Applications of CI

Applications of CI

A wide range of applications of CI has been identified in multidisciplinary and transdisciplinary areas, such as: (1) The architecture of future generation computers; (2) Estimation the capacity of human memory; (3) Autonomic computing; (4) Simulation of human cognitive behaviors using denotational mathematics.

The Architecture of Future Generation Computers

Conventional machines are invented to extend human physical capability, while modern information processing machines, such as computers, communication networks, and robots, are developed for extending human intelligence, memory, and the capacity for information processing [Wang, 2004]. Recent advances in CI provide formal description of an entire set of cognitive processes of the brain [Wang et al., 2006]. The fundamental research in CI also creates an enriched set of contemporary denotational mathematics [Wang, 2006c], for dealing with the extremely complicated objects and problems in natural intelligence, neural informatics, and knowledge manipulation.

The theory and philosophy behind the next generation computers and computing methodologies are CI [Wang, 2004]. It is commonly believed that the future-generation computers, known as the cognitive computers, will adopt non-von Neumann [von Neumann, 1946] architectures. The key requirements for implementing a conventional stored-program controlled computer are the generalization of common computing architectures and the computer is able to interpret the data loaded in memory as computing instructions. These are the essences of stored-program controlled computers known as the von Neumann architecture, which encompasses five essential components to implement general-purpose programmable digital computers.

Definition 4. A von Neumann Architecture (VNA) of computers is a 5-tuple that consists of the components: (a) the arithmetic-logic unit (ALU), (b) the control unit (CU) with a program counter (PC), (c) a memory (M), (d) a set of input/output (I/O) devices, and (e) a bus (B) that provides the data path between these components.

Definition 5. Conventional computers with VNA are aimed at stored-program-controlled data processing based on mathematical logic and Boolean algebra.

A VNA computer is centric by the bus and characterized by the all purpose memory for both data and instructions. A VNA machine is an extended Turing machine (TM), where the power and functionality of all components of TM including the control unit (with wired instructions), the tape (memory), and the head of I/O, are greatly enhanced and extended with more powerful instructions and I/O capacity.

Definition 6. A Wang Architecture (WA) of computers, known as the Cognitive Computers is a parallel structure encompassing an Inference Engine (IE) and a Perception Engine (PE) [Wang, 2006a].

The future generation cognitive computers based on WA are not centered by a CPU for data manipulation as the VNA computers do. The WA computers are centered by the concurrent IE and PE for cognitive learning and autonomic perception based on abstract concept inferences and empirical stimuli perception. The IE is designed for concept/knowledge manipulation according to concept algebra [Wang, 2006d], particularly the 9 concept operations for knowledge acquisition, creation, and manipulation. The PE is designed for sensory and perception processing according to RTPA [Wang, 2002b] and the formally described cognitive process models of the perception layers as defined in the LRMB model [Wang et al., 2006].

Definition 7. Cognitive computers with WA are aimed at cognitive and perceptive concept/knowledge processing based on contemporary denotational mathematics, i.e. concept algebra, Real-Time Process Algebra (RTPA), and system algebra.

As that of mathematical logic and Boolean algebra are the mathematical foundations of VNA computers. The mathematical foundations of WA computers are based on denotational mathematics [Wang, 2006b, 2006c]. As described in the LRMB reference model [Wang et al., 2006], since all the 39 fundamental cognitive processes of human brains can be formally described in CA and RTPA [Wang, 2002b, 2006e]. In other words, they are simulatable and executable by the WA-based cognitive computers.

Estimation the Capacity of Human Memory

Despite the fact that the number of neurons in the brain has been identified in cognitive and neural sciences, the magnitude of human memory capacity is still unknown. According to the Object-Attribute-Relation (OAR) model [Wang, 2007c], a recent discovery in CI is that the upper bound of memory capacity of the human brain is in the order of 108,432 bits [Wang et al., 2003]. The determination of the magnitude of human memory capacity is not only theoretically significant in CI, but also practically useful to explain the human potential, as well as the gaps between the natural and machine intelligence. This work indicates that the next generation computer memory systems may be built according to the OAR model rather than the traditional container metaphor, because the former is more powerful, flexible, and efficient to generate a tremendous memory capacity by using limited number of neurons in the brain or hardware cells in the next generation computers.