AI Background Info

Defining Artificial Intelligence

There is no single agreed upon definition of Artificial Intelligence.  Since inception, the debate has raged as to what actually constitutes AI.  Lots of folks get in the game and provide definitions, from the father of AI, Alan Turing, to think tanks and even dictionaries…. Here are how some different organizations define the term Artificial Intelligence on their public websites.

Alan Turing – Father of AI

AI is the science and engineering of making intelligent machines, especially intelligent computer programs.

Merriam-Webster Dictionary 

AI is software designed to imitate aspects of intelligent human behavior.  Also  : an individual program or set of programs designed in this way, to emulate human behavior.

US Department of State

The term ‘artificial intelligence’ means a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments (NATIONAL ARTIFICIAL INTELLIGENCE ACT OF 2020).

Brooking’s Institute

Today, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.”   These software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. Such systems have three qualities that constitute the essence of artificial intelligence: intentionality, intelligence, and adaptability.

Stanford

Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955,

was defined by him as “the science and engineering of making intelligent machines”. Today, we emphasize machines that can learn, at least somewhat like human beings do.  Intelligence might be defined as the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent but not intelligent.

Gartner

Artificial Intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision and take actions.

IBM

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Accenture

Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence.  It can further be broken down as “narrow” and “general” AI.  Narrow AI Examples include: Weather apps, Digital assistants, Software that analyzes data to optimize a business function.  General AI is where sentient machines emulate human intelligence, thinking strategically, abstractly and creatively.  

McKinsey

Artificial intelligence is a machine’s ability to perform the cognitive functions we usually associate with human minds.

Deloitte

AI is concerned with getting computers to do tasks that would normally require human intelligence. Artificial intelligence is a computerized system that exhibits behavior that is commonly thought of as requiring intelligence. Also, Artificial Intelligence is the science of making machines do things that would require intelligence if done by man.  AI refers to a broad field of science encompassing not only computer science but also psychology, philosophy, linguistics and other areas.

 Oracle Inc.

AI has become a catchall term for applications that perform complex tasks that once required human input, such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning (ML) and deep learning.

 Microsoft

By using math and logic AI simulates the reasoning that humans use to learn from new information and make decisions.  An artificially intelligent computer system makes predictions or takes actions based on patterns in existing data and can then learn from its errors to increase its accuracy, processing new information extremely quickly and accurately.

More Details From Stanford

Plan & Decide.  [These] Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without micro-management.  

Machine Learning (ML) is the part of AI studying how computer agents can improve their perception, knowledge, thinking, or actions based on experience or data. In supervised learning, a computer learns to predict human-given labels, such as dog breed based on labeled dog pictures; unsupervised learning does not require labels, sometimes making its own prediction tasks such as trying to predict each successive word in a sentence; reinforcement learning lets an agent learn action sequences that optimize its total rewards, such as winning games, without explicit examples of good techniques, enabling autonomy.

Deep Learning is the use of large multi-layer (artificial) neural networks that compute with continuous (real number) representations, a little like the hierarchically organized neurons in human brains. It is currently the most successful ML approach, usable for all types of ML, with better generalization from small data and better scaling to big data and compute budgets.

Algorithms lists the precise steps to take, such as a person writes in a computer program. AI systems contain algorithms, but often just for a few parts like a learning or reward calculation method. Much of their behavior emerges via learning from data or experience, a sea change in system design that Stanford alumnus Andrej Karpathy dubbed Software 2.0.

Narrow AI is intelligent systems for one particular thing, e.g., speech or facial recognition. Human-level AI, or Artificial General Intelligence (AGI), seeks broadly intelligent, context-aware machines.