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This paper describes a new approach to problem solving by splitting up problem component parts between software and hardware. Our main idea arises from the combination of two previously published works. The rst one proposed a conceptual environment of concept modelling in which the machine and the human expert interact. The second one reported an algorithm based on recon gurable hardware system which outperforms any kind of previously published genetic data base scanning hardware or algorithms. Here we show h o w e cient the interaction between the machine and the expert is when the concept modelling is based on recon gurable hardware system. Their cooperation is thus achieved with an real time interaction speed. The designed system has been partially applied to the recognition of primate splice junctions sites in genetic sequences.

This paper proposes a new technology for intelligent machines, based on the concept of programmable hardware. To build an intelligent system, the designer has to adapt it t o the problem. First we show that programmable hardware is an intermediate step for building configurations, in order to choose the best architecture. In this case, the tasks are performed in a time period that respects human cognitive capacities. Next is detailed a multilevel model composed of the cognitive, software and hardware levels. An experimental platform has been built based on programmable hardware, and used in a "Grand Challenge" problem: knowledge discovery in genetic sequence databases, to compare the relative efficiencies of programmable hardware and classical Von Neumann based architecture. Programmable hardware has shown to have a significantly faster response time, which is essential for modern day intelligent machine user interaction.