The work by Tenenbaum at MIT is a particularly interesting mix of psychology, computational statistics, and neuroscience. It really challenges the commonly repeated notion that neural networks are completely unrelated to actual brains.
Is there any proof about an existing non-leaky abstraction between natural language and First Order Logic (or logic in general)? Afaik there is none, but I might me wrong. For example (one amongst thousands): “I don’t like you” can actually mean (and it often does) the exact opposite, and the same can be said of a simple statement like “I am ok” which also can mean the exact opposite. Natural language is a very hard problem.
Notice again that I don't think this is feasible in practice. At least not with hand-crafted FOL expressions and certainly not with existing technology.
I'm not sure what "non-leaky abstraction" means. Would formal language theory fit the bill?
https://en.m.wikipedia.org/wiki/Heterophenomenology
In this framework, utterances can be studied without taking their truth value at face value.
I mean, I don't believe that is true any more that I believe calculus, or linear algebra are the way we think. Those are just formal systems that we can use to get various results about the world- but is that how we really think inside our heads? I kind of doubt it.
I'm just talking about the expressive power of first-order predicate calculus here, not about whether it's a good model for human intelligence.
There is a direct trade-off between the expressiveness of your representation and your ability to reason about it.
It seems more likely that First Order Logic can be represented by a subset of natural language.
Otherwise, I agree. I don't understand why everyone has been concentrating so hard on probabilistic ML when there are many applications where inductive inference is more efficient and powerful.
I doubt this is the case since natural language makes a lot of use of metaphors, and our concepts are probably mostly based on the types of bodies we have and how we interact with the world. See embodied cognition and writings of Lakoff and Mark Johnson.
There is ambiguity in natural languages so the determination of underlying meaning is at best probabilistic, but we have ways of handling that too.
In fact, populating a LS/CS knowledgebase with requisite facts was a game stopper when they were first invented and still pose challenges of scale that remain unsolved decades later. If it accomplished anything, Cyc proved that manual curation alone of a rich database of propositions is depressingly infeasible. So how does one automatically curate a general fact KB, no matter what semantic model the facts take? I don't think expressiveness of the propositional representation will ever decide that question.
I think this tradeoff in quality vs quantity has been central to the inability of most Good Old Fashioned AI (GOFAI) formal symbolic methods to 1) scale up beyond toy academic clean-room problems (from the admittedly little I've seen of commercial LS/CS), as well as 2) support the flexibility and imprecision needed to deal with a messy ill-defined very big world with a seemingly unknowable number of unknowns.
It may be that this venerable yin v yang of knowledge modeling will forever bedevil AI. I don't have the sense that any techniques in the past 30 years have really made headway in breaking this log jam. Deep nets have just tilted the playing field in its favor, so long as we're willing to ignore fussy details like dependence, FOL, and causality.
If you include a semantics for supervaluationism (Sorites paradox, Keefe on vagueness)!
Thinking of it, I'd love to train a NN against human responses to '"is this a heap, yes/no?" +/-1 grain, repeat'.
In principle, First Order Logic and equivalent languages can represent anything that can be represented in natural language. The problem is that in practice it is very hard to transfer into a logic language all the knowledge you might need for a useful system.
In fact, this was one motivation for at least one branch of machine learning- the work from Ryszard Michalski, Claude Sammut, Ranan Banerji, Steven Vere, Brian Cohen and later rule-learners like Quinlan's decision tree learners and of course all the later work in Inductive Logic Programming by Plotkin, Shapiro, Muggleton etc. These are all rule-learning systems, that can avoid the knowledge acquisition bottleneck that probably did in for purely rule-based expert systems in the '90s (i.e. getting experts to transfer their knowledge into rules).
I rather agree that further progress in AI will require a, let's say, synchretistic approach- like I say in another comment, the obvious thing for me is to use deep learning for perception, logic for inference and probabilistic modelling to deal with the noisy world. There are some people working in that sort of direction, with different amounts of emphasis on each of the three approaches. For instance, Josh Tenenbaum at MIT, Evans and Grefenstette at DeepMind, Luc De Raedt at KU Leuven, Kristian Kersting at Dortmund, Lise Getoor also at MIT, Pedro Domingos at Washington, and many others.
Apologies for all the name-dropping without links. Let me know and I can provide them if required.