The term "Generative UI" refers to a front-end design approach where an AI model dynamically builds a UI in real time instead of relying on static, hard-coded templates.
Similar to how AI assitants give you answer directly instead of making you search through walls of text. Generative UI builds the right interface based on the context and intent of the user.
Public opinion and politicians will only notice when the job losses are massive, unfortunately. Right now, unemployment rates are still stable. We can only hope they will notice before things fall off a cliff (if they do).
It's like YouTube "Explainer" personalities, like Hank Green and Adam Neely. They do the kind of "learning theater" that makes you feel like you're learning something when you're actually just providing views and ad revenue. You'll come away from a video feeling like you gained knowledge, but:
1) there's a good chance it was subtly misleading (or just wrong)
2) you probably won't ever use the information in any meaningful way and will likely forget all relevant details in a few days
3) you almost certainly could have spent that time better actually doing or creating something - actually doing real learning and making real progress
The algorithm wanted these "educational" videos to be 10 minutes at first and now it seems to want them to be 20 minutes.
Once I saw that I realised just how padded the content really was and started looking elsewhere.
Other good tells the content is low quality or a bad fit for video are excessive amounts of talking head (should have been a podcast) or stock video clips (it's video, show me something).
Listening to YouTube on 2x makes it slightly bearable.
But this was also the joke about TED Talks: everyone came away smugly confident they were part of an amazing tide of progress, ignoring any engineering or scaling challenges.
Ten minutes used to be the cutoff where you could provide an interstitial advertisement. This sort of turned into a sort of wive's tale, where people thought the algorithm would push such videos. It's not the case today.
The reason that 20 minute videos are more popular these days is that more people are watching youtube on larger screens, like on their TV on the couch, and so more reliably watch longer videos than in the past.
The taxi driver claim is not really supported by any evidence. There was an article going around in 2024 claiming something like "taxi drivers die at 68", but the study it referenced had all kinds of methodological problems and was not actually designed to estimate occupational life expectancy.
"Sitting" is just a stand in for "having a sedentary lifestyle". Anyone who doesn't exercise will have these issues.
It's not uncharitable, because the system doesn't actually claim to give you an extra day of notice. It would be more accurate to say: "the model can reach a given level of forecast accuracy roughly a day farther in advance". So your question becomes: are there scenarios where I am 80% sure this is a Cat 5 hurricane 3 days before, where I would die if I was only 65% sure it was Cat 5 on that same day? The answer is - probably not, because even in the example used in the PR article, the NHC was already issuing strong early guidance 5 days before landfall.
Actually it doesn't even say that - they claim to have the same "accuracy" at 3 days that older methods have at 2 days. What does that actually get you? Were the older models so much less accurate at 3 days (compared to 2) that it prevented evacuation of key areas? Looking at the paper, it doesn't really seem like this can be answered yet because there's not enough data over a long enough time.
Keep in mind, DeepMind has a very well documented history of releasing enormously hyped up PR pieces with grandiose claims that are never backed up in real world usage, or are simply lies.
I wish there was a way that your comment could be pinned.
The context is so important here and radically re-frames the impact of GDM's results. Folks need to understand that with modern forecasting tools, we anticipate tropical cyclones to develop 5-10 days before they ever threaten landfall. The "2-day" vs "3-day" improvement in forecast skill is better interpreted as a modest reduction in forecast uncertainty - the "cone" on the hurricane track map gets a little narrower.
It's not like there's a "literal extra day" of preparation time for folks who may be impacted by the storm. They get the same amount of time they always have. Nothing actually changes on-the-ground for really any consumer of hurricane forecast data anywhere in the world.
And that's not a sleight against GDM. It's just a simple statement of how good contemporary weather forecasting is, and how good it was before AI forecast models came onto the scene some 5 years ago.
Very common press release/headline meme: the question in the title of the article is literally never answered (or the opposite of the headline's claim is true). This research plainly DOES NOT explain why certain mice got cancer and others didn't, let alone why smoking/UV rays cause cancer in some humans but not others. There was no control group for exposed mice that would've allowed them to look at which biological differences made one group get tumors and the others not (the study doesn't appear to actually be interested in that question).
The language here is also pretty hyperbolic. We've known for decades that certain gene mutations cause increased cancer risk in the context of a certain genetic background (BRCA1/2, etc.). Using language like "for the first time" is just juvenile, or purposeful lying at worst.
Post-LLMs: anyone can generate implementations, the ideas are what matter
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