Abstract (Summary)

Laser lidar. “Laser Focus World”, 2003, v 46, №3, p45.

The text focuses on the use of laser-based lidar in oceanography.

The ability of lidar to penetrate into the ocean surface to obtain specific data in

murky coastal waters is specially mentioned.

Particular attention is given to the advantage of laser-based lidars over passive

satellite-based systems iN obtaining signals not being contaminated by the water

column or the bottom.

A typical lidar system is described with emphasis on the way it works.

This information may be of interest to research teams engaged in studying

shallow waters.

Task II. Read the texts and write summaries according to given one.

Text 1

Artificial Intelligence at Edinburgh University: a Perspective

Jim Howe

Revised June 2007.

Artificial Intelligence (AI) is an experimental science whose goal is to

understand the nature of intelligent thought and action. This goal is shared with a

number of longer established subjects such as Philosophy, Psychology and

Neuroscience. The essential difference is that AI scientists are committed to

computational modelling as a methodology for explicating the interpretative processes

which underlie intelligent behaviour, that relate sensing of the environment to action

in it. Early workers in the field saw the digital computer as the best device available to

support the many cycles of hypothesizing, modelling, simulating and testing involved

in research into these interpretative processes. They set about the task of developing a

programming technology that would enable the use of digital computers as an

experimental tool. Over the first four decades of AI's life, a considerable amount of

time and effort was given over to the design and development of new special purpose

list programming languages, tools and techniques. While the symbolic programming

approach dominated at the outset, other approaches such as non-symbolic neural nets

and genetic algorithms have featured strongly, reflecting the fact that computing is

merely a means to an end, an experimental tool, albeit a vital one.

The popular view of intelligence is that it is associated with high level problem

solving, i.e. people who can play chess, solve mathematical problems, make complex

financial decisions, and so on, are regarded as intelligent. What we know now is that

intelligence is like an iceberg. A small amount of processing activity relates to high

level problem solving, that is the part that we can reason about and introspect, but

much of it is devoted to our interaction with the physical environment. Here we are

dealing with information from a range of senses, visual, auditory and tactile, and

coupling sensing to action, including the use of language, in an appropriate reactive

fashion which is not accessible to reasoning and introspection. Using the terms

symbolic and sub-symbolic to distinguish these different processing regimes, in the

early decades of our work in Edinburgh we subscribed heavily to the view that to

make progress towards our goal we would need to understand the nature of the

processing at both levels and the relationships between them. For example, some of

our work focused primarily on symbolic level tasks, in particular, our work on

automated reasoning, expert systems and planning and scheduling systems, some

aspects of our work on natural language processing, and some aspects of machine

vision, such as object recognition, whereas other work dealt primarily with tasks at the

sub-symbolic level, including automated assembly of objects from parts, mobile

robots, and machine vision for navigation.

Much of AI's accumulating know-how resulted from work at the symbolic level,

modelling mechanisms for performing complex cognitive tasks in restricted domains,

for example, diagnosing faults, extracting meaning from utterances, recognising

objects in cluttered scenes. But this know-how had value beyond its contribution to

the achievement of AI's scientific goal. It could be packaged and made available for

use in the work place. This became apparent in the late 1970s and led to an upsurge of

interest in applied AI. In the UK, the term Knowledge Based Systems (KBS) was

coined for work which integrated AI know-how, methods and techniques with knowhow,

methods and techniques from other disciplines such as Computer Science and

Engineering. This led to the construction of practical applications that replicated

expert level decision making or human problem solving, making it more readily

available to technical and professional staff in organisations. Today, AI/KBS

technology has migrated into a plethora of products of industry and commerce, mostly

unbeknown to the users.