mafribe

Exactly.

Taiwan has spent the approx 120 years on a very different political, economic, cultural track from the mainland. Taiwan diverged from the other subject of the Qing dynasty before Han nationalists began their century long project to forge a united Chinese nation. In particular, Taiwan did not go through decades of communist terror, but did experience the fruit of democracy.

> How the parameters are passed must be decided by the compiler, not by the programmer.

Consider the program

   def f(x):
      return 17

If omega is a non-terminating expression then the call

  f(omega)

might or might not terminate, depending whether the compiler uses a lazy or eager evaluation strategy. This freedom is not something I would recommend to a programming language designer.

One might decide to act out one's entrepreneurial spirit in a different country ...

Country C's regulators can sanction the companies entities in C including denying access to the market in C. (C.f. how the US got Swiss banks to comply with financial transparency laws.)

It would not matter if C == Lichtenstein, but China is currently the world's biggest semi-conductor market ...

The pipeline stages (say: fetch, decode, execute, memory access, register write back), are organised "parallel in space" as transistors on chip. The point of having a pipeline is so the stages can execute "parallel in time".

More generally, parallel in space is interesting because it is a necessary precondition for parallel in time.

Neuromorphic has been an ongoing failure (for general purpose processors or even AI accelerators), ever since Carver Mead introduced (and quickly abandoned them) them nearly half a century ago. Bill Dally (NVidia CTO) concurs: "I keep getting those calls from those people who claim they are doing neuromorphic computing and they claim there is something magical about it because it's the way that the brain works ... but it's truly more like building an airplane by putting feathers on it and flapping with the wings!" From: Hardware for Deep Learning, HotChips 2023 keynote.

We have NO idea how the brain produces intelligence, and as long as that doesn't change, "neuromorphic" is merely a marketing term, like Neurotypical, Neurodivergent, Neurodiverse, Neuroethics, Neuroeconomics, Neuromarketing, Neurolaw, Neurosecurity, Neurotheology, Neuro-Linguistic Programming: the "neuro-" prefix is suggesting a deep scientific insight to fool the audience. There is no hope of us cracking the question of how the human brain produces high-level intelligence in the next decade or so.

Neuromorphic does work for some special purpose applications.

The paper goes out of its way not to compare the 42% figure with anything. Is "42% within the top 5 suggestions" good or bad?

How would an experienced engineer score on the same task?

Note that "some sort of partitioning" isn't a hyperplane. A partition is a set-theoretic concept. A hyperplane is (a generalisation of) a geometric concept, so has much more structure.

This is wrong!

The term hyperplane already assumes that the hypothesis space that your learning algorithm searches has some kind of dimension and is some variant of an Euclidean / vector space (and its generalisations). This is not the case for many forms of ML, for example grammar induction (where the hypothesis space is Chomsky-style grammars) or inductive logic programming (hypothesis space are Prolog (or similar) programs), or, more generally, program synthesis (where programs form the hypothesis space).

Multi-paradigm languages, and there are several popular ones including Scala, need strong software architecture enforcement. The software architects need to decide which style to use, e.g. purely functional and monadic, vs nicer-looking Java-style OO.