I think our best bet at combatting "fake news" is to repeal the censorship and copyright, and to formalize belief systems (this may seem like arduous work, and it is, but I believe machine learning will soon be able to formalize texts, science, physics, math to the point where we will have package managers for believe systems)
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.
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.