Digital Reach

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One goal of AI work in natural language is to enable commu-

nication between people and computers without resorting to

memorization of complex commands and procedures. Automatic

translation – enabling scientists, business people and just plain

folk to interact easily with people around the world – is another

goal. Both are just part of the broad field of AI and natural

language, along with the cognitive science aspect of using comput-

ers to study how humans understand language.

Knowledge representation and reasoning mechanisms

–Representations

based on logic or structured like frames, scripts and other represen-

tations of this type, like semantics have been also introduced in AI and

have been developed afterwards in classical computing applications

like Object Programming which retakes the concept of frame, repre-

senting entities to which behaviours like data bases are associated.

Expert systems

– In the early 1980s, expert systems were believed

to represent the future of artificial intelligence and of computers

in general. The primary goal of expert systems research is to make

expertise available to decision makers and technicians who need

answers quickly. Portable with computers loaded with in-depth

knowledge of specific subjects can bring decades’ worth of knowl-

edge to a problem. The same systems can assist supervisors and

managers with situation assessment and long-range planning.

Many small systems now exist that bring a narrow slice of in-

depth knowledge to a specific problem, and these provide

evidence that the broader goal is achievable.

These knowledge-based applications of AI have enhanced

productivity in business, science, engineering, and the military

field. With advances in the last decade, today’s expert systems

clients can choose from dozens of commercial software packages

with easy-to-use interfaces.

Today, the original field has been enlarged to include under the

name ‘knowledge engineering’ the conception of application with

the aim of helping the user intelligently.

Formal calculations

– The areas of research include Computer

Algebra (symbolic computation), more specifically polynomial

system solving, Axiom and arithmetic of real numbers and alge-

braic infinitesimals and effective Galois theory. The findings are

useful in various fields: robotics, astronomy, image compression.

The study of mathematical reasoning was at the origin of the

development of logic in the first half of the 20th century, which

led to a precise definition of the notion of effective calculation as

the starting point of computing science. With AI, other types of

reasoning different from the ones made by mathematicians

became subjects of study.

Problem resolution

– To resolve a given problem, we need to break

it up into sub-problems and then break the latter into small ones

until we get problems having an immediate accessible solution.

Computer vision

– Visual perception with the computer is a means

of interaction between human beings and computers. Vision

involves both the acquisition and processing of visual informa-

tion. AI powered technologies have made possible such

astounding achievements as vehicles that are able to safely steer

themselves along our superhighways, and computers that can

recognize and interpret facial expressions.

AI programs make possible the enhancement, interpretation,

recognition, identification and other processing of partial images.

AI vision technology has made possible such applications as:

image stabilization, 3D modelling, image synthesis, surgical navi-

gation, handwritten document recognition, and vision-based

computer interfaces.

Human-machine interface

– This represents a key element in the

use of any information system and determines its success. In this

regard, the human-machine interface must be conceived as an

extension of the user short-term memory and must integrate

temporal aspects of the interactions. Theoretically, it has to be

natural, efficient, reliable and easy to understand and to use.

These aspects are very important for the performers of the human-

machine interface.

Artificial neural networks

– A neural network is a collection of

processing nodes transferring activity to each other via connec-

tions. The clearest example is, of course, the brain.

In computing, ‘neural network’ refers to a class of models

which simulate learning in order to assist us in detecting infor-

mation, predicting outcomes, and making decisions. Neural nets

have the topology of a directed graph, meaning that, like the struc-

ture of the brain, connections between nodes or neurons is

one-way.

Neural networks, along with other analytical and predictive

methods, are today being incorporated into a relatively new field

called Knowledge Discovery and Data Mining (KDD). A primary

goal of KDD tools is to assist the user in detecting relationships

in data which may not be readily perceptible due to the sheer

size of data, missing data elements, or certainly lack of time to

examine data and infer knowledge from them. Today, they are

proving successful in a number of disciplines such as voice recog-

nition and natural language processing.

Distributed Artificial Intelligence (DAI)

–DAI systems can be defined

as cooperative systems where a set of agents act together to solve a

given problem. These agents are often heterogeneous (e.g. in a

Decision Support System, the interaction takes place between a

human and an artificial problem solver).

It is a branch of classical AI which deals with intelligent behav-

iours as the result of the cooperative activity of many agents. DAI

introduced the concept of MULTI-AGENTS whose characteris-

tics are: cooperation, coordination and communication. Every

agent is autonomous and can be conceived independently from

the others. The advantages are both on the methodological and

technical levels.

Genetic algorithms

– A genetic algorithm (GA) is a search tech-

nique used in computer science to find approximate solutions to

combinatorial optimization problems. GAs are a particular class

of evolutionary algorithms that use techniques inspired by evolu-

tionary biology such as inheritance, mutation, natural selection,

and recombination (or crossover). They were formally introduced

in the United States in the sixties by John Holland at the

University of Michigan.

To use a GA, you must represent a solution to your problem as

a genome (or chromosome). The GA then creates a population of

solutions and applies genetic operators such as mutation and

crossover to evolve the solutions in order to find the best one(s).

The three most important aspects of using GAs are: (1) defin-

ition of the objective function, (2) definition and implementation

of the genetic representation, and (3) definition and implemen-

tation of the genetic operators. Once these three have been

defined, the generic genetic algorithm should work fairly well.

Virtual reality (VR)

– VR provides the experience of perception and

interaction through the use of sensors and effectors in a simulated

environment. Advances in simulation technology allow computer

resources to be interconnected with humans through the use of

sensor systems and robotic devices. The goal of the simulation is to

have a viewer see only the simulation – as if he or she were inside

the simulation itself. Modern interfaces allow you to get into virtual

environments, to move and to be exposed to graphical objects created

by the computer as if they were real objects and places.