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How Artificial Intelligence Is Changing Science

quantamagazine.org

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Re: How Artificial Intelligence Is Changing Science

#51

Post by hadsed »

The 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.

Re: How Artificial Intelligence Is Changing Science

#52

Post by [deleted] »

emiliobumachar wrote: post #13

Here'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…

[deleted]

Re: How Artificial Intelligence Is Changing Science

#53

Post by pinouchon »

emiliobumachar wrote: post #13

Here'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…

Sounds like a good fit for probabilistic programming, much like Stuart Russell did here https://www.youtube.com/watch?v=GYQrNfSmQ0M&feature=youtu.be.... His method could find locations of nuclear tests better than the existing UN system at the time by a lot. His model uses bayesian inference in a stochastic/symbolic/simplified/statistic model of physics (how shockwaves propagate on the surface of the earth).

You could do the same here: assuming the relevant laws (kepler? newton law of gravity?), and a prior distribution on the location/mass of your 9th planet, given what we observe for the other planets, what's the posterior distribution on the mass/location of the 9th planet.

The statistical model is likely to be small (the Russell statistical model for Nukes fits on one slide). The issue is how to do inference efficiently. Fortunately, probabilistic systems have come a long way and can do these kind of inferences.

Re: How Artificial Intelligence Is Changing Science

#54

Post by sansnomme »

throwawaywego wrote: post #3

The 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, i…

More money is always good for researchers. At the end of the day, being paid more is the free market doing resource allocation when basic research in other fields isn't being appropriately subsidized.

Re: How Artificial Intelligence Is Changing Science

#55

Post by mr_overalls »

sansnomme wrote: post #54
throwawaywego wrote: post #3

The 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, i…

More money is always good for researchers. At the end of the day, being paid more is the free market doing resource allocation when basic research in other fields isn't being appropriately subsidized.

In classical economic theory, one pre-condition for efficient market allocation of resources is accurate information providing a basis for rational levels of investment.

If AI is subject to crazes, with investors as a whole drastically overestimating its potential, then it's certainly possible to over-allocate capital (human and otherwise) to it in the hopes of a payoff.

Consider the Dutch tulip mania of the 1630s. Imagine if it had lasted a bit longer, long enough for promising scientists and scholars of every type to be trained solely to optimize the growth of tulips.

This allocation of capital would provide a benefit to tulip investors for as long as the craze lasted, but would prove to be a detriment to society once the craze ended.

https://en.wikipedia.org/wiki/Tulip_mania

Re: How Artificial Intelligence Is Changing Science

#56

Post by sansnomme »

mr_overalls wrote: post #55
sansnomme wrote: post #54

Earlier quoted context omitted.

More money is always good for researchers. At the end of the day, being paid more is the free market doing resource allocation when basic research in other fields isn't being appropriately subsidized.

In classical economic theory, one pre-condition for efficient market allocation of resources is accurate information providing a basis for rational levels of investment. If AI is subject to crazes, with investors as a whole drastically overestimating its potential, then it's certainly possible to over-allocate capital (human and otherwise) to it in the hopes of a payoff. Consider the Dutch tulip mania of the 1630s. I…

To be fair, the connectionist variety of ML is extremely compatible with the majority of the hard sciences (Linear Alg, Calculus, not really much CS/discrete math if you think about it). A physics/rigorous CogSci background prepares you just as well as CS for most of the interesting AI stuff. The fact that AI and CS in general has such a low barrier of entry is something to be celebrated. Be glad that our domain do not suffer from the gatekeeping in Medicine and Law.

Re: How Artificial Intelligence Is Changing Science

#58

Post by jl2718 »

robertAngst wrote: post #42
EGreg wrote: post #2

Can we use this to make an app and figure out the optimal diets for everyone? Or a GAN for generating the funniest jokes?

I want to do linear algebra on this data https://efficiencyiseverything.com/food-nutrition-per-dollar... EDIT: Direct link to the data https://efficiencyiseverything.com/data/Nutrition%20Per%20Do...

George Dantzig invented Simplex for this problem.

Re: How Artificial Intelligence Is Changing Science

#59

Post by cheez »

hadsed wrote: post #51

The 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 tre…

It 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.

Re: How Artificial Intelligence Is Changing Science

#60

Post by rramadass »

monocasa wrote: post #24
throwawaywego wrote: post #3

The 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, i…

I'm really afraid that ML is mainly just going to become automated p-hacking, and bring about a dark age to much of science. In a publish or perish world, how can you compete with someone with enough budget to set a bunch of models looking for any specious correlations in data sets and publishing what comes out the other end? Like we'll still have great breakthroughs from the top of the field, but a lot of grunt work…

>ML is mainly just going to become automated p-hacking, and bring about a dark age to much of science

It is already happening. A lot of people who can contribute to actual Science are moving into AI/ML field for the money and the industry/media hype are reinforcing this. Everything is "Deep${NONSENSE}" nowadays whether it is relevant or not. As a beginner, when i started to learn NNs, i couldn't get past my initial hurdle on how to validate the results on actual real-world data. What Statistical metrics do i use to "know" that the blackbox is working correctly? What are the assumptions and limitations that i need to be aware of to understand and have faith in the output? Most people don't seem to know or care; it is "magic" to them. In a world awash with data, reckless application of NN models to any and every problem is only going to drown us in spurious results and muddying all Scientific endeavours.

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