I (and my company) believe in a hybrid approach; it will never be a good idea to use symbolic AI for getting structured data from speech audio or raw images, for example. But once you have those sentences, or those lists of objects, symbolic AI can do a better job of reasoning about them. Pairing ML and symbolics, they can cover each other's weaknesses.

In the past couple of years, several papers have shown that predefined symbolic relationship can improve over vanilla DL. For example, recognizing a picture of numerical arithmetic equations and compute the result. This is very difficult for neural networks to parametrize over the pixel space.

Moreover, statistical generalizability is currently all derived from concentration theories. This means that the knowledge encoded by a neural network model depends on the statistical distribution of the data. If two people have two different sets of data, they might end with two very different models. Symbolic generalizability is quite different. A rigorous mathematical proof holds true as long as we all live in the same world with the same set of axioms. For example, no person can appear in two physical places at the same time. Or, if A causes B, A has to occur before B occurs. These knowledge can't be learned through statistical methods with no symbolic priors. We postulate the logic first, then verified through observations.

At last, problems that statistical learning handles well so far are essentially interpolations of the collected data. Being able to extrapolate well is still an unknown. Will inductive logic programing work better in this scenario?

Symbolic is the signature of human intelligence. All of our scientific breakthroughs are encoded by symbols, even the DL (deep learning) stuffs. It won't be replaced by the numerical paradigm completely in any time soon.

As to why that is, my best guess is a combination of not having enough man-hours (we're still a relatively small company) and how difficult it has historically been for people to jump in and play with Cyc. There could also be a cultural lack of awareness that people still have interest in tinkering with it, which is something I've thought about bringing up for discussion.

As to the accessibility issue, that's been one of our greatest hurdles in general, and it's something we're actively working on reducing. The inference engine itself is something really special, but in the past most of our contracts have been pretty bespoke; we essentially hand-built custom applications with Cyc at their core. This isn't because Cyc wasn't generic enough, it's because Cyc was hard enough to use that only we could do it. We're currently working to bridge that gap. I'm personally part of an effort to modernize our UIs/development tools, and to add things like JSON APIs, for example. Others are working on much-needed documentation, and on sanding off the rough edges to make the whole thing more of a "product". We also have an early version of containerized builds. Currently these quality-of-life improvements are aimed at improving our internal development process, but many of them could translate easily to opening things up more generally in the future. I hope we do so.

That meshes with what I've heard at conferences, that Cyc management was worried people were treating OpenCyc as an evaluation version of Cyc, even though it was significantly less capable, and using its capabilities to decide whether to license Cyc or not. The new approach seems to be that you can get a free version of Cyc (the full version) for evaluation or research purposes, and the open-source version was discontinued.

https://v1.probmods.org/learning-as-conditional-inference.ht...

"Cyc gains an intuition about what kinds of paths of reasoning to follow when searching for conclusions"

The possible paths come purely from symbolics. But that creates a massive tree of possibilities to explore, so ML is used simply to prioritize among those subtrees.

> I (and my company) believe in a hybrid approach

> I don't know of any higher-level hybridization experiments

That contradiction and the admission that Cyc only has "one ML person on staff" signals to me, an outsider, that the belief of parity between Machine Learning and "Symbolic" might be predicated more on faith than on reason.

https://news.mit.edu/2019/teaching-machines-to-reason-about-...

We aren't an ML shop ourselves; we don't claim to be. Given that we have around 100 people, we focus on what we have that's special instead of trying to compete in an overcrowded market. The idea of hybrid AI is something we see as a future part for us to play in the bigger picture of machine intelligence.

[1] https://news.ycombinator.com/item?id=19717680

But I will affirm that Cyc is fundamentally symbolics-based. We don't position ourselves as anti-ML, because it's seriously good at a certain subset of things, but Cyc would still be fully-functional without any ML in the picture at all.

I'd love to experiment with an automated planner (a good old symbolic AI technique) but use deep learning to design the heuristic. It feels like a lot of reinforcement learning techniques are becoming close to this kind of things.

