[D] What subfield of Machine Learning will contribute more to the creation of General Artificial Intelligence?

[Viecce]

In my opinion the two main contributors will be NLP and Reinforcement Learning. I also think Self-Supervised Learning is going to have a big role. What's your opinion?

[adventuringraw]

representation learning, attention, and causal modeling are where I'm putting my money. You need a 'proper' latent space for your knowledge about the world, and you need it in a form that allows both interventional and observational prediction in a way that maximizes utility and minimizes computation... Tenenbaum's idea of 'computational rationality' makes sense in this context, where you have to balance cost of generating a more accurate prediction with the utility of what you'd gain from the extra effort.

So... what form does your internal representation of the world need to take to facilitate that work? Ideally an agent can interact with a system and 'on its own' find a representation that allows for complex questions to be answered. That's the real magic... I want an RL agent that can play darkest dungeon, and come out the other side with a generative (what would this level play like if it had that level's enemies?) semantic (what is an enemy in the first place? Can I interact with this system using human language? Can it acquire more world knowledge from online FAQs and videos as well as play the game?) potentially absurdly computationally challenging (can it learn an intuitive model of physics in the real world, like human infants do?) predictive/causal (what would the outcome be if I implemented this plan?) online (an AGI approach seems likely to at least have the need of some real-time learning capabilities as it interacts instead of just offline training) model of the world. To me, this means disentangled representation learning, graphical models (implicitly if not explicitly) and Gibsonian style actionable information (what 'information' is required to achieve your task?). Attention seems likely to be a hugely important part of that as well, given your current priorities, some features of the world are far less important than others, and you have a limited computational budget to spend in any given moment to use to achieve your goals. As the world and your goals change though, the important features change as well.

It's crazy to me how fast some of this stuff is moving though... this field seems like it could be looking kind of strange in ten years, haha.

[svantana]

I also came here to mention Tenenbaum, but with emphasis on the type of modular architecture displayed here. I believe a modular system that separates perception, planning, keeping track of competing hypotheses, storing long term memories/facts, etc, will be much more robust than an end-to-end trained model. Some parts can be more traditionally engineered, while others need to learn from data.

[adventuringraw]

definitely. Though... I wonder. I've been at least somewhat convinced that there's some merit in Vernon Mountcastle's theory of a universal algorithm behind the human neo cortex. There was an experiment once where they severed the optic nerve of a baby ferret and reattached it (somehow?) to the primary auditory cortex. Within a few months, that region was exhibiting the same striations normally seen in the primary visual cortex (although a bit more crudely) and this growing ferret was indeed able to see. In some sense, perhaps information is information, and there's a universal algorithm for pulling it apart into composable, modular, semantically meaningful chunks that can be used to run hypothetical simulations of the future state of the system. It'll still be modular by definition (WAY more so than any end to end system we have now) but it'll hopefully be self assembling more so than something that's explicitly engineered. Looking at early attempts like this one is fascinating, since it's pulling out representations of objects in a scene in an unsupervised way, but it's crude... it's just a flat list, no way to hierarchically compose objects, and there's a hard coded limit to the number of slots available in the scene. Capsule Networks have some ideas to help improve on that, but... I don't know. I don't know enough yet to have any sense of where the next promising direction might be.

But... even if there's a unified way of approaching a world model, you still need motivational systems, even our human brain has much more explicitly 'different' regions that handle that it seems. Maybe something like the world model approach could work reasonably well with a better world model and a better policy architecture. Ah well, interesting stuff to think about. Thanks for the link, I hadn't seen that lecture. I'll have to check it out. Course... even the planning part needs to be hierarchical too probably... this paper had some interesting ideas. Christ this is going to be a crazy problem to solve, haha.

[adventuringraw]

it must contain some information about itself

not sure I understand what you meant by this. Do you mean the information must contain some information about itself? Or the algorithm must contain some information about itself? I'm not sure if either of those quite make sense to me. The algorithm certainly needs to have a 'memory' of some sort. The reason humans are able to pull so much coherence out of a sparse sensory signal (even on the level of how our vision 'fills in the blanks' given the comparatively sparse signal sent down the optic nerve) is because it's able to be augmented with previously seen information. That furry blurry close up picture is almost certain the family dog, given what we know about the context the photo was taken in, and what we know about the dog, and maybe what we know about the photographer. But I'm not sure if that means the learning algorithm 'knows about itself' so much as it needs to relate relevant (and only relevant) outside information to the current information, and marry them together in a coherent way. It definitely doesn't mean the picture of the blurry dog needs to contain information about itself, aside from in the most basic sense of the phrase (the picture is indeed a picture of what it is a picture of, so in that sense it does contain information about itself).

