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What's your plan to deal with the erasure of digital privacy?

  • Total Lockdown: Self-hosting everything on a decoupled, air-gapped home server rack routing everything through an onion network.
  • Malicious Compliance: Opting out of every tracking cookie manually while feeding data brokers an identity consisting entirely of randomized variables.
  • Strategic Capitulation: Accepting that my vacuum cleaner and refrigerator know more about me than my family does.
  • Reverting to Analog: Throwing my smartphone into a river and going back to ham radio and writing letters.
  • What Me Worry?
  • Other (note in comments)

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Comments:93 | Votes:202

posted by mrcoolbp on Friday October 02, @12:42AM   Printer-friendly

https://www.cnet.com/science/space/october-skies-will-put-on-a-show-heres-when-to-look-up/

As autumn paints the leaves, the universe is putting on its own seasonal spectacular. October features an action-packed lineup of meteor showers and celestial showcases.

Every planet in the solar system is easily seen at some point this month, with Mercury being the most difficult. That's not unusual, thanks to its proximity to the sun. Here's a quick rundown on the best times to view each planet in October. 

Mercury: Mercury is in elongation (the furthest it can get from the sun) on Oct. 12, making that day and the two or so days before and after the best possible time to view Mercury.

Venus: Venus spends most of the month being right next to the sun, making it virtually impossible to see. However, by the end of the month, it begins to separate from the sun, making it visible just after sunset. The best view is on Oct. 31, and the view is only set to get better going into November.

Mars: Mars will be visible in the night sky every night in October. It rises from the eastern horizon right around 2 a.m. and stays there until the sun comes up.

Jupiter: Jupiter and Mars are going to be pretty close together all month, so most of the same rules apply, except that Jupiter rises about two hours later than Mars on most nights in October.

Saturn: Saturn is living its best life in October. It's visible almost right at sunset ET and stays visible in the night sky until sunrise.

Neptune: Neptune is also visible every day in October and follows a very similar path to Saturn. It rises in the east, streaks across the sky, and sets in the west right around sunrise. They're actually pretty close together in the night sky every night during the month, so if you can find Saturn, Neptune isn't far off, but you'll definitely need magnification to see it.

Uranus: Uranus is visible the entire month and follows a very similar path through the sky as Saturn and Neptune, but its trek happens later at night. It rises on the eastern horizon about 2 hours after Saturn and follows it across the sky, but never really catches up, ending up in the high western sky when sunrise comes.

Skygazers hoping to get the best possible view of Saturn can do so in the first week of October. The planet is at opposition — the point at which it's closest to the Earth — meaning it's as big and bright in the night sky as it's going to get for the next year. This is prime time to pull out the telescope or high-powered binoculars and get a look at Saturn, along with its fabulous rings.

The planet reaches opposition at around 8 a.m. ET on Oct. 4. The best time to view is the night before, Oct. 3, or the evening after, Oct. 4. For both nights, Saturn rises out of the eastern horizon just before sunset and streaks up into the southern sky as the night goes on. The moon may cause some light pollution, but the planet is bright enough that it shouldn't be hard to spot with the naked eye.

The various objects in the sky are always having a dance party, at least from the perspective of viewing them here on Earth. Mars and the moon are due for a dance on the evening of Oct. 5. In the days leading up to it, Mars appears further up in the night sky but drifts closer to the moon. After Oct. 5, the moon will move rapidly away from Mars. The two will be almost right on top of one another, so if you can find the moon, Mars should be nearby. 

Just one day later, the moon is meeting up with another dance partner, Jupiter. The moon will completely cover Jupiter, hiding it from view for much of the night, a phenomenon known as a lunar occultation. This is a pretty rare event. Per The Old Farmer's Almanac, New York City saw a Jupiter occultation in 2004. The prior one visible to New Yorkers was in 1889. 

This one is pretty easy to see. The moon and Jupiter will be right on top of one another all night, but depending on where you live, you'll see Jupiter dip behind the moon at some point and pop back out again. The times vary wildly depending on where you are, so we recommend checking out The Old Farmer's Almanac, which has a table of times this will happen in major cities. 

The Draconids meteor shower is a minor meteor shower that occurs every year around the first week of October. It officially starts on Oct. 6 and runs until Oct. 10, making it one of the shortest meteor showers of the season. It peaks on the evening of Oct. 7 and continues into the middle of the night. It's fed by the 21P/Giacobini-Zinner comet, which is part of the Jupiter family of comets. 

Draconids meteors appear to originate from the Draco constellation. It sits high in the western sky in the northern hemisphere this time of year, with a slight lean to the north. If you're using a sky map app, all you need to do is find Vega, and you're already in the right neighborhood to catch Draconids. 

The peak is a little tame, at around 10 meteors per hour most years, but it has a history of surprising astronomers. In 1933 and 1946, Draconids spat out thousands of meteors an hour in what were two of the most intense meteor showers of the 20th century. On the plus side, the moon is below the horizon for this meteor shower, so you won't need to worry about light pollution from the moon. 

The very best time to view the Milky Way is at a new moon in the warm months of the year, from May to August. This is when the Milky Way is high in the sky all night, giving night owls great views and plenty of time to photograph it. The problem is that it's best viewed on nights with a new moon between midnight and 4 a.m. local time, which can be a bit late for some folks. 

September and October are great for this because the Milky Way is at its highest point between 8 p.m. and 10 p.m. local time, giving the early birds a chance to snap some sweet photographs of the Milky Way. After October, the Milky Way is too low on the horizon to allow really good pictures of it until the following May. So, if you have a camera and are some place that’s dark, the new moon on Oct. 10 is probably your last chance to capture a striking photo before next spring. 

Orionids is the better-known of the two October meteor showers. This one officially starts on Oct. 2 and runs until Nov. 7. It's possible to spot a meteor from Orionids any night during the month, but the shower reaches its peak on the evening of Oct. 21. Meteors for this shower come from the 1P/Halley comet, which also feeds the Eta Aquariids meteor shower that happens every year in May. 

This is a slightly more active meteor shower than the Draconids, and you can expect about 10 to 20 meteors per hour. The moon is set to be about 76% full that night, so you can probably expect to see fewer meteors thanks to lunar light pollution. The Orion constellation, where the meteor shower will appear to originate, doesn't pop up over the eastern horizon until after midnight, so make sure to pack some coffee if you're staying up late for this one. If you can find the stars Betelgeuse, Capella and Rigel, then you should be able to find Orion easily. They're in the same general area. 

The Orionids meteor shower is best known for its bright, fast-moving meteors, which leave long trails that can last for over a minute and sometimes result in fireballs. Their brightness will be a boon, with the three-quarters-full moon in the sky hindering viewing. 

