I don't think the quality of discussion about social media suffers from lack of specification. Whether or not you consider HN to be social media, or wherever your decision boundary is, doesn't change that most of the conversation does apply to the general class of apps/websites that have become de-facto short form video platforms. Which lots of people use, so the effects of use are consequential and worth discussing.
The conversations on consciousness though...oh man. I have to steel myself before diving into that mess.
I use the firefox extension "Unhook" to completely hide suggested content on Youtube. Really effective, I kindof can't believe how much time I spent getting suckered into watching video essays that absolutely did not deliver.
Just to be clear, your comment had a general statement about how you perceived the motivations of "people buying vinyl". That's what I was responding to. (People using VHS filters on social media is by definition social signalling so no comment there lol).
And I completely agree with your point about touting film as "easier" than digital. That's a stretch.
There are plenty of people who sincerely enjoy the aspects that make older tech less convenient or practical. Maybe it's an appreciation for the engineering or "comprehensibility," often it's because older tech produces unique outputs that can't be adequately captured by newer technology.
Reducing people's interest to "social signalling" comes off as dismissive.
Not asking adversarially at all here: what do you mean by resisting with "real numbers" without media campaigns, social media, or protesting? What do the vested parties actually do to secure their second amendment rights? Do you just mean having large voting blocs?
It looks to me like OpenAI's image pipeline takes an image as input, derives the semantic details, and then essentially regenerates an entirely new image based on the "description" obtained from the input image.
Even Sam Altman's "Ghiblified" twitter avatar looks nothing like him (at least to me).
Other models seem much more able to operate directly on the input image.
The seahorse emoji is one of the canonical "Mandela effects". These are things that a large group of people collectively (mis)remember, but turn out to have never existed. Classic examples include the cornucopia in the Fruit of the Loom label (never there), and the wording on car mirrors "objects in the mirror may be closer than they appear." (There's no record of 'may be closer', just 'are closer').
Unfortunately, the discussion around Mandela effects gets tainted by lots of people being so sure of their memory that the only explanation must be fantastical (the timeline has shifted!), giving the topic a valence of crazy that discourages engagement. I find these mass mis-rememberings fascinating from a psychological perspective, and lacking satisfying explanation (there probably isn't one).
So here we're seeing LLMs "experiencing" the same mandela effect that afflicts so many people, and I sincerely wonder why? The obvious answer is that the training data has lots of discussions about this particular mandela effect, ie people posting online "where is the seahorse emoji"? But those discussions are probably necessarily coupled with language that ascertains 'no, the seahorse emoji does not exist.' That's why the discussion is there in the first place! so why does the model take on the persona of someone that is sure it does exist? Why does it steer the models into such a weird feedback loop?
I've always been surprised by the official homeless population count, but it turns out there's a lot more to it.
The department of HUD generates this ~771K figure from a "point-in-time" estimate, a single count from a single night performed in January. They literally have volunteers go out, count the number of homeless people they observe, and report their findings.
It's not hard to imagine why this is probably a significant undercount. There is likely a long tail of people that happened to be in a situation that night where they were not able to be counted (i.e. somewhere secluded, sleeping in a friend's private residence that night, etc).
Even if these numbers are correct, to my mind a "crisis" is still more characterized by the trend than the numbers in absolute. From the first link you provided, we saw a 39% increase in "people in families" experiencing homelessness, and 9% in individuals. A resource from the HUD itself suggests a 33% increase in homelessness from 2020-2024, 18% increase from 2023-2024. That is far apace of the population increase in general.
And even then, I would say many people would suggest that the change in visible homelessness they've experienced in the last 10 years would amount to "crisis" levels, at least relative to the past.
It's completely fair to argue that it is not in fact a crisis, but claiming that it is certainly not "baseless."
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.
To be clear, I'm pretty sure the half-trillion figure is the projected combined investment between SoftBank, OpenAI, Oracle, and MGX. Not public, US tax-payer dollars.
If that's not what you meant my apologies. Reason I'm quick to point this out is I think some of the writing/headlines around are suggestive of this misconception.
Exactly. If the whole "deep research" thing pans out, and we have models that can reliably produce proper literature reviews in 10 minutes...that alone will be an enormous boon to research.
Then add all the practical/mundane tasks that you mentioned, and you've got quite the multiplier.