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...
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.
{
"causal_relation": {
"cause": {
"concept": "vaccines"
},
"effect": {
"concept": "autism"
}
}
},
"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"Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."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.
>> "Even though the article was fraudulent and was retracted, 1 in 4 parents still believe vaccines can cause autism."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
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.
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.
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.
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.
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.
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.)
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....
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).
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.
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.
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.
Honestly, I don’t know understand how these so-ontologies have persisted. Who is investing in this space, and why?
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
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.
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.
https://news.ycombinator.com/item?id=43625474 "Obituary for Cyc"
https://news.ycombinator.com/item?id=40069298 "Cyc: History's Forgotten AI Project"
pavlov•3h ago
kolektiv•3h ago