I feel like I see this attitude a lot amongst devs: "If everyone just built it correctly, we wouldn't need these bandaids"
To me, it feels similar to "If everyone just cooperated perfectly and helped each other out, we wouldn't need laws/money/government/religion/etc."
Yes, you're probably right, but no that won't happen the way you want to, because we are part of a complex system, and everyone has their very different incentives.
Semantic web was a standard suggested by Google, but unless every browser got on board to break web pages that didn't conform to that standard, then people aren't going to fully follow it. Instead, browsers (correctly in my view) decided to be as flexible as possible to render pages in a best-effort way, because everyone had a slightly different way to build web pages.
I feel like people get too stuck on the "correct" way to do things, but the reality of computers, as is the reality of everything, is that there are lots of different ways to do things, and we need to have systems that are comfortable with handling that.
Was this written by AI? I find it hard to believe anyone who was interested in Semantic Web would have not known it's origin (or at least that it's origin was not Google).
The concept of a Semantic web was proposed by Tim Berners-Lee (who hopefully everyone recognizes as the father of HTTP, WWW, HTML) in 1999 [0]. Google, to my knowledge, had no direct development or even involvement in the early Semweb standards such as RDF [1] and OWL [2]. I worked with some of the people involved in the latter (not closely though), and at the time Google was still quite small.
The phrase “if everyone just” is an automatic trigger for me. Everyone is never going to just. A different solution to whatever the problem is will be necessary.
eh I feel this but it's a lot simpler than that. Not "if everyone built everything correctly" but "if everyone's work was even slightly better than complete garbage". I do not see many examples of companies building things that are not complete embarrassing shit. I worked at some companies and the things we built was complete embarrassing shit. The reasons are obvious: nobody cares internally to do it, and nobody externally has any standards, and the money still flows if you do a bad job so why do better?
What happens in practice is that the culture exterminates the drive for improvement: not only are things bad, but people look at you if you're crazy if you think things should be better. Like in 2025 people defend C, people defend Javascript, people write software without types, people write scripts in shell languages; debugging sometimes involves looking at actual bytes with your eyes; UIs are written in non-cross-platform ways; the same stupid software gets written over and over at a million companies, sending a large file to another person is still actually pretty hard; leaving comments on it is functionally impossible ... these are software problems, everything is shit, everything can be improved on, nothing should be hard anymore but everything still is; we are still missing a million abstractions that are necessary to make the world simple. Good lord, yesterday I spent two hours trying to resize a PDF. We truly live in the stone age; the only progress we've made is that there are now ads on every rock.
I really wish it was a a much more ruthlessly competitive landscape. One in which if your software is bad, slow, hard to debug, hard to extend, not open source, not modernized, not built on the right abstractions, hard to migrate on or off of, not receptive to feedback, covered in ads... you'd be brutally murdered by the competition in short order. Not like today where you can just lie on your marketing materials and nobody can really do anything because the competition is just as weak. People would do a much better job if they had to to survive.
The semantic web came out of work on Prolog and formal systems for AI which just didnt work well... LLMs and vector databases give us new tools that are pretty usable.
Fresh plums right off the tree taste significantly better than the ones you can get in the produce isle, which are in turn better than canned, which are themselves still better than re-hydrated prunes.
In scaling out computation to the masses, we went from locally grown plums that took a lot of work and were only available to a small number of people that had a plum tree or knew someone that had one, to building near magical prune-cornucopia devices that everyone could carry around in their pockets, giving them an effectively unlimited supply of prunes.
LLMs re-hydrate these for us, making them significantly more palatable; if you're used to gnawing dried fruit, they seem amazing.
Perhaps, but we still failed and not at personal computing, nor just semantic web, but computing and programming in general. The failure is between the original intent (computing was originally more or less AI) along with theory and actual result with every software project turning into unsustainable infinite regress. Things likely broke around ALGOL.
Also LLMs are failing too, for different reasons, but IMO unlikely AI in general will— it will correct a 60 years or so failure in industrial computer science.
I really don't like this analogy, and I really don't like the premise of this article.
Writing software is only so scalable. It doesn't matter all of the shortcuts we take, like Electron and JavaScript. There are only so many engineers with so much time, and there are abundantly many problems to solve.
A better analogy would be to look at what's happening to AI images and video. Those have 10,000x'd the fundamental cost savings, time savings, and personnel requirements. It's an industrialization moment. As a filmmaker who has made several photons-on-glass films, this is a game changer and lifts the entire creative industry to a level where individuals can outdo Pixar.
That is the lens with which to look at what AI will do to software. We're going from hand-carving stone wheels to the Model T.
This is all just getting started. We've barely used what the models of today offer us.
I have often thought about how computers are significantly faster than they were in the early 2000s, but they are significantly harder to use. Using Linux for the first time in college was a revelation, because it gave me the tools to tell the computer "rename all of the files in this directory, keeping only the important parts of the name."
But instead of iterating on better interfaces to effectively utilize the N thousands of operations per second a computer is capable of, the powers that be behind the industry have decided to invest billions of dollars in GPUs to get a program that seems like it understands language, but is incapable of counting the number of B's in "blueberry."
IDK, I think there is something adorable about taking a system that over trillions of iterations always performs the same operation with the same result, reliability unmatched in all of the universe…
And making it more of “IDK what it answered the way it did, but it might be right!!”
They're not claiming AGI yet, so human intelligence is required to operate an LLM optimally. It's well known that LLMs process tokens rather than characters s, so without space for "reasoning" there's no representation of the letter b in the prompt. Telling it to spell or think about it gives it room to spell it out, and from there it can "see" the letters and it's trivial to count.
Is counting the number of B's vital? Also, I'm pretty sure you can get an LLM to parse text the way you want it, it just doesn't see your text as you do, so that simple operation is not straightforward. Similarly, are you worthless because you seem like you understand language but are incapable of counting the number of octects in "blueberry"?
