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CauseNet: Towards a causality graph extracted from the web

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Re: CauseNet: Towards a causality graph extracted from the web

#61

Post by bckr »

Nevermark wrote: post #38
jiggawatts wrote: post #22

Earlier quoted context omitted.

Even more importantly, it's not even a simple probability of death, or a fraction of a cause, or any simple one-dimensional aspect. Even if you can simplify things down to an "arrow", the label isn't a scalar number. At a bare minimum, it's a vector, just like embeddings in LLMs are! Even more importantly, the endpoints of each such causative arrow are also complex, fuzzy things, and are best represented as vectors.…

That was very well said. One quibble, and really mean only one: > a high-dimensional probabilistic causal framework Deep learning models aka neural network type models, are not probabilistic frameworks. While we can measure on the outside a probability of correct answers across the whole training set, or any data set, there is no probabilistic model. Like a Pachinko game, you can measure statistics about it, but the…

What’s the relationship between what you’re saying and the concepts of “temperature” and “stochasticity”? The model won’t give me the same answer every time.

Re: CauseNet: Towards a causality graph extracted from the web

#62

Post by [deleted] »

asplake wrote: post #42
tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

Agreed. About the strongest we can hope for are causal mechanisms, and most of those will be at most hypotheses and/or partial explanations that only apply under certain conditions. Honestly, I don’t know understand how these so-ontologies have persisted. Who is investing in this space, and why?

[deleted]

Re: CauseNet: Towards a causality graph extracted from the web

#63

Post by [deleted] »

bckr wrote: post #61
Nevermark wrote: post #38

Earlier quoted context omitted.

That was very well said. One quibble, and really mean only one: > a high-dimensional probabilistic causal framework Deep learning models aka neural network type models, are not probabilistic frameworks. While we can measure on the outside a probability of correct answers across the whole training set, or any data set, there is no probabilistic model. Like a Pachinko game, you can measure statistics about it, but the…

What’s the relationship between what you’re saying and the concepts of “temperature” and “stochasticity”? The model won’t give me the same answer every time.

[deleted]

Re: CauseNet: Towards a causality graph extracted from the web

#65

Post by kruffalon »

I read it as "casual" rather than "causal", got very dissapointed while reading the article!

An inventory of casual knowledge would be really fun, although it's hard to think what it would consist of now that I think about it...

There is this concept of "hidden knowledge" about all the things you know at work that no one really thinks about is knowledge so it's hard to let newcomers know about it.

But that does sound different than "casual knowledge", and so does "trivia".

Oh well!

Re: CauseNet: Towards a causality graph extracted from the web

#66

Post by thedudeabides5 »

maweki wrote: post #7

It's nice to see more semantic web experiments. I always wanted to do more reasoning with ontologies, etc., and it's such an amazing idea, to reference objects/persons/locations/concepts from the real world with uris and just add labeled arrows between them. This is such a cool schemaless approach and has so much potential for open data linking, classical reasoning, LLM reasoning. But open data (together with RSS) ha…

semantic web/OWL was always way too heavy to imagine humans using, you could imagine AI doing the heavy lifting here though..

Re: CauseNet: Towards a causality graph extracted from the web

#67

Post by qsi »

dr_dshiv wrote: post #34
tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

Democritus (b 460BCE) said, “I would rather discover one cause than gain the kingdom of Persia,” which suggests that finding true causes is rather difficult.

Felix, qui potuit rerum cognoscere causas. [0]

Virgil.

[0] https://en.m.wikipedia.org/wiki/Felix,_qui_potuit_rerum_cogn...

Re: CauseNet: Towards a causality graph extracted from the web

#68

Post by jijijijij »

lemonwaterlime wrote: post #50
kachnuv_ocasek wrote: post #47

Earlier quoted context omitted.

> a system cannot be both sound a complete Huh, what do you mean by this? There are many sound and complete systems – propositional logic, first-order logic, Presburger arithmetic, the list goes on. These are the basic properties you want from a logical or typing system. (Though, of course, you may compromise if you have other priorities.)

My take is that the GP was implicitly referring to Gödel’s Incompleteness Theorems with the implication being that a system that reasons completely about all the human topics and itself is not possible. Therefore, you’d need multiple such systems (plural) working in concert.

I believe, neither the expansion of Gödel's theorems to "everything", non-formalized systems, nor the conclusion of a resolution by harnessing multiple systems in concert, are sound reasoning. I think, it's a fallacious reductionism.

Re: CauseNet: Towards a causality graph extracted from the web

#69

Post by __alexs »

tgv wrote: post #14

This makes little sense to me. Ontologies and all that have been tried and have always been found to be too brittle. Take the examples from the front page (which I expect to be among the best in their set): human_activity => climate_change. Those are such a broad concepts that it's practically useless. Or disease => death. There's no nuance at all. There isn't even a definition of what "disease" is, let alone a way t…

Given we've tried to develop such ontologies constantly for thousands of years now. What do you think the cause for such hopeless optimism might be? If only we had a database of causal relationships to consult...
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