Introduction to Artificial Intelligence

Definitions

There are hundreds of definitions of artificial intelligence. Most contain a bias as to whether the writer of the definition sees AI as dealing with thinking versus acting, and whether they see it as trying to model humans or capturing intelligence (rationality) abstractly.

	Humanly	Rationally
Thinking	Thinking humanly: Cognitive modeling. Systems should solve problems the same way humans do.	Thinking rationally: Using logic. Need to worry about modeling uncertainty and dealing with complexity.
Acting	Acting humanly: The Turing Test approach.	Acting rationally: The study of rational agents, that is, agents that maximize the expected value of their performance measure given what they currently know.

Here are definitions put forth by various experts (who know that the general population’s misunderstanding that the term is limited to chatbots and agents using generative AI is unfortunately widespread):

• The study of agents that receive percepts from the environment and perform actions. (Russell and Norvig)
• The science and engineering of making intelligent machines, especially intelligent computer programs (John McCarthy)
• The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings (Encyclopædia Britannica)
• The study of ideas to bring into being machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention (Latanya Sweeney)
• The scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines (American Association for Artificial Intelligence)
• A branch of science which deals with helping machines find solutions to complex problems in a more human-like fashion (AI depot)
• A field of computer science that seeks to understand and implement computer-based technology that can simulate characteristics of human intelligence and human sensory capabilities (Raoul Smith)

Eight more can be found here. And a handful more here.

You say: "We added AI to our product"
I hear: "We added a bunch more IF statements to our codebase"

— I Am Devloper (@iamdevloper) February 10, 2017

Concerns

AI has both scientific (nature of intelligence) and engineering (design and production of intelligent agents) aspects. There’s another aspect dealing with the ways humans use AI systems.

AI Science: The Nature of Intelligence

AI researchers study the nature of intelligence. They don’t (necessarily) try to build androids because:

1. It was the study of the principles of aerodynamics, not the attempt to make mechanical birds, that enabled human flight.
2. We already know how to make new humans anyway.

Intelligence involves sensing, thinking, and acting.

SENSING	THINKING	ACTING
Translation of sensory inputs (percepts) into a conceptual representation	Manipulation of the conceptual representation	Translation of intent into (physical) actions (reflexive or deliberative)
Computer Vision Speech Recognition Language Understanding	Knowledge Representation Problem Solving/Planning Learning (making improvements based on the results of past actions)	Robotics Speech and Language Synthesis

AI Engineering: Intelligent Agents

An agent is something that senses and acts.

• Agents can be organic, robotic, or pure software.
• Not all agents are intelligent.
• Intelligent agents are agents that can operate autonomously in complex environments.
• AI is concerned with the production of intelligent agents.

An interesting question is whether to build something intelligent, should you study the human brain, or can you build an intelligent system using some other approach?

Jeff Hawkins is a proponent of the idea that understanding the brain's structure is necessary for the development of intelligent systems, but famously had research on this topic rejected by MIT who told him it was not necessary. See The CHM Oral History of Hawkins for more on this story, as well as this article and .

AI Fluency

AI fluency is the ability to understand, evaluate, and apply AI tools in various workflows, interacting with systems in ways that are efficient, ethical, and safe.

You can get an introduction to AI fluency in this short article. Also, check out the 4D AI Fluency Framework. Anthropic defines the four D’s like this:

Or take this course from Anthropic.

So how is AI fluency doing these days? Read this report.

Areas of Study

In no particular order, nor with any thought of completeness, here are a few:

• Vision
• Speech recognition
• Robotics
• Problem Solving
• Searching a Solution Space
• Planning
• Learning
• Natural Language Processing
• Natural Language Understanding
• Knowledge Representation
• Automated Reasoning
• Inference, both in monotonic and non-monotonic logic
• Common Sense Reasoning
• Uncertainty and Probability
• Genetic Programming
• Artificial Life
• Ontology
• Epistemology
• Expert Systems
• Solving problems with no tractable deterministic algorithmic solutions

Influences

From Section 1.2 of Russell and Norvig:

