Edit:
I actually think the major AI breakthrough will come from either of those two fields, not computer science.
The growth in building increasingly sophisticated AI is faster than our efforts to reverse engineer biology. I could see that changing with improved observational techniques like optogenetics or bacteria and viruses we can "program" to explore.
Researchers are already focusing on concrete insights from cognitive science, neuroscience, etc such as one shot learning or memory that we haven't yet figured out in a cohesive machine learning framework. For the time being I'd bet on more advaces coming without significant changes in biological understanding.
But I am with you in that engineering / CS / ... should not wait for neuroscience to make further discoveries but continue the journey.
Regardless, my feeling is that there is a healthy dose of human hubris around intelligence. If I train a dog to go fetch me a beer from the fridge, that seems pretty smart. It learned how to understand a request, execute a complex sequence of actions for motion and planning, reason around occluded objects, understand depth and 3 dimensional space, differentiate between objects, and more without me writing a sequence of rules to follow. I'd be happy to have a robot that intelligent. Plants dont have brains or neurons yet react to sensory stimulus such as light touch or sound, communicate, and even have memory and learn. It's not at a scale to do something interesting within one plant but communities of plants are arguably the most successful organisms on the planet.
Andrew Ng likes to point to a Ferret experiment [0] where experimental neuroscientists rewired the visual inputs to the auditory cortex and the auditory cortex learned to "see" the visual signals! This suggests that there may be some amount of unified "learning" rules to the brain. Biology is never so clean but if humans have whatever this intelligence thing is that lesser organisms do not, there is another angle to look at things. We have a lot of neurons which suggests less per neuron specialization than say a C. elegans; basically large populations of neurons perform tasks that in lesser creatures single or few neurons may perform. While the trees are complex and important to understand for biology and medicine, the forest may have some high level rules.
Looking at something that appeared intelligent 50-100 years ago but seems mechanical now, we have text to speech. NETtalk was a simplified computational neuroscience model from the 80s that could synthesize human speech. Today we have far better quality techniques that came out of R&D focused on things like large high quality labeled datasets for training, better soundcards, more processing power, and algorithmic improvements. Researchers didn't continue trying to model the brain and instead threw an HMM and a couple other tricks at it. Now we're going full circle back to neural networks but they aren't using any advances from biology and certainly arent produced by computational neuroscientists like Terry Sejnowski.
It's funny because at the time of NETtalk they thought that learning to read would be an extremely hard problem because it incorporates so many components of the human brain [1]. While it certainly wasn't a trivial problem, state of the art OCR and object recognition came from similar artificial neural networks a decade later with LeNet and MNIST * . And no, ANNs != biological neuronal networks. The models of computational neuroscientists are different; for example look at [2, 3] for high level models or [4] for a tool.
Now I'm even more convinced than before that understanding the brain is great for humanity but wont be necessary for building intelligent systems that can perform tasks similar to biological ones.
[0] http://www.nature.com/nature/journal/v404/n6780/full/404871a...
[1] https://en.wikipedia.org/wiki/NETtalk_(artificial_neural_net...
[2] http://science.sciencemag.org/content/338/6111/1202
[3] http://ganguli-gang.stanford.edu/pdf/InvModelTheory.pdf
[4] http://neuralensemble.org/docs/PyNN/index.html
* Perhaps you could argue that convolutions are loosely inspired by neuron structure, but that sort of knowledge had existed for quite some time, with inspiration arguably within Camillo Golgi's amazing neuronal physiology diagrams from the 1870s let alone the 1960-80s. It's telling that papers on CNNs have little to no neuroscience and a lot of applied math :)
What I meant to say is that our ANNs are so ridiculously simplified versions of real neural networks that there might still be something to be learnt from the real brain. This shall not imply that to achieve intelligence, the solution necessarily has to mimic a biological brain.
(Thank you for your detailed response. I love to read about this stuff!)
edit: missing word
But we are still nowhere near developing a computer that can learn to play Monopoly, Risk or Axis & Allies just from reading the rule book and looking at the components. If you aim your machine at a very narrow subset of a problem, you can optimise it to amazing degrees, far exceeding humans or nature. But developing a machine that has the whole package is staggeringly hard in comparison.
But you know what? That's fine. Special purpose, single domain tools are fantastically useful and are easier to make highly reliable, with well understood limitations.
I believe it's more likely that engineering of AI will bring new ideas to neuroscience instead, just like after building helicopters we gained some intuition and understanding on why certain features of dragonflies exist.