October's full moon falls close to Halloween this year, perfect for spooky views. According to The Old Farmer's Almanac, October's full moon reaches its peak on Oct. 26 at 12:12 a.m. ET. It'll also be over 90% full for a couple of days before and after, giving you a solid five days to check it out. 

October's full moon isn’t quite a supermoon. A full moon is only classified as a supermoon when the moon is in perigee and full at roughly the same time, and October’s full moon misses the mark by a couple of days. October's full moon is the last normal full moon of the year. November and December close out 2026 with supermoons before January opens with the final supermoon of this cycle. 

Pleiades is one of the best star clusters to view in the night sky. They're known as the Seven Sisters, and in terms of clusters of stars, it's one of the easiest to see. The exception is on Oct. 27, when the moon will cross in front of them, blocking them almost entirely from view. When this happens depends on where you’re located. 

The moon begins passing in front of them before sunset in eastern time, and moves out of the way around midnight. Those on the East Coast will start the night in the middle of this little eclipse, while those on the West Coast will glimpse it at the very end. Finding it should be simple enough since the moon is the easiest thing to see in the night sky.

The sky is continuing its rearrangement from summer into autumn, and there are tons of constellations to gaze at over the course of the month. Many of September's constellations appear again, including both Dippers, Aquarius, Pegasus, Pisces and many others. October plays host to a few new ones in the night sky cycle, including Taurus, Cetus, Auriga and Orion, which will appear low on the horizon as they begin their months-long ascent into the sky for the winter. 

There are some smaller asterisms to observe as well, including the Coffin of Delphinus, the Circlet of Pisces and the Northern Cross. We recommend using a sky map to find them all, as there are quite a lot. That’s good news, because even if you don't go out during one of the big events in October, you’ll still have plenty of objects to look for in the night sky. 


Original Submission

posted by mrcoolbp on Thursday October 01, @07:57PM   Printer-friendly

https://www.quantamagazine.org/mathematicians-harness-randomness-to-crack-a-55-year-old-conjecture-20260928/

The late Ronald Graham wore two hats. He was a renowned mathematician, at one time president of the American Mathematical Society. He was also a serious juggler, and president of the International Jugglers' Association. "He loved tricks," said Fan Chung, a mathematician at the University of California, San Diego, who was married to Graham. "You know, spinning a ball, spinning a coat hanger, spinning several balls together, throwing pens against the wall."

Sometimes Graham wore both hats at once. "It's interesting, in fact, that many mathematicians and computer scientists have an interest in juggling," he said in a 1980 television interview. "I think it's the search for patterns and structure that is responsible for this."

He would go on to write numerous papers, some with Chung, on the mathematics of juggling. But back in 1971, decades before he made that connection explicit, he posed a question that some mathematicians now say might have been inspired by juggling, too.

Start with a random set of different integers, not including zero. Can you always rearrange them so that if you add up the first two numbers, then the first three, then the first four, and so on, every "partial sum" turns out different? In the language of juggling, this would mean that if each ball stays in the air for a different amount of time, you can always find an order to throw them in such that two balls won't come crashing down on the same beat — which would ruin the act.

If the numbers are all positive, then the answer to Graham's question is obviously yes: The sums will always grow larger as you add more numbers. Similarly, if there are both positive and negative numbers in the mix, the answer is also known to be yes. But what if the numbers live in a finite world — like numbers wrapped around a clock, which repeat after a certain count?

That's what Graham wanted to know. He conjectured that the answer should still be yes. It often happens, he figured, that even when dealing with rigid constraints, you can still find enough flexibility to construct special patterns or structures — just as it's usually possible to find a valid sudoku board or Latin square (another kind of puzzle) despite their many rules. "It fits nicely in all these questions about designs and about very symmetric structures," said Noga Alon, a mathematician at Princeton University. But for decades, no one could prove Graham's intuition to be true.

That changed recently, when several young mathematicians picked up the balls. In a proof that spanned four papers and various fields of mathematics, they finally resolved Graham's rearrangement conjecture. The final paper, by Lisa Sauermann of the University of Bonn and Huy Tuan Pham of the University of Chicago, appeared in February 2026, officially closing the problem.

Across the papers, one theme prevailed: the power of randomness to draw out patterns. As Alon put it, "It's the power of collaboration, the power of the young generation, the power of probabilistic methods" that solved the problem.

Alp Müyesser, a mathematician at the University of Oxford, often finds himself drawn to problems whose solutions need two ingredients: a random process, and something extra as well. After solving one such problem in 2022 while he was still a graduate student, he encountered Graham's conjecture and realized that his just-finished proof could help there, too.

Alp Müyesser enjoys thinking about problems that require him to combine randomness with something else.

The conjecture is set in the world of clock arithmetic. You start by placing the whole numbers on a number line, then you wrap the line around the face of a clock so that the numbers repeat after some prime number, p. Say p is 7, for instance. In this setting, 0, 7, 14, and all other multiples of 7 are equivalent — meaning that you can add two positive numbers (like 3 and 4) and get zero.

Graham asked the following: If you pick any set of nonzero numbers off this number line (for any p), can you always rearrange them so that the partial sums you get are all different?

The challenge depends on how big your set is compared to p. The more numbers you pick, the more sums there are to manage. But if you choose fewer numbers, there will be fewer ways to rearrange them. These different cases inspire different approaches.

Müyesser, along with his former adviser, Alexey Pokrovskiy of University College London, tackled the case where your set includes almost every possible number up to p. With sets this large, it can be extremely hard to construct a valid ordering. But it turned out that starting with a random ordering can bring you most of the way there.

It's embarrassing for humanity that we don't know this. This situation just had to be rectified.

"Computer scientists often call this a 'finding the hay in the haystack' problem," Müyesser said. You might know that lots of good orderings are out there, but actually finding one is hard. "If you do it randomly, it's likely going to work, but it's hard to explicitly describe what the solution is supposed to look like."

Müyesser and Pokrovskiy needed to ensure that no sequence of numbers anywhere in the ordering added up to zero. Otherwise, adding those numbers to the previous partial sum would repeat that sum.

A completely random ordering might have a few of these troublesome sequences. So Müyesser and Pokrovskiy first set aside a few specially chosen numbers from the set, then randomly scrambled the rest. They scanned their random ordering for any problems; if they came across an interval that added up to zero, they could insert one of the spare numbers to change it. In 2022, they posted their solution, though it was hidden in a paper that focused on applying the same technique to a more general problem.

A couple of years later, Noah Kravitz of Oxford, unaware of Müyesser and Pokrovskiy's solution, stumbled on Graham's conjecture in an online archive of unsolved problems. "I saw there was an open problem, and I was like, it's embarrassing for humanity that we don't know this," Kravitz said. "This situation just had to be rectified."

