My previous post on Computers and Chess, serves as a good prologue to this.
A little more than two years ago, the IBM Watson played against and defeated the previous champions of Jeopardy!, the TV game show in which the contestants are tested on their general knowledge with quiz-style questions. I remember being so excited while watching this episode that I ended up playing it over and over again, only to have the Jeopardy jingle loop in my head for a couple of days! Now, this is a much harder challenge for the computer scientists to solve than making a machine play chess.
Computers have accomplished so many things that we thought that only humans could do (play chess and jeopardy, drive a car all by itself …). While these examples are by no means small problems that we have solved, we still have a long way to go. While it can solve problems that we as humans often find difficult (such as playing chess, calculating 1234567890 raised to the power 42 etc.), it cannot* do a lot of things that you and I take for granted. For example, it can’t comprehend this post as well as you do (Watson may not be able to answer everything), read it out naturally & fluently (Siri still sounds robotic) and make sense of the visuals on this page (and so on). *At least not yet.
Computers were designed as tools to help us with calculations or computations. By this very definition, are computers are inherently better at handling certain types of problems while in others they fail? Well, we have no answer  to this question now and I at least hope that it isn’t in affirmative so that someday we can replicate human intelligence. As we have seen in the past, we certainly can not say that “X” is something that computers will never be able to do. But we can sure point out the areas in which the researchers are working hard and hoping to improve.
Here’s a video that talks about the topic that I am hinting at. While I promise not to post many TED talks in future, you can be sure of finding this central idea (the first half of the talk) as a common theme on this blog. Also, I prefer the word “Collaboration” over “Cooperation”  :
TLDR Let’s not try to solve big problems solely with computers. Make computers do the boring repetitive work and involve humans for providing creative inputs or heuristics for the machines. Try to improve interfaces that make this possible.
Although this was an idea envisioned in "Man-Computer Symbiosis" (Licklider J. C. R., 1960) more than half-a-century ago, researchers seem to have not given due importance to it when  the computers failed to perform as well as expected. Of course, more the number of “X”s that the computers are able to do by themselves, the more it frees us to do whatever we do best. When we do look around and observe the devices that we use and how we interact with the machines everyday, we seem to have knowingly or unknowingly progressed in the direction shown by Licklider. With the furthering of research in areas such as Human Computing, Social Computing, and (the new buzzword) Crowd-sourcing, the interest shown in such ideas has never been greater.
- Licklider J. C. R. (1960), Man-Computer Symbiosis. IEEE. Available: http://groups.csail.mit.edu/medg/people/psz/Licklider.html.
- More about Watson from IBM here. See also, Jeopardy vs. Chess. ^
- Amazon’s Mechanical Turk does talk about “HITs” or Human Intelligence Tasks ^
- In AI terms, it would indeed be multi-agent co-operation but then again we are not treating humans just as agents in this case. ^
- AI Winter: http://en.wikipedia.org/wiki/AI_winter ^