Future directions in computing

Published on 14/11/2007 by BBC

Silicon electronics are a staple of the computing industry, but researchers are now exploring other techniques to deliver powerful computers. Quantum computers are able to tackle complex problems. A quantum computer is a theoretical device that would make use of the properties of quantum mechanics, the realm of physics that deals with energy and matter at atomic scales. In a quantum computer data is not processed by electrons passing through transistors, as is the case in today's computers, but by caged atoms known as quantum bits or Qubits. "It is a new paradigm for computation," said Professor Artur Ekert of the University of Oxford. "It's doing computation differently." Click for more...

[G.K Comment: This article lists some new technologies that could potentially replace silicon in computers. They are all very exciting prospects that could have a positive effect on the way eBrains are designed in the future.]

Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits

Published by Dileep George and Jeff Hawkins

This paper explores how functional and anatomical constraints and resource optimization could be combined to obtain a canonical cortical micro-circuit and an explanation for its laminar organization. We start with the assumption that cortical regions are involved in Bayesian Belief Propagation. This imposes a set of constraints on the type of neurons and the connection patterns between neurons in that region. In addition there are anatomical constraints that a region has to adhere to. There are several different configurations of neurons consistent with both these constraints. Among all such configurations, it is reasonable to expect that Nature has chosen the configuration with the minimum wiring length. We cast the problem of finding the optimum configuration as a combinatorial optimization problem. A near-optimal solution to this problem matched anatomical and physiological data. As the result of this investigation, we propose a canonical cortical micro-circuit that will support Bayesian Belief Propagation computation and whose laminar organization is near optimal in its wiring length. We describe how the details of this circuit match many of the anatomical and physiological findings and discuss the implications of these results to experimenters and theorists. Click fore more (.pdf)

Attention and consciousness: two distinct brain processes

Published by Christof Koch and Naotsugu Tsuchiya on Neuron.org

The close relationship between attention and consciousness has led many scholars to conflate these processes. This article summarizes psychophysical evidence, arguing that top-down attention and consciousness are distinct phenomena that need not occur together and that can be manipulated using distinct paradigms. Subjects can become conscious of an isolated object or the gist of a scene despite the near absence of top-down attention; conversely, subjects can attend to perceptually invisible objects. Furthermore, top-down attention and consciousness can have opposing effects. Such dissociations are easier to understand when the different functions of these two processes are considered. Untangling their tight relationship is necessary for the scientific elucidation of consciousness and its material substrate. Click for more... (.pdf)

Theory of Brain Function, Quantum Mechanics and Superstrings

Published by D.V. Nanopoulos

"Theory of brain function, quantum mechanics, and superstrings are three fascinating topics, which at first look bear little, if any at all, relation to each other. Trying to put them together in a cohesive way, as described in this task, becomes a most demanding challenge and unique experience. The main thrust of the present work is to put forward a, maybe, foolhardy attempt at developing a new, general, but hopefully scientifically sound framework of Brain Dynamics, based upon some recent developments, both in (sub)neural science and in (non)critical string theory. I do understand that Microtubules are not considered by all neuroscientists, to put it politely, as the microsites of consciousnes." Click for more...

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[G.K Comment: Interesting... but is it all true? More to follow...]

Neural Networks vs. HTMs Part 2: From the OnIntelligence Forum

Quote 1:
"HTMs are similar to Bayesian networks; however, they differ from most Bayesian networks in the way that time, hierarchy, action, and attention are used. An HTM can be considered a form of Bayesian network where the network consists of a collection of nodes arranged in a tree-shaped hierarchy. Each node in the hierarchy self-discovers a set of causes in its input through a process of finding common spatial patterns and then finding common temporal patterns. Unlike many Bayesian networks, HTMs are self-training, have a well-defined parent/child relationship between each node, inherently handle time-varying data, and afford mechanisms for covert attention."

Quote 2:
"HTM's are a type of neural network. But in saying that, you should know that there are many different types of neural networks (single layer feedforward network, multi-layer network, recurrant, etc). 99% of these types of networks tend to emulate the neurons, yet don't have the overall infrastructure of the actual cortex. Additionally, neural networks tend not to deal with temporal data very well, they ignore the hierarchy in the brain, and use a different set of learning algorithms that our implementation. But, in a nutshell, HTMs are built according to biology. Whereas neural networks ignore the structure and focus on the emulation of the neurons, HTMs tend to focus on the structure and ignores the emulation of the neurons. I hope that clears things up.
_________________
Phillip B. Shoemaker Director
Developer Services Numenta, Inc."

