Comment Send out a NASA intern to change the part (Score 1) 58
Ya cheap bastards
Comment Re:quality and reliability. (Score 1) 41
Looks like they are jelly of Alexa's driving features and care more about upselling their AI in cars than selling consumer AI speakers.
I fully expect Google to sunset all their home automation eventually. It is what they do best.
Comment Re:For the full list... (Score 1) 41
Yep, never trust Google with your time or data.
'What Kind of Bubble Is AI?' (locusmag.com) 100
"Of course AI is a bubble," argues tech activist/blogger/science fiction author Cory Doctorow.
The real question is what happens when it bursts?
Doctorow examines history — the "irrational exuberance" of the dotcom bubble, 2008's financial derivatives, NFTs, and even cryptocurrency. ("A few programmers were trained in Rust... but otherwise, the residue from crypto is a lot of bad digital art and worse Austrian economics.") So would an AI bubble leave anything useful behind? The largest of these models are incredibly expensive. They're expensive to make, with billions spent acquiring training data, labelling it, and running it through massive computing arrays to turn it into models. Even more important, these models are expensive to run.... Do the potential paying customers for these large models add up to enough money to keep the servers on? That's the 13 trillion dollar question, and the answer is the difference between WorldCom and Enron, or dotcoms and cryptocurrency. Though I don't have a certain answer to this question, I am skeptical.
AI decision support is potentially valuable to practitioners. Accountants might value an AI tool's ability to draft a tax return. Radiologists might value the AI's guess about whether an X-ray suggests a cancerous mass. But with AIs' tendency to "hallucinate" and confabulate, there's an increasing recognition that these AI judgments require a "human in the loop" to carefully review their judgments... There just aren't that many customers for a product that makes their own high-stakes projects betÂter, but more expensive. There are many low-stakes applications — say, selling kids access to a cheap subscription that generates pictures of their RPG characters in action — but they don't pay much. The universe of low-stakes, high-dollar applications for AI is so small that I can't think of anything that belongs in it.
There are some promising avenues, like "federated learning," that hypothetically combine a lot of commodity consumer hardware to replicate some of the features of those big, capital-intensive models from the bubble's beneficiaries. It may be that — as with the interregnum after the dotcom bust — AI practitioners will use their all-expenses-paid education in PyTorch and TensorFlow (AI's answer to Perl and Python) to push the limits on federated learning and small-scale AI models to new places, driven by playfulness, scientific curiosity, and a desire to solve real problems. There will also be a lot more people who understand statistical analysis at scale and how to wrangle large amounts of data. There will be a lot of people who know PyTorch and TensorFlow, too — both of these are "open source" projects, but are effectively controlled by Meta and Google, respectively. Perhaps they'll be wrestled away from their corporate owners, forked and made more broadly applicable, after those corporate behemoths move on from their money-losing Big AI bets.
Our policymakers are putting a lot of energy into thinking about what they'll do if the AI bubble doesn't pop — wrangling about "AI ethics" and "AI safety." But — as with all the previous tech bubbles — very few people are talking about what we'll be able to salvage when the bubble is over.
Thanks to long-time Slashdot reader mspohr for sharing the article.
The real question is what happens when it bursts?
Doctorow examines history — the "irrational exuberance" of the dotcom bubble, 2008's financial derivatives, NFTs, and even cryptocurrency. ("A few programmers were trained in Rust... but otherwise, the residue from crypto is a lot of bad digital art and worse Austrian economics.") So would an AI bubble leave anything useful behind? The largest of these models are incredibly expensive. They're expensive to make, with billions spent acquiring training data, labelling it, and running it through massive computing arrays to turn it into models. Even more important, these models are expensive to run.... Do the potential paying customers for these large models add up to enough money to keep the servers on? That's the 13 trillion dollar question, and the answer is the difference between WorldCom and Enron, or dotcoms and cryptocurrency. Though I don't have a certain answer to this question, I am skeptical.
AI decision support is potentially valuable to practitioners. Accountants might value an AI tool's ability to draft a tax return. Radiologists might value the AI's guess about whether an X-ray suggests a cancerous mass. But with AIs' tendency to "hallucinate" and confabulate, there's an increasing recognition that these AI judgments require a "human in the loop" to carefully review their judgments... There just aren't that many customers for a product that makes their own high-stakes projects betÂter, but more expensive. There are many low-stakes applications — say, selling kids access to a cheap subscription that generates pictures of their RPG characters in action — but they don't pay much. The universe of low-stakes, high-dollar applications for AI is so small that I can't think of anything that belongs in it.