Yeah dream on. I’m an engineer and know what structured data is. And yet I miserably fail to store my private files in a way that I can find them back without relying on search tools. So how on earth are we ever going to organize all the world’s data and knowledge? Thank god we found this sub-optimal “band aid” called LLMs!
> Remember Semantic Web? The web was supposed to evolve into semantically structured, linked, machine-readable data that would enable amazing opportunities. That never happened.
I think the lesson to be learned is in answering the question "Why didn't the semantic web happen?"
I have literally been doing we development since their was a web, and the companies I developed for are openly hostile to the idea of putting their valuable, or perceived valuable, information online in a format that could be easily scraped. Information doesn't want to be free, it wants to be paid for. Unless the information shared pulls visitors to the site it doesn't need to be public.
The semantic web was theoretically great for data scientists and metadata scrapers, but offered close to zero value for ordinary humans, both on the publishing side an the consumption side. Also, nobody did the hard work of defining all of the categories and protocols in a way that was actually usable.
The whole concept was too high minded and they never got the implementation details down. Even if they did it would have been horrendously complex and close to impossible to manage. Asking every single publisher to neatly categories their data into this necessarily enormous scheme would have resulted in countless errors all over the web that would have seriously undercut the utility of the project anyway. Ultimately the semantic web doesn't scale very well. It failed for the same reason command economies fail: It's too overwhelming for the people in control to manage and drowns in its own bureaucracy.
Semantic web never existed. There was Google and Google had an API to get breadcrumbs to show on search results. And that's what people called "semantic web." A few years later they gave up and made everything look like a breadcrumb anyway. And that sums up the whole semantic web experience.
I find traditional web search and LLM search to be complementary technologies, and this is a good example of that. Both have their uses and if you get the information you need using one or the other, we are all happy.
I think the example query here actually shows a problem with the query languages used in web search rather than an intrinsic inability of web search. It contains what amounts to a natural language subquery starting with "in the same year". In other words, to evaluate this query properly, we need to first evaluate this subquery and then use that information to evaluate the overall query. Google Search and almost all other traditional web search engines use intentionally oversimplified query languages that disallow nested queries let alone subqueries, so this example really is just revealing a problem with the query language rather than a problem with web search overall. With a better query language, we might get better results.
> What animal is featured on a flag of a country where the first small British colony was established in the same year that Sweden's King Gustav IV Adolf declared war on France? ... My point is that if all knowledge were stored in a structured way with rich semantic linking, then very primitive natural language processing algorithms could parse question like the example at the beginning of the article, and could find the answer using orders of magnitude fewer computational resources.
So as well as people writing posts in English, they would need to provide semantic markup for all the information like dates, flags, animals, people, and countries? It's difficult enough to get people to use basic HTML tags and accessible markup properly, so what was the plan for how this would scale, specifically to non-techy people creating content?
This actually happened already and it's part of why llms are so smart, I haven't tested this but I venture a guess that without wikipedia and wikidata and wikipedia clones and stolen articles, LLMs would be quite a lot dumber. You can only get so far with reddit articles and embedded knowledge of basic info on higher order articles.
My guess is when fine tuning and modifying weights, the lowest hanging fruit is to overweigh wikipedia sources and reduce the weight of sources like reddit.
> If all knowledge were stored in a structured way with rich semantic linking, then very primitive natural language processing algorithms could parse question like the example at the beginning of the article, and could find the answer using orders of magnitude fewer computational resources.
In vertical markets, can LLMs generate a "semantic web of linked data" knowledge graph to be parsed with efficient NLP algorithms?
leveraging LLMs to build the special markup so that it can be applied towards other uses.. some type of semantic web format, like JSON-LD or OWL, or some database that can process SPARQL queries.. Palantir is using ontologies as guardrails to prevent LLM hallucinations
Palantir was 90% about ontology creation back when I first used it in 2009. They knew at that point that without some level of structure that mapped to the specific business problem that the data input into the graph was difficult to contextualize.
I always appreciate our weekly Crankypants Take on LLMs.
> AI is not a triumph of elegant design, but a brute-force workaround
You can read and understand Attention Is All You Need in one hour, and then (after just scaling out by a few billion) a computer talks to you like a human. Pretty elegant, if you ask me.
> The web was supposed to evolve into semantically structured, linked, machine-readable data that would enable amazing opportunities.
I missed that memo. The web was, and forever shall be, a giant, unstructured, beautiful mess. In fact, LLMs show just how hopeless the semantic web approach was. Yes, it's useful to attach metadata to objects, but you will still need massive layering and recursion to derive higher-order, non-trivial information.
This entire article is someone unable to let go of an old idea that Did Not Work.
> AI is not a triumph of elegant design, but a brute-force workaround
I think the author is on to something here, but does not realize it, or applies the think to the wrong problem. The issue isn't the web, search was good enough and it's a pretty big problem to solve. We need to go smaller. Applying AI to customer service, internal processes and bureaucracy, among other things, is an inelegant brute-force approach to not just fixing your shit.
The majority of customer service could be fixed by having better self-service, designing better UIs, writing better manuals, having better monitoring, better processes, better trained staff, not insisting on pumping stock numbers and actually caring about your product. AI will never fix the issues your customers are having, they'll just make customer service cheaper and worse because just like real humans the content and levers they need aren't available.
Same for much of the bureaucracy in companies and governments. Rather than having AI attempt to fill out forms or judge if someone is entitled to a pension, insurance pay out or what have you. Take the time to fix your damn processes and built self-service portals that actually works (some of those may have some sort of AI on the backend for things like scanning documents).
Forget the semantic web thing, it never worked, to much data, and AI generated content certainly isn't making the problem easier. Let's work on that at some other time. Still the author is correct, LLMs are a brute-force workaround, regardless of how elegant the design may be. They are applied to a host of problem that should just be eliminated, not hidden and stupefied by a prediction engine.
This take is very naive and should anticipate the obvious criticism: people did try, very very hard, to create structured information systems. They simply did not work. The sort of systems people tend to build are well-scoped for a particular problem, but fall apart completely when moving out of that domain. It is very easy to imagine an information system that results in correct answering of flag questions. It is a wide open problem to come up with a way of tagging information that works well for flag questions and for questions about memes, or art. It's not like Google didn't try!
