They keep tasking these LLMs with things that traditional programming solved a long time ago. There are already vending machines run by computers. They work just fine without AI.
Honestly the computer controlled vending machines are already over-engineered since many of them play ads when you walk up. The last customer-focused feature added was credit card support, and that just needs a credit card reader and a minimal IoT integration. They really shouldn’t even have screens.
Why are they… why are they having autocomplete recommend medical treatment? There are specialized AI algorithms that already exist for that purpose that do it far better (though still not well enough to even assist real doctors, much less replace them).
My point is that this kind of pseudo intelligence has never existed on Earth before, so evolution has had free reign to use language sophistication as a proxy for humanity and intelligence without encountering anything that would put selective pressure against this heuristic.
Human language is old. Way older than the written word. Our brains have evolved specialized regions for language processing, so evolution has clearly had time to operate while language has existed.
And LLMs are not the first sophisticated AI that’s been around. We’ve had AI for decades, and really good AI for a while. But people don’t anthropomorphize other kinds of AI nearly as much as LLMs. Sure, they ascribe some human like intelligence to any sophisticated technology, and some people in history have claimed some technology or another is alive/sentient. But with LLMs we’re seeing a larger portion of the population believing that that we haven’t seen in human behavior before.
My running theory is that human evolution developed a heuristic in our brains that associates language sophistication with general intelligence, and especially with humanity. The very fact that LLMs are so good at composing sophisticated sentences triggers this heuristic and makes people anthropomorphize them far more than other kinds of AI, so they ascribe more capability to them than evidence justifies.
I actually think this may explain some earlier reporting of some weird behavior of AI researchers as well. I seem to recall reports of Google researchers believing they had created sentient AI (a quick search produced this article). The researcher was fooled by his own AI not because he drank the Koolaid, but because he fell prey to this neural heuristic that’s in all of us.
According to Hui’s attorney, she hoped to gain permanent resident status in the U.S. after paying “an American citizen $2,000 to enter into a sham marriage.”
She needs to get a new attorney.
Even more surprising: the droplets didn’t evaporate quickly, as thermodynamics would predict.
“According to the curvature and size of the droplets, they should have been evaporating,” says Patel. “But they were not; they remained stable for extended periods.”
With a material that could potentially defy the laws of physics on their hands, Lee and Patel sent their design off to a collaborator to see if their results were replicable.
I really don’t like the repeated use of the phrase “defy the laws of physics.” That’s an extraordinary claim, and it needs extraordinary proof, and the researchers already propose a mechanism by which the droplets remained stable under existing physical laws, namely that they were getting replenished from the nanopores inside the material as fast as evaporation was pulling water out of the droplets.
I recognize the researchers themselves aren’t using the phrase, it’s the Penn press release organization trying to further drum up interest in the research. But it’s a bad framing. You can make it sound interesting without resorting to clickbait techniques like “did our awesome engineers just break the laws of physics??” Hell, the research is interesting enough on its own; passive water collection from the air is revolutionary! No need for editorializing!
The main issue is that nobody is going to want to create new content when they get paid nothing or almost nothing for doing so.
This is the whole reason copyright is supposed to exist. Content creators get exclusive control over the content they create for the duration of the copyright, so they can make a living off of work that then enriches society. And for the further benefit of society, after 14 years this copyright ends and the works become public domain, where anyone can create derivative works that will have copyright on them going to their own creators and the cycle continues, further enriching society.
Large companies first perverted this by getting Congress to extend the duration of copyright to truly absurd levels so they could continue to extract wealth from works they had to spend very little to maintain (mostly lawyers to enforce their copyrights). Since only they could create derivative works for 100(!) years, they did not have to compete with other creators in society, giving themselves a monopoly on what become cultural icons. Now corporate America has found a way to subvert creation itself, but it requires access to effectively all copyrighted works everywhere simultaneously. So now they just ignore the copyright, since it is impeding their wealth accumulation.
And so now the creative engine copyright is supposed to foster dies, taking the social enrichment it was designed to facilitate with it. People won’t stop making art or generating what’s supposed to be copyrighted works, but when they can’t making a living on it, they have to turn it into a hobby and spend the bulk of their time and energy on work that will put food on the table.
