Intelligent (AI) Information System

An Intelligent (AI) Information System is an intelligent system that is an information processing system and can solve automated intelligence tasks.

• AKA: Artificial Intelligence (AI).
• Context:
  • It can (typically) be an AI Technology.
  • It can (typically) implement AI Algorithms.
  • ...
  • It can range from being a Narrow AI System to being a General AI System.
  • It can range from being an Athletic AI to being a Scholarly AI.
  • It can range from being an Non-Autonomous AI to being an Autonomous AI.
  • It can range from being a Domain-Specific AI System to being an Open-Domain AI System.
  • It can range from being an Non-Cognitive AI to being a Cognitive AI (such as a conscious AI).
  • It can range from being an Engineered AI to being an Evolved AI.
  • It can range from being an Information Providing AI Systems and Tool Using AI System.
  • It can range from being a Black-Box AI System to being an Explainable AI System.
  • It can range from being a Beneficial AI to being a Dangerous AI.
  • ...
  • It can be produced by an AI Creation Task.
  • It can be the focus of an AI Discipline, an AI Industry, ...
  • It can be analyzed by an AI System Analysis Task.
  • It can be applied to an AI Application.
  • It can be related to AI Scaling Laws (such as LLM scaling laws).
  • …
• Example(s):
  • Narrow AI to General AI:
    • Narrow AI Systems, such as: AlphaGo, Deep Blue, YOLO, Facial Recognition AI, GPT-3, BERT, Boston Dynamics' Robots, Industrial Robots, Surgical Robots, DALL-E, Siri, Alexa, Google Assistant, OpenAI Five, Machine Translation AI, MuseNet, AI-generated Poetry
    • General AI Systems, such as: a hypothetical Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) would be examples
  • Athletic AI to Scholarly AI:
    • Athletic AI Systems, such as: AlphaGo, Deep Blue, OpenAI Five, Self-Driving Cars, Boston Dynamics' Robots
    • Scholarly AI Systems, such as: GPT-3, BERT, Machine Translation AI, DALL-E, MuseNet, AI-generated Poetry, IBM Watson, Siri, Alexa, Google Assistant
  • Non-Autonomous AI to Autonomous AI:
    • Non-Autonomous AI Systems, such as: those that rely on human input, design, and maintenance, including Siri, Alexa, Google Assistant, YOLO, Facial Recognition AI, GPT-3, BERT, Machine Translation AI, Industrial Robots, Surgical Robots, DALL-E, MuseNet
    • Autonomous AI Systems, such as: Advanced future AI systems that could operate independently, Self-Driving Cars have some autonomy
  • Non-Cognitive AI to Cognitive AI:
    • Non-Cognitive AI Systems, such as: YOLO, Industrial Robots, Facial Recognition AI
    • Cognitive AI Systems, such as: future AI assistants that aim for human-like language understanding and generation, arguably Siri, Alexa, Google Assistant, GPT-3, BERT, IBM Watson
  • Engineered AI to Evolved AI:
    • Engineered AI Systems, such as: Most current AI systems are manually designed and trained, including Siri, Alexa, Google Assistant, AlphaGo, Deep Blue, OpenAI Five, YOLO, Facial Recognition AI, GPT-3, BERT, Machine Translation AI, Boston Dynamics' Robots, Industrial Robots, Surgical Robots, DALL-E, MuseNet, AI-generated Poetry
    • Evolved AI Systems, such as: Techniques like Evolutionary Algorithms and Reinforcement Learning allow AI to develop its own behaviors to some degree
  • Beneficial AI to Dangerous AI:
    • Beneficial AI Systems, such as: AI Assistants (Siri, Alexa, Google Assistant), Surgical Robots, Machine Translation AI, AI Art and Creativity (DALL-E, MuseNet, AI-generated Poetry)
    • Dangerous AI Systems, such as: Highly advanced AI that could pose existential risk to humanity if misaligned. Current AI could be dangerous if misused.
  • Centralized AI to Distributed AI:
    • Centralized AI Systems, such as: those that operate on a single, powerful machine or within a centralized data center, such as IBM Watson, GPT-3, AlphaGo, Deep Blue
    • Distributed AI Systems, such as: those that are spread across multiple machines or devices, often to process data locally or to provide faster responses, such as Federated Learning, Edge AI, Siri, Alexa, Google Assistant, Self-Driving Cars, Boston Dynamics' Robots
  • Symbolic AI to Sub-Symbolic AI:
    • Symbolic AI Systems, such as: those that use explicit, human-readable symbols and rules to represent knowledge and make decisions, such as Expert Systems
    • Sub-Symbolic AI Systems, such as: those that learn and make decisions using implicit, distributed representations that are not easily interpretable by humans, such as Artificial Neural Networks, Deep Learning systems (GPT-3, BERT, DALL-E), AlphaGo, Deep Blue, OpenAI Five, YOLO, Facial Recognition AI, Machine Translation AI, MuseNet, AI-generated Poetry
  • Information Providing AI to Tool Using AI:
    • Information Providing AI Systems, such as: conversational chatbots.
    • Tool Using AI Systems, such as: industrial robots, surgical robots, web-search tool-using AI system,
  • …
• Counter-Example(s):
  • Biological Intelligence, such as an intelligent human.
  • Unintelligent Machine.
• See: Cognitive Science, Mind, Organizational AI Guideline, Artificial Neural Network, Machine Learning, Natural Language Processing, You Only Look Once (YOLO) System.

