hunch_net hunch_net-2005 hunch_net-2005-50 knowledge-graph by maker-knowledge-mining

50 hunch net-2005-04-01-Basic computer science research takes a hit


meta infos for this blog

Source: html

Introduction: The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim). This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. For example “darpa funded the invention of the internet” is reasonably correct. This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs. The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. Methods of making good automated predictions obv


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. [sent-1, score-1.049]

2 Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim). [sent-2, score-1.02]

3 This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. [sent-3, score-0.954]

4 For example “darpa funded the invention of the internet” is reasonably correct. [sent-4, score-0.483]

5 This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs. [sent-5, score-1.31]

6 The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. [sent-6, score-0.771]

7 Methods of making good automated predictions obviously have a lot of applications that DARPA cares about and the technology often isn’t there yet. [sent-7, score-0.658]


similar blogs computed by tfidf model

tfidf for this blog:

wordName wordTfidf (topN-words)

[('nsf', 0.328), ('darpa', 0.32), ('funded', 0.241), ('budget', 0.208), ('funding', 0.187), ('technology', 0.168), ('factor', 0.165), ('policy', 0.157), ('phone', 0.15), ('aimed', 0.15), ('cares', 0.15), ('defense', 0.15), ('invention', 0.15), ('directed', 0.139), ('staying', 0.139), ('dropped', 0.132), ('cuts', 0.125), ('extensive', 0.125), ('bell', 0.125), ('monopoly', 0.125), ('hit', 0.125), ('remaining', 0.125), ('anecdotal', 0.116), ('department', 0.116), ('grown', 0.116), ('perspective', 0.107), ('grant', 0.107), ('approximately', 0.107), ('universities', 0.107), ('decision', 0.103), ('article', 0.099), ('development', 0.097), ('submissions', 0.092), ('reasonably', 0.092), ('lot', 0.09), ('automated', 0.089), ('response', 0.089), ('partially', 0.086), ('constant', 0.086), ('news', 0.086), ('obviously', 0.085), ('york', 0.083), ('internet', 0.079), ('basic', 0.079), ('times', 0.077), ('context', 0.076), ('predictions', 0.076), ('sort', 0.075), ('towards', 0.072), ('years', 0.069)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 1.0000001 50 hunch net-2005-04-01-Basic computer science research takes a hit

Introduction: The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim). This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. For example “darpa funded the invention of the internet” is reasonably correct. This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs. The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. Methods of making good automated predictions obv

2 0.33234528 36 hunch net-2005-03-05-Funding Research

Introduction: The funding of research (and machine learning research) is an issue which seems to have become more significant in the United States over the last decade. The word “research” is applied broadly here to science, mathematics, and engineering. There are two essential difficulties with funding research: Longshot Paying a researcher is often a big gamble. Most research projects don’t pan out, but a few big payoffs can make it all worthwhile. Information Only Much of research is about finding the right way to think about or do something. The Longshot difficulty means that there is high variance in payoffs. This can be compensated for by funding many different research projects, reducing variance. The Information-Only difficulty means that it’s hard to extract a profit directly from many types of research, so companies have difficulty justifying basic research. (Patents are a mechanism for doing this. They are often extraordinarily clumsy or simply not applicable.) T

3 0.25291398 154 hunch net-2006-02-04-Research Budget Changes

Introduction: The announcement of an increase in funding for basic research in the US is encouraging. There is some discussion of this at the Computing Research Policy blog. One part of this discussion has a graph of NSF funding over time, presumably in dollar budgets. I don’t believe that dollar budgets are the right way to judge the impact of funding changes on researchers. A better way to judge seems to be in terms of dollar budget divided by GDP which provides a measure of the relative emphasis on research. This graph was assembled by dividing the NSF budget by the US GDP . For 2005 GDP, I used the current estimate and for 2006 and 2007 assumed an increase by a factor of 1.04 per year. The 2007 number also uses the requested 2007 budget which is certain to change. This graph makes it clear why researchers were upset: research funding emphasis has fallen for 3 years in a row. The reality has been significantly more severe due to DARPA decreasing funding and industrial

