Reminds me of the early attempts at hand categorising knowledge for AI
CauseNet: Towards a causality graph extracted from the web
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Re: CauseNet: Towards a causality graph extracted from the web
#12I 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" } } }, ... seriously? Then again, they do say these are just "causal beliefs" expressed on the i…
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 "Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."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
Re: CauseNet: Towards a causality graph extracted from the web
#13"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.
Re: CauseNet: Towards a causality graph extracted from the web
#14This 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.
Re: CauseNet: Towards a causality graph extracted from the web
#15This reminds me of an article I read that was posted on HN only a few days ago: Uncertain[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?
Re: CauseNet: Towards a causality graph extracted from the web
#16I 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.
Re: CauseNet: Towards a causality graph extracted from the web
#17This 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…
Exactly. In some cases disease causes death. In others it causes immunity which in turn causes “good health” and postpones death.
Re: CauseNet: Towards a causality graph extracted from the web
#18This reminds me of an article I read that was posted on HN only a few days ago: Uncertain [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
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.
Re: CauseNet: Towards a causality graph extracted from the web
#19I was hoping this would be actual normalized time series data and correlation ratios. Such a dataset would be interesting for forecasting.
Re: CauseNet: Towards a causality graph extracted from the web
#20I 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.