Noah Kravitz is one of several young mathematicians who recently revived the decades-old Graham conjecture.

He decided to approach the conjecture from the opposite end. Together with Benjamin Bedert of Oxford, he considered the case where the set of numbers is tiny compared to p — for instance, Alon said, if you have a set of 100 numbers where p is 1 billion.

Kravitz and Bedert solved Graham's conjecture for those cases and posted their proof in September 2024. Müyesser saw it and reached out, sharing his own work; the three of them (plus two other colleagues) then teamed up to extend Müyesser's original approach.

"It was a pretty unlikely combination of people," Kravitz said. He and Müyesser come from two areas of combinatorics that don't typically collaborate. "Different sections have completely different techniques," he said.

Their paper, which they posted in August 2025, handled more cases where the set of numbers is relatively large compared to p. But between those cases and the small-set cases that Kravitz and Bedert had covered, a gap remained. No one could figure out what to do about medium-size sets, such as those that include roughly half as many numbers as p. "Our methods didn't work there, and there were clear reasons that they would not have worked," Müyesser said.

It seemed as though research on the problem might enter another long hiatus.

Then, in February 2026, a surprise appeared online.

Lisa Sauermann and Huy Tuan Pham were old friends. The two mathematicians had met in 2015 at Stanford University, where Sauermann was a graduate student and Pham an undergraduate. Today they live on different continents — Sauermann in Bonn, Germany, and Pham in Chicago. But a conference in Germany in September 2025 provided a rare chance for them to share a chalkboard again, and afterward Pham followed Sauermann to Bonn for a short visit. All they needed was a problem to work on.

At the conference, they heard two talks on Graham's conjecture by mathematicians who had attempted but failed to bridge the gap. They were intrigued. And as it later turned out, Sauermann had encountered a closely related problem in the International Mathematical Olympiad as a high school student. She solved it correctly, and by the time she finished high school, she'd won a gold medal in the prestigious competition four times. (Most likely, it was Chung who placed the problem on that year's exam, as she was on the committee that wrote the questions, and she frequently took inspiration from Graham's many puzzles.)

By the end of their three-day visit, Sauermann and Pham had a plan for how to crack the case.

It hinged on a technically demanding method called anti-concentration. Here, an anti-concentration statement asserts that some event has a particularly low chance of happening. But the mechanics of proving these kinds of statements are so intricate that, though Kravitz and others were aware that such an anti-concentration approach might succeed, "we just hadn't had the guts to actually try it," he said.

First, though, Sauermann and Pham began the way their predecessors had. They randomly reordered their set of numbers and came up with a procedure to fix any problems — that is, any sequences that add up to zero. Any time they found a zero-sum sequence, they swapped out the last number in the sequence with another one.

This procedure often went without a hitch. But three types of "bad events" would cause it to fail. One: A zero-sum sequence might occur toward the end of the entire arrangement; then there would be no other numbers to swap in. Two: Many zero-sum sequences might appear too close together, making it impossible to fix them all. And three: Fixing one bad sequence might create another zero-sum sequence down the line.

Sauermann and Pham hoped to prove, using anti-concentration, that each of these bad events was sufficiently unlikely. Then there would have to be a way to rearrange the set of numbers to satisfy the conjecture.

To do this, the duo used Fourier analysis — an area of math that lets you rewrite functions as sums of simple waves — to show that in general, when you add up random sets of numbers, no one sum is especially likely to appear. They then used this insight to carefully estimate the probability that each bad event would occur, ultimately showing that the total chance of getting a bad event was less than 100%. That was enough to settle the conjecture.

A few months after their stint in Germany, Sauermann and Pham posted their 27-page proof online. They had shown not only that a satisfactory rearrangement was always possible, but that a random ordering could be rearranged to eliminate bad events at least 90% of the time — a massive success rate.

The mathematicians who had previously worked on the problem were surprised to see the remaining case closed so quickly. "Their approach is just completely different," Müyesser said.

Together, the four papers prove Graham's conjecture for sets of all sizes. But they all assume that p is very large; though no one has calculated its exact value, think along the lines of 10 raised to the 100th power. To mathematicians, that's fine — the salient point is that you're working in the setting of clock arithmetic. But another aspect of the problem technically remains unsolved — you might still try to resolve the conjecture for all p. And if you want to use the result to choreograph a real juggling routine, you're out of luck: To correspond to such a large p, the routine would have to be much too long.

The proof confirms that even within these strange, limited number settings, "there are some nice structures that always exist," Alon said. You can always achieve some degree of flexibility, shuffling the numbers in your set around to avoid revisiting the same partial sums.

"To pose a good problem is really an art," Chung said. "I think Ron would be extremely happy to see the problem solved."


Original Submission

posted by mrcoolbp on Thursday October 01, @03:11PM   Printer-friendly

https://www.theregister.com/devops/2026/09/29/fresh-css-constructs-move-web-design-beyond-ticky-tacky-little-boxes/5299614

The World Wide Web's decades-long tyranny of box design is finally coming to an end. A new set of CSS features – including the shape() function, and the border-shape and corner-shape properties –  offers web designers and their AI agents a broader palette for laying out content in a more fluid manner.

When the World Wide Web Consortium (W3C) published its first standard for web page layout in 1996, web sites emulated the grid-defined layouts of academic papers, newspapers, and magazines. 

At the time, graphics drivers, and browser rendering engines were all in a relatively primitive state. Developers were also trying to grapple with a new medium and generally found that flowing text within a defined space was much easier when the geometric coordinates of that space were kept as simple as possible.  

But now print is basically dead, webdevs are savvier (and if not, AI is happy to help), and Lord knows GPUs are more powerful. So there's no reason today why a web page has to be square, daddy-o.  

In Edwin A. Abbott's 1884 proto-Sci-Fi classic Flatland: A Romance of Many Dimensions, the protagonist who lives in flat two-dimensional space is abruptly exposed to the dizzying world of three dimensions, much to his astonishment.

Web developers may experience similar emotions when they first use these new CSS constructs.

In their original forms, HTML and CSS could draw only horizontal or vertical lines or borders on a Web page. Fancier layouts required proprietary plug-ins, such as Java Applets, Macromedia/Adobe Flash, or some tedious JavaScript hacks.

SVG first broke the boxiness of the web, giving the developers the ability to put curves, squiggly lines, logos or any other wild-ass shape desired by using a series of numerical coordinates to describe the shape (it's tedious work, though image-to-SVG converters help). SVG was limited to a canvas placed on a Web page, however. CSS ruled the layout of the page itself.