---

---

References

2021

• (Pretz, 2021) ⇒ Kathy Pretz (2021). "Stop Calling Everything AI, Machine-Learning Pioneer Says". In: IEEE Spectrum.
  • Stop Calling Everything AI, Machine-Learning Pioneer Says Michael I. Jordan explains why today’s artificial-intelligence systems aren’t actually intelligent
  • QUOTE: ... Despite such developments being referred to as “AI technology," he writes, the underlying systems do not involve high-level reasoning or thought. The systems do not form the kinds of semantic representations and inferences that humans are capable of. They do not formulate and pursue long-term goals.

    “For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations," he writes. “We will need well-thought-out interactions of humans and computers to solve our most pressing problems. We need to understand that the intelligent behavior of large-scale systems arises as much from the interactions among agents as from the intelligence of individual agents." ...

2013

• (Gartner, 2013-10-13) ⇒ "Gartner Says Smart Machines Will Have Widespread and Deep Business Impact Through 2020." Press Release
  • QUOTE: Machines are evolving from automating basic tasks to becoming advanced self-learning systems as capable as the human brain in many highly specialized professions. As such, the next wave of job losses will likely occur among highly valued specialists during the next decade. … "... This marketplace comprises intelligent agents, virtual reality assistants, expert systems and embedded software to make traditional machines 'smart' in a very specialized way, plus a new generation of low-cost and easy-to-train robots and purpose-built automated machines that could significantly devalue and/or displace millions of humans in the workforce." said Kenneth Brant, research director at Gartner. … Gartner believes that the capability and reliability of smart machines will dramatically increase through 2020 to the point where they will have a major impact on business and IT functions. The impact will be such that firms that have not begun to develop programs and policies for a "digital workforce" by 2015 will not perform in the top quartile for productivity and operating profit margin improvement in their industry by 2020. … Citizens will protest higher and more prolonged states of unemployment, electing governments to legislate against smart machines … "We certainly will not approach a state of mass unemployment at any time in the near future," said Mr. Brant. “What is also certain, however, is that many new combinations of technology — from intelligent software agents, expert systems and virtual reality assistants to software systems embedded in smart products and revolutionary new forms of robotics — will emerge and have great impacts in this decade. We won't need to develop a full-functioning artificial brain by 2020 for smart machines to have radically changed our business models, workforce, cost structure and competitiveness."

2012

• (Wikipedia, 2012) ⇒ http://en.wikipedia.org/wiki/Intelligent_agent
  • In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.

    Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA) to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their Autonomy, and so prefer the term autonomous intelligent agents. Still others (notably (Russell & Norvig, 2003) considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, “rational agent”.

    Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.

    Intelligent agents are also closely related to software agents (an autonomous software program that carries out tasks on behalf of users). In computer science, the term intelligent agent may be used to refer to a software agent that has some intelligence, regardless if it is not a rational agent by Russell and Norvig's definition. For example, autonomous programs used for operator assistance or data mining (sometimes referred to as bots) are also called "intelligent agents".

2010a

• The New Oxford American Dictionary, Third Edition
  • QUOTE: … computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

2009a

• (IEEE Intelligent Systems, 2009) ⇒ http://www.computer.org/portal/site/intelligent/
  • QUOTE: … systems that perceive, reason, learn, and act intelligently.

2009b

• (Intelligent Systems, 2009) ⇒ http://www.intelligent-systems.com.ar/intsyst/defintsi.htm
  • It is a system.
  • It learns during its existence. (In other words, it senses its environment and learns, for each situation, which action permits it to reach its objectives.)
  • It continually acts, mentally and externally, and by acting reaches its objectives more often than pure chance indicates (normally much oftener).
  • It consumes energy and uses it for its internal processes, and in order to act.

1998

• (Moravec, 1998) ⇒ Hans Moravec. (1998). “When Will Computer Hardware Match the Human Brain.” In: Journal of evolution and technology, 1(1).
  • QUOTE: This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s.

1959

• (Samuel, 1959) ⇒ Arthur L. Samuel. (1959). “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal of research and development 3, no. 3

---