4 0.17077878 344 hunch net-2009-02-22-Effective Research Funding

Introduction: With a worldwide recession on, my impression is that the carnage in research has not been as severe as might be feared, at least in the United States. I know of two notable negative impacts: It’s quite difficult to get a job this year, as many companies and universities simply aren’t hiring. This is particularly tough on graduating students. Perhaps 10% of IBM research was fired. In contrast, around the time of the dot com bust, ATnT Research and Lucent had one or several 50% size firings wiping out much of the remainder of Bell Labs , triggering a notable diaspora for the respected machine learning group there. As the recession progresses, we may easily see more firings as companies in particular reach a point where they can no longer support research. There are a couple positives to the recession as well. Both the implosion of Wall Street (which siphoned off smart people) and the general difficulty of getting a job coming out of an undergraduate education s

5 0.1338601 396 hunch net-2010-04-28-CI Fellows program renewed

Introduction: Lev Reyzin points out the CI Fellows program is renewed . CI Fellows are essentially NSF funded computer science postdocs for universities and industry research labs. I’ve been lucky and happy to have Lev visit me for a year under last year’s program , so I strongly recommend participating if it suits you. As with last year, the application timeline is very short, with everything due by May 23.

6 0.13097934 282 hunch net-2008-01-06-Research Political Issues

7 0.12492055 48 hunch net-2005-03-29-Academic Mechanism Design

8 0.11846869 228 hunch net-2007-01-15-The Machine Learning Department

9 0.1088917 132 hunch net-2005-11-26-The Design of an Optimal Research Environment

10 0.10366797 355 hunch net-2009-05-19-CI Fellows

11 0.10089106 425 hunch net-2011-02-25-Yahoo! Machine Learning grant due March 11

12 0.10043518 449 hunch net-2011-11-26-Giving Thanks

13 0.093538247 220 hunch net-2006-11-27-Continuizing Solutions

14 0.090962633 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge

15 0.080574676 155 hunch net-2006-02-07-Pittsburgh Mind Reading Competition

16 0.069445223 235 hunch net-2007-03-03-All Models of Learning have Flaws

17 0.06724593 112 hunch net-2005-09-14-The Predictionist Viewpoint

18 0.066979095 193 hunch net-2006-07-09-The Stock Prediction Machine Learning Problem

19 0.066901088 106 hunch net-2005-09-04-Science in the Government

20 0.066754684 269 hunch net-2007-10-24-Contextual Bandits


similar blogs computed by lsi model

lsi for this blog:

topicId topicWeight

[(0, 0.14), (1, -0.024), (2, -0.117), (3, 0.126), (4, -0.107), (5, -0.048), (6, 0.032), (7, 0.117), (8, -0.075), (9, 0.053), (10, 0.143), (11, -0.006), (12, -0.099), (13, -0.193), (14, -0.088), (15, -0.005), (16, -0.049), (17, -0.073), (18, -0.094), (19, -0.022), (20, -0.085), (21, -0.141), (22, -0.004), (23, 0.078), (24, -0.105), (25, -0.085), (26, -0.095), (27, 0.164), (28, 0.054), (29, 0.033), (30, 0.057), (31, -0.04), (32, 0.112), (33, -0.011), (34, 0.014), (35, 0.035), (36, -0.007), (37, 0.051), (38, 0.104), (39, -0.091), (40, -0.048), (41, -0.069), (42, 0.019), (43, 0.024), (44, 0.003), (45, 0.097), (46, -0.042), (47, -0.055), (48, 0.034), (49, -0.056)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.97656798 50 hunch net-2005-04-01-Basic computer science research takes a hit

Introduction: The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim). This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. For example “darpa funded the invention of the internet” is reasonably correct. This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs. The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. Methods of making good automated predictions obv

