there is massive amount of experimental observations on learning and cognition in neuroscience and cognitive sciences (from neurophysiology to psychology) that is largely ignored by Artificial General Intelligence and Machine Learning communities.
On the other hand the progress in Deep Learning, Computer Vision, NLP and Robotics is largely ignored by neuroscientists because these learning models do not respect biological constraints
There is a whole group of narrow domains like Formal Concept Analysis, Statistical Relational Learning, Inductive Logic Programming, Commonsense Reasoning, Probabilistic Graphical Models that don't talk to each other but all deal with cognition and conceptual reasoning using different tools
I think we have a chance to make progress if these fragmented domains converge.
There are researchers in all the different fields who's sole job is to report what other communities and doing and be the agents of cross pollination.
Everyone agrees that artificial general intelligence is a difficult problem.
Practically its not possible to converge all the different fields and also what is the point of that ?
Each researcher is interested in solving their own sets of problems what they find interesting or have the motivation to be part of the solution.
Progress is being made - maybe not at the rate of silicon valley start-ups but hard problems require time to solve.
It would not be ideal that Computer Vision people suddenly stop doing their research and take the massive risk of putting all their shoes into Deep Learning.
People doing Computer Vision have their sets of constraints and goals. If tomorrow the garbage man, cleaner, cook, etc all stop working and stay "we are all going to work on deep learning". The world will stop working.
As absurd as that sounds that is what the implications would be if theses separate fields try to converge. Even if we do solve the problem of AGI today, what direct change or improvement in human condition would be see tomorrow ?
When that AGI needs to be integrated in a framework like computer vision, robotic, or search engine you need that domain experts, practitioners in the those various fields to still exist tomorrow to maximize the economical benefit of such a technology.
I'm not suggesting experts should drop what they're good at and work on integration, it's a job for engineers, think 'Apollo 11' kind of integration of different sciences into a single working product.
there have been decades of research into 'Cognitive Architectures' (http://en.wikipedia.org/wiki/Cognitive_architecture) and 'Artificial Consciousness' (http://en.wikipedia.org/wiki/Artificial_consciousness)
there is massive amount of experimental observations on learning and cognition in neuroscience and cognitive sciences (from neurophysiology to psychology) that is largely ignored by Artificial General Intelligence and Machine Learning communities.
On the other hand the progress in Deep Learning, Computer Vision, NLP and Robotics is largely ignored by neuroscientists because these learning models do not respect biological constraints
There is a whole group of narrow domains like Formal Concept Analysis, Statistical Relational Learning, Inductive Logic Programming, Commonsense Reasoning, Probabilistic Graphical Models that don't talk to each other but all deal with cognition and conceptual reasoning using different tools
I think we have a chance to make progress if these fragmented domains converge.