hunch_net hunch_net-2006 hunch_net-2006-154 knowledge-graph by maker-knowledge-mining

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


meta infos for this blog

Source: html

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


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 The announcement of an increase in funding for basic research in the US is encouraging. [sent-1, score-0.86]

2 There is some discussion of this at the Computing Research Policy blog. [sent-2, score-0.097]

3 One part of this discussion has a graph of NSF funding over time, presumably in dollar budgets. [sent-3, score-1.135]

4 I don’t believe that dollar budgets are the right way to judge the impact of funding changes on researchers. [sent-4, score-1.061]

5 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. [sent-5, score-1.337]

6 This graph was assembled by dividing the NSF budget by the US GDP . [sent-6, score-0.607]

7 For 2005 GDP, I used the current estimate and for 2006 and 2007 assumed an increase by a factor of 1. [sent-7, score-0.46]

8 The 2007 number also uses the requested 2007 budget which is certain to change. [sent-9, score-0.483]

9 This graph makes it clear why researchers were upset: research funding emphasis has fallen for 3 years in a row. [sent-10, score-1.26]

10 The reality has been significantly more severe due to DARPA decreasing funding and industrial research labs (ATnT and Lucent for example) laying off large numbers of researchers about when the governments emphasis on basic research started declining. [sent-11, score-1.902]

11 It is certainly encouraging to see the emphasis on science growing again. [sent-12, score-0.578]


similar blogs computed by tfidf model

tfidf for this blog:

wordName wordTfidf (topN-words)

[('funding', 0.378), ('emphasis', 0.344), ('dollar', 0.337), ('gdp', 0.318), ('budget', 0.251), ('graph', 0.235), ('nsf', 0.176), ('increase', 0.164), ('judge', 0.16), ('research', 0.133), ('announcement', 0.121), ('dividing', 0.121), ('lucent', 0.121), ('upset', 0.121), ('researchers', 0.115), ('requested', 0.106), ('atnt', 0.106), ('decreasing', 0.101), ('governments', 0.101), ('assumed', 0.101), ('encouraging', 0.101), ('discussion', 0.097), ('labs', 0.094), ('industrial', 0.091), ('reality', 0.088), ('presumably', 0.088), ('darpa', 0.086), ('us', 0.079), ('severe', 0.077), ('estimate', 0.072), ('certain', 0.072), ('growing', 0.072), ('computing', 0.071), ('relative', 0.068), ('factor', 0.067), ('numbers', 0.066), ('changes', 0.065), ('basic', 0.064), ('measure', 0.063), ('policy', 0.063), ('way', 0.061), ('certainly', 0.061), ('impact', 0.06), ('started', 0.059), ('significantly', 0.058), ('current', 0.056), ('per', 0.056), ('years', 0.055), ('uses', 0.054), ('terms', 0.053)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 1.0000001 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

2 0.28134012 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 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

4 0.22231965 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 0.15401709 132 hunch net-2005-11-26-The Design of an Optimal Research Environment

Introduction: How do you create an optimal environment for research? Here are some essential ingredients that I see. Stability . University-based research is relatively good at this. On any particular day, researchers face choices in what they will work on. A very common tradeoff is between: easy small difficult big For researchers without stability, the ‘easy small’ option wins. This is often “ok”—a series of incremental improvements on the state of the art can add up to something very beneficial. However, it misses one of the big potentials of research: finding entirely new and better ways of doing things. Stability comes in many forms. The prototypical example is tenure at a university—a tenured professor is almost imposssible to fire which means that the professor has the freedom to consider far horizon activities. An iron-clad guarantee of a paycheck is not necessary—industrial research labs have succeeded well with research positions of indefinite duration. Atnt rese