2 0.87027556 154 hunch net-2006-02-04-Research Budget Changes

Introduction: The announcement of an increase in funding for basic research in the US is encouraging. There is some discussion of this at the Computing Research Policy blog. One part of this discussion has a graph of NSF funding over time, presumably in dollar budgets. I don’t believe that dollar budgets are the right way to judge the impact of funding changes on researchers. A better way to judge seems to be in terms of dollar budget divided by GDP which provides a measure of the relative emphasis on research. This graph was assembled by dividing the NSF budget by the US GDP . For 2005 GDP, I used the current estimate and for 2006 and 2007 assumed an increase by a factor of 1.04 per year. The 2007 number also uses the requested 2007 budget which is certain to change. This graph makes it clear why researchers were upset: research funding emphasis has fallen for 3 years in a row. The reality has been significantly more severe due to DARPA decreasing funding and industrial

3 0.72150785 36 hunch net-2005-03-05-Funding Research

Introduction: The funding of research (and machine learning research) is an issue which seems to have become more significant in the United States over the last decade. The word “research” is applied broadly here to science, mathematics, and engineering. There are two essential difficulties with funding research: Longshot Paying a researcher is often a big gamble. Most research projects don’t pan out, but a few big payoffs can make it all worthwhile. Information Only Much of research is about finding the right way to think about or do something. The Longshot difficulty means that there is high variance in payoffs. This can be compensated for by funding many different research projects, reducing variance. The Information-Only difficulty means that it’s hard to extract a profit directly from many types of research, so companies have difficulty justifying basic research. (Patents are a mechanism for doing this. They are often extraordinarily clumsy or simply not applicable.) T

4 0.65687251 344 hunch net-2009-02-22-Effective Research Funding

Introduction: With a worldwide recession on, my impression is that the carnage in research has not been as severe as might be feared, at least in the United States. I know of two notable negative impacts: It’s quite difficult to get a job this year, as many companies and universities simply aren’t hiring. This is particularly tough on graduating students. Perhaps 10% of IBM research was fired. In contrast, around the time of the dot com bust, ATnT Research and Lucent had one or several 50% size firings wiping out much of the remainder of Bell Labs , triggering a notable diaspora for the respected machine learning group there. As the recession progresses, we may easily see more firings as companies in particular reach a point where they can no longer support research. There are a couple positives to the recession as well. Both the implosion of Wall Street (which siphoned off smart people) and the general difficulty of getting a job coming out of an undergraduate education s

5 0.55508888 48 hunch net-2005-03-29-Academic Mechanism Design

Introduction: From game theory, there is a notion of “mechanism design”: setting up the structure of the world so that participants have some incentive to do sane things (rather than obviously counterproductive things). Application of this principle to academic research may be fruitful. What is misdesigned about academic research? The JMLG guides give many hints. The common nature of bad reviewing also suggests the system isn’t working optimally. There are many ways to experimentally “cheat” in machine learning . Funding Prisoner’s Delimma. Good researchers often write grant proposals for funding rather than doing research. Since the pool of grant money is finite, this means that grant proposals are often rejected, implying that more must be written. This is essentially a “prisoner’s delimma”: anyone not writing grant proposals loses, but the entire process of doing research is slowed by distraction. If everyone wrote 1/2 as many grant proposals, roughly the same distribution

6 0.53625411 106 hunch net-2005-09-04-Science in the Government

7 0.53326219 282 hunch net-2008-01-06-Research Political Issues

8 0.48653623 355 hunch net-2009-05-19-CI Fellows

9 0.46180943 132 hunch net-2005-11-26-The Design of an Optimal Research Environment

10 0.45278996 449 hunch net-2011-11-26-Giving Thanks

11 0.43423063 121 hunch net-2005-10-12-The unrealized potential of the research lab

12 0.41706935 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers

13 0.38573819 51 hunch net-2005-04-01-The Producer-Consumer Model of Research

14 0.38490653 64 hunch net-2005-04-28-Science Fiction and Research

15 0.3761813 193 hunch net-2006-07-09-The Stock Prediction Machine Learning Problem