No AI is impressive because we built a machine that finally understands what we are saying and has a concept of what creativity is. It's rudimentary but it's a milestone for humanity.
It's social media and the endless barrage of generated AI that's creating the illusion that AI isn't impressive. You're inundated everyday with the same crap and is just making it less and less impressive.
I think every field has its own version of this thought, where if we could just manage to categorise and tag things properly we could achieve anything. Our lack of a valid overarching ontology is what is holding us back from greatness.
It might be short lived, who knows, but it's interesting that the recent progress came from capturing/consuming rather than systematically eliminating the nuance in language.
I think I broadly agree with this. I am super frustrated that everythign always wants me to search for things. As an example Finders default search while looking at a folder is the whole machine instead of a filter in the directory you are viewing in seems totally insane to me. It's almost like they don't want me to know where my files are.
I can understand that it's a result, to a degree of cloud services and peoples primary mode swapping to opening and app and opening recents or searching instead of opening a file to open an app but it does mean that you're at the mercy of what I experience as some pretty crap search algorithms that don't seem to want you to find the information you're looking for. I keep encountering searches that rank fuzzy matches over exact matches or aren't stable as you continue to complete the same word and I just don't understand how that's acceptable after being pointed out if search is what I'm supposed to be using.
> It's almost like they don't want me to know where my files are.
I think this might actually be true in some cases.
Especially where companies want your files on their cloud servers. It's better for them if you don't think about what's stored locally or remotely. It's better for them if you don't think at all and just ask them for whatever you want and let them decide what to show you or keep hidden from you. It's easier for them to get metrics on what you're doing if you type it out explicitly in a search box than it is for them to track you as you browse through a hierarchy you designed to get to what you want. You're supposed to feel increasingly helpless and dependent on them.
Uh, the author got so close to make the same realization I had while working on a project [0] for the Wikimedia Foundation: we wouldn't need search engines if we had better tooling to query semantic databases like wikidata.
However, the thing that the author might be missing is that the semantic web exists. [1] The problem is that the tools that we can use to access it are not being developed by Big Tech. Remember Freebase? Remember that Google could have easily kept it around but decided to fold it and shoved it into the structured query results? That's because Google is not interested in "organizing the world's information and make it universally accessible" unless it is done in a way that it can justify itself into being the data broker.
I'm completely out of time or energy for any side project at the moment, but if someone wants to steal my idea: please take a llm model and fine tune so that it can take any question and turn it into a SparQL query for Wikidata. Also, make a web crawler that reads the page and turns into a set of RDF triples or QuickStatements for any new facts that are presented. This would effectively be the "ultimate information organizer" and could potentially replace Wikidata as most people's entry page of the internet.
DBpedia Spotlight and entity-fishing already do something similar to your idea - they extract structured data from text and link to knowledge bases. Combining these with LLM-based query translation to SPARQL could indeed bridge the gap between semantic web's structure and natural language interfaces.
ChatGPT etc does an OK job at SPARQL generation.
Try something like "generate a list of all supermarkets, including websites, country, description" and you get usable queries out.
In a much, much more limited way, this is what I was dabbling with with alltheprices - queries to pull data from wikidata, crawling sites to pull out the schema.org Product and offers, and publish the aggregate.
I would argue that LLM is ultimately making true Semantic Web available, or irrelevant.
It can basically figure out all the semantic knowledge graphs automatically for us, and it's multi-modal to boot. That means it can infer the relationships between any node across text, audio, images, videos, and even across different languages.
The root cause of this is that HTML is not a language for the markup of hypertext.
Because everything has to be copied and/or compressed into a single layer document along with its markup, you almost never see actual markup in the web, just a lot of formatting and layout.
Because you can't have multiple markup of a given source material, adding multiple hierarchies doesn't happen. Any given information structure is necessarily oversimplified and thus only fit for limited use.
It's almost as if someone read Vannevar Bush's description of the memex and decided to actively prevent its reification.
He said the organization of knowledge was the largest challenge facing mankind post war. Clearly he was right, and we've failed miserably.
The thing LLMs provide is an impedance match between the data we do have, and the actual needs of mankind.
The funny thing to me is that now more than ever structured data is important so that AI has a known good data set to train from, and so that search engines have contextual and semantically rich data to search.
AI isn't a solution to this; on the contrary, whatever insufficiency exists in the original data set will only be amplified, as compression artifacts can be amplified in audio.
We also can't trust any data that's created recently, because an LLM can be trained to provide correct-looking structured data that may or may not accurately model semantic structure.
The best source of structured, tagged, contextually-rich data suitable for training or searching in the future will be from people who currently AREN'T using generative AI.
My read is that the author is saying it would have been really nice if there had been a really good protocol for storing data in a rich semantically structured way and everyone had been really really good at adhering to that standard.
"... if all knowledge were stored in a structured way with rich semantic linking..." this sounds a lot like Google's "Knowledge Graph". https://developers.google.com/knowledge-graph. (Disclosure: I work at Google.)
If you ask an LLM where you can find a structured database of knowledge with structured semantic links, they'll point you to this and other knowledge graphs. TIL about Diffbot!
In my experience, it's a lot more fun to imagine the perfect database like this than it is to work with the actual ones people have built.
If you really want to, you know have this super generic indexing thing, why don't you go organize the web with hypercard and semantic web crap and tell us how it worked out for you
One can speak in their native language to a computer now and it mostly understands what is meant and can retrieve information or even throw together a scaffold of a project somewhat reliably.
It's not particularly good at writing software, however. Still feels like a human is needed to make sure it doesn't generate nonsense or apparently pretty insecure code.
So I'm not sure the author got the point across that they wished, but aren't vector databases basically a semantic storage/retrieval technology?
Ok, but Google's result summary got the answer wrong. So did Gemini, when I tried it (Lion, Sierra Leone). And so did ChatGPT when I tried it (Lion, Sri Lanka).
