AI Knowledge Map: How To Classify AI TechnologiesWhat follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. I call it the AI Knowledge Map (AIKM). [Note: See the original source for the author's graphic. This StratML rendition documents as values "the problems AI" and the author's suggested "paradigms" and technologies are documented as goal statements.]I am open to feedback on this first version and am planning to take two additional steps: one is creating a layer for the type of challenges AI is facing (e.g., memory issues and catastrophic forgetting, transfer learning, learning from fewer data with things like zero and one-shot learning, etc.) and what technology can be used to overcome that specific issue. Second, is to apply lenses to look at the different technologies and not the problems they are solving but rather the ones they are creating (e.g., ethical issues, data-intensive problems, black-box and explainability problem, etc.).ForbesFID-8cb83f4c-7d2f-4601-9e2a-83f61bae617dFrancesco Corea, Ph.D.Tech investor and AI technologist -- Francesco Corea, Ph.D., columnist, is a complexity scientist and tech investor who is mainly focusing on science-driven companies in high-social-impact verticals including life sciences, energy, and artificial general intelligence. Francesco has a background that ranges from economics and finance to applied machine learning, and he's been working on a variety of different data problems over the past few years (e.g., sentiment analysis, fraud detection, behavioral science, etc.). Currently, he is working with a few AI companies as well as an emerging VC fund. Dr. Corea holds a Ph.D. in Economics.Users are assisted to scout around for additional information and eventually create new knowledge on AI_f10e061a-026b-11e9-8a9e-85792f6e96adTo propose an AI Knowledge Map_f10e06ec-026b-11e9-8a9e-85792f6e96adReasoningthe capability to solve problemsKnowledgethe ability to represent and understand the worldPlanningthe capability of setting and achieving goalsCommunicationthe ability to understand language and communicatePerceptionthe ability to transform raw sensorial inputs (e.g., images, sounds, etc.) into usable information.ParadigmsIdentify AI paradigms_f10e0778-026b-11e9-8a9e-85792f6e96ad1Logic-Based ToolsRepresent knowledge and solve problems_f10e0872-026b-11e9-8a9e-85792f6e96ad1.1tools that are used for knowledge representation and problem-solvingKnowledge-Based ToolsEnable action based on ontologies and databases_f10e08fe-026b-11e9-8a9e-85792f6e96ad1.2tools based on ontologies and huge databases of notions, information, and rulesProbabilistic MethodsEnable agents to act despite incomplete information_f10e0aa2-026b-11e9-8a9e-85792f6e96ad1.3tools that allow agents to act in incomplete information scenariosMachine LearningEnable computers to learn from data_f10e0b38-026b-11e9-8a9e-85792f6e96ad1.4tools that allow computers to learn from dataEmbodied IntelligenceSupport embodied functions_f10e0bc4-026b-11e9-8a9e-85792f6e96ad1.5engineering 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 intelligenceSearch & OptimizationSupport intelligent searching_f10e0c50-026b-11e9-8a9e-85792f6e96ad1.6tools that allow intelligent search with many possible solutions.TechnologiesProvide a listing of AI technologies_f10e0cdc-026b-11e9-8a9e-85792f6e96ad2Robotic Process Automation (RPA)Extract rules and actions to perform by watching users_f10e0d68-026b-11e9-8a9e-85792f6e96ad2.1technology that extracts the list of rules and actions to perform by watching the user doing a certain taskExpert SystemsEmulate the human decision-making process_f10e0df4-026b-11e9-8a9e-85792f6e96ad2.2a 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 outcomeComputer Vision (CV)Acquire and make sense of digital images _f10e0eee-026b-11e9-8a9e-85792f6e96ad2.3methods to acquire and make sense of digital images (usually divided into activities recognition, images recognition, and machine vision)Natural Language Processing (NLP)Handle natural language data_f10e0f84-026b-11e9-8a9e-85792f6e96ad2.4sub-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)Improve performance without being explicitly instructed on how to do so_f10e101a-026b-11e9-8a9e-85792f6e96ad2.5a 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 SystemsEnable intelligent robotic systems_f10e10b0-026b-11e9-8a9e-85792f6e96ad2.6sub-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)Distribute problems among autonomous “agents” that interact to produce solutions_f10e1150-026b-11e9-8a9e-85792f6e96ad2.7a 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 agentsAffective ComputingDeal with emotions recognition, interpretation, and simulation_f10e11e6-026b-11e9-8a9e-85792f6e96ad2.8a sub-field that deal with emotions recognition, interpretation, and simulationEvolutionary Algorithms (EA)Use mechanisms inspired by biology to look for optimal solutions_f10e1290-026b-11e9-8a9e-85792f6e96ad2.9it 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)Use formal logic to represent facts and formulate hypotheses_f10e1326-026b-11e9-8a9e-85792f6e96ad2.10sub-field that uses formal logic to represent a database of facts and formulate hypothesis deriving from those dataDecision NetworksRepresent variables and their relationships_f10e13c6-026b-11e9-8a9e-85792f6e96ad2.11is 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 ProgrammingWork with probabilistic models_f10e147a-026b-11e9-8a9e-85792f6e96ad2.12a 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)Sense, perceive, and respond to stimuli_f10e151a-026b-11e9-8a9e-85792f6e96ad2.13a framework that demands physical devices into digital environments to sense, perceive, and respond with context awareness to an external stimulus (usually triggered by human action).2018-08-222018-12-17https://www.forbes.com/sites/cognitiveworld/2018/08/22/ai-knowledge-map-how-to-classify-ai-technologies/#1ba82aa87773OwenAmburOwen.Ambur@verizon.net