hunch_net hunch_net-2005 hunch_net-2005-142 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Every year about now hundreds of applicants apply for a research/teaching job with the timing governed by the university recruitment schedule. This time, it’s my turn—the hat’s in the ring, I am a contender, etc… What I have heard is that this year is good in both directions—both an increased supply and an increased demand for machine learning expertise. I consider this post a bit of an abuse as it is neither about general research nor machine learning. Please forgive me this once. My hope is that I will learn about new places interested in funding basic research—it’s easy to imagine that I have overlooked possibilities. I am not dogmatic about where I end up in any particular way. Several earlier posts detail what I think of as a good research environment, so I will avoid a repeat. A few more details seem important: Application. There is often a tension between basic research and immediate application. This tension is not as strong as might be expected in my case. As
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1 Every year about now hundreds of applicants apply for a research/teaching job with the timing governed by the university recruitment schedule. [sent-1, score-1.095]
2 This time, it’s my turn—the hat’s in the ring, I am a contender, etc… What I have heard is that this year is good in both directions—both an increased supply and an increased demand for machine learning expertise. [sent-2, score-1.049]
3 I consider this post a bit of an abuse as it is neither about general research nor machine learning. [sent-3, score-0.558]
4 My hope is that I will learn about new places interested in funding basic research—it’s easy to imagine that I have overlooked possibilities. [sent-5, score-0.559]
5 I am not dogmatic about where I end up in any particular way. [sent-6, score-0.194]
6 Several earlier posts detail what I think of as a good research environment, so I will avoid a repeat. [sent-7, score-0.695]
7 A few more details seem important: Application. [sent-8, score-0.079]
8 There is often a tension between basic research and immediate application. [sent-9, score-0.689]
9 This tension is not as strong as might be expected in my case. [sent-10, score-0.399]
10 As evidence, many of my coauthors of the last few years are trying to solve particular learning problems and I strongly care about whether and where a learning theory is useful in practice. [sent-11, score-0.54]
11 I would like my next move to be of indefinite duration. [sent-13, score-0.291]
12 net) if there is a possibility you think I should consider. [sent-15, score-0.218]
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Introduction: Every year about now hundreds of applicants apply for a research/teaching job with the timing governed by the university recruitment schedule. This time, it’s my turn—the hat’s in the ring, I am a contender, etc… What I have heard is that this year is good in both directions—both an increased supply and an increased demand for machine learning expertise. I consider this post a bit of an abuse as it is neither about general research nor machine learning. Please forgive me this once. My hope is that I will learn about new places interested in funding basic research—it’s easy to imagine that I have overlooked possibilities. I am not dogmatic about where I end up in any particular way. Several earlier posts detail what I think of as a good research environment, so I will avoid a repeat. A few more details seem important: Application. There is often a tension between basic research and immediate application. This tension is not as strong as might be expected in my case. As
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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
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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
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Introduction: This is near the one month point, so it seems appropriate to consider meta-issues for the moment. The number of posts is a bit over 20. The number of people speaking up in discussions is about 10. The number of people viewing the site is somewhat more than 100. I am (naturally) dissatisfied with many things. Many of the potential uses haven’t been realized. This is partly a matter of opportunity (no conferences in the last month), partly a matter of will (no open problems because it’s hard to give them up), and partly a matter of tradition. In academia, there is a strong tradition of trying to get everything perfectly right before presentation. This is somewhat contradictory to the nature of making many posts, and it’s definitely contradictory to the idea of doing “public research”. If that sort of idea is to pay off, it must be significantly more succesful than previous methods. In an effort to continue experimenting, I’m going to use the next week as “open problems we
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Introduction: Every year about now hundreds of applicants apply for a research/teaching job with the timing governed by the university recruitment schedule. This time, it’s my turn—the hat’s in the ring, I am a contender, etc… What I have heard is that this year is good in both directions—both an increased supply and an increased demand for machine learning expertise. I consider this post a bit of an abuse as it is neither about general research nor machine learning. Please forgive me this once. My hope is that I will learn about new places interested in funding basic research—it’s easy to imagine that I have overlooked possibilities. I am not dogmatic about where I end up in any particular way. Several earlier posts detail what I think of as a good research environment, so I will avoid a repeat. A few more details seem important: Application. There is often a tension between basic research and immediate application. This tension is not as strong as might be expected in my case. As
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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
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