursday, an idea dawned on me:  why not implement a route-planner to figure out the fastest route from one location on campus to another?  

The idea is quite straightforward.  Use Dijkstra's shortest path algorithm to find the best way to get from place to place.  The sidewalks could be edges, and buildings and sidewalk crossings could be nodes.  Edge weights would be proportional to distances between sidewalk crossings and buildings in real life.  The most interesting part might be a “Winter” mode, where special emphasis is put on paths that cross through mostly buildings as opposed to sidewalks.  Both Brad and I were constantly ducking through Hamilton Smith on our way along Main St.  

The only issue for this idea:  is this too easy?  Dijkstra's shortest path algorithm isn't all that hard to implement.  Maybe more of a challenge would be a multi-stage algorithm, for multi-part trips?  That way it would be possible to simulate a whole day on campus.  Or possibly a program that analyzes a set of various possible days, and generates a set of rules that could guide a program deciding which paths to take on the fly.  This idea greatly appeals to that Lego maniac I used to be.  Build it with fewer pieces!

 
 

This is the 4th in a series of AI journals for my class at UNH.  The third is missing due to my contracting mono a few weeks ago and spending most of my time actively trying not to die.  In the course of events I managed to forget about my journal entry.  Well, maybe at the end I will throw another on as a bonus.

Anyway, my timely and topical journal entry will revolve around this piece in recent BBC News,  As soon as I saw the headline I started to become extremely skeptical.  Only twenty-two years to match human thought capability?  Then I noticed the quote's originator:  Ray Kurzweil.  Ah-ha!

Ray is a borderline-manic future-holic.  Having read The Singularity Is Near, I would hardly consider him an unbiased source about the future of technology.  However, having considered the source, let us give equal thought to what he is saying.  I have no doubt that most, if not all, of what he claims will happen will eventually come true.  His visions of nanobots and other nanotechnological marvels are almost certainly in the cards, as well as his suggestions of future man-machine symbiosis.  I am even a believer in his suggestions that humans will eventually upload their consciousness into a computer system and live immortally as sentient software.  But in the next twenty-two years?  It seems highly unlikely. 

This started me thinking, though.  If twenty-two years aren't long enough, what is?  I started thinking about the level of change necessary to move from the current level AI is at compared to where it would need to be to be considered human equivalent. Then I considered the length of time it took AI to go from its inception as a concept to what it is today.  Even considering that technological progress as an exponential function over time, I still think it will take more than a few decades to get weakly humanlike AI.  But who knows?  I could be wrong, and Ray could be right.  I hope he is!  The AI Winter has lasted far too long in my opinion, and here's hoping Spring is just around the corner...




 
 

This is the second entry in my series of articles about my experiences with AI, in and out of the classroom at UNH.

I have been doing some thinking about possible projects, and what I might feel like working on at the end of the year concerning artificial intelligence.  One of the most interesting areas of technology for me is the idea of distributed processing.  I firmly believe that many, many processors working in harmony to solve a problem is the future of computing.  The benefits of more hands working on any particular problem are obvious, but so are the drawbacks.  How do you synchronize tens to hundreds of nodes so that no work is repeated?

While we have not discussed in class any intelligent ways of dealing with this problem of many agents working on the same problem without repeating work, I felt as though this was certainly within the realm of artificial intelligence.  I did a little searching to see if I could discover the applications of AI to this problem.

And I found some interesting results!  I was less interested in artificial intelligence in high school because I felt it was too abstract to be of any use, but if I had known better and done my research, I may have ended up paying more attention to my UMASS Amherst acceptance letter, if only to see the MAS Lab in action.  Founded in 1968, they have published a great many papers concerning the many uses of AI to control many hands all working on the same project.  Perhaps most interesting to me is their ANTs project, which created some very interesting Autonomous Negotiating Teams.  One of the coolest aspects of AI, to me, is definitely autonomy.  And to pair autonomy with my interest in control systems made me pretty much pre-destined to write about ANTs!  Give the project a read and check out all their other projects if you have an interest in that stuff. 

 
 

That title is probably the most misleading thing I have ever typed on here.  The Wall Street in New York interests me not at all.  The WallStreet made by Apple is what I am going to discuss (albeit briefly) today.

I always, always liked Apple's hardware design, and since they are primarily a hardware company, I guess this makes me an Apple fan.  For quite some time I trashed them, though, not realizing that Apple was more than just a crumby OS.  I relentlessly slagged them for having such a slow, unreliable, and just plain useless operating system back in the OS 8 and 9 days.  While I secretly admired their sleek hardware, I assumed I would never use it due to its seeming dependence on disgusting (pre-OS X) Apple operating systems.

I had a particular proclivity for the G3 PowerBooks.  They were simply very well-designed and particularly rugged laptops.  I began thinking about them again when reading Douglas Coupland's Microserfs, in which several characters use them and reference them frequently.  Admittedly, they were earlier models than the Wall Street, but it rekindled a desire to own one of the oldies-but-goodies.

So when I saw that I could pick one up for $90 dollars, I jumped on it.  I plan to double the batteries in it (for up to 9 hours of life!) and jam either OpenBSD or Ubuntu on it.  By adding an old Wifi PCMCIA card, I could easily have a super-sweet road machine!  I encourage anyone who liked these old machines to jump on this deal.