We can develop and tune algorithms for diverse industrial, commercial, domestic or public service needs including data mining,
sorting, complex decision software, pattern recognition, forecasting, and estimation.

What is Artificial Intelligence: Artificial Intelligence (AI) finds its place within the overarching discipline of Information Technology (IT). As described in the IT section,
IT is primarily based on logic programming. If we are aiming to simulate processing closer to organic brain functions,
a homogeneous adaptive system is required. This aim is achieved in AI by a number of novel approaches. AI applies statistics and geometric connections made between identical modules that are weighted differently, or uses what is termed tree connection and crossing.

An easy way to conceptualise AI is as a digitalised version of a natural process. There are many examples of natural systems we could consider, for instance,
DNA genetic algorithms, ant society swarm systems, bee orientation optical flow, animal neurone artificial neurones.

Essentially, AI is the art of duplicating a simplified version of natural mechanisms. As capability increases, so does the likelihood these digitised systems will achieve realistic outcomes. Think about photography, as the number of pixels has increased, the closer a digital image has come to replicating the analog. Similarly, as computer power and storage improves, AI becomes closer to achieving the outcomes of natural processes by running more complex models and larger numbers of units. Take the neural network as an example. If you combine a large enough number of artificial neurones within a complex system and the right set of rules/training, in theory a functional artificial brain could be constructed.

In an industrial setting, AI processes are commonly used to optimise recognition systems in either complex environments or with large amounts of data where more classical systems fail. Algorithms are associated with learning processes, weighing a homogeneous system response to an approximated input and specific output. AI technology is used in this way for pattern recognition for voice, visual shapes, motion, and data mining to extract relevant data.


Artificial Intelligence Methods Used:

  • System Expert and Neural Networks
  • Bayesian and Markov Chains
  • Prolog and Genetic Algorithms
  • Fuzzy Logic
  • Game Theory and Alpha/Beta Tree Search
  • General Statistics
  • Cellular Automatons
  • Artificial Inteligence menu

Artificial Intelligence Application Examples:

  • Logic expertise for bureaucratic task adaptive control
  • Pattern recognition
  • Conditional logic for game evolution and strategic
    machine learning.
  • Data mining research, quality control