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How Artificial Intelligence Is Changing Science
- EGreg 7y agoCan we use this to make an app and figure out the optimal diets for everyone? Or a GAN for generating the funniest jokes?
- wpasc 7y agoFor something like optimal diets for everyone, I imagine it's more of an issue with incomplete understanding of diet, microbiome, genetics, epigenetics, etc. Once that's known, I'd venture to say you don't need AI. But a GAN where the discriminator is determining if a joke is made by an AI or a human might be pretty cool :)
- btrettel 7y ago> For something like optimal diets for everyone, I imagine it's more of an issue with incomplete understanding of diet, microbiome, genetics, epigenetics, etc. Once that's known, I'd venture to say you don't need AI. Even if the data exists, doesn't mean the AI folks would use it. In my research (a particular subfield of fluid dynamics), the machine learning/AI papers/talks always seem to have incomplete or even bad data, as if how advanced their algorithm is makes up for that. (I don't think they actually believe that. I think they just have bad habits.) I've published pretty good linear regressions of a much larger data compilation and received much less attention, despite the fact that my linear regressions are probably more accurate than the ML models...
- ImaCake 7y agoYup. As an outsider looking in on bioinformatics, a lot of the datasets I have played with have some real glaring problems. They often have small amounts of data or some serious bias. A naive ML approach on a lot of sequencing data will probably be undermined by the bias of the sequencing machine and the researcher who processed the biological materials.
- mattkrause 7y agoYup. For example, look at this paper from Google: https://www.nature.com/articles/s41746-018-0029-1 https://www.nature.com/articles/s41746-018-0029-1 The whole thing is about how to build these fancy networks, and it created a fair bit of buzz. Table S1, in the supplement, however shows that it's rather pointless. The deep model has an AUC (95% CI) of [0.94, 0.96] for in-patient mortality. The "full feature-enhanced" logistic regression baseline has an AUC of [0.92, 0.95]. Same pattern for 30 remission. Length of stay is the only one that's not overlapping, and it just squeaks that out: [0.86, 0.87] for the deep model vs. [0.84, 0.85] for the baseline.
- jacquesm 7y ago> Or a GAN for generating the funniest jokes? You really want to be careful with that, Monty Python made an excellent documentary about the weaponization of such high grades of humor and the results, to put it mildly, weren't funny.
- EGreg 7y agoSorry what? Can you offer us a link?
- D-Coder 7y agoMore than you want to know: https://en.wikipedia.org/wiki/The_Funniest_Joke_in_the_World https://en.wikipedia.org/wiki/The_Funniest_Joke_in_the_World Just the right amount: https://www.youtube.com/watch?v=_yo9WHrTvks https://www.youtube.com/watch?v=_yo9WHrTvks
- stcredzero 7y agoMonty Python made an excellent documentary about the weaponization of such high grades of humor and the results, to put it mildly, weren't funny. It was a skit, and it was funny. Not their best work.
- gwern 7y agoSpeaking of diet: https://www.nytimes.com/2019/06/10/health/nutrition-diet-genetics-food.html https://www.nytimes.com/2019/06/10/health/nutrition-diet-gen... https://www.nytimes.com/2019/05/08/science/precision-medicine-overtreatment.html https://www.nytimes.com/2019/05/08/science/precision-medicin... https://www.gwern.net/docs/longevity/2019-rose.pdf https://www.gwern.net/docs/longevity/2019-rose.pdf Diet response is so genetically confounded that I think it's going to be a while before you can make any sort of confident prediction, and just plain hard to predict even when you're using identical twins. Probably more leverage in figuring out how to make continuous glucose monitors more feasible to measure individual response directly.
- robertAngst 7y agoI want to do linear algebra on this data https://efficiencyiseverything.com/food-nutrition-per-dollar/ https://efficiencyiseverything.com/food-nutrition-per-dollar... EDIT: Direct link to the data https://efficiencyiseverything.com/data/Nutrition%20Per%20Dollar-v1.5.xlsx https://efficiencyiseverything.com/data/Nutrition%20Per%20Do...
- jl2718 7y agoGeorge Dantzig invented Simplex for this problem.
- jl2718 7y agoThe optimal diet problem was the first problem ever solved in the field of operations research. Get ready for some navy beans.
- throwawaywego 7y agoThe article states positive impacts on science, but there are also negative impacts on science. For instance, the hype of AI has caused a brain-drain on related fields (such as cognitive science or applied mathematics). AI research itself suffers from companies buying up the academic talent. And researchers slap AI (which is usually deep learning) on a decade-old problem, without any care for complexity/benchmarks, implementation/usage, and proper validation methods, just to get published or receive funding.
- khawkins 7y agoI love how many paper titles nowadays follow the pattern: "Deep-<topic>: <Actual title of the paper>". And often they aren't doing anything "deeper" than a fully-connected multilayer neural network--a machine learning algorithm competitive with SVMs and been around well over a decade.
- moultano 7y agoThat's true, but there's a lot of value in waking people up to the idea that ML works, even if what they're doing has worked for a long time. There are a lot of situations where before people would have assumed their best option is to carefully tweak a custom statistical model, whereas now they're just happy to throw a black box at it and see what happens. This is as much a cultural change as a technological change, and it's good that it is finally happening. That's what a "paradigm shift" is after all.