So... it's impressive, sure, because the author is correct that a search engine can't answer that question in a single query. But it's also wildly wrong.
I also vaguely agree with the author that Google Drive sucks, but I wish they'd mentioned that the solution to their problem - using search! - also fucking sucks in Google Drive.
At Engelbart's mother of all demos in 1968, which basically birthed what we call personal computing today, most computer scientists were convinced that AGI was right around the corner and "personal computing" wasn't worth it.
Now, back then AGI wasn't right around, and personal computing was really really necessary, but how did we forget the viewpoint that personal computing was seen as a competing way to use computing power vs AI?
Economic interests, walled gardens, lock-in effects. Providers learned to make it (initially) convenient for us to forget. Of course, once we’re hooked, enshittification ensues: https://news.ycombinator.com/item?id=44837367
Another angle: we're super over provisioned in compute resources because of reasonable limitations in how quickly we can collectively absorb and learn how to use computers. "AI" is simply a new paradigm in our understanding of how to program computers. This isn't a failure, it's just an evolution.
Leading in with what feels like a p-hacked question that almost certainly isn't typical kind of hurts the point. Reminds me of seeing clearly rehearsed demos for things that ignore the reason it worked is because it was rehearsed. A lot.
Am I wrong in that this was a completely organic question?
People have said this since Vannevar Bush theorized the Memex. The unanswered question, the problem unsolved, remains: Who does all the categorization work? How come they're never part of "we," such that the plaint is never "if only we had done the work..."?
Neither chat-gpt nor gemini got the same answer as the article for me
`
What animal is featured on a flag of a country where the first small British colony was established in the same year that Sweden's King Gustav IV Adolf declared war on France?
`
chatgpt: Final answer: Lion
gemini: A lion is featured on the flag of Sri Lanka.
Gemini (2.5 pro) stands firm on its answer even when told that ChatGPT said it is a parrot. It does provide additional reasoning: https://g.co/gemini/share/45232dcc7278
Personal computing, best now it's flav djour we get to slam on cloud based approaches to everything being what they always were. Wasteful, slow and privacy invasive... But still can't just plainly say cloud has been mostly bad.
Cyc[0] tried for 40 years to make handwritten semantic rules a thing, but still don't have much to show for. Humans just aren't good or fast at writing down the rather fuzzy semantics of the real world into a computer readable format.
With RDF specially, there was also the issue of "WTF is this good for?". Semantic Web sounds lofty in theory, but was there ever even a clear plan on how the UI would look like? How would I explore all that semantic data if it ever came into existence? How would it deal with link rot?
And much like with RSS, I think a big failure of RDF is that it's some weird thing outside the Web, instead of just some addition HTML tags to enrich existing documents. If there is a failure, it's that. Even today, a lot of basic semantic tags are missing from HTML, we finally got <date> in 2011, but we still have nothing for names, cities, units, books[1], movie, gps coordinates and a lot of other stuff we use daily.
Another big failure is that HTML has become a read-only format, the idea that one uses HTML as a source format to publish documents seems to have been completely abandoned. HTML is just a UI language for Web apps nowadays, Markdown, LaTeX or whatever is what one uses to write content.
Just complaining that the world is bad is a good way to waste your energy and end up being a cynic.
So why is not all information organized in structured, open formats? Because there's not enough of an incentive to label/structure your documents/data that way. That's if you even want to open your data to the public - paywalls fund business models.
There have been some smaller successes with semantic web, however. While a recipe site might not want to make it easy for everyone to scrape their recipes, people do want Twitter to generate a useful link preview from their sites' metadata. They do that with special tags Twitter recognizes, and other sites can use as well.
The good news is that LLMs can generate structured data from unstructured documents. It's not perfect, but has two advantages: it's cheaper than humans doing it manually, and you don't have to ask the author to do anything. The structuring can happen on the read side, not the write side - that's powerful. This means we could generate large corpuses of open data from previously-inaccessible opaque documents.
This massive conversion of unstructured to structured data has already been happening in private, with efforts like Google's internal Knowledge Graph. That project has probably seen billions in cumultative investment over the years.
What we need is open data orgs like Wikipedia pick up this mantle. They already have Wikidata, whose facts you can query with a graph querying language. The flag example in the article could be decomposed into motifs by an LLM and added to the flag's entry. And then you could use SPARQL to do the structured query. (And that structured query can be generated from LLMs, too!)
Alan Kay has also written about his disappointment with what personal computing delivered. I think he'd agree with this.
Most people can't use the power of the computers they have effectively. Maintaining the data in Excel spreadsheets for most people is like manual labour.
How do LLMs help Google, an ad company, generate more ad revenue. LLMs drive the traffic away from websites, give you answer directly without needing bother with ads. How does it benefit Google's ad business?
Ad clicks are a trifle compared to the possibility of winning the game of displacing vast amounts of labor and delivering ads and narratives undisclosed through conversational AI output. At that point you've won the economy, period.
is it unreasonable to expect some kind of good enough baseline guided by the prior of these llm to be the standard ? Should google's priors (or their training dataset and recipe) be allowed to guide societal's prior? same problem, different era ?
This is a clickbait article one could've written when Google was new and people were getting used to the idea of "You can just search for things on the internet."
It's just dressed up with mentions of AI for better "engagement."
The essential meaning of technological progress is the utter inadequacy of humanity. We build cars that we never figure out how to safely drive, but being to arrogant to admit defeat and reconstruct society around trams like a sensible animal would do, we wait a century for cars that can drive themselves while the bodies pile up on the freeways in the meantime.
Yes it's true, we can't make a UI. Or a personal computer, or anything else that isn't deeply opaque to its intended users. But it's so much worse than that. We humans can't do hardly anything successfully besides build technology and beat each other in an ever expanding array of ridiculous pissing contests we call society.
> They scan the unstructured web and build ephemeral semantic maps across everything. It's not knowledge in the classic sense.. or perhaps it is exactly what knowledge is?
