Watching IBM supercomputer watson play Jeopardy yesterday....
It's an amazing experience that using the state-of-the-art technology nowadays, people can build a machine to understand very complex questions in natural language and answer them correctly most of the time... As a CS students, the feeling that the area you are devoting your energy in is actually challenging the normal human being's intelligence is pretty good~~ Watson proved that it is promising.
Well, there are several basic things I still can not understand, or in other words, limitations.
1. Why they claim "watson" answered those Jeopardy questions by himself, just because it does not connected to the WWW. All the knowledge stored in the machine are from human beings. It still can not create new knowledge by observation and reading.
2. Just like playing Chess, playing Jeopardy proved that using technology the machine can understand questions quite well...
3. HCI is really important to amuse people, look at watson's fancy avatar!
THIS IS A BLOG OF YEZHOU YANG, A PH.D STUDENT AT UNIVERSITY OF MARYLAND, COLLEGE PARK. MOST OF THE POSTS HERE ARE MY STUDY AND RESEARCH NOTES FOR QUICK ONLINE ACCESS. OCCASIONALLY, MY STUPID IDEAS WILL ALSO BE SHARED HERE.
Tuesday, February 15, 2011
Tuesday, February 1, 2011
Machine Learning Reading Group note 1
Topic: Online EM for Unsupervised Models
At the end of the section, Hal raised a problem that: when using Online EM (incremental or stepwise), as we need another parameter to control the learning rate, what makes it better than simple gradient descent? ( As in gradient descent, we need a parameter: learning step while normal EM does not need an extra parameter)
And Zhongqiang Huang claimed that actually when we set
\[ \alpha = -1 \]
in \[ \eta_k = (k+2) ^ {-\alpha} \]
The update step in stepwise EM is reduced to (almost):
\[ \mu = \mu + s \]
At the end of the section, Hal raised a problem that: when using Online EM (incremental or stepwise), as we need another parameter to control the learning rate, what makes it better than simple gradient descent? ( As in gradient descent, we need a parameter: learning step while normal EM does not need an extra parameter)
And Zhongqiang Huang claimed that actually when we set
\[ \alpha = -1 \]
in \[ \eta_k = (k+2) ^ {-\alpha} \]
The update step in stepwise EM is reduced to (almost):
\[ \mu = \mu + s \]
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