What’s the y-axis, and how exactly are you measuring it? Anybody can draw an exponential curve of nothing specific.
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People are making fun of the waffling and the apparent indecision and are missing the point. Trump isn’t flailing and trying to figure out how to actually make things work. He’s doing exactly what he intended: he’s holding the US economy for ransom and building a power base among the billionaires.
He used the poor and ignorant to get control of the public institutions, and now he’s using that power to get control over the private institutions (for-profit companies). He’s building a carbon copy of Russia with himself in the role of Putin. He’s almost there, and it’s taken him 2 months to do it.
That matches the hue, but the added dimension is the saturation. Counties in brighter colors are more impacted by retaliatory tariffs, while duller colors are less impacted. The point is to illustrate that a higher proportion of impacted counties voted for Trump than if the impact were spread evenly, though the effect is not that pronounced. Statistically, people working in different industries tend to vote one direction or the other compared to average, but there are still both Harris and Trump voters in every industry.
Why would I pronounce something with rules of English that’s not an English word? When I say the word jalapeno, I pronounce the tilde on the n even though in English it’s neither written with the tilde nor written with a letter combination that would produce that sound through standard English spelling.
In relation to English, it’s the “ng” sound in the common “-ing” ending or suffix.
Wikipedia has an entire article on it (of course): https://en.wikipedia.org/wiki/Voiced_velar_nasal
I’m sorry, I mostly agree with the sentiment of the article in a feel-good kind of way, but it’s really written like how people claim bullies will get their comeuppance later in life, but then you actually look them up later and they have high paying jobs and wonderful families. There’s no substance here, just a rant.
The author hints at analogous cases in the past of companies firing all of their engineers and then having to scramble to hire them back, but doesn’t actually get into any specifics. Be specific! Talk through those details. Prove to me the historical cases are sufficiently similar to what we’re starting to see now that justifies the claims of the rest of the article.
That’s not what a vomitorium was for. I understand the sentiment, but let’s not perpetuate a misunderstanding of Roman architecture/history.
Are you sure the answer you’re getting from AI about the weight of ginger is right? Before AI I would trust the answer from a smart speaker. Now I don’t trust anything any AI produces that should be fact-based. (Turning on lights and TV I would trust because I can see the results myself.)
I’m not the person you’re replying to, but I think their point is that the bars don’t scale linearly. The red bar (2014 price) for the McChicken is supposed to represent $1 and the yellow bar (2024 price) ~$3, but the yellow bar is not 3 times the length of the red bar. This means the relative differences between the bar lengths doesn’t match the percent increase number printed above then. This is most egregious comparing relative differences between the McChicken and the Quarter Pounder with Cheese meal: why does a 122% increase look so much worse than the 199% increase?
I suspect the cause of problem is that the small bars were stretched a bit to fit printing the dollar value within then, but if it throws off the visual accuracy of the bars, what’s the point of using bars at all?
As students work through lessons on subjects like math, reading, and science, the AI system will analyze their responses, time spent on tasks, and even emotional cues to optimize the difficulty and presentation of content
This will be a nightmare for any neuro-divergent students, or really any student with atypical learning needs.
They didn’t put in the biggest bid. They put in a bid that was a smaller amount of cash bundled with a waiver from the Sandyhook families that were to get a damages payout that they forgo their damages claim.
The third party evaluating the bids decided this was a better deal for Infowars’ creditors, as that meant more of the bankruptcy money would be going to them, so that’s why it was chosen.
The first statement is not even wholly true. While training does take more, executing the model (called “inference”) takes much, much more power than non-AI search algorithms, or really any traditional computational algorithm besides bogosort.
Big Tech weren’t doing the best they possibly could transitioning to green energy, but they were making substantial progress before LLMs exploded on the scene because the value proposition was there: traditional algorithms were efficient enough that the PR gain from doing the green energy transition offset the cost.
Now Big Tech have for some reason decided that LLMs represent the biggest game of gambling ever. The first to find the breakthrough to AGI will win it all and completely take over all IT markets, so they need to consume as much as they can get away with to maximize the probability that that breakthrough happens by their engineers.