The last ten years of consumer web tech was dedicated to SEO slop and crypto. Google has destroyed everything else with its endless promotion of hindustantimes .com listicles.
I sympathize so much with the failure of personal computing to manifest!
> My point is that if all knowledge were stored in a structured way with rich semantic linking, then very primitive natural language processing algorithms could parse question like the example at the beginning of the article, and could find the answer using orders of magnitude fewer computational resources. And most importantly: the knowledge and the connections would remain accessible and comprehensible, not hidden within impenetrable AI models.
It's a pocket hope of mine that AI brings us back to the Semantic Web, or something very like it. In many ways these embeddings are giant non-human lexicons already. Distilling this information out seems so possible.
Even just making an AI to go markup (or uhh perhaps refine) a page with microdata seems conceptually very doable!
More broadly, looking at personal computing: my personal belief is that failure is hugely because of apps. Instead of a broader personal computing that aggregates, that allows constructivism, that enables personal growth & development (barefoot developers & home-cooked software style), computing has been defined by massificstion, by big tech software. The dominant computing paradigm has been mainframe pattern: we the user have an app that acts as a terminal to some far off cloud-y data center. Whatever agency we get is hewn out for us apriori by product teams, and any behavior not explicitly built in is a "Felony Contempt of Business Model" (an oh so accurate Doctorow-ism)! https://maggieappleton.com/home-cooked-softwarehttps://news.ycombinator.com/item?id=40633029https://pluralistic.net/2022/10/20/benevolent-dictators/#fel...https://news.ycombinator.com/item?id=33279274
It is so so sad to see computing squandered so!
The good news is AI is changing this relationship with software. I'm sure we will have no end of AI models built in to our software, that companies will maintain the strict control (tyrant's grip) over software as long as they can! But for AI to flourish, it's going to need to work across systems. And that means tearing down some of the walls, walls that have forcibly kept computing (ardently) anti-personal.
I can go look at https://github.com/punkpeye/awesome-mcp-servers and take such great hope from it. Hundreds of ways that we have eeked out a way to talk interface with systems that, before, people had no say and no control over.
headcanon|6 months ago
To me, it feels similar to "If everyone just cooperated perfectly and helped each other out, we wouldn't need laws/money/government/religion/etc."
Yes, you're probably right, but no that won't happen the way you want to, because we are part of a complex system, and everyone has their very different incentives.
Semantic web was a standard suggested by Google, but unless every browser got on board to break web pages that didn't conform to that standard, then people aren't going to fully follow it. Instead, browsers (correctly in my view) decided to be as flexible as possible to render pages in a best-effort way, because everyone had a slightly different way to build web pages.
I feel like people get too stuck on the "correct" way to do things, but the reality of computers, as is the reality of everything, is that there are lots of different ways to do things, and we need to have systems that are comfortable with handling that.
roadside_picnic|6 months ago
Was this written by AI? I find it hard to believe anyone who was interested in Semantic Web would have not known it's origin (or at least that it's origin was not Google).
The concept of a Semantic web was proposed by Tim Berners-Lee (who hopefully everyone recognizes as the father of HTTP, WWW, HTML) in 1999 [0]. Google, to my knowledge, had no direct development or even involvement in the early Semweb standards such as RDF [1] and OWL [2]. I worked with some of the people involved in the latter (not closely though), and at the time Google was still quite small.
0. https://archive.org/details/isbn_9780062515872/mode/2up
1. https://www.w3.org/TR/PR-rdf-syntax/Overview.html
2. https://www.w3.org/TR/owl-ref/
fouc|6 months ago
Tim Berners-Lee coined it in 1999 and further expanded on the concept in a 2001 Scientific American article by Berners-Lee, Hendler, and Lassila.
rco8786|6 months ago
ajkjk|6 months ago
What happens in practice is that the culture exterminates the drive for improvement: not only are things bad, but people look at you if you're crazy if you think things should be better. Like in 2025 people defend C, people defend Javascript, people write software without types, people write scripts in shell languages; debugging sometimes involves looking at actual bytes with your eyes; UIs are written in non-cross-platform ways; the same stupid software gets written over and over at a million companies, sending a large file to another person is still actually pretty hard; leaving comments on it is functionally impossible ... these are software problems, everything is shit, everything can be improved on, nothing should be hard anymore but everything still is; we are still missing a million abstractions that are necessary to make the world simple. Good lord, yesterday I spent two hours trying to resize a PDF. We truly live in the stone age; the only progress we've made is that there are now ads on every rock.
I really wish it was a a much more ruthlessly competitive landscape. One in which if your software is bad, slow, hard to debug, hard to extend, not open source, not modernized, not built on the right abstractions, hard to migrate on or off of, not receptive to feedback, covered in ads... you'd be brutally murdered by the competition in short order. Not like today where you can just lie on your marketing materials and nobody can really do anything because the competition is just as weak. People would do a much better job if they had to to survive.
justincormack|6 months ago
m463|6 months ago
mom: "you need to clean up your room"
kid: "mom, just give up. The room will always be a mess, just use search"
discostrings|6 months ago
MarkusQ|6 months ago
In scaling out computation to the masses, we went from locally grown plums that took a lot of work and were only available to a small number of people that had a plum tree or knew someone that had one, to building near magical prune-cornucopia devices that everyone could carry around in their pockets, giving them an effectively unlimited supply of prunes.
LLMs re-hydrate these for us, making them significantly more palatable; if you're used to gnawing dried fruit, they seem amazing.
But there's still a lot of work to be done.
random3|6 months ago
Also LLMs are failing too, for different reasons, but IMO unlikely AI in general will— it will correct a 60 years or so failure in industrial computer science.
pavel_lishin|6 months ago
Except sometimes you're expecting a fresh plum, and then you bite into a fig, or an apple, or a banana, or a stick.
selimthegrim|6 months ago
echelon|6 months ago
Writing software is only so scalable. It doesn't matter all of the shortcuts we take, like Electron and JavaScript. There are only so many engineers with so much time, and there are abundantly many problems to solve.
