Ontology Is Overrated: Categories, Links, and Tags (2005)

If you get 5 streams of bits and four of them correlate with some epsilon number of new bits, and the fifth has 10x epsilon new bits, one of things is likely true. Either the odd stream out is corrupted or the stream has changed.

Apply that to news stories where the ontology (O) and knowledge graph (KG) are combined to "error correct" the content of the story. "Error bits" would be defined as relationships that don't match what the ontology says can be true, or facts that are different than those facts already in the knowledge base. You can flag the odd news story as lacking alignment with the existing KG&O, further you can quantify that lack of alignment fairly precisely.

So if the news story has 0 bits of information it correlates very strongly with other existing stories. If it has many bits of information it contrasts with other existing stories. At some number of stories on the same topic you can begin to reason about which are more likely 'true' and which are more likely 'false.'

It does amuse me how much this framing of problems happens. Both in the annoying cases of arm chair quarterbacks, and in the cases of folks that take on problems previously thought of as too difficult.

If that is indeed your argument, it is remarkably similar to the argument "Perfect security is impossible, this system is worthless." I think it is demonstrably obvious that there is a related parameter in both cases, which is the cost of overcoming the system is greater than the gain from overcoming said system.

As an example of how that argument fails in practice, consider textbooks. When I was working on this problem full time the organization had a relationship with a major publisher of textbooks. Consider the system which can construct an ontology and knowledge graph based on the contents of a text book. It may not be 'true' in the Aristotle sense, but it is an effective tool for evaluating whether or not a paper or essay correlates that ontology and knowledge graph or doesn't. In such a system there is value in evaluating the extent with which the knowledge in the textbook has been communicated to the person writing the essay.

Being able to automate the analysis has other benefits as well which extend beyond flagging documents which disagree a previous established canon.

The fact that you can read about the same topic in several (more often opposing) ways is already a reason to red-flag the piece in the first place. This isn’t readily available through any of the current newspapers. Everyone has an opinion.

I would happily pay for an aggregator that simply ejects facts (stated facts, not truths) from the stories. Then combines that with similar other pieces.

Ten years ago, I would have said that this would not happen within 20 years, but now I am much less certain, due to a couple of profound breakthroughs: word embeddings (Glove etc), and their famous linear analogies, and sense disambiguation.

These have not only been achieved in the emergent "look how amazingly bizarre" sense, but have over the last few years gotten a much more rigorous footing: notably the papers:

[1] Linear Algebraic Structure of Word Senses, with Applications to Polysemy (RECOMMEND reading both first and last version, as some things are more lucid in the first version, and some in the last. The main takeaway is that the word vectors are linear combinations of a low number of sense vectors, and these sense vectors can be recovered by sparse coding)

[2] Towards Understanding Linear Word Analogies (Which finally explains the "king" + "woman" - "man" = "queen" phenommena)

Now consider physics for example, running text and equations, then consider

* an "overscored n": is this 1) normal vector or 2) an antineutron or 3) ... ?

* a greek beta symbol: is this 1) v/c as in relativity 2) kT as in thermmal or statistical physics or 3) ... ?

* an e symbol: is this the exponent function, or the electron or ... ?

With today's tools it should already be possible to make this: type a LaTeX formmula, or perhaps even a picture of a formula, then mouse-over an ambiguous symbol to see the sense of the symbol, or alternatively generate a wikipedia style list: "where ... is ..."

It should be possible to do this for math as well, and pretty soon software should be able to formalize quasi-formal mathematics books, papers, derivations, proofs ... into MetaMath, and the beautiful part? MetaMath contains the ground truth upon verifying so it can keep training the formalizer while formalizing without supervision! The verifier will become the supervisor!

Of course MetaMath will not tell you if your postulates and axioms are correct, but it will check if your reasoning is sound. The axioms or postulates will be what characterize the belief systems, and any rammifications or inconsistencies will be linked to in the "belief package" managers so that whenever you encounter a formalized claim you can then reply with how the claim does not follow in your own belief system, and it will hence become netiquette to not just share vague claims, but formalized ones and not just formalized ones but accompanying a reference to the claims proof and in which belief systems they hold, it will become much easier to identify exactly where people who disagree with you differ in assumptions or axioms, and hence easier to collaborate on identifying inconsistensies in the endless stream of forked belief systems to avoid being filtered by the reader's or platform's access to gossiped inconsistency proofs...

A corollary is that the semi-crackpot semi-true realpolitik interpretations from the lower regions of society will force the higher regions of society to either formalize explicit injustice and oppression, or else democractically cede the definition of the boundary between just and unjust to the populace, so that we can have a formalized egalitarian society...

Just like the printing press democratized communication, machine learning and formalization will democratize interpretation.

On top of that what might be considered fake news by some doesn't have to be by others because it might have utility.

And lats but not least. Combatting fake news would mean that you would be able to cover all contextual interpretations of a given statement which we simply don't have access to.

So from my perspective you can't battle fake news in any meaningful ways which won't end up doing more harm than good as "fake news" is not really fake in some objectively defined way.

Someone with an agenda in the symbolic vs statistic AI question could take this parallel as an example of how close ontology-based AI approaches are to the way humans think. And then someone with the opposite agenda would point out that the example is all hypothetical.

I clarified that there's a workaround to give hierarchy.

For example if you're looking for the population of New York between 1850 and 1950, you would see a potentially dramatic shift of the population around the turn of the century, which would be misleading if you didn't already know the caveat about the consolidation of the city in 1898.

However that shouldn't discourage the tagging of data by city. It just means that a city, as metadata, is no more or less functional than country, as the grandparent comment suggests.

My caution is that we have found the equivalent of cheap, fast, microwavable meals and we are now consuming them almost exclusively and in 15 ~ 20 years it's going to become painfully obvious what deleterious side-effects arise as a result of that. We can't see it now, so we don't feel the need not to do it, and that simply is what it is, but in time it will act as a control signal that nudges us to modify our behavior.

This is both a great and terrifying time to be alive. The 20th century was full of some pretty hefty social experiments and the thing with those were they were obvious and in your face. The 21st century versions are much less obvious and reveal themselves very slowly.

I increasingly advocate F#, and many people recommend Elixir for these purposes too (though I'm not familiar with it myself).

The only real trick is to use delegation patterns instead of inheritance, i.e. dispatch requests to class members rather than super/subclasses.

It doesn't feel awkward or unnatural.

Lack of pointers. An event reference is a double pointer: to the method and to the object that is "self" or "this" for the execution of the event handler. Since they refused to have pointers in the language, they devised that convoluted solution.

It's "sold" as enabling extensibility and code reuse, but the only feature it adds to good old-fashioned composition is 'open recursion' a.k.a. late binding, which inherently leads to the "fragile base class" problem-- making behavior of base-class implementations dependent on preserving complex and generally unspecified invariants in the derived classes - so it's basically never what you actually want!

Full-blown OOP (including implementation inheritance and polymorphism) doesn't work. It's a terrible idea that only ever became popular because we didn't quite grok its implications.

The point being - more rigorous methods can definitely be valuable in areas, and I don't think it's a good idea to ignore this fact.

It's a bygone issue because there wasn't a critical mass in place to make it work, but there could be.