[js8]

I have a straightforward definition of "understand". To understand means to be able to give a (representative) example of the (intensionally) given set. Though it is harder than it seems, as it usually means solving the constraint satisfaction problem.

For example, take the classical AI knowledgebase fragment, "bird is animal that flies". If I ask example of bird, it can say "eagle", and exhibit some understanding. We can then probe further and ask for a bird which is not an eagle. If it says "bat" or "balloon", it exhibits that it still doesn't understand birds quite right.

In particular, if the description is nonsensical and thus impossible to understand, we cannot give any examples.

This idea was really inspired by the study, where they asked people to recognize nonsensical and profound sentences, describing certain situation. The profound are the ones where you can create a concrete instance of the situation.

[mjburgess]

Then animals do not understand.

You've rigged this up to operationalize it for current digital machines.

"Understanding", "Intelligence", etc. is a feature of animals in their environment. We need to begin there; and that is what we are talking about.

We "understand" how to drive as a dog "understands" how to play fetch. Understanding is not ever going to be a trivial rule that some digital system may instantiate.

It will always require direct causal contact with an environment. In my view "understanding" is "competent play in a changing environment" -- ie., the ability to modify the environment as it changes in accordance with your goals.

This rough definition is inspired by work in animals to understand the role of the neocortex, and animal learning, and the role of consciousness therein. Roughly: consciousness is "perceptual and cognitive intelligence grappling with environmental change".

[js8]

> Then animals do not understand.

I am agnostic regarding that, as I don't think there is any evidence that they do not attempt to build models that are consistent representations of reality.

I am assuming, based on my own experience, they also have this "internal lightbulb" going on when they think they have built the correct model. But whether they are actually cognizant of it (self-aware), I have no idea. (I guess what I am saying is that understanding and self-awareness are two different things.)

[mjburgess]

I'm not even talking about self-awareness. I'd be happy to raise the bar to that level when (, if) we have mice-level AI.

However the bar is way below that at the moment, and masquerading as "intelligence".

Current machine learning (ie., mere statistical) approaches to AI, that do not explicitly aim to dynamically model environments/goals/behaviour/etc., aren't even meeting an extremely minimal notion of intelligence.

We have at the moment "smart rocks". Electrical current "tumbles down" a "digital mountain" and we all it's path "smart" because it has useful outcomes. Equally, a rock rolling down a hill finds an optimal path -- it aint "smart".

We should look at what the rock does when you start adpating its environment: eg., create a little dip in the mountain side; it gets trapped. A mouse doesnt get trapped in a dip, it continues to explore -- why?

Because animal behaviour is inherently exploratory of the enviornment. A mouse doesnt "solve" a maze, it intelligently navigates it -- so that when unexpected change occurs, it isn't "broken".

At the moment, all AI systems radically break when such changes occur -- because they are statistically trained on mere data. They arent dynamically model building. They aren't in an environment. They're just rocks rolling down a hill.

[sgt101]

There is a lot of evidence that they do though - wolves splitting the pack to ambush the tired deer at the end of the valley, chimps and corvids using tools and water displacement to achieve goals, whales bubble fishing in teams.

[felixyz]

Very good observation, although I'd say this is still just understanding at the micro-level. A lot of what is going on in communication between people depends just as much on what hasn't been said, what would normally be said in this situation, having an idea of what the situation is in the first place, what was said recently or the last time you interacted with this person (which could potentially be a very long time ago), etc. I do believe that a lot or all of this can be posed as CSPs though.

On my reading list is "The proper treatment of events", a book which "studies the semantics of tense and aspect" within a formal framework of constraint logic programming[1]. There is other similar work in this area, like "Good-enough parsing, Whenever possible interpretation:a constraint-based model of sentence comprehension"[2].

[1] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.10.... [2] https://hal.archives-ouvertes.fr/hal-01907632/file/CSLP-Blac...

[russdill]

I gave grover-mega this problem:

Question: What is an example of a bird? Answer: An egret. Question: What is another example? Answer: Canaries.

Seems to do fine. I don't really have a stop though, so it goes on making up new questions on it's own. Make of it what you will. Very few of the answers are correct or even coherent enough to be correct: https://hastebin.com/agululiqif.txt

I do like this one though:

Question: Who is the inventor of the English ham? Answer: Poor old Francis Bacon.

[deepbake]

"bat" would be a correct response based on the knowledge fragment wouldn't it?

[js8]

I think you're right. The word "to understand" has two meanings. In the narrow sense, it's the feeling that we have when we "get it", that is we think that we have built a correct model of reality and it passes the logic consistency check (which is verified by being able to give an example or counterexample). In the broad sense, it means to build and apply correct models of reality.

Above, I am talking in the narrow sense. So the fact that the model itself is wrong shouldn't be an issue. But in the broad sense, we could say that understanding is ability to convert between intensional and extensional (ostensive) representations (models) of the world. Finding an example from intensional representation is just one task that is required.

[yamrzou]

Do you have a link to the mentioned study?

Edit: nvm, I think I found it : http://journal.sjdm.org/15/15923a/jdm15923a.pdf

[js8]

It was on HN before: https://news.ycombinator.com/item?id=17764348

But perhaps I wasn't clear, the study doesn't say this, but it was rather my own experience with the BS sentences in that study that led me to the observation that they have an empty set of examples if we take them as a constraint satisfaction problem of sorts.

[sgt101]

Also to be able to manipulate and use the conceptual realisation ? "I could use this twig (but not that twig) as a hook for ants if I strip the bark off and turn it when I hold it i the hole, then I can eat the ants"

[stared]

Is "profound" the right word here?

> The opposite of a fact is falsehood, but the opposite of one profound truth may very well be another profound truth. - Niels Bohr

And, in fact, it is my rule of thumb test if something is a profound truth.