1 Maybe I should clarify: Are the tasks previously done by bank tellers becoming automated? Yes. The fact that the number bank tellers has increased does not invalidate my statement. If there were no internet banking or ATMs then increase would be much larger right? So its trivial to see that the number of bank tellers can increase at the same time as bank teller jobs are lost to automated systems.
2 I’ll give you an extreme one. I am a few steps away of earning a degree in theoretical physics specializing in quantum information theory. Theoretical quantum information theory is nothing but symbol manipulation in a framework on existing theorems of linear algebra. With enough resources pretty much all of the research could be done by computers alone. Algorithms could in principle put mathematical statements together, other algorithms testing the meaningfulness of the output and so on.. but that a discussion interesting enough to have its own thread. I just mean that theoretical work is not immune to automation.
Organize all the known mathematics and physics of 1915 in a computer running the right algorithms, the ask it: ‘what is gravity?’ Would it output General theory of relativity? I think so.
Organize all the known mathematics and physics of 1915 in a computer running the right algorithms, the ask it: ‘what is gravity?’ Would it output General theory of relativity? I think so.
You have fallen victim to the hindsight bias. The parameter space of the ways of reconciling Special Relativity with Newtonian gravity is quite large, even assuming that this goal would have occurred to anyone but Einstein at that time (well, Hilbert did the math independently, after communicating with Einstein for some time). Rejecting the implicit and unquestionable idea of a fixed background spacetime was an extreme leap of genius. The “right algorithms” would probably have to be the AGI-level ones.
I am a few steps away of earning a degree in theoretical physics specializing in quantum information theory. Theoretical quantum information theory is nothing but symbol manipulation in a framework on existing theorems of linear algebra.
“Theoretical quantum information theory” is math, not a natural science, and math is potentially easier to automate. Still, feel free to research the advances in automated theorem proving, and, more importantly, in automated theorem stating, a much harder task. How would a computer know what theorems are interesting?
1 Hindsight bias? Quite a diagnosis there. I never specified the level of those algorithms.
2 Which part of theoretical physics is not math? Experiments confirm or reject theoretical conclusions and points theoretical work in different directions. But that theoretical work is in the end symbol processing—something that computers are pretty good at. There could be a variety of ways for a computer to decide if a theorem is interesting just as for a human. Scope, generality and computability of the theorems could be factors. Input Newtonian mechanics and the mathematics of 1850 and output Hamiltonian mechanics just based on the generality of that framework.
1 Hindsight bias? Quite a diagnosis there. I never specified the level of those algorithms.
I have, in my reply: probably AGI-level, i.e. too far into the haze of the future to be considered seriously.
2 Which part of theoretical physics is not math?
Probably the 1% that counts the most (I agree, 99% of theoretical physics is math, as I found out the hard way). It’s finding the models that make the old experiments make sense and that make new interesting predictions that turn out to be right that is the mysterious part. How would you program a computer that can decide, on its own, that adding the gauge freedom to the Maxwell equations would actually make them simpler and lay foundations for nearly all of modern high-energy physics? That the Landau pole is not an insurmountable obstacle, despite all the infinities? That 2D models like graphene are worth studying? That can resolve the current mysteries, like the High Tc superconductivity, the still mysterious foundations of QM, the cosmological mysteries of dark matter and dark energy, the many problems in chemistry, biology, society etc.? Sure, it is all “symbol manipulation”, but so is everything humans do, if you agree that we are (somewhat complicated) Turing machines and Markov chains. If you assert that it is possible to do all this with anything below an AGI-level complexity, I hope that you are right, but I am extremely skeptical.
Mathematicians would probably call much less of what physicists do “math” than the physicists. Let me focus on statistical mechanics. A century ago, physicists made assertions that mathematicians could understand, like the central limit theorem and ergodicity. There was debate about whether these were mathematical or physical truths, but it is fine to take them as assumptions and do mathematics. This happens today with spin glasses. But physicists also talk about universality. I suppose that’s a precise claim, though rather strong, but the typical prototype of a universality class is a conformal field theory and mathematicians can’t make heads or tails of that. The calculations about CFT may look like math, but the rules aren’t formal.
PS—bank tellers per capita fell from 1998 to 2008, though not much.
