Hierarchical Temporal Memory is the closest model to the brain that I've seen (http://en.wikipedia.org/wiki/Hierarchical_temporal_memory). They are capable of generalized learning and excel in the same way that humans do. They are capable of abstraction, self categorization, and online learning.
There are elements of past AI models in the HTM model, however to reduce HTM's or any deep learning algorithm to a mere combination of past AI concepts overlooks the power of the right model when it is achieved. It would be like saying that Facebook is just a news feed. Sure, that's what gets most of the eyeballs, but there's a lot more there which would drastically reduce its value if not present.
What I think is most interesting is that we may find that Humans learn pretty inefficiently from the perspective of the amount of input data required over time. This may seem silly at first, but when you consider how many neurons cover the surface area of our ears and eyes and then consider the fact that it takes anywhere from 12 to 14 months for a child to speak its first word, you might start to agree with this line of thought. Also, when I consider the fact that this processing all happens in parallel even further pushes me in this direction.
Whatever the case may be, HTMs are definitely a cool area of research. For those who are interested, you should definitely check out more of Jeff Hawkins work at Numenta. They've been able to demonstrate some pretty novel things. He wrote a book back in 2006 that blew my mind. Went into deeper explanation about how HTMs could model everything from deep learning, to consciousness, creativity, and bunch of other things.
There are elements of past AI models in the HTM model, however to reduce HTM's or any deep learning algorithm to a mere combination of past AI concepts overlooks the power of the right model when it is achieved. It would be like saying that Facebook is just a news feed. Sure, that's what gets most of the eyeballs, but there's a lot more there which would drastically reduce its value if not present.
What I think is most interesting is that we may find that Humans learn pretty inefficiently from the perspective of the amount of input data required over time. This may seem silly at first, but when you consider how many neurons cover the surface area of our ears and eyes and then consider the fact that it takes anywhere from 12 to 14 months for a child to speak its first word, you might start to agree with this line of thought. Also, when I consider the fact that this processing all happens in parallel even further pushes me in this direction.
Whatever the case may be, HTMs are definitely a cool area of research. For those who are interested, you should definitely check out more of Jeff Hawkins work at Numenta. They've been able to demonstrate some pretty novel things. He wrote a book back in 2006 that blew my mind. Went into deeper explanation about how HTMs could model everything from deep learning, to consciousness, creativity, and bunch of other things.