public:t-720-atai:atai-18:lecture_notes_w1
Table of Contents
T-720-ATAI-2018 Main
Links to Lecture Notes
T-720-ATAI-2018
Lecture Notes, W1
What this Course Is / Is not
| Intelligence | This course is about phenomenon we refer to as “intelligence”. A number of features of natural intelligence remain unexplained. |
| AGI | A number of terms have been used to refer to the various aspects that people study wrt intelligence. We use the term “artificial general intelligence” in the most general sense (no pun intended), to refer to the various aspects of intelligence that allows it to deal with variety, incompleteness, and incremental information gathering. |
| Advanced topics | The main focus of course is not the latest and greatest methods to come out of the field called “AI”. However, we will make some references to such methods along the way, and you may even learn something about them. But that is not what is meant by “advanced”. |
| What does “advanced” mean here? | It refers to advancement toward a deeper understanding of the phenomenon of intelligence. |
| History | The phenomenon of intelligence has been studied for ages. Some of the early notable contributions were the Greek philosophers' musings on reasoning and logic. This is not a history course, but we must make some references to the history of philosophy, AI, cybernetics and computer science along the way. |
Course Overview
| Lectures | Every week involves lectures. Listening is good. Listening and asking questions is better. Reading the assignments, listening, and asking questions is best. |
| Software Assignments | To enable you to get insight into principles of machine learning. |
| Short Essay | To allow you to delve into a topic of particular interest to you. |
| Pair Software Project | To give you a bit more in-depth experience in programming AGI systems. |
| Final Exam | To try to gauge how much of the material you actually understand – how much of it you have “ingested”. |
Lines of Research & High-Level Topics
| Intelligence | Phenomenon. Intelligence is a natural phenomenon, but may have more forms than the examples from nature (the only one that everyone agrees on to call 'intelligent': humans). |
| Natural Intelligence | Phenomenon. 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. |
| Cognitive Science | The study of natural intelligence, in particular human. |
| Artificial Intelligence | The study of how to make intelligent machines. |
| Intelligent machines | Systems created by us to display (some or all) features deemed 'intelligent' when encountered in nature. |
| How to define 'intelligence' | Many definitions have been proposed, see e.g.: A Collection of Definitions of Intelligence by Legg & Hutter. |
| Definitions: a word of caution | We must be careful when it comes to definitions – for any complex system there is a world of difference between decent definitions and good accurate appropriate definitions. |
| 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) |
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? |
| AGI | “Artificial general 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. |
Key Concepts in AI
| Problem | A state that should be changed. |
| Goal | The resulting state after a successful change. |
| Task | A problem that is assigned to be solved by an agent. |
| Environment | The constraints that may interfere with achieving a goal. |
| Plan | The partial set of actions that an agent assumes will achieve the goal. |
| Planning | The act of generating a plan. |
| Knowledge | Information that can be used for various purposes. |
| Agent | A system that can sense and act in an environment to do tasks. https://en.wikipedia.org/wiki/Intelligent_agent |
Important Concepts in This Course
| Methodology | Present methods in AI will not suffice for addressing the full scope of the phenomenon of intelligence, as see in nature. |
| Attention | The ability to manage resources, including computational (thought), information from the external environment, energy, and time. |
| Meta-Cognition | The ability of a system to reason about itself. |
| Reasoning | The application of logical rules to knowledge. |
| Learning | Acquisition of knowledge that enables more successful completion of tasks. |
| Life-long learning | Incremental acquisition of knowledge throughout a (non-trivially long) lifetime. |
| Transfer learning | The ability to transfer what has been learned in one task to another. |
| Autonomy | The ability to do tasks without interference / help from others. |
| Constructionist AI | Methodology that relies heavily on human coding for building intelligent systems. |
| Constructivist AI | Methodology that relies on systems acquiring their own knowledge. |
AI is a Broad Field
| AI spans many fields | Psychology, mathematics and computation, neurology, philosophy. |
| 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. |
| Terminology | 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 tendency for creating multiple meanings for terms is the habit of, in the beginning of a new research field to use terms to refer to general concepts in nature, and then as time passes to use the same terms to refer to work already done in the field (e.g. expert system, machine learning, neural net). Needless to say, this regularly makes for some lively but useless debates on many subjects. |
Some Interesting Case Stories of Intelligence
| The Crow | One crow was observed, on multiple occasions, to make its own tools. |
| The Parrot | One parrot was taught multiple concepts in numbers and logic, including the meaning of “not” and the use of multi-dimensional features to dynamically create object groups for the purpose of communicating about multiple objects in single references. Oh, and the parrot talks. |
| The Ape | One ape was taught to use sign language to communicate with its caretakers. It was observed creating compound words that it had never heard, for purposes of clarifying references in space and time. |
Some (Pressing?) Questions
| Isn't AI almost solved? | Short answer: No! If it's almost solved it's been “almost solved” for over 60 years. |
| Should we fear AI? | Short answer: No! 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. By the time the course is finished you will be in a good position to make up your own mind about this. |
How To Study (and not fail)
As you read papers from each of the following categories I want you ask yourself a few questions:
- For each paper on a topic X, ask yourself:
| 1 | What is X? |
| 2 | How does the human mind do X? |
| 3 | Do current computers do X? |
| 4 | Do we need (to replicate or capture the principles of) what the human mind does to achieve X to create a machine that rivals the ability of humans to do X? |
| Result | If you can answer them satisfactorily when you're done reading you're good! Even if you can't you'll be fine if you: Write down the discrepancies and bring them to class in the form of questions. There is no such thing as a 'stupid question' when you're learning something new. |
2020©K.R.Thórisson
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