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the blue brain project


Concepts of intelligence IBM built the computer Deep Blue 3 to compete against and eventually beat Garry Kasparov at chess, shaking the foundations of our concepts of intelligence. Deep Blue combined conventional methods from computer science, but was able to win by brute force, considering 200 million moves per second using if–then-like routines (BOX 1). Nevertheless, this defeat of a human master by a computer on such a complex cognitive task posed the question of whether the relevant world of an organism could simply be described by enough if–then conditions. It could perhaps be argued that artificial intelligence, robotics and even the most advanced computational neuroscience approaches that have been used to model brain function are merely if–then-like conditions in various forms. Adaptation and learning algorithms have massively enhanced the power of these systems, but it could also be claimed that these approaches merely enable the system to automatically acquire more if–then rules. Regardless of the complexity of such an operation, the quality of the operation is much the same during any stage of the computation, and this form of intelligence could therefore be considered as ‘linear intelligence’. From a biological perspective, there are quantum leaps in the ‘quality’ of intelligence between different levels of an organism. Atoms are differentially combined to produce a spectrum of molecules, which are qualitatively very different from atoms in terms of their properties and the information they contain. After all, molecules cannot be understood by the study of atoms alone. DNA molecules can be strung together in numerous sequences to produce different genes, which collectively produce hundreds of thousands of proteins that are qualitatively different from their building blocks. Different combinations of proteins produce qualitatively different types of cell that can be combined in various ways in the brain to produce distinct brain regions that contain and process qualitatively different types of information. The brain seems to make the next quantum leap in the quality of intelligence, beyond the physical structures to form dynamic electrical ‘molecules’. The ultimate question, therefore, is whether the interaction between neurons drives a series of qualitative leaps in the manner in which information is embodied to represent an organism and its world. As computers approach petaFLOPS speeds, it might now be possible to retrace these elementary steps in the emergence of biological intelligence using a detailed, biologically accurate model of the brain. Detailed models

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