There are some promising avenues, like "federated learning," that hypothetically combine a lot of commodity consumer hardware to replicate some of the features of those big, capital-intensive models from the bubble's beneficiaries. It may be that — as with the interregnum after the dotcom bust — AI practitioners will use their all-expenses-paid education in PyTorch and TensorFlow (AI's answer to Perl and Python) to push the limits on federated learning and small-scale AI models to new places, driven by playfulness, scientific curiosity, and a desire to solve real problems. There will also be a lot more people who understand statistical analysis at scale and how to wrangle large amounts of data. There will be a lot of people who know PyTorch and TensorFlow, too — both of these are "open source" projects, but are effectively controlled by Meta and Google, respectively. Perhaps they'll be wrestled away from their corporate owners, forked and made more broadly applicable, after those corporate behemoths move on from their money-losing Big AI bets.
Our policymakers are putting a lot of energy into thinking about what they'll do if the AI bubble doesn't pop — wrangling about "AI ethics" and "AI safety." But — as with all the previous tech bubbles — very few people are talking about what we'll be able to salvage when the bubble is over.
Thanks to long-time Slashdot reader mspohr for sharing the article.
Comment Re:Doesn't get basic facts right (Score 1) 47
Bard will argue with you if you tell it is wrong about some things.
Comment Re: Robotics (Score 2) 238
We still don't have a general purpose sewing robot. 90% of clothing is finished by hand.
Meta's New Rule: If Your Political Ad Uses AI Trickery, You Must Confess (techxplore.com) 110
Press2ToContinue writes: Starting next year, Meta will play the role of a strict schoolteacher for political ads, making them fess up if they've used AI to tweak images or sounds. This new 'honesty policy' will kick in worldwide on Facebook and Instagram, aiming to prevent voters from being duped by digitally doctored candidates or made-up events. Meanwhile, Microsoft is jumping on the integrity bandwagon, rolling out anti-tampering tech and a support squad to shield elections from AI mischief.
Comment Re:That's crazy (Score 5, Informative) 135
Plants kept growing at most latitudes and almost everywhere at lower altitudes. Just not as fast for 15-20 years. Mammal species ate a lot of tubers and nuts from the trees, which spread seeds that exploded in diversity in the years after the dinosaurs.
Comment Re:clueless writer (Score 1) 206
Once some of the newer deeper wells are shut off it will take a lot of money to turn them back on.
Comment Re:That's a good thing (Score 0) 111
The Supreme Court operates without a code of ethics.
Why should anyone trust anything they say?
Comment Re:Chiplets? (Score 1) 59
Shame MISC never took off or we could see 5000 core processors or something ridiculous like that.
Comment Re:"biggest architectural shift in 40 years" (Score 1) 59
The scaling for this new arch in terms of the number of onboard processors is going to be insane though. We might see 32 core processors with 16 big boys and 16 efficiency cores become the norm in a few years. That is huge.
Intel's new server lineup is rumored to be verging on 1000 with 500+ cores already revealed.
Comment The Exec who suggested this policy worked for EA. (Score 1) 127
Where he once suggested charging gamers for bullets when they ran out in MMOFPS games.
Comment Re: Lost for words (Score 2) 324
If you can't change your diction with the times you probably should start looking at retirement.
Younger people don't want to deal with the drama of some old guy who believes he is entitled to never change his behaviour around others since he was hired 20-30-40 years ago. Every single time I hear someone say they can't "act like themselves" the "actions" they are talking about are always demeaning of others. Why is that?
When I first started working in the late 1990's the Network Operation Center I worked at had fold-out Playboy posters in the break room - albeit the nipples were covered up with google-eyed stickers. I complained and they were down the next day, and an old guy who started on Sys V became livid. He then started messing with people's food who he thought had "ratted him out". They fired him, and he had the audacity to list us as references.
The folks who are the most problematic in any gig are the ones who don't realize that everyone can be replaced, and refuse to change.
Comment Re:It depends... (Score 1) 147
Learning standard and a few special CLI commands is important for any operating system.
Knowing commands like SFC
Learning how to use mv, cp, cd, etc on Linux takes only 15-20 minutes.