AlphaGo was a rough implementation of that. Do you know of some efforts from symolic AI in that respect?

And do you still accept candidates ? :-)

> And do you still accept candidates ? :-)

If you mean job candidates, then yes, we definitely do!

I am employed now but I'll probably send a CV when I am looking for a change!

Symbolic reasoning/AI is fantastic when you have the right concepts/words to describe a domain. Often, the hard ("intelligent") work of understanding a domain and distilling its concepts need to be done by humans. Once this is done, it should in principle be feasible to load this "DSL" into a symbolic reasoning system, to automate the process of deduction.

The challenge is, what happens when you don't have an appropriate distillation of a complex situation? In the late eighties and early nineties, Rodney Brooks and others [1] wrote a series of papers [2] pointing out how symbols (and the definiteness they entail) struggle with modeling the real world. There are some claimed relations to Heideggerian philosophy, but I don't grok that yet. The essential claim is that intelligence needs to be situated (in the particular domain) rather than symbolic (in an abstract domain). The "behavior driven" approach to robotics is stems from that cauldron.

[1]: Authors I'm aware of include Philip Agre, David Chapman, Pattie Maes, and Lucy Suchman.

[2]: For a sampling, see the following papers and related references: "Intelligence without reason", "Intelligence without representation", "Elephants don't play chess".

I think there is something (a lot) to this. Consider how much of our learning is experiential, and would be hard to put into a purely abstract symbol manipulating system. Taking "falling down" for example. We (past a certain age) know what it means to "fall", because we have fallen. We understand the idea of slipping, losing your balance, stumbling, and falling due to the pull of gravity. We know it hurts (at least potentially), we know that skinned elbows, knees, palms, etc. are a likely consequence, etc. And that experiential learning informs our use of the term "fall" in metaphors and analogies we use in other domains ("the market fell 200 points today, on news from China...") and so on.

This is one reason I like to make a distinction between "human level" intelligence and "human like" intelligence. Human level intelligence is, to my way of thinking, easier to achieve, and has arguably already been achieved depending on how you define intelligence. But human like intelligence, that features that understanding of the natural world, some of what we call "common sense", etc., seems like it would be very hard to achieve without an intelligence that experiences the world like we do.

Anyway, I'm probably way off on a tangent here, since I'm really talking about embodiment, which is related to, but not exact the same as, situated-ness. But that quote reminded me of this line of thinking for whatever reason.

E.g. human sizing of time is highly linked to physiological timing, may it only be heartbeat pace. More generally, all emotional input can gear reasoning (emotional intelligence).

Only my 2c on this. Not sure how accurate it is.

The amount of confusion I see between two people from different cultures speaking the same language is amazing. 70% of communication is body language. The rest appears to be shared assumptions about what the other person just said.

I don't think we'll ever be able to have a conversation with a dolphin. We know they talk to each other, we know they're able to interact with us, but how would we ever communicate with them? Their world is so different from ours. To use the example above, a dolphin cannot "fall down", so any language concepts that we have around "falling" will be impossible for them to grok. Likewise we won't have mental concepts around sonar that they use every day, and so won't understand what they mean when they refer to that. We may be able to get to "hello, my name is Alice", but beyond that... nope.

Same with "conversational" AI - it's going to need to understand what its like to have a body, so it can understand all the language around bodies. Simulating that, and being able to make references to "falling over" as a brain-in-a-box, is going to lead to misunderstanding, for exactly the reasons you describe.

I hadn't thought about the measurements aspect, but it's true. There's been some research into trees communicating - could be a classic example. They talk (via fungal networks in their roots, apparently), but so slowly that we can't hear them.

And yes, human emotion is linked to human physiology, and hormones. A lot of human communication is about recognising and empathising with human emotion. That's going to be a tough thing for a machine to do...

Actually, the research has found that new born infants can perceive all sorts of things, like human faces and emotional communication. There is also a lot of inborn knowledge about social interactions and causality. The embodied cognition idea is looking at how we experience all that.