• Philosophy considers the nature of knowledge, thought, and learning
• Mathematics considers the notions of formal logic, algorithms, computational complexity, and probability
• Economics studies how agents attempt to maximize their own well-being, even when given uncertain information and in the presence of allies and adversaries
• Neuroscience studies the workings of the human brain
• Psychology studies how humans and animals think and act (process information)
• Linguistics deals with language in a formal-enough way that it can be processed by machine
• Computer Engineering looks to increase the efficiency of computing devices
• Control Theory and Cybernetics consider how autonomous machines can operate

Brief History

Highlights:

• 1940’s — Interest in neurons, neural networks and their relationship to mathematics and learning
• 1950 — Turing’s paper
• 1956 — Dartmouth conference
• 1950’s and 1960’s — enthusiasm and optimism; big promises
• Late 1960’s and 1970’s — Realization that further progress was really hard; disillusionment
• 1980’s — Expert Systems, neural networks, etc.; AI now a little different; quiet successes
• 1990’s to present — Intelligent agents
• 2000’s — robot pets, self-driving cars

More history:

• Section 1.3 of Russell and Norvig
• Brief history of AI from the cool AAAI site
• Wikipedia’s Timeline of Artificial Intelligence
• Another easy-to-read history, with a timeline (feels a little dated, though)

Applications

Today AI is in:

• Game playing (Chess, Go, Risk, Bridge, Checkers, ...)
• Systems that read handwritten addresses to speed mail sorting
• Search Engines
• Theorem Proving
• Cars (stability traction, braking assist, driving, ...)
• Aircraft autolanders
• Medical Diagnosis
• Expert Systems
• Information Retrieval Systems
• Story writers, poetry writers, ...
• Music Composition
• Annoying auto-correct agents in word processors
• Crisis management
• Space Exploration
• Finance
• Retailing
• Manufacturing
• Inventory Control
• Pharmaceutical Research
• Genetic Research
• (Micro)Surgery
• Insurance Underwriting
• Environmental Monitoring
• Protein Structure Determination
• Scheduling Systems
• Assisted Living Support
• Dispensing Legal Advice
• Essay Evaluation
• Detection of Steganography
• Cryptanalysis
• Translation
• Military Planning
• Surveillance
• Traffic Control

See Also

• McCarthy’s "What is Artificial Intelligence" Q&A article
• AI Resources
• AI Topics
• Russell and Norvig Textbook Page
• Curated list of AI Books

Find these at the LMU library:

• Christo El Morr, AI and Society: Tensions and Opportunities
• Emily Bender, The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want
• Shannon Vallor, The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking
• Algorithms, Artificial Intelligence and Beyond: Theorising Society and Culture of the 21st Century by edited by Dariusz Brzezinski, Kamil Filipek, Kuba Piwowar and Malgorzata Winiarska-Brodowska.
• Artificial Intelligence by Arni S. R. Srinivasa Rao (Volume Editor); C. R. Rao (Volume Editor); Steven Krantz (Volume Editor)
• Artificial Intelligence, Finance, and Sustainability: Economic, Ecological, and Ethical Implications by Thomas Walker, Dieter Gramlich, Akram Sadati, editors.
• Artificial Intelligence in Society: Social, Political and Cultural Implications of a Technological Innovation by Michael Heinlein (Editor); Norbert Huchler (Editor)
• A Citizen's Guide to Artificial Intelligence by John Zerilli; John Danaher (Contribution by); James Maclaurin (Contribution by); Colin Gavaghan (Contribution by); Alistair Knott (Contribution by)
• Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI by Karen Hao
• Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Account and Finance Processes by Pethuru Raj Chelliah (Editor); Pushan Kumar Dutta (Editor); Abhishek Kumar (Editor); Ernesto D. R. Santibanez Gonzalez (Editor); Mohit Mittal (Editor); Sachin Kumar Gupta (Editor)
• How AI, Metaverses, Crypto, and Cyber Will Upend the 21st Century by Jon M. Garon
• Introduction to Generative AI by Numa Dhamani; Maggie Engler
• Language Machines: Cultural AI and the End of Remainder Humanism by Leif Weatherby
• Resisting AI by Dan McQuillan
• Thinking with AI: Machine Learning the Humanities by Hannes Bajohr (Editor)

Recall Practice

Here are some questions useful for your spaced repetition learning. Many of the answers are not found on this page. Some will have popped up in lecture. Others will require you to do your own research.

Coming soon

Summary