I agree that the conceptual (non-simply-symbol-processing) part of theoretical physics is the tricky part to automate, and even if I am willing to accept that that last 1% will be kept in the monopoly of human beings, but then that’s it; theoretical physics will asymptotically reduce to that 1% and stay there until AGI arrives. Its not bound to change over night, but the change will be the product of many small changes where computers start to aid us not by just doing the calculations and simulations but more advanced tasks where we can input sets of equations from two different sub-field and letting the computers using evolutionary algorithms try different combinations, operate on them and so on and find links. The process could end where a joint theory in a common mathematical framework succeeds to derive the phenomena in both sub fields.
EDIT: Have to add that it feels a bit awkward to argue against the future necessity of my “profession”..
Are you a grad student? Because I don’t know much about theoretical physics, but I find it very hard to believe much academic research could be automated.
I’m a post-doc doing research on computational linguistics. I can’t imagine automating my work.
Yes I am, and I’ll soon start looking for PhD-positions either in physics or some interdisciplinary field of interest. I know I seem a bit over-optimistic, and that such radical changes may take maybe at least 30-50 years, but I’d guess most of us will be alive by then so its still relevant. My main point is that step by step theoretical tasks will move to the space of computation and the job of the theoretician will evolve to something else. If one day our computers in our computer aided research starts to output suggestions for models, or links between sets of data we haven’t thought about comparing wouldn’t those results actually be a collaboration between us and that system? You maybe cant imagine automating everything you do, but I’m sure you can imagine parts of your research being automated. That would allow you to use more mental resources for the conceptual and creative part of the research and so on..
Well I’m working on a sub-problem of artificial intelligence. So the task of designing a system to do my work is harder than the end-goal my work is trying to achieve!
By human-equivalent i’d guess you mean equivalent in if not all, but in many different aspects of human intelligence. I wouldn’t dare to have an opinion at the moment.
Organize all the known mathematics and physics of 1915 in a computer running the right algorithms, the ask it: ‘what is gravity?’ Would it output General theory of relativity? I think so.
Do you have any idea how to do it? What does organising the mathematics and physics actually look like? What are the right algorithms?
Nobody probably doubts that theoretical work can be done by machines. But your original claim was stronger: that these tasks are “fairly easy to represent virtually” and that the conjecture that they are going to be the last to go is a misconception.
1 Maybe I should clarify: Are the tasks previously done by bank tellers becoming automated? Yes. The fact that the number bank tellers has increased does not invalidate my statement. If there were no internet banking or ATMs then increase would be much larger right? So its trivial to see that the number of bank tellers can increase at the same time as bank teller jobs are lost to automated systems.
2 I’ll give you an extreme one. I am a few steps away of earning a degree in theoretical physics specializing in quantum information theory. Theoretical quantum information theory is nothing but symbol manipulation in a framework on existing theorems of linear algebra. With enough resources pretty much all of the research could be done by computers alone. Algorithms could in principle put mathematical statements together, other algorithms testing the meaningfulness of the output and so on.. but that a discussion interesting enough to have its own thread. I just mean that theoretical work is not immune to automation.
Organize all the known mathematics and physics of 1915 in a computer running the right algorithms, the ask it: ‘what is gravity?’ Would it output General theory of relativity? I think so.
You have fallen victim to the hindsight bias. The parameter space of the ways of reconciling Special Relativity with Newtonian gravity is quite large, even assuming that this goal would have occurred to anyone but Einstein at that time (well, Hilbert did the math independently, after communicating with Einstein for some time). Rejecting the implicit and unquestionable idea of a fixed background spacetime was an extreme leap of genius. The “right algorithms” would probably have to be the AGI-level ones.
“Theoretical quantum information theory” is math, not a natural science, and math is potentially easier to automate. Still, feel free to research the advances in automated theorem proving, and, more importantly, in automated theorem stating, a much harder task. How would a computer know what theorems are interesting?
1 Hindsight bias? Quite a diagnosis there. I never specified the level of those algorithms.
2 Which part of theoretical physics is not math? Experiments confirm or reject theoretical conclusions and points theoretical work in different directions. But that theoretical work is in the end symbol processing—something that computers are pretty good at. There could be a variety of ways for a computer to decide if a theorem is interesting just as for a human. Scope, generality and computability of the theorems could be factors. Input Newtonian mechanics and the mathematics of 1850 and output Hamiltonian mechanics just based on the generality of that framework.