By the way, Kant demonstrated a couple of centuries ago that the blank slate idea was unworkable.

yes, thats called sensory input.... a child deprived of sensory input when newborn can die because there is nothing there to show the baby of its existence, this the cause of crib death (notice that crib death is not called arm death because a baby doesnt die in the mothers arms)

>There is also a lot of inborn knowledge about social interactions and causality.

no, babies are not born with any knowledge at all of even the existence of society or beings. causality is learned from the result of human experience, causality is not known at birth

That's a very limited subset of what I mean by "human like intelligence". And within that specific subset, yes, AI/ML can and have achieved "human level" results. But that same ML model can recognize cats in vectors of pixels doesn't know anything about falling down. It's never tripped, stumbled, fallen, skinned it's palms, and felt the pain and seen the blood that results. It's never know the embarrassment of hearing the other AI kids laughing at it for falling, or the shame of having it's AI parent shake it's head and look away after it fell down. It's never been in love with the pretty girl AI (or pretty boy AI) and been had to wonder "did he/she see me fall and bust my ass?"

Now giving a computer program some part of the experience of falling we could do. We could load the AI into a shell of some sort, and pack it with sensors: GPS receiver, accelerometers, ultrasonic distance detectors, cameras, vibration sensors, microphones, barometric pressure sensor, temperature detector, etc., and then shove it off a shelf. Now it would "know" something about what falling actually is. And that's what I mean by the need for experiental learning in a situated / embodied setting.

While it might be possible in principle to get that knowledge into a program in some other way, my suspicion is that it would be prohibitively difficult to the point of being effectively impossible.

I think the key term here is "concept formation" as well as "knowledge representation". How do we form concepts, and how are they represented internally to make them tractable?

Symbols are one way to represent concepts (or rather, point to them). But with symbols we are limited to surface-level transformations according to a syntax (I'm pretty sure Chomsky said something similar?). What do the concepts actually point to, though, and can we represent that underlying structure programmatically?

As I wrote in another comment, I'm very inspired by the conceptual spaces model:

https://mitpress.mit.edu/contributors/peter-gardenfors

https://www.youtube.com/watch?v=Y3_zlm9DrYk

Could someone please steelman my thinking here a bit? Would love to advance my own thinking on this matter.

In a well designed autoencoder, the network ends up discovering an abstract representation of inputs and a conceptual space to express it.

Original comment:

https://news.ycombinator.com/item?id=19703867

See his books “What Computers Can’t Do”, “Being-in-the-world”, and the paper “Why Heideggerian AI Failed and Why Fixing It Would Require Making it More Heideggerian” (something like that).

A basic point is that ordinary human coping does not involve conceptual thinking, schematic rules, or the manipulation of symbols. It’s sort of like “Thinking Fast and Slow”.

We do not fundamentally live by constantly consulting our inner symbolic representation of the world, though we do that too. The more fundamental way of being is to just cope and care directly without explicit cognitive representation.

So I could attempt to codify an “expert system” for my way of coping with and caring for my cat, let’s say. But it would only be a kind of symbolic ghost of my real way of being, and it would never be sufficient. The more precise I tried to make it, the more complex it would become, until it became a gigantic mess, because it’s fundamentally an inaccurate model.

Dreyfus “Being-in-the-world” brings up many examples of the way the intelligence of daily life is informal, unconscious, and nonsymbolic. The way we maintain distance from other bodies which is only roughly approximated by the idea of a “personal space”, or the ways in which we live out masculinity and femininity.

“There are no beliefs to get clear about; there are only skills and practices. These practices do not arise from beliefs, rules, or principles, and so there is nothing to make explicit or spell out. We can only give an interpretation of the interpretation already in the practices.”

“Being and Time seeks to show that much of everyday activity, of the human way of being, can be described without recourse to deliberate, self-referential consciousness, and to show how such everyday day activity can disclose the world and discover things in it without containing any explicit or implicit experience of the separation of the mental from the world of bodies and things.”

