AI Knowledge Map: how to classify AI technologies

A sketch of a new AI technology landscape

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  • Knowledge-based tools: tools based on ontologies and huge databases of notions, information, and rules;
  • Probabilistic methods: tools that allow agents to act in incomplete information scenarios;
  • Machine learning: tools that allow computers to learn from data;
  • Embodied intelligence: engineering toolbox, which assumes that a body (or at least a partial set of functions such as movement, perception, interaction, and visualization) is required for higher intelligence;
  • Search and optimization: tools that allow intelligently searching through many possible solutions.
  • Knowledge: the ability to represent and understand the world;
  • Planning: the capability of setting and achieving goals;
  • Communication: the ability to understand language and communicate;
  • Perception: the ability to transform raw sensorial inputs (e.g., images, sounds, etc.) into usable information.
  • Expert Systems: a computer program that has hard-coded rules to emulate the human decision-making process. Fuzzy systems are a specific example of rule-based systems that map variables into a continuum of values between 0 and 1, contrary to traditional digital logic which results in a 0/1 outcome;
  • Computer Vision (CV): methods to acquire and make sense of digital images (usually divided into activities recognition, images recognition, and machine vision);
  • Natural Language Processing (NLP): sub-field that handles natural language data (three main blocks belong to this field, i.e., language understanding, language generation, and machine translation);
  • Neural Networks (NNs or ANNs): a class of algorithms loosely modeled after the neuronal structure of the human/animal brain that improves its performance without being explicitly instructed on how to do so. The two majors and well-known sub-classes of NNs are Deep Learning (a neural net with multiple layers) and Generative Adversarial Networks (GANs — two networks that train each other);
  • Autonomous Systems: sub-field that lies at the intersection between robotics and intelligent systems (e.g., intelligent perception, dexterous object manipulation, plan-based robot control, etc.);
  • Distributed Artificial Intelligence (DAI): a class of technologies that solve problems by distributing them to autonomous “agents” that interact with each other. Multi-agent systems (MAS), Agent-based modeling (ABM), and Swarm Intelligence are three useful specifications of this subset, where collective behaviors emerge from the interaction of decentralized self-organized agents;
  • Affective Computing: a sub-field that deal with emotions recognition, interpretation, and simulation;
  • Evolutionary Algorithms (EA): it is a subset of a broader computer science domain called evolutionary computation that uses mechanisms inspired by biology (e.g., mutation, reproduction, etc.) to look for optimal solutions. Genetic algorithms are the most used sub-group of EAs, which are search heuristics that follow the natural selection process to choose the “fittest” candidate solution;
  • Inductive Logic Programming (ILP): sub-field that uses formal logic to represent a database of facts and formulate hypothesis deriving from those data;
  • Decision Networks: is a generalization of the most well-known Bayesian networks/inference, which represent a set of variables and their probabilistic relationships through a map (also called directed acyclic graph);
  • Probabilistic Programming: a framework that does not force you to hardcode specific variable but rather works with probabilistic models. Bayesian Program Synthesis (BPS) is somehow a form of probabilistic programming, where Bayesian programs write new Bayesian programs (instead of humans do it, as in the broader probabilistic programming approach);
  • Ambient Intelligence (AmI): a framework that demands physical devices into digital environments to sense, perceive, and respond with context awareness to an external stimulus (usually triggered by a human action).

Written by

Research Lead @Balderton. Formerly @Anthemis @UCLA. All opinions are my own.

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