samloveshummus

No, it's not wasted; that's my point. Why don't you think Disney's investors demand a $7bn annual saving by cutting the "waste" on advertising?

The spend on fundraising does go towards the mission, because it increases the amount of money for the mission. You're making the incorrect assumption that donations are constant.

Imagine that a charity hiring a fundraiser for $50k garnered $150k of additional donations. Let's say without that fundraiser, they got $100k in donations. So with the fundraiser, they got $100k + $150k - $50k = $200k net to spend on the charitable purpose.

20% of all donations go to the fundraiser's salary while she's employed. But if the fundraiser is sacked, then 100% of donations go to the mission, yet the mission takes a loss of 50%.

The criticism that too much goes to fundraisers is meaningless without an explanation of how much would be optimal. If spending an additional $1m on fundraising meant an additional $1.1m in donations, then that's $100k more for Wikipedia that wouldn't have been there before.

If huge profit-making companies like Disney, Coca-Cola and McDonald's spend so much on marketing and sales, then it must be profitable. Similarly, there's no reason that fundraising spend wouldn't be financially advantageous to a non-profit.

If you support Wikipedia enough to donate, then it makes sense to want them to raise as much as they can. In which case you should enthusiastically support them running like a business-savvy organization.

I had a major epiphany deriving this myself as a teenager, because it was the first time I used algebra to calculate something "real". The equation was trivial, but the principle of mathematical modelling struck me like lightning. In an instant the world felt more comprehensible and tractable, I suddenly felt like Neo seeing the Matrix.

Meat is a $2 trillion industry globally but you're primarily worried about the corrupting influence of activists' donations?

Good for you, but NAC has a host of effects so you couldn't say it was due to glutamate for sure.

For example, NAC mainly increases glutathione, the primary antioxidant your body uses, so it may reduce the level of oxidative stress and lower your body's stress response. It also increases the level of SAM-e in your body (as you can transform sulfur containing amino acids methionine and cysteine into each other). SAM-e is a crucial cofactor for many processes including creation of neurotransmitters such as dopamine and serotonin. And an increase in sulfur amino acids (including NAC) also modulates your gut microbiome.

I'm not sure that those really are the extremes. I think quite a lot of people believe that justice may or may not ever happen depending on how we choose to act as a civilization. Even further than that, many people believe injustice is an inevitable feature of existence.

No, 63.3% of all participants got a favourable outcome, so 13% cheated (suggesting 26% would cheat if necessary, as half got a favourable outcome without cheating).

The participants all had varying degrees of belief in a just world which were measured with a six-item test. The results of that test correlated significantly with the chance of a favourable self-reported coin toss.

It is meaningless to draw inferences from comparisons to another study, as they were selected differently so there's no control, and it isn't necessary as this study contains a random and varied sample of participants.

Yes, there is a question over the representativeness of Mechanical Turk workers. But even though they're surely different on average from the average human, this study is controlled by comparing them to each other rather than to some pre-determined statistics, so the sampling bias should be largely cancelled out, barring second-order interactions.

The Bristol Four acquittal was not an act of jury nullification.

Under the Criminal Damage Act 1971, no offense is committed if there is a "lawful excuse" for the damage.

The defence argued that there was a lawful excuse on several grounds:

1. The defendants believed they were preventing a more serious crime (public indecency because of the statue's offensiveness).

2. The defendants believed the statue was owned by the citizens of Bristol (as stated on its plaque), who they believe consented to its removal.

3. Their right to freedom of expression and assembly under the European Convention on Human Rights.

The judge instructed the jury that they were allowed to consider questions such as the statue's offensiveness in deciding whether these excuses applied.

I see why you would say that: these neural networks probably have thousands or millions of weights while the equations of motion can probably be written on an index card.

But I would argue that this parsimony is illusory. There's a lot of implicit knowledge needed for the interpretation of physical laws. The laws are written using specialized mathematical notation such as special functions, partial differential equations, in a certain conceptual framework such as Lagrangian mechanics. You need to understand the concept of abstracting and quantifying a dynamic system (most people wouldn't imagine you can do this) and then you have to learn all the tips and tricks of how to reformulate and solve systems.

For example, I could write a mathematical representation of quantum electrodynamics (the theory of how electrons and photons interact) on a single index card. However, I would need to dig into my two shelves of QFT textbooks to actually make any quantitative experimental predictions, on top of my degree, PhD and post doc experience, which I need to even be able to read the textbooks (and I would still mess up the minus signs).

I think it's important to remember that these neural networks are doing all of that - not just finding the physics, but also all the abstraction, calculation and interpretation that is usually taken for granted but actually very non-trivial.

I would say it's essentially equivalent, especially if you choose a neural network architecture with a very low-dimensional layer in the middle with only a handful of variables.

Then the first half of the network (before the low-dimensional layer) will learn how to "encode" the state of the system in the video in as few variables as possible, such as the orientations and angular momenta of the double pendulum. This is equivalent to what humans do when we look at a messy physical system like the Solar System and model it with a few quantitative parameters.

The bottleneck layer will represent the handful of state variables, and then finally the other half of the network will learn the mathematical function that predicts the system's evolution. This is equivalent to what humans do when we work out physical laws and equations of motion.

It is just a model though! Everything in science is just a model. We better hope it's just a model, because it's incompatible with quantum field theory, which is another very accurate model. The only consistent model that bridges the two, superstring theory, says that spacetime and gravity could fundamentally be many things, from closed strings travelling between D3-branes to the holographic projection of a conformal theory - and you still get the same predictions.

I don't see any fundamental difference. A deep neural network is a universal function approximator. It uses different language from what we're used to (weights and activations instead of analytic functions and calculus) but that's not a big deal. The point is that it uses only a handful of latent variables to describe the state of the system at a given time, and these can be used to predict the system's behaviour, which is fundamentally the same thing that a scientist would try to do.

But he's saying it is bad to do it that way instead of doing traditional physics because you get no understanding, which is true, but in this study they're not using it as a "physics engine" to pilot aircraft or whatever, they're using it as a trick to generate novel hypotheses, which could then be theorised and investigated properly, not as a replacement to theory.

Who cares if there's no gravity? Gravity wasn't sent down to us from heaven on a stone tablet, it's just a concept that lets us make predictions. At school I was taught it was a force and at university I was taught it was a pseudoforce resulting from fixing a non-inertial frame. Both approaches give correct answers, even though they're conceptually very different. There's no objective way to say which is right; they're just different approaches to modelling.

And maybe the 4.7 is actually more correct? The 4-parameter model is an approximation that neglects friction and air resistance. Moreover the double pendulum is a chaotic system and chaotic systems sometimes have dynamics described by laws with non-integer exponents such as Lyapunov dimension. I'm just spitballing, but the point is that it's not a priori ridiculous.

It's definitely possible to estimate mass from images. How do you think we know the masses of asteroids and planets? No-one put them on a scale, we just record their motion and work out which value fits best.

It's really easy to test whether or not it works - see how well the model predicts on out-of-training sample data. That wouldn't work with astrology.

There's no such thing as "physical properties of the system" other than measurable quantities that can be used to make predictions, which is what this does. There's no reason to be sure that temperature, for example, is a "real" physical property of a system rather than just one of many variables that would help us model it and understand it.

Do you think it's pseudoscientific because there's no theory-ladenness in the predictions?