“The traditional approach to skills as theories has gained attention with the supposed success of expert systems. If expert systems based on rules elicited from experts were, indeed, successful in converting knowing-how into knowing-that, it would be a strong vindication of the philosophical tradition and a severe blow to Heidegger's contention that there is no evidence for the traditional claim that skills can be reconstructed in terms of knowledge. Happily for Heidegger, it turns out that no expert system can do as well as the experts whose supposed rules it is running with great speed and accuracy. Thus the work on expert systems supports Heidegger's claim that the facts and rules ‘discovered’ in the detached attitude do not capture the skills manifest in circumspective coping.”

And yet he conveys these examples using somewhat formal, conscious and symbolic method - printed words.

But not even philosophers actually work in the schematic way of an AI based on formal logic...

Dreyfus’s critique is about the first order (or whatever) logic programs, and I don’t think neural nets are cognitivistic in the same way, but there’s also the point that until they live in the human world as persons they will never have “human-like intelligence”.

I think it’s interesting to think of AI in a kind of post-Heideggerian way that includes the possibility that it can be desirable or necessary for us human beings to submit and “lower” ourselves to robotic or “artificial” systems, reducing the need for the AIs to actually attain humanistic ways of being. If the self-driving cars are confused by human behaviors, we can forbid humans on the roads, let’s say. Or humans might find it somehow nice to let themselves act within a robotic system, like maybe the authentic Heideggerian being in the world is also a source of anxiety (anxiety was a big theme for Heidegger after all).

I suspect that eventually there will be an "ImageNet Moment" of sorts starring a statistical/symbolic hybrid system and we'll see an explosion of interest in a family of architectures (but it hasn't happened yet).

[0] http://news.mit.edu/2019/teaching-machines-to-reason-about-w...

[1] https://dtai.cs.kuleuven.be/problog/

[2] https://bitbucket.org/problog/problog

[3] https://bitbucket.org/problog/deepproblog

[4] https://github.com/mthom/scryer-prolog

[5] https://github.com/friguzzi/cplint

In contrast, ML/DL AI is still shiny and new and we have a much less clear grasp of what its ultimate capabilities are, which makes it a ripe target for research.

My strong hunch is that deep learning results will continue to be very impressive and that with improved tooling basic applications of deep learning will become fairly much automated, so the millions of people training to be deep learning practitioners may have short careers; there will always be room for the top researchers but I expect model architecture search, even faster hardware, and AIs to build models (AdaNet, etc.) will replace what is now a lot of manual effort.

For hybrid systems, I have implemented enough code in Racket Scheme to run pre-trained Keras dense models (code in my public github repos) and for a new side project I am using BERT, etc. pre-trained models wrapped with a REST interface, and my application code in Common Lisp has wrappers to make the REST calls so I am treating each deep learning model as a callable function.

Have database admins disappeared? How about front end devs?

Even though I'm pretty sure AWS needs a lot of them; although not one (or more) for each and every single one of their customers.

Still; queries are a lot less to learn and much easier to fix when I do them wrong then also queries + taking care of (maintaining, scaling, fixing, backing up,...) the database itself.

Based on my limited contact with AI during the aughts' semantic web/description logic symbolic heyday (we were exploring ways in which multiple communicating knowledge bases might resolve conflicting information): symbolics with uncertainty is too hard, maybe in a very far future. When ML and symbolics are successfully put together, I expect the symbolics to focus on what they do best: ignore uncertainty and change, leave all that to the ML part. For example, when you do the "obvious thing" and run symbolic reasoning on top of classifications supplied by ML (which is maybe a naive approach not working out at all, I have no idea), you would feed back corrective training updates into the classification layer instead of softening the concepts when the outcome of reasoning is not satisfactory.

Imaginary toy example: if your rules state that cars always stop at stop signs, but the observed reality is that this hardly ever happens, this first iteration of ML-fed symbolics would not adopt by adjusting the rules to "cars carefully approach stop signs, but don't actually stop", it would eventually adopt by classifying the red octagonal shape as a yield sign, keeping the rules for stop signs as is (but never seeing any).

