Does AI mean we don't need the Semantic Web?
shkspr.mobi
shkspr.mobi
This has really always been the disconnect between the semantic web advocates and the skeptics like me. It's not that I don't buy your analysis of the benefits... it's that I do not and never have bought your analysis of the costs. The only question I have is how many orders of magnitude they undersell it by. It's certainly a non-trivial number.
What AI will do is basically swamp the semantic web. The problems the world will be dealing with with AI, like the deluge of AI-generated meaningless garbage, training AIs accidentally on other AI-generated garbage, the complete overwhelming of all human voices by AI voices by sheer weight [1], will be so large and pressing that the problems of the semantic web will just get washed away in the flood. And, sadly, the tools of the semantic web will be effectively useless to help; all the signals they are based on are even easier to forge than a human voice in arbitrary text. No help at all.
[1] What will it look like to live in a world where for every kilobyte of bespoke humanity I produce, an AI can produce megabytes? What will it look like when someone can afford to use AI to build an entire Reddit, just for me and only me, and isolate me there?
If you're happier with a unique personalized reddit, is that worse than preferring video games to movies?
In which the rich have been getting richer, and asset inflation has been growing ever higher.
>If you're happier with a unique personalized reddit
The question here is this depends. Simulacra of which Reddit? Of the one that shows you a good world where we try to become better people? Of the Reddit that tells you "They" are out to get you so you should be afraid? Personalized of the 'Real' world, or just made up and trending towards the imaginary?
I mean, when I play a video game, I know I'm not playing reality. The line starts breaking down as the level of fidelity of reality is neared.
Of course the majority of the traffic on the internet by raw bytes is video, but that was clearly not what I'm talking about.
"If you're happier with a unique personalized reddit"
There is an element of deception there. If I think I'm interacting with real humans, I want to be interacting with real humans, not bots. And nobody but nobody will just generously spend the money to do that, and then leave me a perfect simulation of a human space. They're going to have some ulterior motive and it will not be in my best interests.
I will agree that a shocking-to-me number of people are not bothered by this prospect. Perhaps you are one of them who would not mind. You do you, as the saying goes. I am bothered about being deceived. Truth matters to me and I have no interest in being comprehensively lied to like that.
AI is complicated, but seeing it as mimicry of pre-AI reality is far too narrow to capture the real change (and the real concerns).
I'm also not just talking about someone hooking up the publicly-available and known language models of May 2023 to this. You going to guarantee me that "nobody" will be deceived in even just another year's worth of public progress? Or five? Ten?
And "nobody" is a really tall bar... should we at least set the bar at 100IQ or 110, just in the interests of fairness? 2022 discussions of "income inequality" will have nothing on 2032's if a 150 IQ and heavy education becomes necessary to navigate the internet successfully.
Than the throw-more-data-at-the-wall-leveraging-ever-growing-parallel-GPU-compute-and-hope-the-black-box-that-comes-out-is-good-enough shortcut to AI* :p
But otherwise, yes, I would agree with you, the LLM-based AIs that we have right this very second don't "do" the Semantic Web. But we're clearly much closer on that front than by asking Facebook to pretty please grease up everything in pristine RDF files, let alone the entire web.
The commoditised reality of the WWW is that content is lucrative, but the content need not be of any social or educational value as long as the ice cream cone licks itself.
Cyberlarceny is more lucrative than order, usability, and education. And now, algorithmic text generation will provide the full experience of half-baked verbiage refreshed daily.
Society does need reliable, true information. But we will not have a semantic Web for economic reasons far beyond the value of education.
I believe otherwise. They are perfectly happy with the errors. I see this as wikipedia 2.0. Wikipedia was once derided for being full of errors and holes. Now it is a de facto encyclopedia, the go-to place for the "true" description of most anything. AI will do the same. Eventually its error and subtle biases will become the accepted version of reality. Society needs only actionable information. Accuracy is great, but volume of content wins the audience.
If you sort content by what leads to maximum engagement the stuff that comes out on top is absolutely not informative, intelligent, or positive. You get repetitive addictive filler, inflammatory fear/hate/outrage porn, and lurid tabloid style trash.
When you scroll the limbic system appears to be in charge, not the neocortex. The lambic system seeks only dopamine. The easiest path to dopamine wins.
This means that on an ad supported web content like the above will swamp everything else. There is zero incentive to produce anything else.
AI can now produce maximally engaging content on demand, so the future is Twitter or TikTok but largely without human creators. The future of the open web and free apps is a Skinner box full of AI generated outrage porn and silly filler that scrolls forever, only pausing for ads.
The dynamics can be very different when people pay directly for content. The neocortex appears to get more involved in buying decisions than passive consumption. But very very few people pay for content so there is either not much of this or it will have to be expensive.
We may be going back to the days when actual knowledge and quality discourse was costly, not because of the cost of printing it but as a filter mechanism to find it in the vast sea of trash.
The net was already headed here before AI. Generative models will just accelerate this.
I wonder if AI trained on other AI content inevitably leads to a garbage in garbage out scenario that gets worse over time or whether it actually improves, maybe even at a non linear rate?
I believe the former is true, because of financial and political incentives, but the latter isn’t unthinkable.