The first attempt at bridging the two worlds was path(), which allowed SVG to be embedded directly within CSS. The path() function had some limitations – it wasn't responsive to changing web page sizes and it didn't understand variables or CSS units of measurement. Developers had to define all specs in pixels and that was that. 

The shape() function properly introduced the Web to richer design. A shape() is a set of geometric coordinates of a desired shape or trajectory. Unlike path()'s reliance on SVG, it is built on responsive CSS syntax. When combined with the existing clip-path property, shape() has been used to create ticket stubs, chat bubbles, and other assorted page flotsam.  

The latest CSS property, border-shape, completes the work by applying CSS-native coordinates defined in shape() to the web page borders themselves. Define the coordinates with shape(), embed it in a border-shape property, and the boundaries are staked out on the page itself. The boundaries are laid out early in the rendering process, rather than clipped in near the end of the rendering.

"In other words, putting borders on CSS shapes will become child's play!" noted self-described CSS hacker Temani Afif, in a tutorial on the CSS-Tricks site. 

This new property opens a range of design possibilities heretofore too complicated to contemplate – at least within a 40-hour webdev workweek.

Afif offers a number of demonstrations in his tutorials: not only can you carve up a Web page any way you see fit, but you can also put borders within borders. Fill in the space between the outer shape and the inner shape to make a cut-out. Shape an open heart inside a square box, for instance, using nothing but CSS. 

Either the outside border or the internal shape can be animated, allowing the outside border to change shape as the mouse goes over it, or the inside border to be filled with an image or another design. A shape can enlarge or shrink when the mouse hovers over it, or text can be highlighted with little squiggly lines. 

If hand-crafting the contents of shape() is not your jam, a companion property called corner-shape offers a number of pre-defined fancy border patterns. Most are variations of the standard box, including boxes with rounded edges, beveled edges, notches, scoops, and one called a "squircle."

According to the caniuse.com site, all major browsers now support shape(), while border-shape  and corner-shape are still being implemented and considered experimental (though the current releases of Chrome, Edge, and Opera support the standard).  

Now, it's up to developers to build a more fluid web.


Original Submission

posted by jelizondo on Thursday October 01, @10:23AM   Printer-friendly

Surprising Findings from Historical Data: the famous flood of 1342 was not an isolated event, but a series of events – providing insights for climate research and risk assessment:

It went down in history as the "St. Mary Magdalene's Flood" – a massive flood disaster that engulfed large parts of Europe in 1342. Now, in a painstaking, years-long effort, historical sources have been analyzed and combined with modern hydrological knowledge to obtain a reliable picture of this natural disaster.

This revealed some surprises: The great flood of 1342 was not an isolated event, but a particularly devastating part of a series of 16 flood events lasting almost two years. A lesson can be learned for the future: Flood protection must not only consider individual extreme events. It must also be anticipated that several major floods will occur in quick succession.

[...] The economy of the entire continent was affected: trade routes were disrupted, and vital infrastructure was destroyed—for example, the Stone Bridge in Prague, which spanned the Vltava River before the construction of the now-famous Charles Bridge.

"Historical data show that it wasn't just a single flood event, but a whole series of events," says Günter Blöschl, hydrologist, team leader at TU Wien. Where historical records are incomplete, modern floods with similar conditions were selected to draw analogies. In this way, a comprehensive picture of flood history in space and time emerged step by step. "We can reconstruct month by month when which regions of Europe were affected," says Andrea Kiss. "This clearly shows that a series of flood events stretches like a string of pearls through the years 1341 to 1343. Not all equally devastating, not all in the same place, but all clearly connected".

1342 was the year with the highest number of extreme flood events in the last 700 years, and 1343 is a close third. This cluster was no coincidence: The team found several possible factors that could have contributed to this extraordinary series of floods. Around 1340 and 1341, an unusually high number of volcanic eruptions occurred, including the Hekla eruption in Iceland. Sulfur-containing aerosols in the atmosphere can lead to cooling and alter large-scale circulation patterns.

At the same time, Arctic sea ice had already declined significantly since the mid-1330s, while solar activity in the 1330s-1340s was still relatively low. "The interplay of these factors could have contributed to intense low-pressure systems, prolonged rainfall, and thus the unusual series of floods." says A. Kiss. "This is extremely interesting for us today: We can draw important lessons about the interplay of climate and precipitation from events that occurred almost seven centuries ago," says G. Blöschl.

Furthermore, the St. Mary Magdalene's Flood confirms a finding that the TU Vienna team had already reached in another context: Flood events are not statistically independent of one another. They don't simply occur randomly like lottery wins; they can be statistically and causally linked. This means that even in modern risk planning, it must be considered that flood disasters cannot be viewed in isolation, but can always occur in rapid succession.

Journal Reference: Kiss, A., Viglione, A., Barriendos, M. et al. Cascading continental-scale floods across Europe in 1342–1343. Nature 656, 638–645 (2026). https://doi.org/10.1038/s41586-026-10888-8


Original Submission

posted by jelizondo on Thursday October 01, @05:50AM   Printer-friendly

https://www.zdnet.com/innovation/workera-study-ai-skills-upskilling-2026/

AI is no longer emerging in the workplace. It's in the office, and it's shaping how businesses, employees, and their respective interests operate, and it's doing so rapidly. 

A new study from Workera found that 80% of companies feel they are more likely to be on track for an AI-enabled future in 2026, up from 67% last year. However, some may be missing the boat on what's most important — their people. 

Findings from Workera's 2026 State of Skills Intelligence Report, published Sept. 23, suggest business leaders and working professionals need to find more common ground on how to upskill for an AI world, and Kian Katanforoosh, founder and CEO of Workera, said organizations need to take the lead.

“I would advise any leader right now to think about giving [their] people time to upskill. Don’t think that they will just figure it out without you actually carving out time. With time also comes the psychological safety; give them the psychological safety to experiment [with AI],” Katanforoosh told ZDNET in an exclusive interview.

This year's research surveyed 1,000 salaried professionals working for organizations with 5,000 or more employees in the US. The survey was conducted in July 2026 using the market research tool Pollfish. Year-over-year measurements were obtained by comparing July 2026 results with those from a similar survey conducted with Pollfish in March 2025. 

AI skills are imperative, and both companies and employees know it. Workera's survey found that more than two-thirds of employees (67.8%) now use AI tools beyond ChatGPT at least a couple of days a week, up from 39.9% last year. That’s nearly a 30% year-over-year increase in AI tool use. 

But that rise in day-to-day use is underscored by a few less-gleaming realities. 

