The classifier isn't breaking down - it was trained to do well across the entire training set using a small amount of computation for each inference and a reasonable (larger) amount of computation for training.

Human's fastest recognition capability still takes 100 ms or so, and operating in that mode (rapid visual presentation), human inference accuracy is considerably less capable than modern ANNs - which classify using less time and also around 1000x less neurons/synapses.

I would bet that humans often make similar mistakes in fast recognition. And even if humans don't make this specific mistake, it doesn't matter because they make more total mistakes in other categories.

The fact that humans can do better given considerably more time and enormously more neural resources is hardly surprising (involving more complex multi-step inference steps).

Also, the ImageNet training criterion is not really a good match for human visual intuitions. It assigns the same penalty for mistaking a dog for a cat as it does for mistaking two closely related species of dogs. Humans have a more sensible hierarchical error allocation. This may be something that is relatively easy to improve low-hanging fruit for ANNs, not sure - but someone is probably working on that if it hasn't already been done.

Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say “Usually Not” by Andrew C. Chang and Phillip Li

We attempt to replicate 67 papers published in 13 well-regarded economics journals using author-provided replication files that include both data and code. Some journals in our sample require data and code replication files, and other journals do not require such files. Aside from 6 papers that use confidential data, we obtain data and code replication files for 29 of 35 papers (83%) that are required to provide such files as a condition of publication, compared to 11 of 26 papers (42%) that are not required to provide data and code replication files. We successfully replicate the key qualitative result of 22 of 67 papers (33%) without contacting the authors. Excluding the 6 papers that use confidential data and the 2 papers that use software we do not possess, we replicate 29 of 59 papers (49%) with assistance from the authors. Because we are able to replicate less than half of the papers in our sample even with help from the authors, we assert that economics research is usually not replicable. We conclude with recommendations on improving replication of economics research.

Thanks for writing this; a couple quick thoughts:

For example, it turns out that a learning algorithm tasked with some relatively simple tasks, such as determining whether or not English sentences are valid, will automatically build up an internal representation of the world which captures many of the regularities of the world – as a pure side effect of carrying out its task.

I think I've yet to see a paper that convincingly supports the claim that neural nets are learning natural representations of the world. For some papers that refute this claim, see e.g.

http://arxiv.org/abs/1312.6199 http://arxiv.org/abs/1412.6572

I think the Degrees of Freedom thesis is a good statement of one of the potential problems. Since it's essentially making a claim about whether a certain very complex statistical problem is identifiable, I think it's very hard to know whether it's true or not without either some serious technical analysis or some serious empirical research --- which is a reason to do that research, because if the thesis is true then that has some worrisome implications about AI safety.

Sure. But if we know or suspect any correlation between A and Y, there's nothing strange about the common information between them being expressed in the prior, right?

Granted, H-T will have nice worst-case performance if we're not confident about A and Y being independent, but that reduces to this debate http://lesswrong.com/lw/k9c/can_noise_have_power/.

Every judge being close to 50% would be bizarre. If I flip 13 coins 53 times I would expect that many of those sets of 13 will stray from the 6.5/13 expected ratio. The big question is whether anyone scored high enough or low enough that we can say "this wasn't just pure chance".

I think you should distinguish between "average score across judges is close to 50%" and "every single judge is close to 50%". I suspect the latter is not true, as pointed out in one of the other comments.

The only way I think you could see the Superhappies' solution as acceptable if you don't think jokes or fiction (or other sort of arts involving "deception") are something humans would value as part of their utility function.

Um, that's the opposite of how utility functions work. They don't have sacred components. You can and should trade off one component for a larger gain in another component. That's exactly what the super happies were offering.

I've only gotten up to doing an MSc (currently volunteering for Vikash Masinghka in my Copious Free Time), but I do know a hell of a lot of academics.

From my (second-hand) knowledge, easy quals are an artifact of something very like economic privilege: your school is very prestigious and doesn't need to cull its grad-student herds as much as others, so quals are allowed to be easy. In other places, quals are used to evict many grad-students from their PhD program because resources are more scarce.

but it seemed like MIT grad students also often started doing research fairly early on, from my perspective as an undergrad there.

I don't know anywhere where grad-students don't start doing research as early as possible. Do some programs really involve whole years of just classes?

Is there any way to do these things without paying a large pricetag? Could you just lurk around campus or something? Only half-joking here.

be sure to first consider the most useful version of grad that you could reliably make for yourself... and then decide whether or not to do it.

Planning fallacy is going to eat you alive if you use this technique.

PhD programs in mathematics, statistics, philosophy, and theoretical computer science tend to give you a great deal of free time and flexibility, provided you can pass the various qualifying exams without too much studying.

Bolding the parts to which I object.

I have never seen anyone in a rigorous postgraduate program who had a lot of free time and could pass their quals without large amounts of studying.

Of course, I could just be, like magic, on the lower part of the intelligence curve for graduate school, but given that my actual measured IQ numbers are pretty in-the-middle for scientific academia (I won't tell what they are, though), and given that almost everyone else says they have little free time and have to study hard in graduate school, I'm inclined to believe the bolded phrases only accurately describe a narrow slice of lucky individuals.