T-720-ATAI-2018 Main
Links to Lecture Notes
| What It Is | The establishment of axioms for the world and applying logic to these. |
| But The World Is Non-Axiomatic ! | Yes. But there is no way to apply logic unless we hypothesize some pseudo-axioms. The only difference between this and mathematics is that in science we must accept that the so-called “laws” of physics may be only conditionally correct (or possibly even completely incorrect, in light of our goal of figuring out the “ultimate” truth about how the universe works). |
| Deduction | Results of two statements that logically are necessarily true. Example: If it's true that all swans are white, and Joe is a swan, then Joe must be white. |
| Abduction | Reasoning from conclusions to causes. Example: If the light is on, and it was off just a minute ago, someone must have flipped the switch. |
| Induction | Generalization from observation. Example: All the swans I have ever seen have been white, hence I hypothesize that all swans are white. |
| What It Is | A concept that people use all the time about each other's cognition. With respect to achieving a task, given that the target of the understanding is all or some aspects of the task, more of it is generally considered better than less of it. |
| Why It Is Important | Seems to be connected to “real intelligence” - when a machine does X reliably and repeatedly we say that it is “capable” of doing X qualify it with “… but it doesn't 'really' understand what it's doing”. |
| What Does It Mean? | No well-known scientific theory exists. Normally we do not hand control of anything over to anyone who doesn't understand it. All other things being equal, this is a recipe for disaster. |
| My Theory | Understanding involves the manipulation of causal-relational models (like we discussed in the context of the AERA AGI-aspiring architecture). |
| Evaluating Understanding | Understanding any X can be evaluated along four dimensions: 1. Being able to predict X, 2. being able to achieve goals with respect to X, 3. being able to explain X, and 4. being able to “re-create” X (“re-create” here means e.g. creating a simulation that produces X and many or all its side-effects.) |
| What It Is | Something of great importance to people. Meaning seems “extracted” from other people's actions, utterances, attitudes, etc. It is generally considered to require intelligence. |
| Why It Is Important | Meaning seems to enter almost every aspect of cognition. |
| My Theory | Meaning is generated when a causal-relational model is used to compute the implications of some action, state, event, etc. Any agent that does so will extract meaning when the implications interact with its goals in some way. |
| Status of Understanding in AI | Since the 70s the concept of understanding has been relegated to the fringes of research. The only AI contexts it regularly appears in are “language understanding”, “scene understanding” and “image understanding”. |
| What Took Its Place | What took the place of understanding in AI is common sense. Unfortunately the concept of common sense does not capture at all what we generally mean by “understanding”. |
| Projects | The best known project on common sense is the CYC project, which started in the 80s and is apparently still going. It is the best funded, longest running AI project in history. |
| Main Methodology | The foundation of CYC is formal logic, represented in predicate logic statements and structures. |
| Key Results | Results from the CYC project are similar to the expert systems of the 80s - these systems are brittle and unpredictable. Apparently the CYC system is being commercialized by a company called Lucid REF. |
| What It Is | The tendency of a learner to seek out novel inputs that may not have any relevance to its currently active goals. |
| Why It Is Important | Curiosity may be an inherent/inevitable feature of all intelligent systems that live in an uncertain environment: Because one of their top-level goals will always be self-preservation, and because they cannot fully predict what threats to this preservation the future may hold, they are forced to collect information which may become useful at a later time. (Of course we sometimes call people “curious” who keep sticking their nose into things which may be relevant to them but which societal norms consider outside their obvious range of access - this is a different, more anthropocentric side of curiosity which is less interesting for our purposes. |
| What It Is | This word has many meanings. 1. In its simplest sense it is the ability to produce solutions to problems. - This meaning treats it as a single dimension (or many that may be collapsed into one) along which we simply put a threshold for when we will classify something as “creative”. \\2. A more complex version references in some way the complexity of a problem, such that solutions that address the problem in a better way (other things being equal) or achieve a similar solution with less cost (other things being equal) are more creative than others. \\3. In reference to some sort of “obviousness”, a solution to a problem may be more creative if it is “less obvious”, with respect to some population, time, society, education, etc. |
| How It Is Measured | Creativity is always measured with respect to some goal: If I just “do something” you cannot tell that I am creative; it is only when I tell you what the goal was (and even better, if I show you what others did with respect to that goal) that you can say for sure whether what I did qualifies as “creative” in some sense. Jackson Pollock was not creative because he splattered paint onto canvas, his work was creative because of the context in which it was done. |
| Why It Is Important | It is difficult to tease apart the concepts of intelligence and creativity: It is hard to imagine a great intelligence that is not creative. Likewise, it is also difficult to imagine a creative agent that is also not intelligent. |
2018©K. R. Thórisson
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