BTW: I have a clear vision of how AI can be used to study and improve learning and teaching practices on a large scale (and how education practices can advance AI development). In particular, I have developed a specific strategy for using advances in AI to developing a new type of content knowledge measuring instruments in physics, mathematics, and chemistry. Based on my experience of teaching problem-solving and knowledge of how mind learns, I also envision a specific strategy which will lead to the development of AI capable of solving physics problems, potentially even win a physics competition, and then be capable of becoming an artificial physics teacher (no the best one, but better than many current ones). When one creates a solution to a physics problem one has not solved in the past, his/her reasoning process follows the steps a scientists uses when uncovering laws of nature. DARPA wants to support research leading to development of AI Research Assistant. Creating AI which can solve physics problems and then teach how to solve physics problems is the natural first step in that direction.
Firstly, I am not just any physics teacher. I am a very good physics teacher who for a long period of time has been successfully using his own natural human intelligence. For example, this is an excerpt from one of many student evaluations: “I hated physics before taking this course, and now after taking both 105 and 106 with Mr. V, I actually really enjoy it. He is one of the best teachers I've ever had. Thank you” (ten more pages on this link :) ).
Why am I good a teaching physics?
Because: (1) I know patterns needed for creating solutions to physics problems (and problems in general); (2) I know patterns needed for learning how to create solutions to physics problems; (3) I know patterns needed for teaching how to create solutions to physics problems; (4) I am good at employing those patterns in my teaching practice.
There are many definitions of "Machine Learning".
For example, “Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.”
None of those "definitions" are definitions. They merely describe the scientific field of machine learning but do not define machine learning. Mostly because they do not define "learning".
If they did, the rest would be obvious.
This is the definition of Machine Learning: "machine learning it is what people do when they learn, but this time it is done by an artificially made object, i.e. by a machine". After a short general introduction most of the authors offer some version of a brief description of specific methods used to process different patterns (could be found in any textbook on AI or ML).
The true goal is to define "learning".
"Learning" also has many definitions. The most common one (which comes in various forms) is "learning is the process of the acquisition of knowledge", or "the knowledge obtained during the processes of learning". Both definitions are correct, in their way, because they do describe learning. But that type of learning is not the type AI professionals have in mind when they say "machine learning". Those two definitions do not allow to establish if learning has actually occurred beyond mere memorization (a.k.a. "acquisition of knowledge"). Machine learning as a memorization is clearly of no interest, because these days we all know very well that machines can accumulate, store and retrieve huge amount of information. Of course, the algorithms, techniques for acquiring and processing that information represent important technical part of machine learning.But that part has little to do with the actual process called "learning". Even blind and deaf people can learn to the highest level (up to getting PhD).
As an expert in human intelligence, I define "learning" (more accurately, "productive learning") as a processes leading to a production of knowledge; as the first approximation (the scientific thinking in action), learning is a process of utilization of currently active knowledge in order to produce new knowledge (for example, the statement "I learned how to do it" represents some of the new knowledge developed during learning). The criterion of "learning" ("actual learning", "real learning", "true learning", "productive learning") is the ability to use existing knowledge to generate knowledge previously not available to the actor of learning. Machine learning is happening when a machine (an artificially manufactured object) produces new knowledge based on the knowledge currently available to the machine.
BTW: what is "knowledge"? Without an operational definition of "knowledge" how do we know if the new knowledge has been produced? If a machine takes a text and randomly permute and recombine letters, words, sentences will it be "new knowledge"? More importantly, what types of knowledge exist? how does knowledge evolve? what is the structure of knowledge? how is the structure of knowledge reflected in the structure of neural network processing that knowledge? People in AI don't seem interested in those questions. At least there is no single page from 1100 pages of "Artificial Intelligence: A modern Approach" (by Stuart J. Russell, Peter Norvig; 3d edition) where those questions about knowledge would be discussed. They talk about "knowledge" as if it is something obvious, or define "knowledge" as "information", which is a severe simplification, in part because it ignores an important feature of "knowledge" - it has a vector; it is purposeful (in general). They define learning as making a match between a hypothetical knowledge and the factual knowledge (meaning "information"). This does formally describe a procedure leading to "new knowledge": (1) state a hypothesis; (2) gather facts; (3) compare; (4) decide. For example: (1) this is a banana? (2) run image recognition; (3) correlation 0.98; (4) ye, that is a banana! (if needed, e.g. to decrease % of mistakes, learning can be "reinforced", and "deepened"). But (A) for people true learning usually begins after learning how to recognize various shapes; (B) this learning ignores "learning as a skill development"; (C) and also it ignores the central feature of learning - its intentionality (humans have a desire to learn, including about themselves, built into the genetic code; good teaching is based on that; bad teaching ignores or even tries to break this desire).
Finally, since the ultimate mission of learning is progress:
(1) acquisition of knowledge is useless if it does not lead to the development of new practice (starting from the development of new individual skills).
(2) the development of new practice (starting from the development of new individual skills) always lead beyond acquisition of knowledge to development of new knowledge.
