What do you plan to do in AGI? The problem with AGI is that many of the fundamental tools necessary for AGI are only in a preliminary state of development. If you’re going to end up working on developing those tools, you might as well do so in a more lucrative area, such as statistics.
you might as well do so in a more lucrative area, such as statistics.
I was going to launch into some of my interests, but you’ve got my attention. Could you expand on this point more? I have an idea or two about why you might be saying that statistics is more lucrative, but I want to know what your point off view is here.
Money is of course only one factor; I think you could easily make a career in statistics without compromising your interests.
There are many researchers in statistics who work on areas which are extremely relevant to artificial intelligence. One of the best examples is Cosma Shalizi (http://www.cscs.umich.edu/~crshalizi/), who developed the “Causal State Splitting Reconstruction” algorithm for automatically detecting patterns in time series.
Your background is also quite suited for statistics: both the computer science and the philosophy. Nowadays, the computational element is becoming more and more prominent in statistics; the best example is the rapid growth of Markov Chain Monte Carlo sampling for Bayesian analysis. MCMC methods originated in statistical physics, but they are now being employed to analyze complex probability models in exotic settings such as social networks.
As for the philosophical aspect of statistics (which is usually understated,) you would be surprised at how many foundational questions still remain on the question of how to draw conclusions from data. There are a few basic tenets for statistical procedures, e.g. the likelihood principle and the conditionality principle, but none of them are universally accepted as “axioms” and in fact some of them contradict each other. The choice of which principle to adopt results in an explosion of competing methodologies: frequentist, Bayesian, empirical Bayesian, conditional, “pragmatic” (cross-validation) etc. Yet the question of how to do inference from data is obviously a central question to be addressed for the goal of designing an inductive agent.
That is interesting. I was aware of the growth of Monte Carlo sampling due to its use in Go AI, and I have an interest in the philosophical aspects of statistical inference though I haven’t follwed up on it quite to the extent that I would like. One of the things I’m currently picking at is An Introduction to Kolmogorov Complexity by Li and Vitanyi. I started looking into it after seeing the discussions about complexity and Solomonoff induction on this site.
However, I am still unclear as to why this is more lucrative than other areas related to AGI.
Ah, now I see what angle you are coming at this from. Yes, data mining techniques would certainly be invaluable to an AGI (which needs to be able to organize its input data into useful information) as well as lucrative to any number of companies. I saw a talk by John Hopcroft a while back that made it seem very appealing.
What do you plan to do in AGI? The problem with AGI is that many of the fundamental tools necessary for AGI are only in a preliminary state of development. If you’re going to end up working on developing those tools, you might as well do so in a more lucrative area, such as statistics.
I was going to launch into some of my interests, but you’ve got my attention. Could you expand on this point more? I have an idea or two about why you might be saying that statistics is more lucrative, but I want to know what your point off view is here.
Money is of course only one factor; I think you could easily make a career in statistics without compromising your interests.
There are many researchers in statistics who work on areas which are extremely relevant to artificial intelligence. One of the best examples is Cosma Shalizi (http://www.cscs.umich.edu/~crshalizi/), who developed the “Causal State Splitting Reconstruction” algorithm for automatically detecting patterns in time series.
Your background is also quite suited for statistics: both the computer science and the philosophy. Nowadays, the computational element is becoming more and more prominent in statistics; the best example is the rapid growth of Markov Chain Monte Carlo sampling for Bayesian analysis. MCMC methods originated in statistical physics, but they are now being employed to analyze complex probability models in exotic settings such as social networks.
As for the philosophical aspect of statistics (which is usually understated,) you would be surprised at how many foundational questions still remain on the question of how to draw conclusions from data. There are a few basic tenets for statistical procedures, e.g. the likelihood principle and the conditionality principle, but none of them are universally accepted as “axioms” and in fact some of them contradict each other. The choice of which principle to adopt results in an explosion of competing methodologies: frequentist, Bayesian, empirical Bayesian, conditional, “pragmatic” (cross-validation) etc. Yet the question of how to do inference from data is obviously a central question to be addressed for the goal of designing an inductive agent.
That is interesting. I was aware of the growth of Monte Carlo sampling due to its use in Go AI, and I have an interest in the philosophical aspects of statistical inference though I haven’t follwed up on it quite to the extent that I would like. One of the things I’m currently picking at is An Introduction to Kolmogorov Complexity by Li and Vitanyi. I started looking into it after seeing the discussions about complexity and Solomonoff induction on this site.
However, I am still unclear as to why this is more lucrative than other areas related to AGI.
See http://www.sciencemag.org/site/special/data/
Ah, now I see what angle you are coming at this from. Yes, data mining techniques would certainly be invaluable to an AGI (which needs to be able to organize its input data into useful information) as well as lucrative to any number of companies. I saw a talk by John Hopcroft a while back that made it seem very appealing.