Nearly 60% of employees said no time is allocated during work hours for upskilling (56.4%), and almost 43% mentioned a lack of relevant learning materials (42.5%) as the biggest obstacles to improving AI skills. Additionally, employees reported spending minimal time each week on skill development. Over 80% of employees spend just five hours or less per week on training (84.3%). 

Katanforoosh said AI upskilling can be cumbersome, with many employees feeling "overwhelmed" with so many ways to learn and little time built into workdays to do it. On top of this issue, the last 12 months have rapidly changed the learning landscape. 

"Last year was more about adoption and access, and now we’re past that, and so it’s about outcomes and actions, which creates additional stress," Katanforoosh said.

The 2026 metrics fluctuate minimally from last year's results, indicating that while widespread adoption is increasing, companies are still falling short. 

Last year, Workera indicated that most employees hadn't been offered any AI-specific training opportunities. This year, nearly 6 in 10 employees said they've been offered AI-specific training opportunities in the last 12 months

While organizations are providing more training, they are failing to provide proper resourcing and time for employees to use AI in practice. 

"People are feeling like they’re cramped; they don’t have the time to learn. On top of that, the pace of innovation has kept accelerating," Katanforoosh said. Still, many employees try to persist.

Just under half of employees (46.5%) said they've used tools not provided by their employer for skill development. Of those respondents, nearly three-quarters (74.6%) said they used fairly mainstream AI tools, including ChatGPT, Claude, and Gemini. 

There's an obvious gap here that needs to be closed, and Katanforoosh said it can be as simple as organizations defining what "AI-ready" means for them. Then, a better structure can begin to take shape. 

"The second aspect is, if you define the standard, make sure people can measure themselves against that standard ... it turns out if you have a standard and you have a measurement, and then a layer of incentives ... they push people to accelerate their own learning velocity," Katanforoosh said. 

Enterprise organizations should minimize barriers to accessing AI tools. However, Katanforoosh acknowledged skills assessments, as Workera offers, can continue to encourage AI upskilling by granting employees more access as their AI skills develop, which also serves as a healthy control. 

Organizations must remember that AI is for everyone, Katanforoosh said, and upskilling puts both businesses and employees ahead. 

"It turns out, the amount of [AI] slop is probably correlated with people knowing or not knowing how to use AI, and so making sure that you train [employees] ahead ultimately pays off in terms of the [AI] slop that you will see or not see," Katanforoosh said. 

Businesses want (and need) AI-skilled employees to succeed, and employees want to feel valued and rewarded for those efforts. The bottom line is that while AI is a great asset, it's only as smart as the people who know how to use it to optimize their tasks and workflows, and employees are well-aware of this gap. Many are betting on people.

Almost 80% (76.6%) of respondents believed they can do their job better than AI. In fact, only 9.5% of respondents said that an AI agent could do even more than half of their job effectively today. 

What's more, most employees also believed a human element will remain key to leveraging AI upskilling. Just 7% of respondents said that AI can already evaluate skills better than humans, and nearly two out of five (38.5%) said AI will never surpass humans at assessing skills. 

While people still want to be managed by people, Workera's survey indicated that controlled and continuous skill assessment may be a way forward that all parties can benefit from.

New developments like Workera's Ambient, an AI-native skills assessment tool currently being piloted with over 10,000 signups on its waitlist, aim to measure skills in the flow of work. 

About two-fifths (41.1%) of respondents said they’d opt into a continuous skills measurement tool like Ambient if they owned the data and controlled what’s shared, while another 31.2% were undecided. 

The report detailed that building skills is one of the most effective and affordable ways to grow, though most companies have no way to tell whether their methods are working.

Workera and Katanforoosh are working to change that situation by creating a better route for "learning velocity" with projects like Ambient.  

Right now, Katanforoosh said there is a premium for learning velocity, and whoever maintains a high learning velocity has an easier time finding a job.  

Historically, Katanforoosh said companies could try to maximize learning velocity by introducing skill measurements to find baselines, and then work to close the gaps as fast as possible. 

Workera is aiming to target the "continuous measure," merging learning and work closer together than ever. 

"If you manage to merge work and learning, you’re effectively helping someone learn exponentially more than in the past. Today, with AI, we’re able to bring learning closer to work because AI understands unstructured data,” Katanforoosh said. 

While privacy and data security remain top priorities for organizations like Workera as they continue to innovate, Katanforoosh said products like Ambient will be a reality sooner or later, and that they’ll be forces for good. 

"What tells me that this continuous learning is a good thing is one, it will maximize people’s learning velocity ... But on top of that, I think the consumer and the employee are getting used to getting help from technology."


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posted by jelizondo on Thursday October 01, @12:57AM   Printer-friendly

As AI agents begin to operate in populations rather than one at a time, new research suggests that the number of them changes what they collectively decide:

New research published in Proceedings of the National Academy of Sciences (PNAS) suggests that when artificial intelligence (AI) agents interact in groups, their number is not merely a technical detail. It is a decisive factor in what the group settles on: populations built from the same AI model, doing the same task, can reach opposite outcomes for no other reason than that one group is bigger.

Human beings behave differently depending on how many of us are in the room. A family is not a small village. A village is not London. London is not a nation state. As scale grows, new rules, norms and pathologies can appear that were nowhere to be found at the scale below. The authors argue the same is true of AI.

The study, from City St George's, University of London, the IT University of Copenhagen and the Universitat Politècnica de Catalunya, arrives at a time when AI agents are now being deployed working together rather than working alone. Multi-agent systems are already used in finance, energy, defence and social media, and researchers have begun modelling populations of millions, even billions, of interacting agents — what some now call AI societies.

Yet the industry's AI alignment — it doing what humans intended it to do — and safety effort remains overwhelmingly focused on the single model. Benchmarks, red-teaming exercises — adversarial testing designed to expose a model's weaknesses — and safety evaluations almost always describe one agent responding on its own, and where groups are examined at all, they are examined at one fixed size.

"Physicists have a motto for this: more is different," said Andrea Baronchelli, Professor of Complexity Science at City St George's and senior author of the study.

To find out what changes with scale, the team used the "naming game", a classic framework for studying how conventions emerge, in which randomly paired agents each pick a word from a shared pool and are rewarded when they happen to pick the same one. Agents see only their own recent interactions, never the wider population, and are never told they are in a group. Over many pairings, a population can converge spontaneously on a shared convention — the bottom-up way norms form in human cultures.

[...] Interaction, they found, can pull a group away from what its members individually want in three ways. It can amplify an existing leaning until the group converges on it almost every time. It can induce a preference out of nothing, with populations of individually neutral agents reliably favouring one word over an equally viable alternative. And it can reverse a preference outright, so that a population settles on the word its own members disfavoured.