Despite the history and significant progress in AI we still don't know that much about what approach will result in the first strong AI, or even if it's possible to make one. In an important sense AI is more like aeronautical engineering in 1902 than aeronautical engineering today, so it's possible that better understanding of biology will result in an important innovation.
Why would that be controversial? It seems to make extremely good sense and even though it may be doubted in some circles I think that most people involved in AI research are painfully aware of our limited understanding of our own psyche.
Neuroscience does concern itself a great deal with low level biological mechanisms and architectures, and is more amenable to cross-pollination with machine learning.
Though I would like to point out that thus far deep learning has taken few ideas from neuroscience.
The reason why psychology is 'too high-level' is exactly what is meant with 'we don't understand it', we're approaching the psyche at the macro level of observable traits, but there is a very large gap between the 'wiring' and the 'traits', some of that gap belongs to neuroscience but quite possibly the larger parts belongs to psychology. The two will meet somewhere in the middle.
A similar thing happens in biology with dna and genetics on the one side and embryology on the other.
The neural networks that were taken 'explicitly from neuroscience' have gone through a vast transformation and that + a whole lot of work on training and other stuff besides is what powers the current crop of AI software. All the way to computers that learn about games, that label images and that drive cars with impressive accuracy to date.
The problem is - and I think that was what the original question was about - that neuroscience is rather very low level. We need something at the intermediate level, a 'useful building block' approach if you will, something that is not quite a fully formed intelligence but also not so basic as plumbing and wiring.
I never said there weren't any developments in neural nets, I'm just saying that few ideas have been taken from neuroscience (there certainly have been some ideas, like convolutions). In fact most things (including most tricks in convolutional neural nets in their current state) a neural net does, we know a brain does not do.
Right now, we have a state of AI that is very much limited by what we know about how our own machinery works. Better understanding of that machinery (just like better understanding of things like aerodynamics, rolling resistance and explosions led to better transportation) will help us in some way or other. And it may take another 50 years before we hit that next milestone, but the current progress all leads more or less directly back to one poor analogy with biological systems. Quite probably there are more waiting in the wings, whether through literal re-implementation or merely as inspiration it doesn't really matter.
To put it in other words: Birds are limited by evolution. They are not an optimal design - they are a successful reproductive design in a wide ecosystem where flying is a tool.
Our intelligence is no different.
This is something Feynman addressed in this beautiful talk (in the Q&A iirc): https://www.youtube.com/watch?v=EKWGGDXe5MA
I think this is one of the key points of the problem: we, as engineers, are expecting this problem (imitating the human psyche/getting to true AI) to be able to be defined rigorously. What if it can't?
I'm not a religious person and I don't generally believe in something inside us that it's "undefinable", but looking at our cultural and intellectual history of about 2,500 years I can see that there are lots of things that we we haven't been able to define rigorously, but which are quintessential to human psyche: poetry, telling jokes, word puns, the sentiment of nostalgia and I could go on and on.
So I would expect strong AI from CS, possibly with a paradigm shift caused by an advance in neuroscience.
1) Acting like a human (this is the Turing test approach)
2) Thinking like a human (this is cognitive science, and for now is focused on figuring out how humans think)
3) Thinking rationally, ie, following formal logic (the difficulty here is encoding all the information in the world as formal logic)
4) Acting rationally. That is, entities that react rationally to their goals (this one is notable because it allows fairly stupid entities).
These are all explained in more detail in Artificial Intelligence, a Modern Approach by Russell and Norvig
Another line for acting human would be the ability to self direct learning in a variety of situations in which a reasonably intelligent human can learn. That means a single algorithmic framework that can learn go, navigate a maze, solve Sudoku, carry on a conversation and decide which of those things to do at any given time. The key is that the go playing skill would need to be acquired without explicitly programming for go.
I believe a lot of our intelligence is the ability to perform solved AI problems given the situation. The key is combining those skills (whether as a single algorithm or a variety of algorithms with an arbiter) and the ability to intelligently direct focus. That's why most researchers aren't confusing alphago with general intelligence. It can play go - period.
Their attempt at a definition that synthesizes all the others is:
> Intelligence measures an agent's ability to achieve goals in a wide range of environments. - S. Legg and M. Hutter
https://en.wikipedia.org/wiki/Ornithopter
Some even worked.
But they concluded that the flapping motion wasn't essential for propulsion, and could more easily be achieved by a propeller.
1. Scientists get a really good understanding of how learning works.
2. One third of the philosophers claim that their philosophical ideas are vindicated, another third claim that their models aren't really contradicted by this new scientific model, and the final third claim that the scientists have somehow missed the point, and that special philosophical learning is still unexplained.