- gubbrora 7y agoI think something is lost when doing this. I'd bet the researcher who first builds a model and then reaches for ml will outperform the researcher who goes straight for ml. Building a custom model will help with feature selection. It will provide a baseline to compare the ml model to which can help debug problem points of the ml model. And finally it serves as a sanity check that you aren't leaving a lot of performance on the table.
- mattkrause 7y ago
- ngcc_hk 7y agoVery odd and may I say wrong article. The basic about AI provide a breakthrough is ok. But I would not call newton law as simulation. The real development is verbal non-maths theory. Not simulation. In fact this kind of theory go first. If Aristotle etc. said ... sun must be revolving about earth. Some basic maths (geometry and algebra). Observation is the second approach. And a breakthrough. Kepler and later Galilei watching juipter’s Moon. Then maths as a tool. Not just simple verbal theory. Calculus, non-Euclid geometry, wave mechanics, ... to these days physics is nothing but maths like. The sad thing about social science is only data. Only economic has some maths. Still data and verbal theory. Then computer provide data analysis tool as well as simulation and visualisation. This is an aid more. Then data as a tool. And the new breakthrough is AI helping to suggest models. Theory, Mathematical model, Data observation (to disprove, to hint, to post question and to generate theory based on pattern) then Various tool to assist above including AI.
- emiliobumachar 7y agoHere's my crackpot idea, in case anyone out there is willing and qualified to put in the hard work: Start with a detailed model of the solar system. Make a million copies of it. In each copy, insert a planet in a random orbit, with random mass. Measure the orbits of everything, perturbed by the new planet. Feed the measurements of everything, except the new planet, to an A.I., and have it estimate the position of the new planet. Give it feedback on how accurate is was. Repeat a million times. It should learn to pinpoint ninth planets in solar systems like ours, from the perturbations on orbits of known bodies. Then feed it the real measurement history from the real Solar System. It should output the location of Planet Nine. https://en.wikipedia.org/wiki/Planet_Nine https://en.wikipedia.org/wiki/Planet_Nine
- starpilot 7y agoOrbital equations are straightforward and deterministic, I am wondering why an AI would be needed for this? You could solve explicitly.
- crimsonalucard 7y agoAlso one perturbed configuration actually has multiple solutions.
- jackpirate 7y agoJust to clarify, the GP is describing what is essentially the n body problem in physics [1]. There is in general no closed form solution for solving n-body problems, but and so numeric solutions are generally required. [1] https://en.wikipedia.org/wiki/N-body_problem https://en.wikipedia.org/wiki/N-body_problem
- opportune 7y agoWell, an unperturbed orbit is straightforward and deterministic. But three+ body problems (which I believe the original comment is describing) do not have closed form solutions and are general simulated: https://en.wikipedia.org/wiki/Three-body_problem https://en.wikipedia.org/wiki/Three-body_problem. The reason planet nine is suspected to exist is due to the commonalities in the orbits of trans-neptunian objects. That is, there appears to be a large gravitational influence on TNOs that causes the distribution of their orbits to exhibit irregularities that don't make sense with only two factors influencing their orbits.
- yters 7y agoEver more algorithms and models and data, ever less understanding and scientific theories. Soon, instead of "theory of gravity" we'll have "generative DNN of science papers and grant writing" that no one will understand, but can generate papers that pass peer review and earn grants and pull in all the monies, effectively monopolizing and halting all government funded scientific progress. Meanwhile, actual science will continue on in the amateur ranks, from which has always come the true breakthroughs.
- thrwayxyz 7y ago[[citation needed]] What has been an amateur science breakthrough in the last century or two which didn't have at it's base some billions of dollars of government funding.
- AnimalMuppet 7y agoThe Special Theory of Relativity, from some moonlighting patent clerk.
- nradclif 7y agoThat was over a century ago (although within the “century or two” limit specified). But it’s not at all characteristic of the majority of scientific discoveries made in the last century, which were largely made by professional scientists and grad students on their way to becoming professionals. The list of Nobel Prizes in various sciences over the past century I think demonstrates this.
- cheez 7y agoMaybe government money has squeezed out innovation a la Einstein.
- teekert 7y agoIn my field of science the only thing that changed is that people started calling algorithms ai. For marketing purposes.
- readhn 7y agoAI is only as good as the programmer who created it. We are really in the stone age when it comes to AI.
- hadsed 7y agoThe negative comments here are disappointing. ML is a fantastic tool for science, where it can propose a model that works as a starting point for getting to a model that works AND that you can understand. This is quite common in physics, for example, where people are happy to build elaborate experiments just to poke at the universe in weird ways. An ML algorithm is a theorist's particle accelerator where they can treat it as something to be explored to gain insight. The reason people are pissed about this is that we're doing this breadth-first, because the incentives make it that way. People are right to be concerned if we never get back to deeper analyses, but I'm not at all concerned. At some point the low hanging fruit will be gone and every scientific community will be better off having these new results. As we get better at probing the black box, and we will because there's a lot of value behind doing so, we will start to shift back to the deeper questions.
- cheez 7y agoIt can't really propose a model that is understandable by most human means but I agree that it can find new relations that we can explore.