Or the work of Paul Werbos, the inventor of backpropagation, was heavily influenced by -- though itself perhaps outside the cannon of -- strictly symbolic approaches

Databases. (Isn't Terry Winograd's SHDRLU conversation that kind of conversation that you have with the SQL monitor?) Compilers. (e.g. programming languages use theories developed to understnad human languages) Business Rules Engines. SAT/SMT Solvers. Theorem proving.

There is sorta this unfair thing that once something becomes possible and practical it isn't called A.I. anymore.

Space tech is when you try to go to space. AI is when you aim at AGI.

I can't answer your main question but, as a practical matter, if you don't hear of new research in the field it probably means you're not tuned in to the right channels, which is to say, the proceedings of the main AI conferences that cover a broad range of subjects: AAAI, IJCAI, IROS, ICRA, plus the more specialised ones, like AAMAS, ICAPS, UAI, KR, JELIA, etc. Any interestting research in symbolic AI is going to be there.

If you get your news from the tech press and the internet, you won't hear of any of that stuff and won't even know it's going on, because, let's face it- the large tech companies are championing a very specific kind of AI (statistical machine learning with deep neural nets) and, well, they have the airwaves, they have the hype engines and their noise is drowning out all other information on the same channels.

For the record, work on symbolic AI is still going on. For instance, the subject area of my PhD research is in Inductive Logic Programming, a branch of symbolic, logic-based machine learning. This is not just active, but going strong with a recent explosion on research in learning Answer Set Programs and the work of my group, on a new ILP technique, Meta-Interpreterive Learning. If we're inventing new stuff, we're still alive and well.

This is also the biggest weakness of statistical AI, and why so many people are mad at companies that employ it. If you train on what people do, you also capture all the behavior that people wish they didn't do. Thus you get all the racism, sexual fetishes, discord, unpopular views, irrational views, tribalism, and general stupidity that folks would prefer to pretend doesn't exist, but shows up all the time to an objective observer of humanity.

I often think about symbolic AI, and how it relates to Boolean satisfiability. This is an integer problem. We don't seem to have a similar technology to GPUs that would be transferable to the problem domain. If we had that, maybe things would be different. I looked into this a bit, and Microsoft seems to have put some resources into a SAT computing ASIC.

To get the same kind of progress in symbolic AI, perhaps we need massively parallel/scalable SAT solving hardware. The gaming industry gave us the initial floating point hardware; maybe the cryptocurrency industry will gift us with analogous integer hardware that could push symbolic AI further.

See eg here:

https://mitpress.mit.edu/contributors/peter-gardenfors

https://www.youtube.com/watch?v=Y3_zlm9DrYk

From reading some papers, it seems his approach is a third way beyond symbolic and connectionist.

Indeed the title of that lecture is "The Geometry of Thinking: Comparing Conceptual Spaces to Symbolic and Connectionist Representations of Information"

Would you say conceptual spaces is a third way and how does it apply to the topic discussed in this thread?

What we call symbolic AI usually makes inference by exploring the space of possibilities generated by recombining the basic symbols of a (fixed) domain language.

Gärdenfors approach has a lot of this, in that it has a symbolic representation of data, a well-defined set of symbols that stand for objects in the observed domain (animals, in the example given); additionally, each symbol has a numerical value which represents how much of each property the object possesses.

This is somewhat similar to the knowledge systems of the 70s and 80s for incomplete, approximate rule-bad reasoning. But those were problematic because it was very difficult to do reasoning with their numerical values. When combining facts within the database, the respective combinations of their numerical values often had nonsensical meanings. The algebras used in those systems were not a good fit.

If Gärdenfors is right and concepts can be treated as mathematical spaces with convex regions, his approach could solve some major problems of those systems that made them impractical, and maybe bring them to prominence again.

If by symbolic AI we mean GOFAI, expert systems etc, I don't think that there will be ever a resurgence. But if by symbolic AI we mean machine learning algorithms that are somehow based on symbolic reasoning, I do think that there will be a resurgence. In particular, this resurgence will start when: a) Deep learning arrives to its limit (ie. research gets stuck) and/or b) Someone finds a scalable and SOTA-ish way to integrate symbols into gradient based algorithms.