I have, in my reply: probably AGI-level, i.e. too far into the haze of the future to be considered seriously.
Probably the 1% that counts the most (I agree, 99% of theoretical physics is math, as I found out the hard way). It’s finding the models that make the old experiments make sense and that make new interesting predictions that turn out to be right that is the mysterious part. How would you program a computer that can decide, on its own, that adding the gauge freedom to the Maxwell equations would actually make them simpler and lay foundations for nearly all of modern high-energy physics? That the Landau pole is not an insurmountable obstacle, despite all the infinities? That 2D models like graphene are worth studying? That can resolve the current mysteries, like the High Tc superconductivity, the still mysterious foundations of QM, the cosmological mysteries of dark matter and dark energy, the many problems in chemistry, biology, society etc.? Sure, it is all “symbol manipulation”, but so is everything humans do, if you agree that we are (somewhat complicated) Turing machines and Markov chains. If you assert that it is possible to do all this with anything below an AGI-level complexity, I hope that you are right, but I am extremely skeptical.
Mathematicians would probably call much less of what physicists do “math” than the physicists. Let me focus on statistical mechanics. A century ago, physicists made assertions that mathematicians could understand, like the central limit theorem and ergodicity. There was debate about whether these were mathematical or physical truths, but it is fine to take them as assumptions and do mathematics. This happens today with spin glasses. But physicists also talk about universality. I suppose that’s a precise claim, though rather strong, but the typical prototype of a universality class is a conformal field theory and mathematicians can’t make heads or tails of that. The calculations about CFT may look like math, but the rules aren’t formal.
PS—bank tellers per capita fell from 1998 to 2008, though not much.
I agree that the conceptual (non-simply-symbol-processing) part of theoretical physics is the tricky part to automate, and even if I am willing to accept that that last 1% will be kept in the monopoly of human beings, but then that’s it; theoretical physics will asymptotically reduce to that 1% and stay there until AGI arrives. Its not bound to change over night, but the change will be the product of many small changes where computers start to aid us not by just doing the calculations and simulations but more advanced tasks where we can input sets of equations from two different sub-field and letting the computers using evolutionary algorithms try different combinations, operate on them and so on and find links. The process could end where a joint theory in a common mathematical framework succeeds to derive the phenomena in both sub fields.
EDIT: Have to add that it feels a bit awkward to argue against the future necessity of my “profession”..
Are you a grad student? Because I don’t know much about theoretical physics, but I find it very hard to believe much academic research could be automated.
I’m a post-doc doing research on computational linguistics. I can’t imagine automating my work.
Yes I am, and I’ll soon start looking for PhD-positions either in physics or some interdisciplinary field of interest. I know I seem a bit over-optimistic, and that such radical changes may take maybe at least 30-50 years, but I’d guess most of us will be alive by then so its still relevant. My main point is that step by step theoretical tasks will move to the space of computation and the job of the theoretician will evolve to something else. If one day our computers in our computer aided research starts to output suggestions for models, or links between sets of data we haven’t thought about comparing wouldn’t those results actually be a collaboration between us and that system? You maybe cant imagine automating everything you do, but I’m sure you can imagine parts of your research being automated. That would allow you to use more mental resources for the conceptual and creative part of the research and so on..
You can’t. Most can’t, probably.
You have to come with some good reasons WHY that would be impossible.
Well I’m working on a sub-problem of artificial intelligence. So the task of designing a system to do my work is harder than the end-goal my work is trying to achieve!
When do you believe the first human-equivalent GAI will be created?
By human-equivalent i’d guess you mean equivalent in if not all, but in many different aspects of human intelligence. I wouldn’t dare to have an opinion at the moment.
Anyone else?
Do you have any idea how to do it? What does organising the mathematics and physics actually look like? What are the right algorithms?
Nobody probably doubts that theoretical work can be done by machines. But your original claim was stronger: that these tasks are “fairly easy to represent virtually” and that the conjecture that they are going to be the last to go is a misconception.