INTRO TO AI RESEARCH

Intelligence: A Natural Phenomenon

Intelligence	A phenomenon encountered in nature. Intelligence is a phenomenon with good examples in the natural world, but may have more forms than the examples from nature. The only one that everyone agrees on to call 'intelligent': humans.
Natural Intelligence	Intelligence as it appears in nature. Some kinds of animals are considered “intelligent”, or at least some behavior of some individuals of an animal species other than humans are deemed indicators of intelligence.
Intelligent Machines	Systems created by humans intended to display (some - but not all?) features required of beings encountered in nature.
How to define 'intelligence'	Many definitions have been proposed. See e.g.: A Collection of Definitions of Intelligence by Legg & Hutter.
Related quote	Aaron Sloman says: “Some readers may hope for definitions of terms like information processing, mental process, consciousness, emotion, love. However, each of these denotes a large and ill-defined collection of capabilities or features. There is no definite collection of nec- essary or sufficient conditions (nor any disjunction of conjunctions) that can be used to define such terms.” (From Architectural Requirements for Human-like Agents Both Natural and Artificial by A. Sloman)

Researching Intelligence

What is “intelligence”?	It is important to know what you're studying and researching! …A researcher selects their methods from their (premature) understanding of the phenomenon they'd like to 'figure out'. Without a good working definition, questions, methods and approach may be flawed.
BUT: Beware of Premature Definitions	You cannot define something precisely until you understand it! Premature precise definitions may be much worse than loose definitions or even bad-but-rough definitions: You are very likely to end up researching something other than what you set out to research.
What Can We Do?	List the requirements. Even a partial list will go a long way towards helping steer the research. Engineers use requirements to guide their building of artifacts. If the artifact doesn't meet the requirements it is not a valid member of the category that was targeted. In science it is not customary to use requirements to guide research questions, but it works just the same (and equally well!): List the features of the phenomenon you are researching and group them into essential, important but non-essential, and other. Then use these to guide the kinds of questions you try to answer.
Before Requirements, Look At Examples	To get to a good list it may be necessary to explore the boundaries of your phenomenon.
Create a Working Definition	It's called a “working definition” because it is supposed to be subject to (asap) scrutiny and revision. A good working definition avoids the problem of entrenchment, which, in the worst case, may result in a whole field being re-defined around something that was supposed to be temporary. One great example of that: The Turing Test.
Two Key Ingredients	Well, this is a simplification, but it is a useful one. Intelligence can be thought of as being composed of classification and control. The former concept is all the rage in contemporary AI research, the latter sits by the wayside. No system with real intelligence can be built without both.

Key Concepts in Control

Sensor	A transducer that changes one type of energy to another type.
Transducer	A device that changes one type of energy to another, typically amplifying and/or dampening the energy in the process.
Actuator	A physical (or virtual) transduction mechanism that implements an action that a controller has committed to.
Control Connection	Predefined causal connection between a measured variable V and a controllable variable Vc where V = f(Vc).
Mechanical Controller	Fuses control mechanism with measurement mechanism via mechanical coupling. Adaptation would require mechanical structure to change. Makes adaptation very difficult to implement.
Digital Controller	Separates the stages of measurement, analysis, and control. Makes adaptive control in machines feasible.
Feedback	For a variable V, information of its value at time t1 is transmitted back to the controller through a feedback mechanism as V', where V'(t1) > V(t0) that is, there is a latency in the transmission, which is a function of the speed of transmission (encoding (measurement) time + transmission time + decoding (read-back) time).
Latency	A measure for the size of the difference between V and V'.
Jitter	The change in Latency over time. Second-order latency.

Key Concepts in Classification

What it is	The separation of one “thing” from “another thing” through measurement.
Why it matters	No intelligence can be brought about without the ability to separate something from something else.
Background / Foreground	The 'central things' and 'contextual' or 'unimportant' things.
Dimensions	The number of variables with measurable values that can help separate one thing from another.
Classification Methods	AI is replete with classification methods. This is the bulk of AI research for the first 70 years of its history.
Examples	Artificial neural networks, decision trees, nearest neighbor, support vector machines, autocorrelation, …

Historical Concepts

AI	In 1956 there was a workshop at Dartmouth College in the US where many of the field's founding fathers agreed on the term to user for their field, and outlined various topics to be studied within the field.
GOFAI	“Good old-fashioned AI” is a term used nowadays to describe the first 20-30 years of research in the field.
Cybernetics	Going back to WWI the field of cybernetics claimed a scope that could easily be said to subsume AI. Many of the ideas associated with information technology came out of this melting pot, including ideas by von Neumann. However, cybernetics has since all but disappeared. Why?
GMI or AGI	“Artificial general intelligence” or “general machine intelligence”: What we call the machine that we hope to build that could potentially surpass human intelligence at some point in the future – a more holistic take on the phenomenon of intelligence than the present mainstream AI research would indicate. Will we succeed? Only time will tell.

