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 to express that myxomatosis is lethal for only European rabbits, not humans, nor gold fish.

• dr_dshiv

  today at 9:30 AM

  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.

  • s1mplicissimus

    today at 11:34 AM

    "According to the Greek historian Herodotus, Xerxes's first attempt to bridge the Hellespont ended in failure when a storm destroyed the flax and papyrus cables of the bridges. In retaliation, Xerxes ordered the Hellespont (the strait itself) whipped three hundred times, and had fetters thrown into the water."

    Not so sure one should take stories about who said something in ancient times at face value ;)

    [1] https://en.wikipedia.org/wiki/Xerxes_I

  • hugh-avherald

    today at 10:19 AM

    Or is less of a hassle.

• DrScientist

  today at 9:07 AM

  I totally agreed that in the past years of hammering out an ontology for a particular area just results in a common understanding between those who wrote the ontology and a large gulf between them and the people they want to use it ( everyone else ).

  What's perhaps different is that the machine, via LLM's, can also have an 'opinion' on meaning or correctness.

  Going fully circle I wonder what would happen if you got LLM's to define the ontology....

  • Xmd5a

    today at 10:08 AM

    >what would happen if you got LLM's to define the ontology.

    https://deepsense.ai/resource/ontology-driven-knowledge-grap...

    >hammering out an ontology for a particular area just results in a common understanding between those who wrote the ontology and a large gulf between them and the people they want to use it

    This is the other side of the bitter lesson, which is just the empirical observation of a phenomenon that was to be expected from first principles (algorithmic information theory): a program of minimal length must get longer if the reality it models becomes more complex.

    For ontologists, the complexity of the task increases as the generality is maintained while model precision is increased (top down approach), or conversely, when precision is maintained the "glue" one must add to build up a bigger and bigger whole while keeping it coherent becomes more and more complex (bottom up approach).

• tomaskafka

  today at 11:13 AM

  But “disease => death” + AI => surely at least few billion in VC funding.

  • taneq

    today at 11:35 AM

    The best thing about this statement is that it can be read as 'the fact that disease causes death, plus the application of AI, will surely lead to billions VC funding' but it can also be read as 'disease is to death as AI is to a few billion in VC funding'. :D

• notrealyme123

  today at 8:16 AM

  Koller and Friedman write in "Probabilistic Graphical Models" about the "clarity test", so that state variables should be clear for an all seeing observer.

  States like "human_activity" are not objectively measurable.

  Fairly PGMs and causal models are not the same, but this way of thinking about state variables is an incredible good filter.

  • eru

    today at 11:39 AM

    > States like "human_activity" are not objectively measurable.

    Well, or at least they would need a heavy dose of operationalisation.

• koliber

  today at 7:55 AM

  Exactly. In some cases disease causes death. In others it causes immunity which in turn causes “good health” and postpones death.

  • Nevermark

    today at 9:38 AM

    Contradictory cause-effect examples, each backed up with data, are a reliable indicator of a class of situations that need a higher chain-effect resolution.

    Which is directly usable knowledge if you are building out a causal graph.

    In the meantime, a cause and effect representation isn't limited to only listing one possible effect. A list of alternate disjoint effects, linked to a cause, is also directly usable.

    Just as an effect may be linked to different causes. Which if you only know the effect, in a given situation, and are trying to identify cause, is the same problem in reverse time.

• asplake

  today at 10:15 AM

  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?

• tossandthrow

  today at 8:23 AM

  Ontology, not ontologies, have been tried.

  We have quite a good understanding that a system cannot be both sound a complete, regardless people went straight in to make a single model of the world.

  • kachnuv_ocasek

    today at 10:51 AM

    > 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.)

    • lemonwaterlime

      today at 11:15 AM

      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.

      • eru

        today at 11:40 AM

        That doesn't make much sense.

        If you take multiple systems and make them work in concert, you just get a bigger system.

        • tossandthrow

          today at 11:46 AM

          Concerts - again plural. And naturally you only bring in appropriate instruments.

  • Xmd5a

    today at 10:13 AM

    Could you define sound and complete in this context ? IIRC Rust's borrow checker is sound (will not mark something dysfunctional as functional) but not complete: some programs would take too long to verify, the checker times out, and compilation fails even though the program is potentially correct.

    • tossandthrow

      today at 11:48 AM

      The meaning of the word person is ~sound (ie. Well defined) when two lawyers speak.

      But when a doctor tells the lawyer that they operated a person, the lawyer can reasonably say "huh" - the concept of a person has shifted with the context.

• jiggawatts

  today at 8:19 AM

  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. I.e.: diseases aren't just simple labels like "Influenza". There's thousands of ever-changing variants of just the Flu out there!