6 0.13593584 282 hunch net-2008-01-06-Research Political Issues

7 0.12142606 425 hunch net-2011-02-25-Yahoo! Machine Learning grant due March 11

8 0.11019739 48 hunch net-2005-03-29-Academic Mechanism Design

9 0.10597496 355 hunch net-2009-05-19-CI Fellows

10 0.097116806 449 hunch net-2011-11-26-Giving Thanks

11 0.095995806 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers

12 0.074907117 142 hunch net-2005-12-22-Yes , I am applying

13 0.074091345 51 hunch net-2005-04-01-The Producer-Consumer Model of Research

14 0.073838659 121 hunch net-2005-10-12-The unrealized potential of the research lab

15 0.073437467 469 hunch net-2012-07-09-Videolectures

16 0.068603791 73 hunch net-2005-05-17-A Short Guide to PhD Graduate Study

17 0.063373148 394 hunch net-2010-04-24-COLT Treasurer is now Phil Long

18 0.063251443 173 hunch net-2006-04-17-Rexa is live

19 0.062981479 228 hunch net-2007-01-15-The Machine Learning Department

20 0.059810732 464 hunch net-2012-05-03-Microsoft Research, New York City


similar blogs computed by lsi model

lsi for this blog:

topicId topicWeight

[(0, 0.116), (1, -0.037), (2, -0.097), (3, 0.129), (4, -0.13), (5, -0.047), (6, 0.043), (7, 0.083), (8, -0.079), (9, 0.089), (10, 0.105), (11, -0.006), (12, -0.092), (13, -0.173), (14, -0.03), (15, -0.004), (16, -0.063), (17, -0.01), (18, -0.149), (19, -0.058), (20, -0.086), (21, -0.066), (22, -0.003), (23, 0.066), (24, -0.041), (25, -0.057), (26, -0.094), (27, 0.111), (28, 0.008), (29, -0.0), (30, 0.063), (31, -0.125), (32, 0.137), (33, -0.042), (34, 0.008), (35, 0.031), (36, -0.044), (37, 0.077), (38, 0.086), (39, -0.117), (40, -0.064), (41, -0.103), (42, -0.003), (43, 0.055), (44, 0.01), (45, 0.023), (46, -0.049), (47, -0.02), (48, 0.008), (49, -0.018)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.97689027 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

2 0.88120198 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 0.81062365 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 0.73687905 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 0.60743475 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 0.55534905 121 hunch net-2005-10-12-The unrealized potential of the research lab

7 0.54274201 132 hunch net-2005-11-26-The Design of an Optimal Research Environment

8 0.50568074 449 hunch net-2011-11-26-Giving Thanks

9 0.48297283 51 hunch net-2005-04-01-The Producer-Consumer Model of Research

10 0.47858214 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers

11 0.44237077 106 hunch net-2005-09-04-Science in the Government

12 0.43628007 282 hunch net-2008-01-06-Research Political Issues

13 0.42056322 255 hunch net-2007-07-13-The View From China

14 0.41755897 142 hunch net-2005-12-22-Yes , I am applying

15 0.3899515 425 hunch net-2011-02-25-Yahoo! Machine Learning grant due March 11

16 0.38293129 355 hunch net-2009-05-19-CI Fellows

17 0.37761927 296 hunch net-2008-04-21-The Science 2.0 article

18 0.37054107 241 hunch net-2007-04-28-The Coming Patent Apocalypse

19 0.3682602 208 hunch net-2006-09-18-What is missing for online collaborative research?

20 0.36556154 73 hunch net-2005-05-17-A Short Guide to PhD Graduate Study


similar blogs computed by lda model

lda for this blog:

topicId topicWeight

[(27, 0.164), (30, 0.434), (53, 0.027), (55, 0.079), (89, 0.044), (94, 0.015), (95, 0.111)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.89800477 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