Symbolic representations are clean, this is both a strength and a weakness. You might have perfectly separated categories but the real world frequently presents inputs that break taxonomies.

We invented symbols like letters and numbers to reduce the complexity of the real world. Language and mathematics are lossy representations but also incredibly useful models.

Given the value that symbols and symbolic methods have for us I have little doubt that they will be an integral part of efficient AI systems in the future. You could train a neural world model on the ballistic properties of a rocket, but if it's orders of magnitude more efficient why not learn to calculate instead?

1. For all the accomplishments made with Deep Learning and other "more modern" techniques (scare quotes because deep learning is ultimately rooted in ideas that date back to the 1950's), one thing they don't really do (much of) is what we would call "reasoning". I think it's an open question whether or not "reasoning" (for the sake of argument, let's say that I really mean "logical reasoning" here) can be an emergent aspect of the kinds of processes that happen in artificial neural networks. Perhaps if the network is sufficiently wide and deep? After all, it appears that the human brain is "just neurons, synapses, etc." and we manage to figure out logic. But so far our simulated neural networks are orders of magnitude smaller than a real brain.

2. To my mind, it makes sense to try and "shortcut" the development of aspects of intelligence that might emerge from a sufficiently broad/deep ANN, by "wiring in" modules that know how to do, for example, first order logic or $OTHER_THING. But we should be able to combine those modules with other techniques, like those based on Deep Learning, Reinforcement Learning, etc. to make hybrid systems that use the best of both worlds.

3. The position stated in (2) above is neither baseless speculation / crankery, nor is it universally accepted. In a recent interview with Lex Fridman, researcher Ian Goodfellow seemed to express some support for the idea of that kind of "hybrid" approach. Conversely, in an interview in Martin Ford's book Architects of Intelligence, Geoffrey Hinton seemed pretty dismissive of the idea. So even some of the leading researchers in the world today are divided on this point.

4. My take is that neither "old skool" symbolic AI (GOFAI) nor Deep Learning is sufficient to achieve "real AI" (whatever that means), at least in the short-term. I think there will be a place for a resurgence of interest in symbolic AI, in the context of hybrid systems. See what Goodfellow says in the linked interview, about how linking a "knowledge base" with a neural network could possibly yield interesting results.

5. As to whether or not "all of intelligence" including reasoning/logic could simply emerge from a sufficiently broad/deep ANN... we only just have the computing power available to train/run ANN's that are many orders of magnitude smaller than actual brains. Given that, I think looking for some kind of "shortcut" makes sense. And if we want a "brain" with the number of neurons and synapses of a human brain, that takes forever to train, we already know how to do that. We just need a man, a woman, and 9 months.

[1]: https://quoteinvestigator.com/2013/10/20/no-predict/

[2]: https://www.youtube.com/watch?v=Z6rxFNMGdn0&feature=youtu.be...

[3]: http://book.mfordfuture.com/

Geoff Hinton comments on a Reddit AMA that "The brain has about 10^14 synapses and we only live for about 10^9 seconds. So we have a lot more parameters than data. This motivates the idea that we must do a lot of unsupervised learning since the perceptual input (including proprioception) is the only place we can get 10^5 dimensions of constraint per second."

That sounds to me like humans don't take "forever to train" and definitely don't learn from "big data" compared to the size of data we feed into a small machine neural network. Brains must already have a lot of shortcuts built-in.

(comment is from https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama... )

I was just being glib about that. "Forever" is just hyperbole, but the 10+ some odd years it takes to go from birth to useful for most intellectual tasks is a pretty long-time in relative terms.

Brains must already have a lot of shortcuts built-in.

Oh absolutely. My point is just that there's no reason for us to not pursue "shortcuts" - as opposed to trying to build an ANN that's big enough to essentially replicate the actual mechanics of a real brain.