A better analogy would be to look at what's happening to AI images and video. Those have 10,000x'd the fundamental cost savings, time savings, and personnel requirements. It's an industrialization moment. As a filmmaker who has made several photons-on-glass films, this is a game changer and lifts the entire creative industry to a level where individuals can outdo Pixar.
That is the lens with which to look at what AI will do to software. We're going from hand-carving stone wheels to the Model T.
This is all just getting started. We've barely used what the models of today offer us.
sudohalt|6 months ago
unknown|6 months ago
[deleted]
askl|6 months ago
Kapura|6 months ago
But instead of iterating on better interfaces to effectively utilize the N thousands of operations per second a computer is capable of, the powers that be behind the industry have decided to invest billions of dollars in GPUs to get a program that seems like it understands language, but is incapable of counting the number of B's in "blueberry."
SV_BubbleTime|6 months ago
And making it more of “IDK what it answered the way it did, but it might be right!!”
svachalek|6 months ago
They're not claiming AGI yet, so human intelligence is required to operate an LLM optimally. It's well known that LLMs process tokens rather than characters s, so without space for "reasoning" there's no representation of the letter b in the prompt. Telling it to spell or think about it gives it room to spell it out, and from there it can "see" the letters and it's trivial to count.
xandrius|6 months ago
bkummel|6 months ago
yeyeyeyeyeyeyee|6 months ago
ctoth|6 months ago
jolt42|6 months ago
I think of the Whalphin and it took Sea World era to discover. Who would see that coming?
foundart|6 months ago
> Remember Semantic Web? The web was supposed to evolve into semantically structured, linked, machine-readable data that would enable amazing opportunities. That never happened.
I think the lesson to be learned is in answering the question "Why didn't the semantic web happen?"
cognivore|6 months ago
I have literally been doing we development since their was a web, and the companies I developed for are openly hostile to the idea of putting their valuable, or perceived valuable, information online in a format that could be easily scraped. Information doesn't want to be free, it wants to be paid for. Unless the information shared pulls visitors to the site it doesn't need to be public.
neilalexander|6 months ago
Advertising.
Workaccount2|6 months ago
Because web content is generated by humans, not engineers.
jandrese|6 months ago
The whole concept was too high minded and they never got the implementation details down. Even if they did it would have been horrendously complex and close to impossible to manage. Asking every single publisher to neatly categories their data into this necessarily enormous scheme would have resulted in countless errors all over the web that would have seriously undercut the utility of the project anyway. Ultimately the semantic web doesn't scale very well. It failed for the same reason command economies fail: It's too overwhelming for the people in control to manage and drowns in its own bureaucracy.
Mikhail_Edoshin|6 months ago
AlienRobot|6 months ago
seydor|6 months ago
open_|6 months ago
"Standing on the shoulders of giants, it is clear that the giants failed to reach the heights we have reached."
AlienRobot|6 months ago
Everything that is bad in UI is a direct consequence of that.
1. No tooltips, right click, middle click behavior because touch doesn't have that. No ctrl+click either.
2. Large click areas wasting screen space with padding and margins.
3. Low density UI so it can shape-shift into mobile version.
4. Why type on a phone when you can talk? Make everything a search box.
5. Everything must be flat instead of skeumorphic because it's easier to resize for other screen sizes.
6. Everything needs a swipe animation and views instead of dialogs because smartphones can't have windows.
atrettel|6 months ago
I think the example query here actually shows a problem with the query languages used in web search rather than an intrinsic inability of web search. It contains what amounts to a natural language subquery starting with "in the same year". In other words, to evaluate this query properly, we need to first evaluate this subquery and then use that information to evaluate the overall query. Google Search and almost all other traditional web search engines use intentionally oversimplified query languages that disallow nested queries let alone subqueries, so this example really is just revealing a problem with the query language rather than a problem with web search overall. With a better query language, we might get better results.
seanwilson|6 months ago
So as well as people writing posts in English, they would need to provide semantic markup for all the information like dates, flags, animals, people, and countries? It's difficult enough to get people to use basic HTML tags and accessible markup properly, so what was the plan for how this would scale, specifically to non-techy people creating content?
TZubiri|6 months ago
This actually happened already and it's part of why llms are so smart, I haven't tested this but I venture a guess that without wikipedia and wikidata and wikipedia clones and stolen articles, LLMs would be quite a lot dumber. You can only get so far with reddit articles and embedded knowledge of basic info on higher order articles.
My guess is when fine tuning and modifying weights, the lowest hanging fruit is to overweigh wikipedia sources and reduce the weight of sources like reddit.
walterbell|6 months ago
In vertical markets, can LLMs generate a "semantic web of linked data" knowledge graph to be parsed with efficient NLP algorithms?
https://news.ycombinator.com/item?id=43914227#43926169
nrjames|6 months ago
khazhoux|6 months ago
> AI is not a triumph of elegant design, but a brute-force workaround
You can read and understand Attention Is All You Need in one hour, and then (after just scaling out by a few billion) a computer talks to you like a human. Pretty elegant, if you ask me.
> The web was supposed to evolve into semantically structured, linked, machine-readable data that would enable amazing opportunities.
I missed that memo. The web was, and forever shall be, a giant, unstructured, beautiful mess. In fact, LLMs show just how hopeless the semantic web approach was. Yes, it's useful to attach metadata to objects, but you will still need massive layering and recursion to derive higher-order, non-trivial information.
This entire article is someone unable to let go of an old idea that Did Not Work.
mrweasel|6 months ago
I think the author is on to something here, but does not realize it, or applies the think to the wrong problem. The issue isn't the web, search was good enough and it's a pretty big problem to solve. We need to go smaller. Applying AI to customer service, internal processes and bureaucracy, among other things, is an inelegant brute-force approach to not just fixing your shit.
The majority of customer service could be fixed by having better self-service, designing better UIs, writing better manuals, having better monitoring, better processes, better trained staff, not insisting on pumping stock numbers and actually caring about your product. AI will never fix the issues your customers are having, they'll just make customer service cheaper and worse because just like real humans the content and levers they need aren't available.
