How about collaboration?

My previous post on Computers and Chess, serves as a good prologue to this one.

watson
That’s me geeking out at the Jeopardy stage setup.

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.[1] 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 [2] 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” [3] :

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 [4] 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.

References

  1. Licklider J. C. R. (1960), Man-Computer Symbiosis. IEEE. Available: http://groups.csail.mit.edu/medg/people/psz/Licklider.html.

Footnotes

  1. More about Watson from IBM here. See also, Jeopardy vs. Chess. ^
  2. Amazon’s Mechanical Turk does talk about “HITs” or Human Intelligence Tasks ^
  3. 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. ^
  4. AI Winter: http://en.wikipedia.org/wiki/AI_winter ^

Computers and Chess

Deep Blue vs Kasparov '96 Game 1
Deep Blue vs. Kasparov: 1996 Game 1. Deep Blue won this game but Kasparov went on to win the match by 4-2. In the 1997 re-match, however, Deep Blue won 3½–2½.

To design an algorithm for playing the game of chess has been one of the challenges that has attracted the attention of many mathematicians and computer scientists. The sheer number of combinatorial possibilities make it hard to predict the result for both humans and computers alike. There have been many highly publicized games pitting humans against the (super) computers in the ’90s and ’00s, such as the Deep Blue vs. Kasparov one.

It was around the same time that I was starting out with chess and was interested in learning how to play better. My father had gifted me a copy of a computer game called Maurice Ashley Teaches Chess. It included playing strategies, past-game analysis and video coaching by the chess grandmaster Maurice Ashley. It also had a practice mode where you could compete and play against the computer. I didn’t end up being a good chess player but if my memory serves me right, it did not take me long to start beating the in-game AI. But things have changed a lot since then. Computers are not only faster and more powerful now (to explore more number of moves) but are also equipped with better algorithms to evaluate a decision. Let’s compare excerpts from the introductory chapters from two of my textbooks:

From "Cognitive Psychology" (Medin et.al., 2004):

The number of ways in which the first 10 moves can be played is on the order of billions and there are more possible sequences for the game than there are atoms in the universe! Obviously neither humans nor machines can determine the best moves by considering all the possibilities. In fact, grandmaster chess players typically report that they consider only a handful of the possible moves and “look ahead” for only a few moves. In contrast, chess computers are capable of examining more than 2,000,000 potential moves per second and can search quite a few moves ahead. The amazing thing is that the best grandmasters (as of this writing) are still competitive with the best computers.

Now consider, "Artificial Intelligence: A Modern Approach (3rd Edition)" (Russell et.al., 2010):

IBM’s DEEP BLUE became the first computer program to defeat the world champion in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an exhibition match (Goodman and Keene, 1997). Kasparov said that he felt a “new kind of intelligence” across the board from him. Newsweek magazine described the match as “The brain’s last stand.” The value of IBM’s stock increased by $18 billion. Human champions studied Kasparov’s loss and were able to draw a few matches in subsequent years, but the most recent human-computer matches have been won convincingly by the computer.

So, what happened in the six year gap between the publishing of these books? It turns out that there has indeed been such a shift in the recent years. The computers’ superior performance stats can be seen on this Wikipedia entry. We have come a long way since the Kasparov vs. Deep Blue matches due the the advancements in both hardware and AI algorithms. Computers have now started not only wining but dominating in the human-computer chess matches so much so that even mobile phones running slower hardware are reaching Grandmaster levels. Guess, time’s right for switching to new board games! Btw, Checkers is a solved problem since 2007: http://www.sciencemag.org/content/317/5844/1518.full! It will end up in a draw (they have a computational proof of that) if both players use the perfect strategies, i.e. the one that never loses.

Image Credits: en:User:Cburnett / Wikimedia Commons / CC-BY-SA-3.0 / GFDL

References

  1. Russell et.al. (2010), Artificial Intelligence: A Modern Approach (3rd Edition), 49. Prentice Hall. Available: http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597.
  2. Medin et.al. (2004), Cognitive Psychology, 8. Wiley. Available: http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0471458201.

My First Post

Lately I have found myself reading a lot about academic blogging. There is no dearth of articles that aggressively advertise blogging by academics and its benefits; such as the ones here, here and here. Evidently, I have been able to convince myself to start a new blog (and hence this post). Along the way I have also been able get some insights on their drawbacks as well but the positives seem to overwhelmingly outweigh the reasons for not blogging.

If you have been through my about me page, you’d know that I am a first year graduate student. In fact, I’ll be starting my graduate studies this week and it would be nice to try my hands at blogging at the beginning of my grad school journey. I have a couple of my own reasons for taking up this project. Allow me to discuss some of them and a bit on how do I plan on taking this blog further.

My First Post
Composing my first blog post!
  1. Blogging as a writing exercise
    Learning to write for a wider audience is one important skill that can be developed by blogging. It may not be easy for you to read through my initial posts but I hope to improve upon their quality over time. It would be a good plan to cut one’s dependency on advisors, course-instructors and co-authors for improving the quality of their writing. In order to succeed, researchers ought to be able to communicate their ideas well enough.
  2. Blogging for fun
    Coming up with ideas for a blog post and the planning process could actually be taken up as a recreational activity. Blogging, being so much more flexible than writing formal academic writing, has a lot of scope for creativity. Acting upon the crazy-burst-of-inspirations during the process has been a source of new ideas for their own research work for some of the academics who blog. But here’s a caveat; and it is because of the very same reason that it is useful. One could risk spending a little too much time and energy blogging, getting lost in their train of thoughts and procrastinating about more pressing matters (deadlines!). That’s something that I’d like to keep at the back of my head.
  3. Blogging for record keeping
    A searchable data-base containing all my ideas and reviews about other projects could potentially serve a very useful resource down the line. WordPress offers a variety of ways for posting using mobile devices, email etc; it is never too difficult to quickly write about anything worth noting. While there are tools with which I could do this privately (more on this later), blog posts could encourage other readers to pitch in their own ideas and work in a collaborative way. What better way to explore social computing than participating in it!

Hope this also motivates some more people to start blogging. It would be exciting to see where this leads to. And if you have made it till here, thanks for reading my first post!