Multiplayer Roam + NFTs
The LLM web doesn't need people to explain the role of headings, paragraphs, etc. It can figure those out. It's faster, cheaper, and more effective than manually applying markup.
Imagine GPT-4 at 1/1000th the cost. That's where we're going. And you can bet your ass Nvidia is working on it as we speak. Or maybe someone else will leapfrog them like ARM did to Intel.
If something is possible but not cheaper than people on a computer today, then is very very much likely it will become cheaper than people in a few years. Computers keep scaling and individuals don't.
They used to keep scaling. Comparisons to supercomputers from decades ago are mostly irrelevant at this point, Moore's law is dead.
Also, didn't these models learn from the semantic web? No idea if that matters, but still.
Ditto with semantic data created by automated systems that were created by humans.
There will always be incorrect data in the world. AI doesn't change that, but so what?
Reinforcement learning seems pretty analogous to how we correct people who are wrong about something. I'm really not seeing a category difference here?
LLMs are complex like humans, but you lack the ability to make it do what you want, or to have recourse. If the model has gained the impression that Paris is the capital of Germany (a more concrete and fixable error than most, frankly), this is woven into its parameters throughout, and it’s really hard to fix, and for LLMs we basically have no idea how to actually truly do so. With such a simple error as “Paris is the capital of Germany”, we do know how to fine-tune the model to fairly effectively mask that error, but the important errors are often closer to what you might consider procedural than factual—rather than its “memory” containing a wrong fact, it’s “thinking” in the wrong way.
But all up, I’d call “lack of recourse” the most important difference.
That's not a use, that's a cost.
[0] https://libreddit.de/r/ProgrammerHumor/comments/13lgv3u/chat...
You may think that's a joke, but from the performance perspective, it's utterly impressive. Summarizing a paper is even more impressive, and is equally far outside the scope of the semantic web.
On the other hand, the semantic web has been a complete failure. Even present attempts at semantically understanding the web (aka what search engines do all day) require highly complex heuristic and machine-learned solutions... so the "fully deterministic" thing is only technically true, but not meaningfully useful in any sense.
Even the actual semantic web standards that exist (and are poorly adopted) don't seem to really solve the core problem with the semantic web: there's no such thing as a universal ontology. Every attempt to produce a broad-based shared ontology has failed - there are fundamental disagreements about how to organize information, and information organization needs turn out to nearly always be application-specific, and be highly unstable over time, preventing there from being a useful, durable universal standard for various topics.
So I guess long story short: we need AI (or just highly advanced heuristic methodologies) to scrape useful information out of natural content. We always have, because the deterministic alternative has never actually worked.
It seems like LLMs and vector representations for words are more practical ways to explore semantic data. Then the specific, reccuring queries may be optimized through graph representations.
The only practicable version of the semantic web needs to embrace that reality. We need tools for converting, extracting, and interpreting data from one application-specific data representation to another.
Sometimes that's LLMs, sometimes it can be done heuristically, but either way, the notion of the universal data structure simply isn't realistic.
Of course, ChatGPT can help a human to write the tags to put on the website:
> Me: I have a shop that was the following opening hours: "Our opening hours are: Weekdays 10 until 7. Weekend 10 until 10 (Early closing 9 o'clock Sunday)." I would like to include these opening hours on the website of the shop using meta tags, so that search engines can easily read and parse them.
> ChatGPT 3.5:
<!-- Schema.org markup for opening hours -->
<meta itemprop="openingHours" content="Mo-Fr 10:00-19:00">
<meta itemprop="openingHours" content="Sa 10:00-22:00">
<meta itemprop="openingHours" content="Su 10:00-21:00">
CS researchers: Hey guys, here's the niche marginal improvement our grad student spent 200 hours developing. Here are the guidelines for you domain experts on how to encode and load your knowledge. START!
Domain experts: euh ... what you are asking us to do would take 9 manyears for the initial base and a 2 FTE upkeep. Why exactly would we do this?
CS researchers: not my problem. Paper has been submitted and accepted. Your question was out of scope. Want to join our next proposal?
>"Our opening hours are: Weekdays 10 until 7. Weekend 10 until 10 (Early closing 9 o'clock Sunday)."
Pardon me, but have you visited a local business' website recently? Rarely is information like this presented in such an explicit way; it's usually crammed into some corner of the footer and without the preamble describing the relevant information. And if the business has multiple locations? Now the visual blocking and semantics become relevant to accurately parse the thing.
In fact, my own involvement in the field started with Jim Hendler suggesting I look into the possibility of developing a spider that would wander across HTML pages and glean knowledge from them with an NLP technique. I worked on that for a while, then abandoned it and proposed to him instead that web pages should be marked up with formally parseable structured semantic data. Why parse a student's page saying he "Goes to U Maryland" when he could just write <claim obj1="me" obj2="UMaryland" rel="attend" ontology="http://ontology.org/university-ontology/">. That was 1995.
So what was this about disambiguating homonyms again?
For example, there is an old effort called Cyc, which is an ontology and knowledge base of common sense. Something like this can be used to align LLMs. However, what is common sense is not necessarily universal across cultures. The underlying ideas behind semantic web can be used for ontologies that don’t agree with each other.
Granted, based on the dataset, it's quite a bit more likely than winning big at the casino - but regardless, it's still not deterministic like intentional semantic markup.