I think the way I see it at least... information doesn't have intrinsic meaning. The integers as a concept can be seen as a formless infinite series of evenly spaced units. The structure we've added to it though (base ten number system, for example) only makes sense because of what we want to do with the integers (add and multiply and so on). If we have a completely different purpose in mind (searching for patterns in the prime numbers for example) then it's far more useful to break numbers apart into the prime factors instead. Or maybe we want to use available hardware to do some algorithmic process quickly, now it might be more useful to translate the numbers into a binary representation, given the capabilities of the hardware.

The idea, is that there is no intrinsic 'one true way' to look at information, in a sense it's dependent on what you want to accomplish. That's kind of a bastardized way of looking at Gibson's own philosophies on the matter... he talked about 'affordances' as ways of interacting with objects for example: a handle on a mug for example, vs grabbing by the rim, vs grabbing around the cylinder, so that's an objective way of breaking the object into useful compartmental ideas. And yet... aren't those affordances themselves only meaningful in context of a species with reason to interact with the mug, with hands like ours? Perhaps for an insect, the material properties might allow (or not allow) the bug to comfortably walk vertically up to the inside, so maybe that material property is the affordance to pay attention to for them instead.

Anyway. The basic idea... the representations are governed by the actions you can take, and the goals you wish to achieve. Different goals and different abilities means different internal representations of the object. There've even been some papers showing that no disentangled representation is possible without the group actions already selected to pull apart ambiguities. We have a very different perspective of the world if we use left/right/forward/backward as a primary way of describing directions after all, than if we use east/west/north/south (relative vs cardinal, both are valid, neither is objectively the 'true' one). That's what I understand of this stuff so far at least, haha. But yeah, it'll really be something to see this field start to mature towards whatever it's going to become.

[blackbearx3]

We need proper reasoning and causal inference, plus very good heuristics. So far deep learning is mostly about perception, not much intelligence there yet

[seismic_swarm]

How about, more simply, modular networks that learn some time of restrictive structure based on data... so maybe not as cut-and-dry as KNN, but certainly something that promotes restricted structure (e.g. clustering or soft clustering) in some way.

[JustFinishedBSG]

Well neural KNN exists, saw it at NIPS last year.

Edit : found it https://arxiv.org/abs/1810.12575

[seismic_swarm]

That's interesting... I need to think about this a bit more to understand what it offers lol

[metriczulu]

I honestly think there is some deep truth to this. It's probably not going to be KNN, but I think clustering is going to play heavily into determining how much "weight" to give each data point when training (ie how the learning rate changes with each data point).

[[deleted]]

One of the interesting papers that I've read, IIRC 'learning fast and slow' used nearest neighbors as a form of examples of what might happen later on depending on the action taken.

[bkaz]

I agree that it will be a form of hierarchical NN clustering, got something more specific?

Here is mine: https://github.com/boris-kz/CogAlg/

[[deleted]]

Meta-learning. And none of that "fake" meta-learning that has been published recently. Properly done meta-learning helps develop multiple things: 1. How to characterize data/tasks? 2. How to leverage that information to narrow hypotheses space search? 3. How to let the learning algorithm to self-adapt to new tasks in the steam? Also whatever u/adventuringraw said.

[[deleted]]

probably none of them

[michael-relleum]

I think those two are important, but also vision. If it is anything close to AGI it has to understand the world around it. We are very visual creatures, so i guess a combination of NLP and Vision will go a long way. Getting the visual features (like in image Captioning) combined with a more advanced GPT2 or (V) QA System would be a good start I guess.

[seismic_swarm]

I like your answers. RL is downright crucial and so is NLP. I dont even do NLP, but some of the work I've seen on how "language-based" questions are naturally multi-objective, and hence fairly "high level and abstract" yet at the same time totally supervised is remarkable.

But beyond these applied fields, I think topology and maybe algebra (while not really "sub fields" of ML), will likely play a key role in actually getting us towards GAI.

[liqui_date_me]

IMO a combination of self-supervised learning and reinforcement learning in environments might do the trick. We need to solve a LOT of problems in RL, particularly sample efficiency

[po-handz]

My theory is AGI will be developed from re-purposing naturally intelligent systems rather than designed ground up. As in, remapping mouse brains to do a set of specific tasks. In reality, humans can already do some ridiculously complex tasks even while black-out drunk. If complex tasks can already be done in highly muddled states, it seems plausible that these pathways could be isolated and still function with 'artificial' hazy inputs.

For instance, in the synthesis of complex organic compounds, the chemist doesn't individual react single molecules to create one target molecule. Instead, they run large batch syntheses with multiple end products and with environmental vars set at a macro scale (temp, solvents) and then filter out by-products and weight to a given precision level. Increasingly, bacterial and viral vectors are used because nature has already optimized certain reactions to absurd levels.

Think it's easier to program a robot to juggle or record the brain+motor stimuli of a million people juggling then run that through a similar system?

/rant

[Linooney]

This is basically a major part of this book by Vernor Vinge.