  A proper representation of a "disease" would be a vector also, which would likely have interesting correlations with the specific genome of the causative agent. [1]

  Next thing is that you want to consider the "vector product" between the disease and the thing it infected to cater for susceptibility, previous immunity, etc...

  A hop, skip, and a small step and you have... Transformers, as seen in large language models. This is why they work so well, because they encode the complex nuances of reality in a high-dimensional probabilistic causal framework that they can use to process information, answer questions, etc...

  Trying to manually encode a modern LLM's embeddings and weights (about a terabyte!) is futile beyond belief. But that's what it would take to make a useful "classical logic" model that could have practical applications.

  Notably, expert systems, which use this kind of approach were worked on for decades and were almost total failures in the wider market because they were mostly useless.

  [1] Not all diseases are caused by biological agents! That's a whole other rabbit hole to go down.

  • Nevermark

    today at 9:49 AM

    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 game itself is topological. As you point out very clearly, these models perform topological transforms, not probabilistic estimations.

    This becomes clear when you test them with different subsets of data. It quickly becomes apparent that the probabilities of the training set are only that. Probabilities of the exact training set only. There is no probabilistic carry over to any subset, or for generalization to any new values.

    They are estimators, approximators, function/relationship fitters, etc. In contrast to symbolic, hard numerical or logical models. But they are not probabilistic models.

    Even when trained to minimize a probabilistic performance function, their internal need to represent things topologically creates a profoundly "opinionated" form of solution, as apposed to being unbiased with respect to the probability measure. The measure never gets internalized.

    • bckr

      today at 12:26 PM

      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.

• vintermann

  today at 9:12 AM

  As I understand it, this is a dataset of claimed causation. It should contain vaccines->autism, not because it's true, but because someone, in public, claimed that it was.

  So, by design, it's pretty useless for finding new, true causes. But maybe it's useful for something else, such as teaching a model what a causal claim is in a deeper sense? Or mapping out causal claims which are related somehow? Or conflicting? Either way, it's about humans, not about ontological truth.

  • morpheuskafka

    today at 9:40 AM

    Also, it seems to mistake some definitions as causes.

    A coronavirus isn't "claimed" to cause SARS. Rather, SARS is a name given to the disease cause by a certain coronavirus. Or alternatively, the name SARS-nCov-1 is the name given to the virus which causes SARS. Whichever way you want to see it.

    For a more obvious example, saying "influenza virus causes influenza" is a tautology, not a causal relationship. If influenza virus doesn't cause influenza disease, then there is no such thing as an influenza virus.

    • vintermann

      today at 10:17 AM

      Yes, I agree there are a lot of definitions or descriptions masquerading as explanations, especially in medicine and psychology. I think maybe insurance has a lot to do that. If you just describe a lot of symptoms, insurance won't know whether to cover it or not. But if you authoritatively name that symptom set as "BWZK syndrome" or something, and suddenly switch to assuming "BWZK syndrome" is a thing, the unknown cause to the symptoms, then insurance has something it can deal with.

      But this description->explanation thing, whatever the reason, is just another error people make. It's not that different from errors like "vaccines cause autism". Any dataset collecting causal claims people make is going to contain a lot of nonsense.

• cantor_S_drug

  today at 8:42 AM

  [dead]

Isn't this like Cyc? There have been a couple of interesting articles about that on HN:

https://news.ycombinator.com/item?id=43625474 "Obituary for Cyc"

https://news.ycombinator.com/item?id=40069298 "Cyc: History's Forgotten AI Project"

• HarHarVeryFunny

  today at 11:40 AM

  Seems like a subset of CYC - attempting to gather causal data rather than declarative data in general.

  It's a bit odd that their paper doesn't even mention CYC once.

The sample set contains:

    {
        "causal_relation": {
            "cause": {
                "concept": "boom"
            },
            "effect": {
                "concept": "bust"
            }
        }
    }

• kolektiv

  today at 7:16 AM

  Plus, regardless of what you might think of how valid that connection is, what they're actually collecting, absent any kind of mechanism, is a set of all apparent correlations...

This reminds me of an article I read that was posted on HN only a few days ago: Uncertain<T>[1]. I think that a causality graph like this necessarily needs a concept of uncertainty to preserve nuance. I don't know whether this would be practical in terms of compute, but I'd think combining traditional NLP techniques with LLM analysis may make it so?

[1] https://github.com/mattt/Uncertain

• notrealyme123

  today at 8:26 AM

  I get some vibes of fuzzy logic from this project.

  Currently a lot of people research goes in the direction that there is "data uncertainty" and "measurement uncertainty", or "aleatoric/epistemic" uncertainty.

  I foumd this tutorial (but for computer vision ) to be very intuitive and gives a good understanding how to use those concepts in other fields: https://arxiv.org/abs/1703.04977

• 9dev

  today at 8:02 AM

  Right. The first example on the site shows disease as a cause, and death as an effect. This is wrong on several levels: There is no such thing as healthy or sick. You’re always fighting off something, it just becomes obvious sometimes. Also, a disease doesn’t necessarily lead to death, obviously.