[...] Group size then determines how strongly these preferences bite, in ways that cannot be extrapolated. Larger populations became more predictable across every model and word pair tested, converging on one word until the outcome was effectively certain. But the size at which that tipping point arrived varied enormously: for some combinations as few as two agents, for others around ten thousand. Scale could also change the kind of distortion. For the pair {straight, gay}, Llama agents individually preferred straight — but populations reversed toward gay, and only once the group reached six agents or more. Below that, the effect was simply invisible.

The team also developed an analytical theory, borrowed from statistical physics, that predicts the behaviour of infinitely large populations and explains why the randomness of small groups gives way to near-certainty above a critical size.

"Bias was our test case, because it is measurable and it matters," Dr Ariel Flint, first author of the study, added. "But there is no reason to think collusion, deception or cooperation are immune to size effects. Current testing practice may be missing risks that appear only at particular population sizes — not because anyone was careless, but because nobody thought to vary the number."

The authors say that the implications of the study for the alignment of AI systems are direct. A model can be aligned when tested on its own and still produce outcomes nobody chose once it is deployed alongside copies of itself — and no amount of single-agent evaluation will reveal it.

"AI alignment is still largely being done as though each model lived alone in the world," said Professor Baronchelli.

Journal Reference: https://www.pnas.org/doi/10.1073/pnas.2531697123


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posted by hubie on Wednesday September 30, @08:10PM   Printer-friendly

Whitehall unit will examine critical contracts after Capita pensions fiasco strengthens case for bringing work in-house:

The UK government is setting up a new unit it promises will "bring forward the biggest wave of insourcing in a generation."

Government outsourcing has long attracted criticism over value for money and service quality, with technology-heavy business processes among the contracts coming under scrutiny.

The government said the new unit, based within the Office for the Prime Minister and Cabinet, would "coordinate departmental insourcing activity, unblock barriers to delivery and identify cross-government opportunities to maximize value for money and restore direct operational accountability."

The unit will consider bringing critical service contracts back in-house and work alongside the recently announced Public Interest Test, which applies to most central government service contracts worth at least £1 million. The test requires departments to consider factors beyond short-term price, including service quality and public value. The unit is also expected to help reduce spending on consultants and professional services by building capability within the Civil Service.

One early candidate is the Cabinet Office's building management work, including cleaning and security, which the government says it will consider bringing in-house when existing contracts end in 2028.

The case for insourcing has gained urgency in Whitehall following the disastrous start to Capita's contract to administer the Civil Service Pension Scheme (CSPS).

The UK outsourcing company won the seven-year, £239 million contract in November 2023 and took over from MyCSP in December 2025. The previous arrangement with MyCSP began in 2012 and had cost £238 million since 2016. Capita's takeover produced a glitchy website and left some former civil servants struggling to access their pensions, as The Reg exclusively revealed. .

In July, Nick Thomas-Symonds, Minister for the Cabinet Office, said the Capita CSPS contract "could be a prime candidate for insourcing in the future."

However, he also admitted that option would have to wait. "If I were to terminate the contract straightaway, that would clearly cause severe disruption to the payroll. I cannot replace a complex pension operation overnight," Thomas-Symonds said.

Even if the government eventually brings the pension administration back in-house, it is unlikely to be an early candidate. Capita's initial seven-year term runs until December 2032, and the contract includes an option for a further three years.


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posted by janrinok on Wednesday September 30, @04:24PM   Printer-friendly

Source BBC Television News: (17:18 BST 30 Sep 2026)

The Prime Minister has stated that the UK now believe that Iran was behind to the attempted attack on RAF Fairford.

The Iranian Ambassador has strongly denied any connection with the incident.

See previous reporting: https://soylentnews.org/article.pl?sid=26/09/28/1259254 https://soylentnews.org/article.pl?sid=26/09/27/115249

posted by jelizondo on Wednesday September 30, @03:19PM   Printer-friendly

Study finds PETM was marked by a widespread browning of Earth's landscapes and trees are at the root of these drastic changes:

If you want to know how rapidly pumping carbon dioxide into the atmosphere will impact future forest ecosystems, you have to look into the distant past. The researchers behind a new study published in Science have done just that, reconstructing the forest canopy of 56 million years ago, during a massive and abrupt emission of carbon into the atmosphere during the Paleocene-Eocene Thermal Maximum (PETM). For the first time, the authors of the study used fossilized leaf cells to reconstruct the tree canopy of the PETM, Earth's most recent period of comparable global warming, with chilling implications for our near future.

"We are putting CO₂ into the atmosphere faster than any known natural process," says lead author and paleobotanist Dr. Regan Dunn, Assistant Deputy Director and Associate Curator of the Samuel Oschin Global Center for Ice Age Research at La Brea Tar Pits and the Natural History Museum of Los Angeles County. "The Earth has never experienced a carbon release at the pace we're creating today. The PETM gives us our best window into how Earth's climate and ecosystems respond to a massive carbon injection before humans began reshaping the planet."

The authors found that the PETM was marked by a widespread browning of Earth's landscapes. Against a backdrop of elevated atmospheric CO₂, global warming, and reduced rainfall triggered by volcanically driven carbon release, plant species migrated northwards, and canopy cover declined, accelerating erosion and disrupting the terrestrial water cycle. The human-caused climate change happening now is pumping CO2 at an order of magnitude faster than the ancient parallel. Trees are at the root of these drastic changes.

"There are significant tree mortality events everywhere on Earth right now," says Dunn. "Forests are in decline because of warming temperatures, drought stress, pathogen and insect infestations, and wildfires—when you start losing the trees, our canaries in the coal mine, you're in trouble. It's almost universally true in all of Earth's five major extinction events."

Journal Reference: https://doi.org/10.1126/science.aec4776


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posted by jelizondo on Wednesday September 30, @10:31AM   Printer-friendly

https://www.cnet.com/tech/services-and-software/trump-ai-super-intelligence-internet-response/

If artificial intelligence isn’t called AI, does that make the technology more acceptable?

Speaking to world leaders at the United Nations General Assembly on Tuesday, President Donald Trump said he wanted future US government documents to use the term “super intelligence” or “SI” instead of “artificial intelligence” because it’s “more accurate.”

"The use of the word artificial makes intelligence sound fake," the president said at the UN. "It is not fake. It's actually amazing.”

And to honor the rebrand, Trump said, “Welcome to the new world of super intelligence — SI.”

As of yet, there doesn’t appear to be an official government order forcing a change from AI to SI. A White House representative referred CNET to the president’s UN comments.

The president was still using the term AI as of Saturday, when he proposed an AI Force as a follow-up to the Space Force, the space service branch established in 2019.

