Unless I misread it, if a project is deemed to have an "FBA" then the project leader is an "FN", in the author's opinion. And yes, some such projects have been explicitly called out.
I think the article misses the point of what physics is. It is not a collection of "sparse" models and principles, rather, it is a scientific discipline from which such models have emerged.
You will notice the article conflates the two things: physics and the known laws of physics (e.g. first para in section 1.2). Simplicity of the latter does not imply simplicity of the former, but the article assumes that it does in order to tackle/state the question as posed: "Why is AI hard and physics simple?".
The author is talking about how a given physics model appears simple when they are presented with it, e.g. a particular quantum field theory. This is the kind of limited perspective about research that an undergraduate physicist may develop simply by solving the hand crafted problems that are presented to them.
However, the true difficulty in physics is arriving at that model in the first place. Decades of work offered up against experiment, the associated conceptual leaps in understanding required to get to e.g. a quantum field theory which succesfully predicts things are nothing short of a monumental achievement. To say that physics is simple is ludicrous.
"The counterfactuals that matter to science and physics, and that have so far been neglected, are facts about what could or could not be made to happen to physical systems; about what is possible or impossible."
On the contrary, if I had to describe theoretical physics in a nutshell, I would say it is entirely about what is impossible. Pick any physical law or theorem. I cannot exceed the speed of light. I cannot globally decrease entropy. I cannot measure a force between two static electric charges in vacuum that deviates from the Coulomb force law.
It is interesting that they chose to use a first person shooter as an example screenshot since gameplay would be broken by visual changes in occlusion.
I think we need more clever people making more predictions (and especially from people quoted in the article like Gross, Witten, Rattazzi etc), and fewer blog articles like this designed to discourage them.
The last time a huge, costly, dedicated collider was built, it was in service of the Higgs prediction, and that worked out quite nicely.
IANAL but I expect not. I would imagine the rental company would then just bill or sue the driver for the loss and inconvenience (or otherwise claim it on insurance).
On a similar note, I would like to think a rental car company would suffer consequences if a drunk person waddled up to the rental desk and they were handed a set of keys.
> astronomers use supernovae to measure distances, which is important for cosmologists to study, for instance, the expansion of the universe and dark energy.
AFAIK supernovae datasets are usually obtained as a survey, resulting in a statistical sample which can be used to compute cosmological observables. Here it seems that there is a new sample bias introduced by the neural network classifier. Can this bias be accurately quantified?
There is a fundamental reason not to publish scientific code.
If someone is trying to reproduce someone else's results, the data and methods are the only ingredients they need. If you add code into this mix, all you do is introduce new sources of bias.