Words of Wisdom:

"to rid yourself of desire to be empty to attain enlightment" - Gautam

The Robot

  • Date Submitted: 12/31/2010 11:38 PM
  • Flesch-Kincaid Score: 55.2 
  • Words: 1411
  • Essay Grade: no grades
  • Report this Essay
| | |

|   |
Able to consider 400 million chess moves per second, IBM’s "Deep Blue" beat reigning chess champ Garry Kasparov in 1997. [Click for a larger image.] Photo courtesy of IBM. |   |
  |   |
Have you ever had someone yell "Think fast!" and then toss something at you? It’s a dirty trick, but the amazing part is that you can usually manage to dodge, block, or catch the item, as you see fit. Not only is your brain fast, it can make judgements "on the fly," and adapt to changing situations.
In short, the human mind is one tough act to follow.
Traditionally, efforts to mimic human thought have centered around rule-based logic. The computer in front of you now uses rule-based logic: binary data is stored and manipulated according to a set of pre-programmed rules. Most robotic "brains" are rule-based, often contained on a single chip that functions as a computer–a microcomputer.
Rule-based systems can be used to create artificial intelligence, by programming vast amounts of information into a computer. Relying on this enormous set of data, such a computer is able to mimic intelligence, for example, helping to diagnose diseases by comparing symptoms to those in a database. Such "expert systems" can know more, fact-wise, than any single person, and yet they have only a very narrow range of useful function. Also, they can’t learn. They can only make connections they’ve been programmed to make.
  | |
Cog is a humanoid stimulus-response robot designed to learn from its environment, the way a child does. (7.3MB) Learn more about Cog. [Need help?] Image courtesy of MIT University. |
  |   |
Another approach to artificial intelligence is neural networks. Neural networks are modeled after the human brain, with the advantage that they are better at handling ambiguity than rule-based systems. A neural net "learns" by exposure to lots of inputs and corresponding outputs. Once trained, the neural net responds to an input with a likely output....


Express your owns thoughts and ideas on this essay by writing a grade and/or critique.

  1. No comments