Sorry seem to be on an AGI bent this month…
Read an article the other day about a new book (The myth of AI, by Erik. J. Larson) that explains how the present direction of AI-ML-DL will be very unlikely to achieve artificial general intelligence (AGI) given it’s current direction. Amazon and others offer a short preview of the book which is where most of this discussion comes from.
Types of (human) reasoning
Near as I can tell, (don’t have the book), the book discusses the three types of reasoning that exist in human intellect, i.e., deduction, induction and abduction.
- Deduction uses formal logic (or its equivalents) to derive facts or theorems from basic principles.
- Induction uses a multitude of samples and constructs general principles from the analysis of them
- Abduction uses a set of probabilistic assertions and formal logic, to come up with a probabilistic principle.
Deduction is most famously observed in geometry and arithmetic proofs and was most evident in the early years of AI through its use of expert systems. The challenge with expert systems is that the real world is vastly more complex than any geometrical or arithmetical artifice that humankind can produce.
Expert systems became champions of checkers, chess and some other games but in the end was not easily generalizable beyond a few (gaming and medically) restricted domains.
Induction is presently all the rage and represents what machine learning and deep neural networks (DNN) are doing with all that training data and resultant classification inferencing.
Today we have DNNs that can classify the objects in an image, can learn to play any game on the planet better than humans, and can even safely drive a car down the road.
The current AI world view is that this form of reasoning, DNN induction, will if taken to its extreme will ultimately result in some level of AGI, or human-equivalent levels of intelligence in a system. The author of the book begs to differ.
Abduction is less well known or discussed in rational circles. It’s essentially what any human does when presented with real world examples/experiences to derive an understanding (or principe) of what happened.
For example, a plate full of cookies last night becomes an almost empty plate of crumbs and two cookies. So what happened, your son woke up early, consumed most if not all of them, and left for work. This is a probabilistic (most likely) inference, but has a high probability of being true.
Any AGI will need all forms of reasoning
The challenge is that AI has been through the deduction phase through the rise of expert systems which crashed and burned because of the cost and time required to produce an exhaustive and correct expert system. And AI is currently in the induction phase, via DNN training, which seems to be entirely more generalizable and successfully usable in many different domains, but no one is talking seriously about doing abduction in AI (anymore).
The author claims (again, have not read the book) that any AGI will require as much abduction as induction (as well as perhaps deduction), and therefore, AGI is not inevitable based on our current AI DNN (or induction) intensive path.
Previous and current attempts at abduction reasoning
Some may recall fuzzy logic as one of the avenues taken after expert systems seemed to fail at doing successful and realistic inferencing around the end of last century. Fuzzy logic was a way of bring probabilities into deduction, not unlike abduction as defined above. With fuzzy logic each assertion or base assumption was given a probabilistic value (of being true) and the final derivation was assigned some level of probability of being true.
The wikipedia article has definitions for fuzzy logic and, or and not which of course would allow any system to make these assertions. But fuzzy logic (like expert systems above) suffered from the inability to exhaustively cover all examples in a real world situation.
Furthermore, the (funny) thing about DNNs is that they are much more probabilistic than it appears. If one examines classification outputs of any DNN, it is extremely rare to see some sort of boolean (true or false) yes or no answers. Mostly one sees a series of probabilities that are assigned to each classification bucket.
DNN systems hide these probabilities by just selecting the maximum (or minimum) probability generated as its final classification. This is entirely an artifact of needing to have some discrete output (classification selection). But DNN (internal) results always result in probabilistic values.
So although, pure induction doesn’t include probabilities, DNN induction as practiced today in AI systems, uses probabilistic reasoning in every layer of a DNN and in its final results.
What else may be missing from AI to allow AGI to be developed
Personally, AGI seems to require not just the reasoning approaches above, but a more workable and general purpose planning solution. I’ve tried to identify to see whether some researchers are using DNNs to provide general purpose planning solutions but have been yet to find any (in publcly available research). These are probably the one place where expert (or control) fuzzy systems still shine. But again they are hard to generalize and prove almost impossible to be completely exhaustive.
Nonetheless, in the end, I think that all the above just proves, that there are a number of distinct reasoning and other (planning) techniques that may need to come together to provide AGI. As any of us can attest, all of these different approaches are available within any human intellect.
And if we assume that any AGI will need to follow the human design to intelligence (not a given), they will all need to be stitched together, combined and brought to bear to realize AGI.
But, at present, with all the focus on DNN/induction, we, as AI researchers, are not making any progress on using these other techniques or in combining them into a single system.
And for that I am happy. I would be very pleased to have any AGI be farther out than nearer term. Because for the life of me, AGI scares the s&#t out of me.
Mostly because I don’t see any real way to control AGI, once unleashed. That and given the diversity of motives around this world, I don’t see any realistic mechanism to instill a universal and firm (unalterable) belief in the sanctity of human and other life, the dependance this life has on our environment/biosphere and the rule of law needed to maintain peace across humankind (and I’m probably missing a half dozen more things that we would want any AGI to adhere to).
Maybe, if I saw more effort on how, we as a species can come up with universal views on these and other topics and can come up with some way of instilling, essentially a system of programs, with these unalterable beliefs and AGI controls based on these, I’d be less fearful of AGI emerging.
Lacking that, any way of delaying its emergence, is fine by me.
- A Symbolics Lisp Machine an early platform for expert systems, by Michael L. Umbricht and Carl R. Friend (Retro-Computing Society of RI), CC BY-SA 3.0
- Mastermind players use abduction to infer secret colors … By Thomas Steiner – Inkscape and de:Bild:Mastermind beispiel.png, CC BY-SA 3.0,