To extend this overall point though... it may be that as we learn newer/better algorithms and techniques we find out that you can actually make an ANN that would, for example, learn to do logical reasoning. And it might do so without need to use anywhere near the number of neurons and synapses that a real brain uses. But until such a time as it becomes apparent that this is likely, I think it's a good idea to continue researching "hybrid" systems that hard-wire in elements like various forms of symbolic/logical reasoning and anything else that we at least sorta/kinda understand.

This kind of rapid cognitive development argues strongly (IMO) against the kind of experimental/experiential training that a tabula-rasa nn approach would indicate.

Human plasticity and logical reasoning are the apex of other processes and approaches, I think that because we have so much access (personally through introspection and socially via children) to models of theses processes, and the results are so spectacular and intrinsically impressive.

I used to go to the SAB conferences in the 90's, they're still going, but somewhat diminished I think. This was where the "Sussex School" of AI had it's largest expression - Phil Husbands, Maggie Boden and John Maynard Smith all spoke about the bridges between animal cognition and self organising systems. I am pretty sure that they were all barking up the wrong tree (he he he) but there was and is a lot of mileage in the approach.

I have no inside information on why they're interested, but it's also intriguing that DARPA continues to pour money into planning system R&D.

I believe the various aspects of the Semantic Web are a continuation of symbolic AI. My two cents as a complete outsider on the topic.

"let's use Description Logic and F-logic because we both cannot do the science or maths to decide between them as a community (oh the irony) and hope that because they are tractable the fact that they are not expressive isn't going to matter"

5 years and £250m tax money later...

"It turns out that it matters, and there isn't an alternative, and we don't know what to do"

Meanwhile on another planet, AI researchers :

"Answersets and FOL offer a potential solution, we just have to slog away on a shoestring for 15 years to get there."

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding https://arxiv.org/abs/1810.02338

It actually brews on ideas that prof.Tenenbaum has been presenting and discussing over the past few years.

There is also the question if ML is not simply moving us into a ditch of optimum locality. I think it does.

I'd love to see symbolic AI take a foothold only to have it explain back its rationalizations (which ML can't).

There appear to be two classes of knowledge. Pattern knowledge, such as riding a bicycle, which we tend to learn in ways similar to the current machine learning trend. In some ways, this is "deductive knowledge". On the other hand, Explicit knowledge, such a learning to reason about proofs, which we tend to learn by teaching is symbolic. In some ways, this is "inductive knowledge.

The current machine learning trend leans heavily on Pattern knowledge. I don't believe it will extend into the Explicit knowledge domain. I fear that once this distinction becomes important it will be seen as a "limit of AI", leading to yet another AI winter. I tried to bring this up in the Open AI Gym (https://gym.openai.com/) but it went nowhere.

My experience leads me to hold the very unpopular opinion that AI requires a self-modifying system. Computers differ from calculators because they can modify their own behavior. I'm of the opinion that there is an even deeper kind of self-modification that is important for general AI. The physical realization of this in animals is due to the ability to grow new brain connections based on experience. One side-effect is that two identical self-modifying systems placed in different contexts will evolve differently. (A trivial example would be the notion of a "table" which is a wood structure to one system and a spreadsheet to the other system). Since they evolve different symbolic meanings they can't "copy their knowledge" but have to transfer it by "teaching".

Self-modification allows for adaptation based on internal feedback rather than external patterns (e.g. imagination). It allows a kind of hardware implementation of "genetic algorithms" (https://en.wikipedia.org/wiki/Genetic_algorithm). It allows "Explicit knowledge" to be "compiled" into "Pattern knowledge". This effect can be seen when you learn a skill like music or knitting. After being taught a manual skill you eventually "get it into your fingers", likely by self-modification, growing neural pathways.

Of all of the approches I've seen I think Jeff Hawkins of Numenta (https://www.amazon.com/Intelligence-Understanding-Creation-I...) is on the right track. However, he needs to extend his theories to handle self-modification in order to get past the "pattern knowledge" behavior.

Deduction is given a rule and cause, find (deduce) the effect, whereas Induction is given cause and effect, induce the rule. Isn't machine learning more inductive (given observations and outcome, induce the decision function)?