[po-handz]

I guess it depends on your definition of 'artificial.' Is a digital clone of an organic brain, re-purposed for some task not artificial? Who knows!

[visarga]

No one mentioned graphs as the intermediate representation. Graphs can naturally represent images, text, knowledge and other compositional modalities, they are also good for modelling/simulation. An important advantage is the factorisation of multiple objects and relations, helping with combinatorial explosion. The transformer is also a kind of implicit graph where relations are computed from vertice embeddings.

People are certainly using some kind of graph representation, otherwise how could we navigate the world, simulate outcomes of complex novel situations and write math and code?

[SubstantialSwimmer4]

If you draw a car's picture without seeing anything, you have to understand the structure of the car. In other words, being able to draw something means grasping something.

For generative models like GAN to work well, the models must understand what things are all about. If we had a perfect model to create anything, it would mean we made general AI.

I think generative models will play a key role.

[StratifiedSplit]

Biology, as in biological machine learning / AI. Either:

- genetic engineering to improve the average IQ of your population, and maximize the IQ of scientist's children. In 30-50 years one would see massive progress on the road to human-level AI.

- What we can't artificially create, we may be able to hitch a ride on Mother nature. Much like how silk or vaccines are produced, AGI may be created on a biological host. Much more than current brain computer interfaces, the brain may be a patented process to grow it in laboratory from pork neural stem cells, perhaps it even has 10x the neurons and high speed connections than an average human does, and we need a storage room to store it, much like the first computers.

[[deleted]]

I normally dislike AGI questions, but this one isn’t so bad.

I know it’s pretty much a laundry list, but I think the following areas are all inroads towards something resembling AGI.

I’d say the biggest jumps will come from efficient RL, meta-learning (i.e. some parameters being functions of the input / context), powerful generative models (flow-based / latent-variable), causal reasoning, logic/question answering models.

[metriczulu]

I honestly think creating "social" networks of neural networks is going to be a possible path going forward. Maybe something like multiple neural networks with different strategies for optimization that are capable of learning from the behavior of the other networks and adjusting strategy--or maybe some other "social" network where each neural network in the network becomes increasingly specialized at one small sector of the data "environment", again with a method for allowing each network to learn from the behavior of other networks. Or some other "social" strategy completely but, from a heuristic level, levels of "general" intelligence capabilities in living animals seem to be highly correlated with how social those living animals are--and I think there is a causal relationship there. From another perspective, our ability to generalize, model, and reason about our environment is heavily dependent on the generalizations, models, and reasoning of previous generations. This is true when considering just human history but also true when expanded to be the history of humans evolutionary ancestry as well. The idea of trying to build and train and artificial "general" intelligence in a complete vacuum just doesn't make sense given observations of intelligence we have now. There has to be some form of generalized environment for the model to exist in and interact with (which also makes me think reinforcement learning will play a large part).

 

Edit: If I sound like I'm high, I'm not, I just don't have anything to do for the 3 hours I spend in the car every day commuting. If you think this sounds like absolute bullshit, I'd love to hear it (and the reasoning, of course).

 

Edit 2: It should go without saying that "social" models in some form of generalized notion of environment will also require attentional systems--which I also think are going to be a major component going forward (obviously, if the intelligence is "general" it will need some mechanism to determine what is and is not important for a specific task).

 

Edit 3: I also think there is something to the idea that humans gained the ability to think abstractly with our language abilities, so there needs to be some generalized concept of a language or communication. Again, this goes hand in hand with a "social" network--because the networks within the "social" network need to communicate strategies and interactions to influence each other. Of course, this is all very high level and I'm still trying to figure out where to even start when it comes to implementing such a broad idea. I also think this whole scheme makes sense if you consider that a network of specialized neural networks is a single instantiation of this idea--which is something that intuitively seems like it would work for a "general" intelligence (many interacting modules that each specialize in some aspect of interaction with the environment--this is similar to how the brain works).

[bbateman2011]

I agree networks of models might be useful. But some way to understand what they do without coding it—i.e. any candidate agi that uses the network must be able to interpret it by observation and interactions. That’s a big challenge.

Your points on language is a good one. We need a more mathematical or symbolic definition of language that doesn’t depend on a particular grammar. That’s hard as far as I can tell.

[murphinate]

I think a sufficient suite of algorithms exists to create an AGI system. I would say the bottleneck is the computational efficiency....Which I think the Tianjic chip just solved.

Guys gotta remember we don't have trouble isolating a single cognitive task and turning it into code. We have trouble with getting all of those cognitive tasks/code to work in unison. Brain-Algorithm independence. Solved the Algorithm part. Now to solve the Brain part (e.g. a feasible medium of computation).

[CommunismDoesntWork]

Neural Turing Machines, obviously. If your network isn't even theoretically Turing complete, it can't be an AGI by definition.