As impressive as it may be, these people should refrain from claiming 'human-like' translation from a system that has no way of 'knowing' anything about context, other than statistical occurrences.
It is certain that, on occasion, the system will make such mistakes as stating the opposite of what is being said in the first place, or attribute one action to the wrong person, and what not. Perhaps on average it's as good as a person, but this system will make mistakes that disqualifies it from being used without a bucket of salt.
Let's just define that "human like" in context of machine translation from now on mean "with full legal responsibility". Then let's see who claim their translator is "human like".
If you defined it that way, not even human translators would meet the standard. Treaties and other official documents published in multiple languages always specify one as the "official" one for purposes of legal interpretation and that, in the event of conflict or confusion, the translations are subservient to it. Setting a bar for AI performance so high that even humans don't reach it seems unhelpful.
Actually most international treaties specify all language versions to be equally authentic. Multilingual contracts on the other hand generally have a single authoritative version.
Humans, on the other hand, have all kinds of biases (intentional and unintentional, conscious or not) that creep in because they do have context.
The important thing is that we build systems that account for the process-based problems, not that we build components that are perfect. Things that matter more are how frequently these mistakes happen? What's the impact? Can we eliminate these errors with multiple layers of processes designed to identify the exploits?
This was much the point that John Searle made in his criticism of AI prognostications. Ultimately I think he'll be shown to be wrong but the time frame for this will be (I suggest) much longer than is currently touted.
Neural networks modify their behaviour by changing their data. Any non-trivial program does that. Some GOFAI programs (e.g. Eurisko) could modify their instructions, not just their data.
Searle's argument is confusing, but how the program in the Chinese Room is implemented doesn't matter. His argument is solely against strong AI. He claims that the Chinese Room (or a suitably programmed computer) cannot be conscious of understanding Chinese in the same way that people can. He doesn't deny that a suitably programmed computer could, in principle, behave as if it understood Chinese, even if it wasn't conscious of anything at all.
However, the machine translation program mentioned in the article behaves as if it understands Chinese only within the limited context of the translation. It wouldn't be able to answer wider questions about things mentioned in an article it had just translated.
Previously it was thought that machine translation systems would have to understand the text they were translating in the way a person does, to produce a useful translation, but that's now shown not to be true. Without hindsight, it's a surprising result, but less surprising when you think of translation as pattern recognition, and think of how a person might go about translating text on a highly technical subject they don't understand.
It's a completely meaningless and plain stupid argument though. There's no reason to believe human consciousness is special and that "understanding" Chinese is at all related to it. One can speculate that the perception of consciousness is simply the product of some form of introspective sensory system and internally-directed actions, of which the former ironically clearly doesn't quite work when it comes to language (or it wouldn't be as much of a reverse-engineering exercise). Nothing really stops you from throwing that into your system and having it consciously understand chinese, assuming it already maintains state.
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))
I agree, the argument seems to come down to saying that human consciousness can't be replicated because human consciousness stems from a "soul" (a non-physical and undetectable element of someone that's at the root of their consciousness).
The amount of attention this argument has received has made me wonder whether the "rigor" used by philosophy departments is mostly just a way to obfuscate bad arguments.
That's not his argument at all. His argument is that just because you can do some task doesn't mean you "understand" it.
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.
Thanks for rebutting Chathamization's gross misrepresentation of Searle's argument.
> 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).
Not even expanding the context changes the situation. The proof of a theorem can be memorized without any understanding just as easily as algebraic rules.
> 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.
This is correct and the same principle applies not just to humans but to computers too (which was the point of Searle's argument). No amount of computation is going to make a computer aware or understand the meaning of the symbols. Ultimately "meaning" is our perceptual awareness of existence but that is a long proof for another day.
Any finite computational system can be implemented as a lookup table in the manner that Searle suggests. But that's not essential to the argument. You can imagine that the man in the room is following instructions for simulating a (finite approximation of) a Turing machine, or any other computational device that you like.
It is certain that, on occasion, the system will make such mistakes as stating the opposite of what is being said in the first place, or attribute one action to the wrong person, and what not. Perhaps on average it's as good as a person, but this system will make mistakes that disqualifies it from being used without a bucket of salt.