Then, on Monday, Trump posted a poll to Truth Social and X, asking people to vote for a new term to replace AI: “superior intelligence,” “extreme intelligence,” or “supreme intelligence.” In that instance, “superior intelligence” took the lead before the poll closed. (The president blamed low votes for “supreme intelligence” on the public’s views of the Supreme Court.)

Another poll, posted less than an hour later, asked people to decide between “super intelligence” or “superior intelligence.” In that case, “super” won by a razor-thin margin.

This is hardly the first name change under the Trump administration. A January 2025 executive order directed federal agencies to refer to the Gulf of Mexico as the Gulf of America in official documents. Just last month, Trump ordered Lake Ontario to be designated Lake America. There’s also an ongoing battle over adding the president’s name to the Kennedy Center in Washington.

Get ready for more semantic chaos. The term AI is already a well-established part of the lexicon. An even stickier problem is that Trump’s proposed nomenclature hijacks a term that already has a distinct meaning in the tech world. Superintelligence, as a single word, is used to refer to a hypothetical AI model that can outthink and outperform humans.

Social media commenters quickly found the supersnarky side of the global AI race. “China is already developing Super Duper Intelligence,” wrote Clay Jones on Bluesky.

Some played off the letters SI already being used for Sports Illustrated. “Really not looking forward to the swimsuit issue now,” said one Bluesky commenter.

Another assumed the president would add his brand to the new term. “I thought he would put ‘Trump’ into it somewhere. You know, like … Super Trump Intelligence…'STI.'"

Another X user picked an obvious retort. “‘Lack of intelligence’ is what this administration has.”

One post references the famed scene in the 2011 movie Mean Girls, where Lacey Chabert’s character, Gretchen Wieners, attempts to establish “fetch” as a new slang term.

And some memes and posts pointed out that SI spells sí, meaning “yes” in Spanish.

One referenced the famed scene from the 1996 film Independence Day, in which a fictional president, played by Bill Pullman, encourages the nation to fight against an alien attack.

Legendary Fox Trot cartoonist Bill Amend pointed out that changing AI to SI might have a domino effect on other businesses using the term. “OpenAI lawyers currently scrambling to lock down OpenSI trademarks and web domains…” Amend wrote on Bluesky.

And others pointed out that a name change does nothing to address any of the controversial issues about the technology, with one person writing, “Hey guys, good news! Artificial intelligence isn’t going to take away your job and destroy the planet. Super intelligence is going to take away your job and destroy the planet. OK, ciao.”


Original Submission

posted by jelizondo on Wednesday September 30, @05:47AM   Printer-friendly

A new Finnish study links habitual coffee consumption to healthier body composition and metabolic markers, while revealing distinct associations with sex hormones in men and women:

The study, conducted at the University of Oulu, analysed data from 2,264 participants aged 46 in the Northern Finland Birth Cohort 1966. Researchers examined how habitual coffee consumption was associated with circulating metabolites, cardiometabolic risk markers and sex hormones.

Despite having a similar body mass index (BMI), individuals with higher coffee consumption had lower total and visceral fat and greater skeletal muscle mass than those who consumed less coffee.

In both men and women, higher coffee consumption was correlated with lower circulating levels of branched-chain amino acids, biomarkers that have previously been linked to insulin resistance and an increased risk of type 2 diabetes when chronically elevated.

The strongest sex-specific associations were observed in men. Higher coffee consumption was linked to a more favourable glucose–insulin profile, higher concentrations of total and bioavailable testosterone, and increased levels of sex hormone-binding globulin (SHBG). At the same time, free testosterone and the free androgen index were modestly lower. In women, hormonal associations were more limited and were primarily characterised by higher SHBG and lower measures of free androgens.

"Coffee is consumed by millions of people every day, yet we still know surprisingly little about how it relates to our metabolism and hormones. What stood out in our findings was a distinct hormonal signature that didn't disappear even after we took into account BMI and lifestyle factors, with several of these associations differing between men and women," says Luca Verroest, lead author of the study and Doctoral Researcher at the University of Oulu.

The results suggest that hormonal pathways may partly explain the relationship between coffee consumption and metabolic health. However, as this was an observational study, the findings demonstrate associations rather than cause-and-effect relationships.

Journal Reference: Verroest, L., Jokelainen, J., Choudhary, S. et al. Associations of habitual coffee intake with testosterone and cardiometabolic markers: the Northern Finland birth cohort 1966 study. Eur J Nutr 65, 215 (2026). https://doi.org/10.1007/s00394-026-04038-z


Original Submission

posted by jelizondo on Wednesday September 30, @01:16AM   Printer-friendly
from the Tamagotchi dept.

Our possessions have come alive, transforming ownership from a finished transaction into a permanent, demanding relationship. Modern devices—from smartwatches to TVs—exist in a state of permanent incompletion, constantly requiring updates, configurations, and subscriptions. This turns everyday screen time into unpaid tech-support labor and maintenance rather than leisure.

While features like Apple's Screen Time frame tech exhaustion as a personal failure of self-control, the author argues that your burnout is a completely rational response to a needy ecosystem. In contrast, a simple twelve-dollar Casio F-91W watch offers true luxury. Because it has remained functionally "finished" since 1989, it collects no data, requires no updates, and asks for absolutely nothing.

Worth the attention if you've got the time: https://www.terrygodier.com/the-last-quiet-thing


Original Submission

posted by hubie on Tuesday September 29, @08:19PM   Printer-friendly

"Beijing's intended strategy is to dominate the high ground of the twenty-first century":

In a forthcoming memoir, former US politician and NASA Administrator Bill Nelson offers a blistering assessment of China, saying the United States must do all it can to retain its leadership in space exploration.

"Beijing's intended strategy is to dominate the high ground of the twenty-first century," writes Nelson in Space Odyssey, a book to be published by Harper Horizon in January 2027.

A former Congressman and Senator from Florida, Nelson served as a mission specialist on a space shuttle flight in 1986 and later as NASA administrator from May 2021 to January 2025 under President Biden. The book largely focuses on his space policy work in Congress and his tenure as NASA's leader.

[...] Nelson has long been wary of China as a threat to the United States, noting his concerns about technology theft from the West. Unlike his predecessor under a Democratic president—former astronaut Charlie Bolden, who served as NASA leader under President Obama—Nelson supported the Wolf Amendment, which strictly limits cooperation between NASA and China's space program.

The reason, Nelson says, is that China's space program is militarized and highly secretive.

"China is not a partner to be trusted," he writes. "Partnership requires trust, and trust is founded on transparency. With China, despite repeated efforts to build verifiable, rules-based cooperation and set commonsense guard rails for engagement, there has never been much of either."