  • kaashif

    today at 8:24 AM

    Since you're always going to die, the problem is solved - the implication is true by the right side always being true, and the left side doesn't matter.

    • 9dev

      today at 8:57 AM

      Then it’s correlation instead of causation and the entire premise of a causation graph is moot.

Might as well go ahead and add https://tylervigen.com/spurious-correlations?page=135 from the looks of it.

Why not use PROLOG then, is the essence of cause and effect in programming. And also can expound syllogisms.

• orobus

  today at 11:26 AM

  The conditional relation represented in prolog, and in any deductive system, is material implication (~PvQ), not causation. You can encode causal relationships with material implication but you’re still going to need to discover those causal relationships in the world somehow.

  • cubefox

    today at 11:45 AM

    Conditional statements don't really work because "if A, then B" means that A is sufficient for B, but "A causes B" doesn't imply that A is sufficient for B. E.g. in "Smoking causes cancer", where smoking is a partial cause for cancer, or cancer partially an effect of smoking.

    "A causes B" usually implies that A and B are positively correlated, i.e. P(A and B) > P(A)×P(B), but even that isn't always the case, namely when there is some common cause which counteracts this correlation.

    Thinking about this, it seems that if A causes B, the correlation between A and B is at least stronger than it would have been otherwise.

    This counterfactual difference in correlation strength is plausibly the "causal strength" between A and B. Though it doesn't indicate the causal direction, as correlation is symmetric.

Causality is literally impossible to deduce...

Reminds me of the early attempts at hand categorising knowledge for AI

I wonder how they will quantize causality. Sometimes a particular cause has different, and even opposite, effects.

Alcohol causes anxiety. At the same time it causes relaxation. These effects depend on time frame, and many individual circumstances.

This is a single example but the world is full of them. Codifying causality will involve a certain amount of bias and belief. That does not lead to a better world.

"The map is not the territory" ensures that bias and mistakes are inextricable from the entire AI project. I don't want to get all Jaron Lanier about it, but they're fundamental terms in the vocabulary of simulated intelligence.

I was hoping this would be actual normalized time series data and correlation ratios. Such a dataset would be interesting for forecasting.

A cool idea, in desperate need of an example use case.

I know it's a reductive take to point to a single mistake and act like the whole project might be a bit futile (maybe it's a rarity) but this example in their sample is really quite awful if the idea is to give AI better epistemics:

    {
        "causal_relation": {
            "cause": {
                "concept": "vaccines"
            },
            "effect": {
                "concept": "autism"
            }
        }
    },

• kykat

  today at 7:40 AM

  In the precision dataset, there are the sentences that led to this, some are:

  >> "Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."

  >> On 28 February 1998 Horton published a controversial paper by Dr. Andrew Wakefield and 12 co-authors with the title "Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children" suggesting that vaccines could cause autism.

  >> He was opposed by vaccine critics, many of whom believe vaccines cause autism, a belief that has been rejected by major medical journals and professional societies.

  All that I've seen don't actually say that vaccines cause autism

• kolektiv

  today at 7:39 AM

  Oh, ouch, yeah. We already know that misinformation tends to get amplified, the last thing we need is a starting point full of harmful misinformation. There are lots of "causal beliefs" on the internet that should have no place in any kind of general dataset.

  • Amadiro

    today at 9:17 AM

    It's even worse than that, because the way they extract the causal link is just a regex, so

    "vaccines > autism"

    because

    "Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."

    I think this could be solved much better by using even a modestly powerful LLM to do the causal extraction... The website claims "an estimated extraction precision of 83% " but I doubt this is an even remotely sensible estimate.

I wonder what is this for.

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) has been dead for a while as all big companies have become just data hoarders. And frankly, while the concept and the possibilities are so cool, the graph databases are just not that fast and also not fun to program.

> CauseNet aims at creating a causal knowledge base that comprises all human causal knowledge and to separate it from mere causal beliefs

Pretty bold to use a picture of philosophers as your splash page and then make a casual claim like this. To say the least, this is an impossible task!

The tech looks cool and I'm excited to see how I might be able to work it into my stuff and/or contribute. But I'd encourage the authors to reign in the rhetoric...

the cyc of this current ai winter

I don’t know if it’s inadvertent, but it’s headed toward just becoming an engine for over fitted generalizations. Each casual pair will just emerge based on frequency, which will reinforce itself in preemptively and prematurely classifying all future information.

Unfortunately, frequency is the primary way AI works, but it will never be accurate for causality because causality always has the dynamic that things can happen just “because”. It’s hacked into LLMs via deliberate randomness in next-token prediction.

this will be super cool if it can be done!

[dead]