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

An easy way to conceptualize AI is as a digitalized version of a natural process. We can consider many examples of natural systems; for instance, DNA genetic algorithms, ant society swarm systems, bee orientation optical flow, and animal neuron artificial neurons.

Essentially, AI involves duplicating a simplified version of natural mechanisms. As capability increases, the likelihood of these digitized systems achieving realistic outcomes also rises. Think about photography; as the number of pixels has increased, digital images have come closer to replicating analog ones. Similarly, as computer power and storage improve, AI gets closer to achieving the outcomes of natural processes by running more complex models with larger numbers of units. Take the neural network as an example. If you combine a large enough number of artificial neurons within a complex system and apply the right set of rules/training, in theory, you could construct a functional artificial brain.

In an industrial setting, AI processes commonly optimize recognition systems in either complex environments or with large amounts of data where classical systems fail. Algorithms are associated with learning processes, where they weigh a homogeneous system response to an approximated input and specific output. AI technology is used in this way for pattern recognition in 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