[O]ur best bet is not just to have a single classification with filtering. ...[I]nstead... take the low level of input and get a whole universe of features that is interrelated. ...[W]e have different levels of determinations. At the lowest level we have percepts. At a slightly higher level we have simulations, and on an even higher level we have a concept landscape.

[T]he types of models that we form right now are not sparse enough... which means that, ideally, every potential model state should correspond to a potential world state. So... if you vary states in your model, you always end up with valid world states. ...[O]ur mind is not quite there... an indication is especially what we see in dreams. The older we get, the more boring our dreams become, because we incorporate more and more constraints that we learned about how the world works. So many of the things that we imagine to be possible as children turn out to be constrained by physical and social dynamics, and as a result fewer and fewer things remain possible. It's not because our imagination scales back, but the constraints under which it operates become tighter and tighter. ...So the constraints under which our neural networks operate are almost limitless, which means it's very difficult to get a neural network to imagine things that look real.

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At some point you have to understand the comedy of your own situation. If you take yourself seriously, and you are not functional, it ends in tragedy, as it did for Nietzsche. ...[Y]ou find the same thing in Hesse... The Steppenwolf syndrome is classic in all its sense, where you... feel misunderstood by the world and you don't understand that all the misunderstandings are the result of your own lack of self-awareness, because you think that you are [the] prototypical human and the others around you should behave the same way as you expect... based on your innate instincts; and it doesn't work out, and you become a transcendentalist to deal with that. ...It's very... understandable and I have great sympathies for this, to the degree that I can have sympathy for my own intellectual history. But you have to grow out of it.

If we want to understand music we have to go beyond understanding sound. We have to understand the transformations that sound can have if you play a different pitch. We have to arrange the sound in a sequencer that gives you rhythms, and so on, and then we want to identify some kind of musical grammar that we can use to... control the sequencer. So we have stacked structures that simulate the world. ...If you want to model a world of music you need to have the lowest level of the precepts, then the higher levels of mental simulations, which give the sequences... and the grammars of music... [B]eyond this you have the conceptual landscape that you can use to describe the different styles of music. ...[I]f you go up in the hierarchy, you get to more and more abstract models, more and more conceptual models, and more and more analytic models. ...[T]hese are causal models...

You need to become unimportant as a subject, that is, if you are a philosopher, believe is not a verb. ...You have to submit to the things that are possibly true and... follow wherever your inquiry leads, but it's not about you, it has nothing to do with you.

Robots are... not going to be the singular route to achieving AGI, and successfully building robots that are performing well in a physical environment does not necessarily engender the solution of the problems of AGI. Whether robotics or virtual agents will be first to succeed in the quest of achieving AGI remains an open question.

[N]egligence of internal states of the mind makes it difficult to form conclusive theories of cognition, especially with regard to language... and consciousness, so radical behaviorism... lost its foothold. Yet, methodological behaviorism is still prevalent...

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Symbolic reasoning falls short not only in modeling low level behaviors but is also difficult to ground into real world interactions and to scale upon dynamic environments... This has lead many... to abandon symbolic systems... and... focus on parallel distributed, entirely sub-symbolic approaches... well suited for many learning and control tasks, but difficult to apply [in] areas such as reasoning and language.

[I] think of the concepts as the address space for our behavior programs. The behavior programs allow us to recognize objects [also mental objects] and react... [A] large part of that is the physical world that we interact with, which is this thing... basically the navigation of information in space... [I]t's similar to a ... a physics engine that you can use to describe/predict how things that look in a particular way, that feel... a particular way, enough , enough auditory perception... the geometry of all these things... [T]his is probably 80% of what our brain is doing... dealing with that... real time simulation... [I]t's not that hard to understand... [O]ur game engines are already approximating the fidelity of what we can perceive... in the same ball park... just a couple of orders of magnitude away from saturating our perception, from the complexity that [the brain] can produce. ...[T]he computer that you can buy... is able to give a perceptual reality that has the detail that is already in the same ball park as what your brain can process.

There are some animals like elephants that have larger brains than us and they don't seem to be smarter. ...Elephants seem to be autistic. They have very, very good motor control and they're really good with details, but really struggle to see the big picture. ...[Y]ou can make them recreate drawings stroke by stroke... but they cannot reproduce a still life... of a scene... Why is that? Maybe smarter elephants would meditate themselves out of existence because their brains are too large. So... that elephants that were not autistic, they didn't reproduce.

General intelligence is not only the ability to reach a given goal (and usually, there is some very specialized, but non-intelligent way to reach a singular fixed goal, such as winning a game of chess), but includes the setting of novel goals, and most important of all, about exploration. ...[A]n environment with fixed tasks, scaled by an agent with pre-defined goals is not going to make a good benchmark problem for AGI.