A.I. + M.L.

Artificial intelligence is apparently intelligent behaviour by machines, rather than the natural intelligence (NI) of humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

Machine Learning (ML) is the subfield of computer science that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.

ML “evolved from the study of pattern recognition and computational learning theory in artificial intelligence” and it “explores the study and construction of algorithms that can learn from and make predictions on data—such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.”

As per Wikipedia, ML can be broken down into the following three categories:

  1. Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
  2. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  3. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.

There are so many use cases for ML and deep learning in gaming that it is impossible to create an exhaustive list here, but it is particularly useful for marketing personalization, customer recommendation, spam filtering, network security, optical character recognition (OCR), voice recognition, computer vision, fraud detection, predictive asset maintenance, optimization, language translations, sentiment analysis, and online search, amongst many others use cases.

Machine Learning

 

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