NEWS

Technology Research News recently highlighted self-improving software developed by Princeton computer science Professor Bernard Chazelle and his students.
posted 5/5/2006

“If people are expected to learn on the job, why isn't software?” writes TRN. “Although some kinds of software are capable of learning, it's more difficult to design software that learns as it works without requiring a separate training process.

Chazelle
Bernard Chazelle, professor in the
Computer Science department

“Princeton University researchers have designed algorithms -- the logic underlying software -- that learn from data that they don't know anything about ahead of time and then tune themselves to better handle those types of data. The key is that the algorithms learn from how the pieces of data fit within the range of possibilities, rather than having to learn the data's details.



“It turns out that even though any given piece of data is random, individual pieces fall into relatively narrow ranges that an algorithm can learn from. An algorithm can also improve after learning from a relatively small number of samples.

“The researchers built two self-improving algorithms, a sorting algorithm and a clustering algorithm. Sorting algorithms put pieces of data into some type of order and clustering algorithms group like pieces of data.

“The algorithms promise to be forerunners of software that alters its default configuration on its own as it learns how it is used.”

Chazelle and graduate students Nir Ailon, Ding Liu, and Seshadhri Comandur wrote a paper (pdf) about their work, which Comandur presented at an ACM-SIAM Symposium on Discrete Algorithms in January.

Source: http://www.trnmag.com

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Chazelle and graduate students Nir Ailon, Ding Liu, and Seshadhri Comandur wrote a paper (pdf) about their work, presented at an ACM-SIAM Symposium on Discrete Algorithms in January.

Source: http://www.trnmag.com