AI is a Broad Field of Research

A Scientific Discipline	As an empirical scientific discipline, AI aims to figure out the general principles of how to implement the phenomenon of intelligence, including any subset thereof. Does empiricism mean there is no theory? Absolutely not! Physics is an empirical science, yet is the scientific field that has the most advanced, accomplished theoretical foundation. What would a proper theory of intelligence contain, if it existed? A general theory of intelligence would allow us to implement intelligence, or any subset thereof, successfully on first try, in a variety of formats meeting a variety of constraints.
An Engineering Discipline	What separates AI from e.g. cognitive science? AI explicitly aims at figuring out how to build machines that are intelligent. Note that CogSci may also build intelligent machines, but the goal is not the machine itself, or the principles of its construction, but rather its role as a tool to figure out how intelligence works. The outcome from such work is unlikely overlap much due to the difference in working constraints,* although in the long term the two are likely to converge – and the two different approaches should be able to help each other.
AI spans many fields	Psychology, mathematics and computation, neurology, philosophy.
Alternative View	Psychology, mathematics & computation, neurology, and philosophy all were sooner than AI to address concepts of high relevance to the study of intelligence.
Is AI a subfield of computer science?	Yes and No. Yes, because this is the field that has the best and most tools for studying it as a phenomenon. No, because the field does not address important concepts and features of intelligence.
Isn't intelligence 'almost solved'?	Short answer: No! If it's almost solved it's been “almost solved” for over 60 years. And yet we still don't have machines with real intelligence.
Should we fear AI?	Short answer: No! Or, no more than genetic engineering. The threat lies with humans, not with machines – human abuse of knowledge goes back to the stone age.
Is the Singularity near?	Short answer: Who's to say? Predictions are difficult, especially wrt the future.

* Like the difference between constructing brick walls to study the stability of rock formations in nature versus the engineering principles of building brick walls: If the principles are well understood (weight distribution and stability), you should be able to build walls out of many materials.

Reasoning

What is Reasoning?	A systematic way of thinking about implications.
How is it done?	Via processes that observe rules.
What are the main types?	Deduction: All men are mortal. Socrates is a man. Hence, Socrates is mortal Abduction: How did this come about? (Sherlock Holmes) Induction: What is the general rule? Analogy: 'This' is like 'that' (in 'this' way).
How is it used in science?	In empirical science to unearth the “rules of the universe”. In mathematics as axioms. In philosophy as a way to construct arguments. In computer science to write code.
Why does it work?	Because the world's behavior is/seems rule-based, some of which we have figured out to date (a very small - but important - part!).
How is Reasoning relevant to AI & Ethics?	To manage rules requires reasoning. Intelligence makes models of the rules of the physical world. Therefore, reasoning is a necessary part of any intelligence, whether artifiical or natural. AI seeks to create machines that contain intelligence, thus AI must seek methods for machines that reason. Ethics can only make sense if it can be encoded in rules.

Models in Science, Engineering & Math

What it is	A model is a “cartoon” of a phenomenon – an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question.
The universe: Nothing is given	How do we know that the sun will come up tomorrow? What evidence do we have? Can we prove it mathematically that the sun will come up tomorrow? The only thing we know for sure is that we can perceive things in the world and that “I am here now”. (This principle is most famously captured by Rene Descartes who wrote “I think, therefore I am”.) But since that perception is provided/generated by the same universe that we want to claim “exists” through those senses, using those grey cells, we cannot possibly know for sure what that really is, and hence whether it can be trusted. Therefore, the universe is (and cannot be anything but) non-axiomatic.
All scientific theories seek a model	No matter how explicit or implicit, all scientific theories are models of the world. Good scientific theories tie a lot of knots, leaving few loose ends. Best known example: E=mc^2
Science vs. Mathematics	Mathematics is axiomatic: Some a-priori premises are (and must be) known and complete. In contrast, science is non-axiomatic: We do not know the full set of rules that govern the universe, and we will never know.
Science vs. Engineering	Science sees an undifferentiated world and looks for the model. In engineering the model is given; the world is modified to behave like the model.
Science + Math	We strive to make scientific theories (models of the world) mathematical because of the compactness, precision, and specificity this can give us. However, it is not guaranteed solely through the use of math because a theory must detail how it maps to the thing it is a model of. If this is not done properly the math provides no benefits. Mapping a model to its reference: A good scientist does it properly; a bad scientist does it sloppily; the wannabe ignores it happily. Bottom line: Being mathematical is no guarantee for good science - it is neither necessary nor sufficient.
Science + Engineering + Math: The Holy Trinity	The three fields so defined support each other: Building better scientific models helps us engineer better; engineering better helps us build new tools for doing science better. Both are bootstrapped by philosophy and clarified through math.
Computer Science	A creative mix of empirical science, engineering and mathematics. Direct testing of applications and programs; user studies. Models and simulations. Logical and mathematical proofs.

Footnote: Terminology

Terminology is important!	The terms we use for phenomena must be shared to work as an effective means of communication. Obsessing about the definition of terms is a good thing!
Beware of Definitions!	Obsessing over precise, definitive definitions of terms should not extend to the phenomena that the research targets: These are by definition not well understood. It is impossible to define something that is not understood! So beware of those who insist on such things.
Overloaded Terms	Many key terms in AI tend to be overloaded. Others are very unclear. Examples of the latter include: intelligence, agent, concept, thought. Many terms have multiple meanings, e.g. reasoning, learning, complexity, generality, task, solution, proof. Yet others are both unclear and polysemous, e.g. consciousness. One source of the multiple meanings for terms is the tendency, in the beginning of a new research field, for founders to use common terms that originally refer to general concepts in nature, and which they intend to study, about the results of their own work. As time passes those concepts then begin to reference work done in the field, instead of their counterpart in nature. Examples include reinforcement learning (originally studied by Pavlov, Skinner, and others in psychology and biology), machine learning (learning in nature different from 'machine learning' in many ways), neural nets (artificial neural nets bear almost no relation to biological neural networks). Needless to say, this regularly makes for some lively but more or less pointless debates on many subjects within the field of AI (and others, in fact, but especially AI).

Center for Analysis and Design of Intelligent Agents

Table of Contents