That means that AI developers also need to define "skills", "new skills", "machine skills", "skill development", etc., in a way assessable for a machine and by a machine.
Machine learning is happening when a machine (an artificially manufactured object) develops new skill based on the skills currently available to the machine.
Back to the main topic.
The current meaning of AI becomes the equivalent of AGI (artificial GI), or AHI, which includes HI and another “A”I (animal I).
The second approach is also introducing new definitions:
i.e. keep the word "Intelligence" for "an ability to solve problems which have never been solved before (by the host).", but name the animal behavior differently, e.g. "Animal Intelligence", or "Pre-Intelligence", or "Quasi-Intelligence", "Pseudo-Intelligence", "Intelligent Orientation", ... . Of course, there is some overlapping between the "Intelligence" and "Animal Intelligence", some gray area when intelligent species look acting like animals, and animals look acting like intelligent species - that is inevitable - but it does not make the definition less useful.
When current AI recognizes a pattern (visual, audio, numerical) it only makes a statement in the form "yes - that is that thing", "no - that is not that thing". But the processes in the network which lead to the final statement also have their own patterns. In a brain, there is at least one another network of a higher level which analyzes and recognizes the patterns happening in the lower network making a decision about the pattern/object. That higher level network generates another signal - a doubt - "are your sure"? And then there is another network which makes another decision "yes, I am sure" or "no, I am not sure". And then ... - long story, but you see the pattern.
No AI is even close to mirror this type of pattern/pattern/pattern recognition (that requires developing the hierarchy of networks analyzing the hierarchy of patterns).
That is why I also added another post on the matter: "Relax, the real AI is not coming any soon" (that post also has some insights on what "common sense" is).
P.S. The field of AI training will become much more important than it is today. Although, not many AI professionals see it so far.
Appendix I: a conversation with a professional
2. a definition of something, including Intelligence, should be concise, sufficient on its own, without the need for additional explanation of a possible interpretation.
3. the host of intelligence does NOT have to use it wide, the definition should allow to observe (measure, assess) one individual and make a conclusion if the host has or doesn’t have Intelligence (e.g. Turing tests).
4. Intelligence should not depend on a specific field of action; the property/ability/feature called “Intelligence” should be “field-independent”, which makes it “field-universal”, meaning, if it works in one field, it will work any any/every field. The ability to create solutions to problems which have never been solved before is exactly of that type.
NB: This response of mine effectively concluded our communication; since then I have not heard back a word. As an expert in Human Intelligence, which includes human psychology, I know the reason for having our communication severed. I shook the ego of the authors of the paper. They had a nice construct of what they called "Intelligence", but some guy from the streets, someone with no name, no recognition, poked and made a big hole in that construct. So, they did what most people do in this situation, they pretended that nothing happened. Of course, those people are not idiots. In their minds they continue to mull over our conversation, their argument, my counterarguments. Eventually they will come up with their new definition of Intelligence, one which will have something from their old definition but will also have crucial elements of mine, and they will think that they came up with this new definition completely on their own (or another idea from any of my posts). Which is fine. All this AI stuff for me is just a hobby on a side (at least for now). Once in a while I just like poking a sleeping bear and see what happened. So far 100 % of my expectations turned out to be correct.
From my view, this is not yet the final definition, because these conditions are necessary, but not sufficiently sufficient; but it grasps the essence of what a game is.
P.S. After the letter was sent I came up with this version of the definition: a game is (1) a pretended life (in that what people call "life" they would not do "it"); or (2) a life pretended to look like a game (in that what people call "life" they want do "it" but do not want to show that they want to do it).
Well, I was not the first one to venture a similar sentiment: "Life is a theater".
Interesting fact: one can replace word "art" in the first quote with basically anything ("teaching", "managing", researching", ...) and it still will stand!
P.P.S. After I have developed my own definition of game, and its shortest version (the sentence in blue) I, naturally, looked it up online. All sources are basically say that a game is a play or a competition, or ..., and there is a list of possible activities, which is technically not a definition, but an analogy.
Appendix III: on the general structure of a problem-solving process
Everyone who has a slightest interest in AI should do it, too. I would like to point at only three (of many) interesting moments.
I am one of those professionals who the President of M.I.T. Prof. Rafael Reif calls "bilinguals". M.I.T. designates $1 billion to create a new college with the sole goal to study AI. In the past I called for creating an institution which would concentrate on the use of AI in education. I would hope the new M.I.T. college will have such a lab.
Here is our brief communication.
Thank you for your note and your interest in the Schwarzman College.
We are in the very early days of the College, with a search for a new dean just getting started. The dean, of course, will be critical in shaping the College's educational programs and opportunities. I encourage you to reach out to the dean once he or she is named.
For more on AI:
Who Will Train our Artificial Puppies?
The Dawn of The New AI Era.
Dr. Valentin Voroshilov
Education Advancement Professionals
The voices of my students
"The Backpack Full of Cash": pointing at a problem, not offering a solution
Essentials of Teaching Science
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