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Title: Asynchronous Transfer Mode
Description: Asynchronous Transfer Mode

ATM does not stand for automatic teller machine. In the telecommunication, it stands for Asynchronous Transfer Mode, in which data sends asynchronously. This mode is another fast packet switching mode. ATM is regarded as the technology of the 21st century and its impact is expected to be similar to PCM (pulse code modulation) which is used widely around the world in telecommunication. Asynchronous transfer mode (ATM) is a technology that has his its history in the development of broadband ISDN in the 1970s and 980s. Technically, it can be viewed as an evolution of pocket switching. Like packet switching for data , ATM integrates the multiplexing and switching functions, is well suited for bursty traffic and allows communications between devices that operate at different speeds . Unlike packet switching , ATM is designed for high-performance multimedia networking.

ATM is also a set of international interface and signaling standards defined by the International Telecommunication Union- Telecommunications (ITU-T) Standards Sector (formerly the CCITT). The ATM forum has played a pivotal role in the ATM market since its formulation in 1991. The ATM forum is an international voluntary organization composed of vendors , service providers, research organization, and users. Its purpose is to accelerate the use of ATM products and services through the rapid convergence of interoperability specifications, promotion of industry cooperation , and other activities. Developing multivendor implementation agreements also furthers this goal.

What is ATM ?

Asynchronous transfer mode is a high speed; packet based multiplexing technique in which information flow is organized in short fixed length cells.

The cells are generated when there is new data to send, which allows an element of data compression (e.g there is no need to send the pauses that occurs in speech). It also means that cells with different bandwidth in each direction are possible- a fax connection, for example uses little bandwidth in return direction. This allows more efficient use of the network resourses.

ATM provides a way of multiplexing many sources of data onto a common cells stream. Regardless of speed of the inputs. This greatly improves flexibility, enabling provision of bandwidth on demand.

We start by making a distinction between mind and cognition, and by positing that cognition is an aspect of mind. We propose as a working hypothesis a Separability Hypothesis which posits that we can factor off an architecture for cognition from a more general architecture for mind, thus avoiding a number of philosophical objections that have been raised about the "Strong AI" hypothesis. Thus the search for an architectural level which will explain all the interesting phenomena of cognition is likely to be futile. There are a number of levels which interact, unlike in the computer model, and this interaction makes explanation of even relatively simple cognitive phenomena in terms of one level quite incomplete.

I. Dimensions for Thinking About Thinking

A major problem in the study of intelligence and cognition is the range of often implicit assumptions about what phenomena these terms are meant to cover. Are we just talking about cognition as having and using knowledge, or are we also talking about other mental states such as emotions and subjective awareness? Are we talking about intelligence as an abstract set of capacities, or as a set of biological mechanisms and phenomena? These two questions set up two dimensions of discussion about intelligence. After we discuss these dimensions we will discuss information processing, representation, and cognitive architectures.

A. Dimension 1. Is intelligence separable from other mental phenomena?

When people think of intelligence and cognition, they often think of an agent being in some knowledge state, that is, having thoughts, beliefs. They also think of the underlying process of cognition as something that changes knowledge states. Since knowledge states

are particular types of information states the underlying process is thought of as information processing. However, besides these knowledge states, mental phenomena also include such things as emotional states and subjective consciousness. Under what conditions can these other mental properties also be attributed to artifacts to which we attribute knowledge states? Is intelligence separable from these other mental phenomena? It is possible that intelligence can be explained or simulated without necessarily explaining or simulating other aspects of mind. A somewhat formal way of putting this Separability Hypothesis is that the knowledge state transformation account can be factored off as a homomorphism of the mental process account. That is: If the mental process can be seen as a sequence of transformations: M1 -->M2 -->..., where Mi is the complete mental state, and the transformation function (the function that is responsible for state changes) is F, then a subprocess K1 --> K2 -->. . . can be identified such that each Ki is a knowledge state and a component of the corresponding Mi, the transformation function is f, and f is some kind of homomorphism of F. A study of intelligence alone can restrict itself to a characterization of Ks and f, without producing accounts of Ms and F. If cognition is in fact separable in this sense, we can in principle design machines that implement f and whose states are interpretable as Ks. We can call such machines cognitive agents, and attribute intelligence to them. However, the states of such machines are not necessarily interpretable as complete Ms, and thus they may be denied other attributes of mental states.

B. Dimension 2: Functional versus Biological

The second dimension in discussions about intelligence involves the extent to which we need to be tied to biology for understanding intelligence. Can intelligence be characterized abstractly as a functional capability which just happens to be realized more or less well by some biological organisms? If it can, then study of biological brains, of human psychology, or of the phenomenology of human consciousness is not logically necessary for a theory of cognition and intelligence, just as enquiries into the relevant capabilities of biological organisms are not needed for the abstract study of logic and arithmetic or for the theory of flight. Of course, we may learn something from biology

about how to practically implement intelligent systems, but we may feel quite free to substitute non-biological (both in the sense of architectures which are not brain-like and in the sense of being un- constrained by considerations of human psychology) approaches for all or part of our implementation. Whether intelligence can be characterized abstractly as a functional capability surely depends upon what phenomena we want to include in defining the functional capability, as we discussed. We might have different constraints on a definition that needed to include emotion and subjective states than one that only included knowledge states. Clearly, the enterprise of AI deeply depends upon this functional view being true at some level, but whether that level is abstract logical representations as in some branches of AI, Darwinian neural group selections as proposed by Edelman, something intermediate, or something physicalist is still an open question.

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