Machine Intelligence

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This post is part of the A to Z Challenge, in which each day of April a post is made inspired by a letter of the alphabet. Each post will be related to the research done on upcoming trends for the near-future techothriller series Shadow Decade.

Machine Learning is a subfield of artificial intelligence that covers the construction and study of algorithms capable of learning from data without being programmed to do so. In general, they build a model of the real world from input and use that to make predictions.

There are three categories of the tasks performed by machine intelligence:

Supervised learning

This is the sort of intelligence shown in my story, Simple Harmonic Motion. A computer is being taught to operate a spacecraft, and the data given is the different crew members training it. Generally, that’s the form it takes: A teacher trains the computer to map behavior to the desired output.

If you want a copy of Simple Harmonic Motion, you can download the half-hour audio drama free by signing up for Burning Brigid Media’s mailing list.

Unsupervised learning

Unsupervised leaves the algorithm to find its own patterns in order to solve a problem, or as an end in itself.

Reinforcement learning

The algorithm interacts with an environment, working towards a stated goal, learning which actions are optimal – for example, in playing a game.

Generally speaking, the goal isn’t to teach the algorithm a task, but you’re trying to teach it how to learn tasks.

Types of Machine Intelligence

    • Genetic Algorithms –¬† Heuristics that mimic natural selection, using mutation to find the best solution or method for a given problem or task. Iterations can be processed very quickly.
    • Decision Tree – The AI creates a map of possibilities and outcomes to maximize the liklihood of a desired end state.
    • Association Rule Learning – The AI discovers correlations between variables; certain behaviors are likely associated with other behaviors, which allows prediction.
    • Artificial Neural Networks – Algorithms inspired by biological neural structures using artificial neurons. They find patterns and create statistical structures.
    • Inductive Logical Programming – Paradigms based on formal logic. They build logic statements of examples, databases, and hypothesis, and create a program involving all of the positive examples, then suggest a theory to explain the facts present.
    • Support Vehicle Machines – Allow the classification of new data. Provided with samples of data, they categorize new data based on shared traits.
    • Cluster Analysis – Observations are categorized into sets called clusters, based on whatever criteria are considered important. This is a method of Unsupervised Learning.
    • Belief Network – Focused on the probable relationship between elements; what causes what. This can be used to infer probabilities.

Real World Applications of Machine Intelligence

  • Search Engines
  • Diagnostics
  • Information Security
  • Data Mining
  • Robotics

Michael Coorlim

Michael Coorlim is a teller of strange stories for stranger people. He collects them, the oddballs. The mystics and fire-spinners, the sages and tricksters. He curates their tales, combines their elements and lets them rattle around inside his rock-tumbler skull until they gleam, then spills them loose onto the page for like-minded readers to enjoy.
Michael Coorlim

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