Comment by Animats
5 days ago
Not learning from new input may be a feature. Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Today's locked-down pre-trained models at least have some consistency.
Incredible to accomplish that in a day - it took the rest of the world another decade to make Twitter sound like 4chan, but thanks to Elon we got there in the end.
This has little to do with the bot, and everything with this being the heyday of Twitter shitstorms; we didn't have any social immunity to people getting offended about random things on-line, and others getting recursively offended, and then "adults" in news publishing treating that seriously and converting random Twitter pileups into stock movements.
In a decade since then, things got marginally better, and such events wouldn't play out so fast and so intensely in 2026.
> In a decade since then, things got marginally better, and such events wouldn't play out so fast and so intensely in 2026.
Are you saying the internet would not do it again, or Microsoft would not do the same approach? Because I think the internet would absolutely do it again.
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> c) goes against the concept of true democracy (which I like
You mean one person, one vote. Or in the case of Twitter/X - one person one voice/account.
Don't spaces like these become dominated by fanatics or money, or fanatics with money? All trying to manufacture consent?
Unregulated != democratic
Just like unregulated != free market [1]
Sure it's difficult to get the balance right - but a balance is required.
[1] As the first step of anybody competing in an unregulated market is to fix the market so they don't have to compete - create a cartel, monopoly, confusopoly ( deny information required for the market to work ) etc etc.
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I quite like current Twitter (x). It's not really like 4chan which was all idiots - you get some quite thoughtful thinkers on it, including pg who built this thing. Also the 'ask Grok' thing for fact checking actually works surprisingly well - it you reply something like "is that true @grok?" to a comment the LLM replies with usually quite an accurate answer.
If you want to understand something like US politics which is mostly a battle between the left and the right it lessens your understanding to filter out one sides viewpoints and then be surprised by reality.
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People say BlueSky is like pre-Musk Twitter, i.e. leftist opinions in today’s Twitter style.
Which is a bit strange because BlueSky is supposed to be decentralized (no central moderation); and although in practice it’s not, the BlueSky team seems pro-freedom (see: Jesse Signal controversy). I know there are some rightists (including the White House), but are they a decent presence? Are they censored? Are there other groups (e.g. “sophisticated” politics, fringe politics, art, science)?
Mastodon is interesting. Its format is like Twitter, but most posts seem less political and less LCD-CW (e.g. types.pl, Mathstodon). I suspect because it’s actually decentralized (IIRC Truth Social is a fork; I didn’t write all posts are less CW). I’m curious to find other interesting instances here too.
Pre-Musk, I remember seeing screenshots of the stupidest, most echo-chamber-y Tweets imaginable. e.g. “why do the cows all have female names, that’s misogynistic” (that one was deliberate satire but I’m sure most were). I’ll brag, I left around 2013 because I felt it was rotting my brain. I enjoyed a few more years off social media, with a healthy dopamine system. Unfortunately, now I’m here.
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You make it seem like it's not predominantly skewed right wing, just a "healthy" mix of right wingers and left wingers due to not banning anyone. Which might be an unpopular take, but in this scenario I think it's unpopular simply because it is demonstrably wrong.
> A study published by science journal Nature has examined the impact of Elon Musk’s changes to X/Twitter, and outlines how X’s algorithm shapes political attitudes, and leans towards conservative perspectives. They found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm. https://www.socialmediatoday.com/news/x-formerly-twitter-amp...
> Sky News team ran a study where they created nine new Twitter/X accounts. Right-wing accounts got almost exclusively right-wing material, all accounts got more of it than left-wing or neutral stuff. (Notably, the three “politically neutral” accounts got about twice as much right-wing content as left-wing content. https://news.sky.com/story/the-x-effect-how-elon-musk-is-boo...
> New X users with interests in topics such as crafts, sports and cooking are being blanketed with political content and fed a steady diet of posts that lean toward Donald Trump and that sow doubt about the integrity of the Nov. 5 election, a Wall Street Journal analysis found. https://www.wsj.com/politics/elections/x-twitter-political-c...
> A Washington Post analysis found that Republicans are posting more, getting followed more and going viral more now that the world’s richest Trump supporter is running the show. https://www.washingtonpost.com/technology/2024/10/29/elon-mu...
I don't think there are tons of "leftists".
Ever since Twitter changed into the tilted X insignia, led by a guy who keeps on raising his right arm, a gazillion of folks left. And I think more "leftists" left than "rights". It is an echo-chamber now.
Weak minded folks are at least 40-50% of the population and there is a reasonable risk of them killing the human race or at least immiserating it.
Unhinged leftists want what public ownership of the means of production whilst unhinged right wingers want concentration camps and may get them. I don't think it's reasonable to equate these things.
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Not an unpopular take, just one not tied to reality.
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Twitter is not like it always was. The presence of oranges doesn’t speak to the volume or rot-level of the apples.
Twitter has lost advertisers, credibility, and legitimacy. That’s objectively demonstrable in the calibre, quantity, and aims of their advertisers, and their loss of revenue.
