hunch_net hunch_net-2013 hunch_net-2013-486 knowledge-graph by maker-knowledge-mining
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Introduction: David McAllester starts a blog .
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same-blog 1 1.0 486 hunch net-2013-07-10-Thoughts on Artificial Intelligence
Introduction: David McAllester starts a blog .
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Introduction: It’s been almost two years since this blog began. In that time, I’ve learned enough to shift my expectations in several ways. Initially, the idea was for a general purpose ML blog where different people could contribute posts. What has actually happened is most posts come from me, with a few guest posts that I greatly value. There are a few reasons I see for this. Overload . A couple years ago, I had not fully appreciated just how busy life gets for a researcher. Making a post is not simply a matter of getting to it, but rather of prioritizing between {writing a grant, finishing an overdue review, writing a paper, teaching a class, writing a program, etc…}. This is a substantial transition away from what life as a graduate student is like. At some point the question is not “when will I get to it?” but rather “will I get to it?” and the answer starts to become “no” most of the time. Feedback failure . This blog currently receives about 3K unique visitors per day from
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Introduction: his blog on information markets and other research topics .
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Introduction: David Pennock notes the impressive set of datasets at datawrangling .
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Introduction: Maverick Woo and the Aladdin group at CMU have started a CS theory-related blog here .
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same-blog 1 0.99922794 486 hunch net-2013-07-10-Thoughts on Artificial Intelligence
Introduction: David McAllester starts a blog .
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Introduction: his blog on information markets and other research topics .
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Introduction: Hal Daume has started the NLPers blog to discuss learning for language problems.
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Introduction: Jonathan Chang has a research blog on aspects of machine learning.
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Introduction: Maverick Woo and the Aladdin group at CMU have started a CS theory-related blog here .
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same-blog 1 1.0 486 hunch net-2013-07-10-Thoughts on Artificial Intelligence
Introduction: David McAllester starts a blog .
2 0.71946263 205 hunch net-2006-09-07-Objective and subjective interpretations of probability
Introduction: An amusing tidbit (reproduced without permission) from Herman Chernoff’s delightful monograph, “Sequential analysis and optimal design”: The use of randomization raises a philosophical question which is articulated by the following probably apocryphal anecdote. The metallurgist told his friend the statistician how he planned to test the effect of heat on the strength of a metal bar by sawing the bar into six pieces. The first two would go into the hot oven, the next two into the medium oven, and the last two into the cool oven. The statistician, horrified, explained how he should randomize to avoid the effect of a possible gradient of strength in the metal bar. The method of randomization was applied, and it turned out that the randomized experiment called for putting the first two pieces into the hot oven, the next two into the medium oven, and the last two into the cool oven. “Obviously, we can’t do that,” said the metallurgist. “On the contrary, you have to do that,” said the st
3 0.4459956 39 hunch net-2005-03-10-Breaking Abstractions
Introduction: Sam Roweis ‘s comment reminds me of a more general issue that comes up in doing research: abstractions always break. Real number’s aren’t. Most real numbers can not be represented with any machine. One implication of this is that many real-number based algorithms have difficulties when implemented with floating point numbers. The box on your desk is not a turing machine. A turing machine can compute anything computable, given sufficient time. A typical computer fails terribly when the state required for the computation exceeds some limit. Nash equilibria aren’t equilibria. This comes up when trying to predict human behavior based on the result of the equilibria computation. Often, it doesn’t work. The probability isn’t. Probability is an abstraction expressing either our lack of knowledge (the Bayesian viewpoint) or fundamental randomization (the frequentist viewpoint). From the frequentist viewpoint the lack of knowledge typically precludes actually knowing the fu
4 0.41821232 76 hunch net-2005-05-29-Bad ideas
Introduction: I found these two essays on bad ideas interesting. Neither of these is written from the viewpoint of research, but they are both highly relevant. Why smart people have bad ideas by Paul Graham Why smart people defend bad ideas by Scott Berkun (which appeared on slashdot ) In my experience, bad ideas are common and over confidence in ideas is common. This overconfidence can take either the form of excessive condemnation or excessive praise. Some of this is necessary to the process of research. For example, some overconfidence in the value of your own research is expected and probably necessary to motivate your own investigation. Since research is a rather risky business, much of it does not pan out. Learning to accept when something does not pan out is a critical skill which is sometimes never acquired. Excessive condemnation can be a real ill when it’s encountered. This has two effects: When the penalty for being wrong is too large, it means people have a
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Introduction: This post is about a technology which could develop in the future. Right now, a new drug might be tested by finding patients with some diagnosis and giving or not giving them a drug according to a secret randomization. The outcome is observed, and if the average outcome for those treated is measurably better than the average outcome for those not treated, the drug might become a standard treatment. Generalizing this, a filter F sorts people into two groups: those for treatment A and those not for treatment B based upon observations x . To measure the outcome, you randomize between treatment and nontreatment of group A and measure the relative performance of the treatment. A problem often arises: in many cases the treated group does not do better than the nontreated group. A basic question is: does this mean the treatment is bad? With respect to the filter F it may mean that, but with respect to another filter F’ , the treatment might be very effective. For exampl
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