Same for much of the bureaucracy in companies and governments. Rather than having AI attempt to fill out forms or judge if someone is entitled to a pension, insurance pay out or what have you. Take the time to fix your damn processes and built self-service portals that actually works (some of those may have some sort of AI on the backend for things like scanning documents).
Forget the semantic web thing, it never worked, to much data, and AI generated content certainly isn't making the problem easier. Let's work on that at some other time. Still the author is correct, LLMs are a brute-force workaround, regardless of how elegant the design may be. They are applied to a host of problem that should just be eliminated, not hidden and stupefied by a prediction engine.
codr7|6 months ago
tempfile|6 months ago
ninetyninenine|6 months ago
It's social media and the endless barrage of generated AI that's creating the illusion that AI isn't impressive. You're inundated everyday with the same crap and is just making it less and less impressive.
jddj|6 months ago
It might be short lived, who knows, but it's interesting that the recent progress came from capturing/consuming rather than systematically eliminating the nuance in language.
octoberfranklin|6 months ago
jvreeland|6 months ago
I can understand that it's a result, to a degree of cloud services and peoples primary mode swapping to opening and app and opening recents or searching instead of opening a file to open an app but it does mean that you're at the mercy of what I experience as some pretty crap search algorithms that don't seem to want you to find the information you're looking for. I keep encountering searches that rank fuzzy matches over exact matches or aren't stable as you continue to complete the same word and I just don't understand how that's acceptable after being pointed out if search is what I'm supposed to be using.
autoexec|6 months ago
I think this might actually be true in some cases. Especially where companies want your files on their cloud servers. It's better for them if you don't think about what's stored locally or remotely. It's better for them if you don't think at all and just ask them for whatever you want and let them decide what to show you or keep hidden from you. It's easier for them to get metrics on what you're doing if you type it out explicitly in a search box than it is for them to track you as you browse through a hierarchy you designed to get to what you want. You're supposed to feel increasingly helpless and dependent on them.
rglullis|6 months ago
However, the thing that the author might be missing is that the semantic web exists. [1] The problem is that the tools that we can use to access it are not being developed by Big Tech. Remember Freebase? Remember that Google could have easily kept it around but decided to fold it and shoved it into the structured query results? That's because Google is not interested in "organizing the world's information and make it universally accessible" unless it is done in a way that it can justify itself into being the data broker.
I'm completely out of time or energy for any side project at the moment, but if someone wants to steal my idea: please take a llm model and fine tune so that it can take any question and turn it into a SparQL query for Wikidata. Also, make a web crawler that reads the page and turns into a set of RDF triples or QuickStatements for any new facts that are presented. This would effectively be the "ultimate information organizer" and could potentially replace Wikidata as most people's entry page of the internet.
[0]: https://meta.wikimedia.org/wiki/QuickStatements_3.0
[1] https://guides.library.ucla.edu/semantic-web/datasets
ethan_smith|6 months ago
throwaway346434|6 months ago
In a much, much more limited way, this is what I was dabbling with with alltheprices - queries to pull data from wikidata, crawling sites to pull out the schema.org Product and offers, and publish the aggregate.
fouc|6 months ago
It can basically figure out all the semantic knowledge graphs automatically for us, and it's multi-modal to boot. That means it can infer the relationships between any node across text, audio, images, videos, and even across different languages.
jacobgkau|6 months ago
mikewarot|6 months ago
Because everything has to be copied and/or compressed into a single layer document along with its markup, you almost never see actual markup in the web, just a lot of formatting and layout.
Because you can't have multiple markup of a given source material, adding multiple hierarchies doesn't happen. Any given information structure is necessarily oversimplified and thus only fit for limited use.
It's almost as if someone read Vannevar Bush's description of the memex and decided to actively prevent its reification.
He said the organization of knowledge was the largest challenge facing mankind post war. Clearly he was right, and we've failed miserably.
The thing LLMs provide is an impedance match between the data we do have, and the actual needs of mankind.
d-mz|6 months ago
AI isn't a solution to this; on the contrary, whatever insufficiency exists in the original data set will only be amplified, as compression artifacts can be amplified in audio.
We also can't trust any data that's created recently, because an LLM can be trained to provide correct-looking structured data that may or may not accurately model semantic structure.
The best source of structured, tagged, contextually-rich data suitable for training or searching in the future will be from people who currently AREN'T using generative AI.
captainclam|6 months ago
Is that the main thrust of it?
jkaptur|6 months ago
If you ask an LLM where you can find a structured database of knowledge with structured semantic links, they'll point you to this and other knowledge graphs. TIL about Diffbot!
In my experience, it's a lot more fun to imagine the perfect database like this than it is to work with the actual ones people have built.
throwmeaway222|6 months ago
?
If you really want to, you know have this super generic indexing thing, why don't you go organize the web with hypercard and semantic web crap and tell us how it worked out for you
butterisgood|6 months ago
One can speak in their native language to a computer now and it mostly understands what is meant and can retrieve information or even throw together a scaffold of a project somewhat reliably.
It's not particularly good at writing software, however. Still feels like a human is needed to make sure it doesn't generate nonsense or apparently pretty insecure code.
So I'm not sure the author got the point across that they wished, but aren't vector databases basically a semantic storage/retrieval technology?
ralfd|6 months ago
By the way: His blog getting a few dozen hn comments is only impressive, because he failed to write a better blog.
pavel_lishin|6 months ago
Ok, but Google's result summary got the answer wrong. So did Gemini, when I tried it (Lion, Sierra Leone). And so did ChatGPT when I tried it (Lion, Sri Lanka).
So... it's impressive, sure, because the author is correct that a search engine can't answer that question in a single query. But it's also wildly wrong.