2 0.88971502 189 hunch net-2006-07-05-more icml papers

Introduction: Here are a few other papers I enjoyed from ICML06. Topic Models: Dynamic Topic Models David Blei, John Lafferty A nice model for how topics in LDA type models can evolve over time, using a linear dynamical system on the natural parameters and a very clever structured variational approximation (in which the mean field parameters are pseudo-observations of a virtual LDS). Like all Blei papers, he makes it look easy, but it is extremely impressive. Pachinko Allocation Wei Li, Andrew McCallum A very elegant (but computationally challenging) model which induces correlation amongst topics using a multi-level DAG whose interior nodes are “super-topics” and “sub-topics” and whose leaves are the vocabulary words. Makes the slumbering monster of structure learning stir. Sequence Analysis (I missed these talks since I was chairing another session) Online Decoding of Markov Models with Latency Constraints Mukund Narasimhan, Paul Viola, Michael Shilman An “a

3 0.87236708 364 hunch net-2009-07-11-Interesting papers at KDD

Introduction: I attended KDD this year. The conference has always had a strong grounding in what works based on the KDDcup , but it has developed a halo of workshops on various subjects. It seems that KDD has become a place where the economy meets machine learning in a stronger sense than many other conferences. There were several papers that other people might like to take a look at. Yehuda Koren Collaborative Filtering with Temporal Dynamics . This paper describes how to incorporate temporal dynamics into a couple of collaborative filtering approaches. This was also a best paper award. D. Sculley , Robert Malkin, Sugato Basu , Roberto J. Bayardo , Predicting Bounce Rates in Sponsored Search Advertisements . The basic claim of this paper is that the probability people immediately leave (“bounce”) after clicking on an advertisement is predictable. Frank McSherry and Ilya Mironov Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contende

4 0.83335984 455 hunch net-2012-02-20-Berkeley Streaming Data Workshop

Introduction: The From Data to Knowledge workshop May 7-11 at Berkeley should be of interest to the many people encountering streaming data in different disciplines. It’s run by a group of astronomers who encounter streaming data all the time. I met Josh Bloom recently and he is broadly interested in a workshop covering all aspects of Machine Learning on streaming data. The hope here is that techniques developed in one area turn out useful in another which seems quite plausible. Particularly if you are in the bay area, consider checking it out.

5 0.6958586 444 hunch net-2011-09-07-KDD and MUCMD 2011

Introduction: At KDD I enjoyed Stephen Boyd ‘s invited talk about optimization quite a bit. However, the most interesting talk for me was David Haussler ‘s. His talk started out with a formidable load of biological complexity. About half-way through you start wondering, “can this be used to help with cancer?” And at the end he connects it directly to use with a call to arms for the audience: cure cancer. The core thesis here is that cancer is a complex set of diseases which can be distentangled via genetic assays, allowing attacking the specific signature of individual cancers. However, the data quantity and complex dependencies within the data require systematic and relatively automatic prediction and analysis algorithms of the kind that we are best familiar with. Some of the papers which interested me are: Kai-Wei Chang and Dan Roth , Selective Block Minimization for Faster Convergence of Limited Memory Large-Scale Linear Models , which is about effectively using a hard-example

6 0.69263083 85 hunch net-2005-06-28-A COLT paper

7 0.61927384 292 hunch net-2008-03-15-COLT Open Problems

8 0.52812606 132 hunch net-2005-11-26-The Design of an Optimal Research Environment

9 0.45535323 344 hunch net-2009-02-22-Effective Research Funding

10 0.44407067 456 hunch net-2012-02-24-ICML+50%

11 0.43450433 466 hunch net-2012-06-05-ICML acceptance statistics

12 0.43085718 140 hunch net-2005-12-14-More NIPS Papers II

13 0.42789602 373 hunch net-2009-10-03-Static vs. Dynamic multiclass prediction

14 0.42225793 301 hunch net-2008-05-23-Three levels of addressing the Netflix Prize

15 0.42098922 462 hunch net-2012-04-20-Both new: STOC workshops and NEML

16 0.41934356 127 hunch net-2005-11-02-Progress in Active Learning

17 0.41657433 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers

18 0.41390201 492 hunch net-2013-12-01-NIPS tutorials and Vowpal Wabbit 7.4

19 0.41264769 36 hunch net-2005-03-05-Funding Research

20 0.41155949 464 hunch net-2012-05-03-Microsoft Research, New York City