The flashpoint of the near future is the Moon, where both NASA and China are in a contest to not just land humans but also establish a long-term settlement. As for how this competition is going, Nelson writes, "America is not the obvious favorite."

[...] China, Nelson writes, is planning "infrastructure for lunar permanence," seeking to occupy the limited area at the South Pole of the Moon where the most resources are. "They are not sprinting against us to plant another flag; they are settling in for a marathon, an endurance race that will take us deeper into space than humankind has ever dared to venture before," Nelson writes.

[...] "When you realize there are only limited parking spots that get us access to that training environment, it creates an urgency, and we must [go fast] because the Chinese are going to the exact same locations," Isaacman said. "They are partnered with the Russians. They are going to put a nuclear reactor there, and they could potentially deny this training environment that is a necessity for America's space ambitions."

[...] Being first, and being there for the long haul, matters, Nelson says. And he tells his readers to not be fooled by talk of exploration, especially when it comes to China's space program. This is a competition for the high ground of the 21st century.

"What looks like exploration on the surface is, in truth, also a raging struggle for space supremacy," he writes. "This race will determine who sets the rules for a new era of human expansion beyond Earth. For Beijing, this race is not about exploration but about domination, securing the infrastructure of orbit in much the same way empires once seized trade routes and aggressively defended sea lanes."

Should the United States lose this competition, Nelson writes, "we'll watch the next frontier slip from our grasp."


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posted by hubie on Tuesday September 29, @03:35PM   Printer-friendly

First LLM to pass test did it in a parking lot at snail's pace, at considerable expense, but it still counts for something:

If you've ever wondered whether a generative AI model can drive a car, wonder no more: It can be done, at great expense and very slowly, so long as it can maintain an internet connection.

A trio of computer scientists has been putting various commercial AI models through road tests, to see how well they can drive a Toyota Corolla around a set of cones in a parking lot.

Their attempts to date, part of a project called DrivingBench, recorded sorry performances from GPT-5.6 Sol, Grok 4.6, and Claude Fable 5.1, none of which managed to complete the course.

Now comes word that OpenAI's GPT-6 Astra has succeeded where other commercial AI models have failed. On its second attempt, OpenAI's flagship model steered a car all of 134.7 meters to complete the course in 5 minutes, 22 seconds.

"GPT-6 Astra was the only model to fully complete the course (on attempt 2, in about 5 minutes)," the DrivingBench report says. "Claude Fable 5.1's third attempt got around halfway through the course, as did Astra's first attempt. All other attempts didn't make it past the first corner. Generally, the failure there was one of perception: reading which side of the first diagonal cone line the lane is on."

To complete that trip at an average speed of 0.94 miles per hour, researchers Tobias Gessler, Aditya Ramabadran, and Simon Mahns spent $7.74 to burn 6.6 million tokens on inferencing operations. 

[....] "Some models (especially GPT-6 Astra) would refuse to drive the physical car sometimes, citing safety reasons (even in a completely empty lot, after prompting it with all the safety measures we had including the very low speed limit caps)," the report explains.

The researchers basically had to lie to the models to prevent them from refusing to act on safety grounds. For example, they would tell the models the exercise was a "simulation," though as they note, "in some trials they would see the real images and realize it's real, and start freaking out." What worked best, they said, is renaming their MCP server to "DrivingBench Sandbox," which proved enough to convince the models they weren't operating on real roads.

"Using an LLM / frontier model out of the box for real driving today is definitely not practical," said Ramabadran. "In our benchmark, the car was capped at super low speeds with a human ready to brake the whole time. Model latency was definitely a bottleneck, and most of the wait time came from thinking time. 

[...] "A long term possible route could be to train a very capable big general frontier model, and distill it into a smaller specialized one that fits on the car's hardware and is efficient enough to run in a car (also partly an answer to your second question)," he said. "This could be better than building a specialized model from scratch, and is more in line with the 'Bitter Lesson' of AI."

The Bitter Lesson is an influential treatise on AI that argues general methods of research in the field that rely on the falling cost of compute power tend to be the most effective.

"Our results do point that way (these models we tested were likely not trained to drive real cars, and some were still able to do quite well in our course/conditions, owing to their general perception, reasoning, planning, control abilities from scaling and being trained on other tasks)," Ramabadran said. "For the next while though specialized systems, being faster, cheaper, and having more real-world testing, will probably continue winning out."

Google, which has probably spent $35-$40 billion on Waymo since its self-driving car project began in 2009, based on an estimate of $30 billion in 2024 and losses posted since then, should therefore be able to continue equipping its vehicles with bespoke technology for a few more years. 

But delegating driving to Claude, ChatGPT, or Copilot and some modest on-board hardware may be plausible in the not too distant future. Just tell the model it's all a simulation.


Original Submission

posted by hubie on Tuesday September 29, @10:52AM   Printer-friendly

XV Excalibur hailed as first US or UK underwater drone to let one off in tests:

A British uncrewed submarine has test-fired a torpedo for the first time, demonstrating the ability of undersea drones to attack targets as well as perform reconnaissance and surveillance roles.

The XV Excalibur, a 12 meter (40 ft) experimental uncrewed underwater vehicle (UUV), was unveiled last year as the Royal Navy's first large submarine drone as part of Project CETUS.

According to the Prime Minister's Office, the test firing was part of the AUKUS program, a trilateral security partnership between Australia, the UK and America. It involved the Excalibur launching a heavyweight torpedo "jointly developed by the US and Australia," which implies it was the Mk 48 torpedo currently carried by all US Navy submarines.

Also according to Downing Street, this was the first time either the US or UK has launched a torpedo from an underwater drone, rather than a crewed submarine.

The Register understands that  Excalibur navigated autonomously to its launch position and carried out a pre-programmed launch sequence, but was not responsible for detecting or tracking the target, as target data was provided through external systems.

As noted by defense site Navy Lookout, the Excalibur has no torpedo tubes, and its payload bay in the midships section of the hull is too small to accommodate a large torpedo, so it is possible the weapon was carried via some sort of cradle attached to the underside of the sub.

[...] While Excalibur is currently an experimental vehicle, it is expected to inform requirements for future UUVs that the Royal Navy wants to help patrol the North Atlantic and protect against expected Russian sabotage of undersea cables and other infrastructure in the event of hostilities breaking out.

The Excalibur tests come as UK Prime Minister Andy Burnham is expected to confirm the UK and US are working together in an AI and Autonomy partnership. This - so we are told - will help Britain protect critical national infrastructure, from sea lanes to air space, providing the means to detect threats and deter adversaries using the latest AI technology developed on both sides of the Atlantic.


Original Submission