I also vaguely agree with the author that Google Drive sucks, but I wish they'd mentioned that the solution to their problem - using search! - also fucking sucks in Google Drive.
cgh|6 months ago
seanmcdirmid|6 months ago
Now, back then AGI wasn't right around, and personal computing was really really necessary, but how did we forget the viewpoint that personal computing was seen as a competing way to use computing power vs AI?
tempodox|6 months ago
gsmt|6 months ago
blurbleblurble|6 months ago
dawnofdusk|6 months ago
kbelder|6 months ago
taeric|6 months ago
Am I wrong in that this was a completely organic question?
throwanem|6 months ago
nullorempty|6 months ago
` What animal is featured on a flag of a country where the first small British colony was established in the same year that Sweden's King Gustav IV Adolf declared war on France? `
chatgpt: Final answer: Lion
gemini: A lion is featured on the flag of Sri Lanka.
vrwarp|6 months ago
autoexec|6 months ago
getting wildly different and unpredictable answers from the same input is one of the features AI offers
rob_c|6 months ago
grumbel|6 months ago
With RDF specially, there was also the issue of "WTF is this good for?". Semantic Web sounds lofty in theory, but was there ever even a clear plan on how the UI would look like? How would I explore all that semantic data if it ever came into existence? How would it deal with link rot?
And much like with RSS, I think a big failure of RDF is that it's some weird thing outside the Web, instead of just some addition HTML tags to enrich existing documents. If there is a failure, it's that. Even today, a lot of basic semantic tags are missing from HTML, we finally got <date> in 2011, but we still have nothing for names, cities, units, books[1], movie, gps coordinates and a lot of other stuff we use daily.
Another big failure is that HTML has become a read-only format, the idea that one uses HTML as a source format to publish documents seems to have been completely abandoned. HTML is just a UI language for Web apps nowadays, Markdown, LaTeX or whatever is what one uses to write content.
0. https://en.wikipedia.org/wiki/Cyc
1. <a href="urn:isbn:..."> exists, but browsers don't support it natively
pradn|6 months ago
So why is not all information organized in structured, open formats? Because there's not enough of an incentive to label/structure your documents/data that way. That's if you even want to open your data to the public - paywalls fund business models.
There have been some smaller successes with semantic web, however. While a recipe site might not want to make it easy for everyone to scrape their recipes, people do want Twitter to generate a useful link preview from their sites' metadata. They do that with special tags Twitter recognizes, and other sites can use as well.
The good news is that LLMs can generate structured data from unstructured documents. It's not perfect, but has two advantages: it's cheaper than humans doing it manually, and you don't have to ask the author to do anything. The structuring can happen on the read side, not the write side - that's powerful. This means we could generate large corpuses of open data from previously-inaccessible opaque documents.
This massive conversion of unstructured to structured data has already been happening in private, with efforts like Google's internal Knowledge Graph. That project has probably seen billions in cumultative investment over the years.
What we need is open data orgs like Wikipedia pick up this mantle. They already have Wikidata, whose facts you can query with a graph querying language. The flag example in the article could be decomposed into motifs by an LLM and added to the flag's entry. And then you could use SPARQL to do the structured query. (And that structured query can be generated from LLMs, too!)
LLMs and structured data are friends.
nicemountain|6 months ago
Most people can't use the power of the computers they have effectively. Maintaining the data in Excel spreadsheets for most people is like manual labour.
smusamashah|6 months ago
add-sub-mul-div|6 months ago
wagwang|6 months ago
octoberfranklin|6 months ago
if it's a space issue, "semantic web" is far more relevant to the article than "personal computing".
yen223|6 months ago
seydor|6 months ago
thanhdotr|6 months ago
unknown|6 months ago
[deleted]
yongjik|6 months ago
It's just dressed up with mentions of AI for better "engagement."
antithesizer|6 months ago
Yes it's true, we can't make a UI. Or a personal computer, or anything else that isn't deeply opaque to its intended users. But it's so much worse than that. We humans can't do hardly anything successfully besides build technology and beat each other in an ever expanding array of ridiculous pissing contests we call society.
gorfian_robot|6 months ago
camgunz|6 months ago
ubercow13|6 months ago
SoftTalker|6 months ago
mwkaufma|6 months ago
Betteridge's law
p3rls|6 months ago
jauntywundrkind|6 months ago
> My point is that if all knowledge were stored in a structured way with rich semantic linking, then very primitive natural language processing algorithms could parse question like the example at the beginning of the article, and could find the answer using orders of magnitude fewer computational resources. And most importantly: the knowledge and the connections would remain accessible and comprehensible, not hidden within impenetrable AI models.
It's a pocket hope of mine that AI brings us back to the Semantic Web, or something very like it. In many ways these embeddings are giant non-human lexicons already. Distilling this information out seems so possible.
Even just making an AI to go markup (or uhh perhaps refine) a page with microdata seems conceptually very doable!
More broadly, looking at personal computing: my personal belief is that failure is hugely because of apps. Instead of a broader personal computing that aggregates, that allows constructivism, that enables personal growth & development (barefoot developers & home-cooked software style), computing has been defined by massificstion, by big tech software. The dominant computing paradigm has been mainframe pattern: we the user have an app that acts as a terminal to some far off cloud-y data center. Whatever agency we get is hewn out for us apriori by product teams, and any behavior not explicitly built in is a "Felony Contempt of Business Model" (an oh so accurate Doctorow-ism)! https://maggieappleton.com/home-cooked-software https://news.ycombinator.com/item?id=40633029 https://pluralistic.net/2022/10/20/benevolent-dictators/#fel... https://news.ycombinator.com/item?id=33279274
It is so so sad to see computing squandered so!
The good news is AI is changing this relationship with software. I'm sure we will have no end of AI models built in to our software, that companies will maintain the strict control (tyrant's grip) over software as long as they can! But for AI to flourish, it's going to need to work across systems. And that means tearing down some of the walls, walls that have forcibly kept computing (ardently) anti-personal.
I can go look at https://github.com/punkpeye/awesome-mcp-servers and take such great hope from it. Hundreds of ways that we have eeked out a way to talk interface with systems that, before, people had no say and no control over.
cdoctorow|6 months ago
unknown|6 months ago
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unknown|6 months ago
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reriufj|6 months ago
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