16 0.3761346 296 hunch net-2008-04-21-The Science 2.0 article

17 0.36963814 142 hunch net-2005-12-22-Yes , I am applying

18 0.36825505 241 hunch net-2007-04-28-The Coming Patent Apocalypse

19 0.36170977 485 hunch net-2013-06-29-The Benefits of Double-Blind Review

20 0.35629195 396 hunch net-2010-04-28-CI Fellows program renewed


similar blogs computed by lda model

lda for this blog:

topicId topicWeight

[(27, 0.141), (53, 0.035), (55, 0.092), (89, 0.45), (94, 0.069), (95, 0.103)]

similar blogs list:

simIndex simValue blogId blogTitle

1 0.93581307 328 hunch net-2008-11-26-Efficient Reinforcement Learning in MDPs

Introduction: Claude Sammut is attempting to put together an Encyclopedia of Machine Learning . I volunteered to write one article on Efficient RL in MDPs , which I would like to invite comment on. Is something critical missing?

same-blog 2 0.88797009 50 hunch net-2005-04-01-Basic computer science research takes a hit

Introduction: The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim). This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. For example “darpa funded the invention of the internet” is reasonably correct. This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs. The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. Methods of making good automated predictions obv

3 0.80836952 480 hunch net-2013-03-22-I’m a bandit

Introduction: Sebastien Bubeck has a new ML blog focused on optimization and partial feedback which may interest people.

4 0.72265369 84 hunch net-2005-06-22-Languages of Learning

Introduction: A language is a set of primitives which can be combined to succesfully create complex objects. Languages arise in all sorts of situations: mechanical construction, martial arts, communication, etc… Languages appear to be the key to succesfully creating complex objects—it is difficult to come up with any convincing example of a complex object which is not built using some language. Since languages are so crucial to success, it is interesting to organize various machine learning research programs by language. The most common language in machine learning are languages for representing the solution to machine learning. This includes: Bayes Nets and Graphical Models A language for representing probability distributions. The key concept supporting modularity is conditional independence. Michael Kearns has been working on extending this to game theory. Kernelized Linear Classifiers A language for representing linear separators, possibly in a large space. The key form of

5 0.68646228 118 hunch net-2005-10-07-On-line learning of regular decision rules

Introduction: Many decision problems can be represented in the form FOR n =1,2,…: — Reality chooses a datum x n . — Decision Maker chooses his decision d n . — Reality chooses an observation y n . — Decision Maker suffers loss L ( y n , d n ). END FOR. The observation y n can be, for example, tomorrow’s stock price and the decision d n the number of shares Decision Maker chooses to buy. The datum x n ideally contains all information that might be relevant in making this decision. We do not want to assume anything about the way Reality generates the observations and data. Suppose there is a good and not too complex decision rule D mapping each datum x to a decision D ( x ). Can we perform as well, or almost as well, as D , without knowing it? This is essentially a special case of the problem of on-line learning . This is a simple result of this kind. Suppose the data x n are taken from [0,1] and L ( y , d )=| y – d |. A norm || h || of a function h on

6 0.63216293 299 hunch net-2008-04-27-Watchword: Supervised Learning

7 0.49609503 36 hunch net-2005-03-05-Funding Research

8 0.42925337 464 hunch net-2012-05-03-Microsoft Research, New York City

9 0.41279304 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers

10 0.40936232 154 hunch net-2006-02-04-Research Budget Changes

11 0.40796006 344 hunch net-2009-02-22-Effective Research Funding

12 0.40594548 449 hunch net-2011-11-26-Giving Thanks

13 0.39889109 132 hunch net-2005-11-26-The Design of an Optimal Research Environment

14 0.3979584 357 hunch net-2009-05-30-Many ways to Learn this summer

15 0.39583725 373 hunch net-2009-10-03-Static vs. Dynamic multiclass prediction

16 0.39505595 315 hunch net-2008-09-03-Bidding Problems

17 0.3919403 466 hunch net-2012-06-05-ICML acceptance statistics

18 0.39000022 127 hunch net-2005-11-02-Progress in Active Learning

19 0.38839608 360 hunch net-2009-06-15-In Active Learning, the question changes

20 0.38827068 389 hunch net-2010-02-26-Yahoo! ML events