Using AI to match human performance in translating news from Chinese to English

You don't need some fancy philosophy and complex thought experiments to see what he means. Just look at how people learn math. You can do calculations by memorizing algebraic rules, but that's not the same as understanding why those rules exist and what they mean. Even though you will calculate answers correctly in both cases, we all know there is a qualitative difference between them.

Back to Searle. His argument is that everything computers do is analogous to rote memorization and that transition to understanding requires something computers don't have.

Whether you buy his argument, two things are clear. First, there is a difference between just producing results and understanding the process. We all experienced this difference. It's all theoretical as long as you stick to simple tests (like multiple-choice exams), but becomes relevant when you suddenly expand the context (like requiring the student to prove some theorem instead of doing a calculation). Second, we also know that for humans this difference isn't just quantitative. Memorizing more algebraic rules and training in their application will not automatically result in students gaining understanding of mathematical principles.

You can however make a fairly solid argument that a CNN alone (as used in image/object recognition) is fundamentally incapable of dealing with images (but maybe not language), on the assumption that it can be faithfully described as Satan's boolean satisfiability problem, then by virtue of complexity theory it can only be solved in constant time with a sufficiently massive lookup table (which there wouldn't be enough atoms in the universe to store). Microsoft are actually dealing with this in their system by repeatedly applying the network and revisioning the text.

Regardless though, accurate NLP is going to come down to managing to codify how humans deals with objects, concepts and actions, because that's what the languages encode; GOFAI wasn't really too off (and the original effort was doomed from the start by the state of hardware and linguistics). Consider how distinguishing objects as masculine-feminine-neuter and animate-inanimate(-human) is universal (but doesn't necessarily affect the grammar), and that the latter is based purely on how complex/incomprehensible the behaviour of something is (unlike grammatical gender which seems to be fairly arbitrary). Of course that's arguable, but you can see animacy appear in english word choices (unrelated to anthromorphic metaphors) and in how "animate" objects tend to be referred to as having intent. You could try and figure all this out the wrong way around using statistical brute force and copious amounts of text, but that's pretty roundabout isn't it?

(Also, the assumption that an objects animacy is determined by predictability offers a pretty concise explanation of why the idea of human consciousness being produced by simpl(er) interacting systems often fails to compute so spectacularily, why most programmers appear to be immune to that, and also why the illusion that image recognition CNNs perform their intended function is so strong (regardless of how useful they are, the failures make it blatant that they're only looking at texture and low-level features, and are extremely sensitive to noise, which is the opposite of what anyone intended))

Apparently the translation of the Japanese response to the Allies ultimatum calling for their surrender might have been faulty.