Twitter is hurting humanity, and has swaths of the population trapped in misinformation clouds. Arguably Elon bought the last election by purchasing it, and current administration issues are the result. But for the slow acclimatization and general brain fog of the “etch a sketch voters” we’d see Twitters direct reprogramming of opinion and behaviour as a psychic virus. You can tell which app people are hooked on by the lies they believe (with great emotional resonance).
Social Media is becoming increasingly restricted from children based on objective developmental and cognitive impacts, I dare speculate we and our parents are the asbestos eating unfiltered cigarette smoking pre-modern victims who misused something terribly until we figured out how bad that shizz is for us.
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I think models should be “forked”, and learn from subsets of input and themselves. Furthermore, individuals (or at least small groups) should have their own LLMs.
Sameness is bad for an LLM like it’s bad for a culture or species. Susceptible to the same tricks / memetic viruses / physical viruses, slow degradation (model collapse) and no improvement. I think we should experiment with different models, then take output from the best to train new ones, then repeat, like natural selection.
And sameness is mediocre. LLMs are boring, and in most tasks only almost as good as humans. Giving them the ability to learn may enable them to be “creative” and perform more tasks beyond humans.
That one 4chan troll delayed the launch of LLM like stuff by Google for about 6 years. At least that's what I attribute it to.
I was always curious about how Tay worked technically, since it was build before the Transformers era.
Was it based on a specific scientific paper or research?
The controversy surrounding it seemed to have polluted any search for a technical breakdown or a discussion, or the insights gained from it.
People have tried to suss this out on the ML subreddit, and it is confusing. Most of the worst messages from Tay were just people discovering a "repeat after me: __" function, so it's hard just to figure out which Tay messages to consider as responses of the model.
There seems to have been interest in a model which would pick up language and style of its conversations (not actually learning information or looking up facts). If you haven't trained an LSTM model before - you could train on Shakespeare's plays and get out ye olde English in a screenplay format, but from line to line there was no consistency in plot, characters, entrances and exits, etc. in a way which you'd expect after GPT-2. Twitter would be good for keeping a short-form conversation. So I believe Tay and the Watson that appeared on Jeopardy are more from this 'classical NLP' thinking and not proto-LLMs, if that makes sense.
https://blogs.microsoft.com/blog/2016/03/25/learning-tays-in...
https://arxiv.org/abs/1812.08989
Exactly. The notion of online learning is not new, but that approach cedes a lot of control to unknown forces. From a theoretical standpoint, this paper is interesting, there are definitely interesting questions to explore about how we could make an AI that learns autonomously. But in most production contexts, it's not desirable.
Imagine deploying a software product that changes over time in unknown ways -- could be good changes, could be bad, who knows? This goes beyond even making changes to a live system, it's letting the system react to the stream of data coming in and make changes to itself.
It's much preferable to lock down a model that is working well, release that, and then continue efforts to develop something better behind the scenes. It lets you treat it more like a software product with defined versions, release dates, etc., rather than some evolving organism.
> Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Well it shows that most humans degrades into 4chan eventually. AI just learned from that. :)
If aliens ever arrive here, send an AI to greet them. They will think we are totally deranged.
> Not learning from new input may be a feature.
Ugh HN is so tedious with these remarks. These people are trying to get computers to learn, not just train on data, and HN goes nOt LeArNiNg Is A fEaTuRe. Where's the wonder and the curiosity?
This is an astonishing claim and if true, will make AI a lot less useful in real life scenario.
In real life, take programming as an example, we want Claude to be strong in capability at first, but what is more important is for it to learn our code base, be proficient in it, as it gains experience around it. In other words, become a domain expert.
Because our code base is proprietary I don't expect ( not do I want) the AI to be familiar with it on the first day. So learning on the job is the only way to go.
Only in that way it will resemble a human programmer, and only then we can truly talk about replacing human programmer.
> Not learning from new input may be a feature.
Learning is OpenClaw's distinguishing feature. It has an array of plugins that let it talk to various services - but lots of LLM applications have that.
What makes it unique is it's memory architecture. It saves everything it sees and does. Unlike an LLM context its memory never overflows. It can search for relevant bits on request. It's recall is nowhere near as well as the attention heads of an LLM, but apparently good enough to make a difference. Save + Recall == memory.
Yes I like that /clear starts me at zero again and that feels nice but I am scared that'll go away.
Like when Google wasn't personalized so rank 3 for me is rank 3 for you. I like that predictability.
Obviously ignoring temperature but that is kinda ok with me.
I just had to reply because this is one of the most important things to me and I didn't put it in words before but you said it perfectly. Down to the Google example which is the one always on my mind. Humans really are all the same.
Yeah deep learning treats any training data as the absolute god given ground truth and will completely restructure the model to fit the dumbest shit you feed it.
The first LLMs were utter crap because of that, but once you have just one that's good enough it can be used for dataset filtering and everything gets exponentially better once the data is self consistent enough for there to be non-contradictory patterns to learn that don't ruin the gradient.