A Thousand Brains
I posted this review of "A Thousand Brains" on Amazon. Bottom line, there are great ideas but, at bottom, the "science" in it is extremely shakey. If you toss out the hardware ideas and focus on the systems analysis, it's brilliant. This reminds me of another great book on the workings of the mind, Surfaces, and Essences. In that book, the authors wisely steer clear of speculating on how analogy and metaphor are implemented in the brain. "A Thousand Brains" can be profitably read in this spirit.
In subsequent posts, I will be seeing what can be done with Hawkins' theory if we see it as systems analysis rather than neuroscience.
AMAZON REVIEW
If this is the only neuroscience book you read, you'd think neural columns are a "thing". I am reminded of the brilliant idea of "memes", which fell apart due to the inability to define them rigorously. It is no coincidence that Richard Dawkins (inventor of the meme) is over the moon about columns and frameworks.
Even so, Hawkins' insights about the constraints a brain theory must satisfy is insightful, and probably a major stepping stone.
But while he tells me a lot of new interesting facts about neuron function, his idea that a column is a little prediction machine is presented without evidence. Even if the idea is true, Hawkins seems to miss the fact that a single column MUST participate in multiple frames, therefore there is no one-to-one correspondence between frames and columns. Frames are created by a set of related neurons firing. If these relationships are, as he claimed, determined by the synapses at the neuron, the frame is ultimately a network of synapses, or the connected neurons firing together. This cannot be a new insight. The book repeatedly leaves the impression that is the column that is "learning" to predict, rather than the network of columns. As they said about Freud, what's true ain't new, what's new ain't true.
The fallacy is quite evident in the title, which hints that our brain is really a thousand "micro-brains". While the number is impressive, we seem to be able to learn and recognize an infinite number of frames. Such a huge number can be reached if "frames" are combinations of synapses of which we have 125 trillion. The number of possible COMBINATIONS of 125 trillion synapses is, for all practical purposes, infinite.
This is not to say that "frameworks" are not a great idea. They satisfy a lot of the constraints that the author sets out. The brain probably does work this way. But if we look deeply, the frame theory works on its own and has nothing to do with the hardware that supposedly implements it. The hardware in the title.
As an engineer, the author sails off into Science Fiction when he claims he knows enough to build a general-purpose AI. This is not to say that he will not come up with something interesting.
A general-purpose machine that implements frames already exists: the Internet. If we examine any Web page in detail, we will see it is bristling with links under the hood (these can be seen in the HTML that creates the page). Your iPhone screen is a frame, as are all the icons. The software that drives the icons is "object-oriented", full of links and references to other objects in the world. Individual objects participate in multiple frames. This is technology now 30 years old. Its fundamentals can be grasped by any intelligent layman. They are simple, but not beyond understanding. You don't need to be (as they say) a brain surgeon.
In spite of its vast size, the Internet is not yet "intelligent" in the way our individual brains are, but this is perhaps a hint that Hawkins will not be cooking up a general-purpose machine any time soon.
In spite of the specific failings of Hawkins' theory, there are enough promising elements in it for me to pursue what makes sense and leave aside a few specifics. Hawkins is a perfect example of the importance of asking impertinent questions.
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