Example research essay topic: Neo Cortex Visual Cortex - 1,311 words

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By a "super intelligence" we mean an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills. This definition leaves open how the super intelligence is implemented: it could be a digital computer, an ensemble of networked computers, cultured cortical tissue or what have you. It also leaves open whether the super intelligence is conscious and has Entities such as companies or the scientific community are not superintelligence's according to this definition. Although they can perform a number of tasks of which no individual human is capable, they are not intellects and there are many fields in which they perform much worse than a human brain - for example, you can't have real-time conversation with Super intelligence requires software as well as hardware. There are several approaches to the software problem, varying in the amount of top-down direction they require.

At the one extreme we have systems like CYC which is a very large encyclopedia-like knowledge-base and inference-engine. It has been spoon-fed facts, rules of thumb and heuristics for over a decade by a team of human knowledge entered. While systems like CYC might be good for certain practical tasks, this hardly seems like an approach that will convince AI-skeptics that super intelligence might well happen in the foreseeable future. We have to look at paradigms that require less human input, ones that make more use of bottom-up methods. Given sufficient hardware and the right sort of programming, we could make the machines learn in the same way a child does, i.

e. by interacting with human adults and other objects in the environment. The learning mechanisms used by the brain are currently not completely understood. Artificial neural networks in real-world applications today are usually trained through some variant of the Backpropagation algorithm (which is known to be biologically unrealistic).

The Backpropagation algorithm works fine for smallish networks (of up to a few thousand neurons) but it doesn't scale well. The time it takes to train a network tends to increase dramatically with the number of neurons it contains. Another limitation of backpropagation is that it is a form of supervised learning, requiring that signed error terms for each output neuron are specified during learning. It's not clear how such detailed performance feedback on the level of individual neurons could be provided in real-world situations except for certain well-defined specialized tasks. A biologically more realistic learning mode is the Hebbian algorithm. Hebbian learning is unsupervised and it might also have better scaling properties than Backpropagation.

However, it has yet to be explained how Hebbian learning by itself could produce all the forms of learning and adaptation of which the human brain is capable (such the storage of structured representation in long-term memory - Boston 1996). Presumably, He's rule would at least need to be supplemented with reward-induced learning (Mozilla 1992) and maybe with other learning modes that are yet to be discovered. It does seems plausible, though, to assume that only a very limited set of different learning rules (maybe as few as two or three) are operating in the human brain. And we are not very far from knowing what these rules are. Creating super intelligence through imitating the functioning of the human brain requires two more things in addition to appropriate learning rules (and sufficiently powerful hardware): it requires having an adequate initial architecture and providing a rich flux of sensory input. The latter prerequisite is easily provided even with present technology.

Using video cameras, microphones and tactile sensors, it is possible to ensure a steady flow of real-world information to the artificial neural network. An interactive element could be arranged by connecting the system to robot limbs and a speaker. Developing an adequate initial network structure is a more serious problem. It might turn out to be necessary to do a considerable amount of hand-coding in order to get the cortical architecture right. In biological organisms, the brain does not start out at birth as a homogenous tabula rasa; it has an initial structure that is coded genetically. Neuroscience cannot, at its present stage, say exactly what this structure is or how much of it needs be preserved in a simulation that is eventually to match the cognitive competencies of a human adult.

One way for it to be unexpectedly difficult to achieve human-level AI through the neural network approach would be if it turned out that the human brain relies on a colossal amount of genetic hard wiring, so that each cognitive function depends on a unique and hopelessly complicated inborn architecture, acquired over aeons in the evolutionary learning process of our species. Is this the case? A number of considerations that suggest otherwise. We have to contend ourselves with a very brief review here. For a more comprehensive discussion, the reader may consult Phillips & Singer Quartz & Sejnowski (1997) argue from recent neurobiological data that the developing human cortex is largely free of domain-specific structures. The representational properties of the specialized circuits that we find in the mature cortex are not generally genetically pre specified.

Rather, they are developed through interaction with the problem domains on which the circuits operate. There are genetically coded tendencies for certain brain areas to specialize on certain tasks (for example primary visual processing is usually performed in the primary visual cortex) but this does not mean that other cortical areas couldn't have learnt to perform the same function. In fact, the human neo cortex seems to start out as a fairly flexible and general-purpose mechanism; specific modules arise later through self-organizing and through interacting with the environment. Strongly supporting this view is the fact that cortical lesions, even sizeable ones, can often be compensated for if they occur at an early age. Other cortical areas take over the functions that would normally have been developed in the destroyed region. In one study, sensitivity to visual features was developed in the auditory cortex of neonatal ferrets, after that region's normal auditory input channel had been replaced by visual projections (Sur et al. 1988).

Similarly, it has been shown that the visual cortex can take over functions normally performed by the somatosensory cortex (Schlaggar & O'Leary 1991). A recent experiment (Cohen et al. 1997) showed that people who have been blind from an early age can use their visual cortex to process tactile stimulation when reading Braille. There are some more primitive regions of the brain whose functions cannot be taken over by any other area. For example, people who have their hippocampus removed, lose their ability to learn new episodic or semantic facts.

But the neo cortex tends to be highly plastic and that is where most of the high-level processing is executed that makes us intellectually superior to other animals. (It would be interesting to examine in more detail to what extent this holds true for all of neo cortex. Are there small neo cortical regions such that, if excised at birth, the subject will never obtain certain high-level competencies, not even to a limited degree? ) Another consideration that seems to indicate that innate architectural differentiation plays a relatively small part in accounting for the performance of the mature brain is the that neo cortical architecture, especially in infants, is remarkably homogeneous over different cortical regions and even over different species: Laminations and vertical connections between lamina are hallmarks of all cortical systems, the morphological and physiological characteristics of cortical neurons are equivalent in different species, as are the kinds of synaptic interactions involving cortical neurons. This similarity in the organization of the cerebral cortex extends even to the specific details of cortical circuitry. (White 1989, p. 179). In the seventies and eighties the AI field suffered some stagnation as the exaggerated expectations from the early heydays failed to materialize and progress nearly ground to a...

Research essay sample on Neo Cortex Visual Cortex