This line of research sounds similar to causal entropic forcing [1] - the idea that you can get intelligent-seeming behavior from an agent that maximizes the future entropy of states in some non-deterministic system.
> If Ben and Jerrys left a bunch of ice cream on the sidewalk and a bunch of people ate it and got sick then there would be zero liability on Ben and Jerrys.
Citation? That doesn't seem right, based on my anecdotal knowledge that restaurants take care to throw leftovers into a garbage bin rather than leave them out somewhere where someone could eat them and expose the business to liability.
I looked up this claim myself. In 1996, President Clinton signed the Good Samaritan Food Donation Act into law to limit liability for those who donate food. [1] The majority of restaurants still discard leftover food due to concerns over liability, though. [2] Clearly, liability was a real issue at some point in the past. I don't know enough about the current law to know how easy it is to take advantage of the new protections; I can understand why people are still concerned.
In summary, liability issues vary by country and are not clear-cut. As an analogy for this open-source situation, they don't clarify matters.
The type of a curried function in Typescript is just something like:
(a: number) => (b: number) => (c: number) => number
Sure, the parameter names and parentheses are a bit annoying, but I wouldn't call that "very verbose". Comparable concepts in C++ or Java would be a nightmare to type out.
Why do you say that? The first demo they provide shows that the adversarial image, when printed and then manipulated, still fools the algorithm. That means that the example is robust to various affine transformations but also to the per-pixel noise that is a result of a printing something and then viewing it again through a camera.
Suppose you were to place an example like that on a stop sign that fooled a car into thinking that it was a tree. The car might blow through an intersection at speed as a result.
The training strategy they used provides a template for doing even more exotic manipulations. For example, you could train an adversarial example that looked like one thing when viewed from far away but something quite different up close. Placing an image like that by a road could result in an acute, unexpected change in the car's behavior (e.g. veering sharply to avoid a "person" that suddenly appeared).
[1] https://www.alexwg.org/publications/PhysRevLett_110-168702.p...