Neural Networks vs. HTMs: What does Jeff Hawkins think?

Published by Evan Ratliff on Wired.com

Neural networks rose to prominence in the 1980s. But despite some successes in pattern recognition, they never scaled to more complex problems. Hawkins argues that such networks have traditionally lacked “neuro-realism”: Although they use the basic principle of inter-connected neurons, they don’t employ the information-processing hierarchy used by the cortex. Whereas HTMs continually pass information up and down a hierarchy, from large collections of nodes at the bottom to a few at the top and back down again, neural networks typically send information through their layers of nodes in one direction — and if they send information in both directions, it’s often just to train the system. In other words, while HTMs attempt to mimic the way the brain learns — for instance, by recognizing that the common elements of a car occur together — neural networks use static input, which prevents prediction. Click for more...

Learn Like A Human

Published by Jeff Hawkins
IEEE Spectrum magazine, April 2007

By the age of five, a child can understand spoken language, distinguish a cat from a dog, and play a game of catch. These are three of the many things humans find easy that computers and robots currently cannot do. Despite decades of research, we computer scientists have not figured out how to do basic tasks of perception and robotics with a computer. Our few successes at building "intelligent" machines are notable equally for what they can and cannot do. Computers, at long last, can play winning chess. But the program that can beat the world champion can't talk about chess, let alone learn backgammon. Today's programs-at best-solve specific problems. Where humans have broad and flexible capabilities, computers do not. Perhaps we've been going about it in the wrong way. Click for more...

[G.K Comment: An excellent article by Jeff Hawkins on Hierarchical Temporal Memory. ]

“Forward” software engineering: "Brain-like software architecture... Confessions of an ex-neuroscientist"

Published by Bill Softky

Which comes first: the problem or the solution?

Reverse engineering starts with hardware, works backward. Usually only succeeds if problem is understood. “Forward” software engineering starts with the problem, and saves hardware for last. Click for more... (.ppt file)

[G.K Comment: An interesting presentation by Bill Softky on how we could use forward software engineering to solve hard problems, such the "brain" one.]

Neural Darwinism

Published by David Cofer of MindCreators.com

"There are a multitude of different theories on the mind. Many more than have been discussed in this document. However, of all the ones I have seen before, I feel that this one offers the greatest hope of coming up with a real, working understanding of the science and neurobiology of how the mind works and what consciousness really is. Its author, Gerald Edelman, is a former Nobel laureate who was instrumental in cracking the mystery of how our immune systems work. After that he turned his attention to something far more difficult, attempting to understand how the neurobiology of the brain forms the mind. The main thrust of his theory of neural Darwinism is that the brain is a somatic selection system similar to evolution, and not an instructional system. (Somatic means that is over the time scale of your body instead of being on the time scale of evolution.)" Click for more...

MindCreators.com

Published by David Cofer of MindCreators.com

"I built this website to document the progress on my research into machine intelligence. Specifically, I am currently focused on building a computer simulation that behaves like a common, everyday insect using neural networks. Most researchers in the field of Artificial Intelligence (AI) try to understand and replicate human thought and abilities. I believe this is a mistake. You must start small and work your way up the evolutionary ladder, not immediately start with the most complicated thing in the known universe. Insects seem pretty stupid when compared with humans, but they are capable of a variety of intelligent, adaptive behaviors in a very unpredictable environment. And that is something that no man made system is yet capable of emulating. Also, when you get groups of insects working together in a cooperative manner they are capable of almost miraculous accomplishments. Once we begin to understand how these tiny brains work to produce such incredible behaviors then we will be able to harness that power for useful purposes. " Click for more...

[G.K Comment: David Cofer takes the approach of simulating a relatively simple insect's brain and then following the evolutionary ladder to understand more complex brain formations. Although this could make sense for solving some other real life problems, I believe that understanding an insect's brain is far more difficult than understanding a human baby's brain! I mean it! The reason is that human beings are the most incapable living organisms the moment of their birth. We also take a long time before we can perform even the most basic tasks such as walking & talking. In my opinion, we can develop a functional brain that looks nothing like any existing organism, as long as it can sense its environment and gain knowledge about it without pre-programming.]