Martin Hellman "achieved legendary status as co-inventor of the Diffie-Hellman public key exchange algorithm, a breakthrough in software and computer cryptography," notes a new interview in InfoWorld.

Nine years after winning the Turing award, the 78-year-old cryptologist shared his perspective on some other issues: What do you think about the state of digital spying today?

Hellman: There's a need for greater international cooperation. How can we have true cyber security when nations are planning — and implementing — cyber attacks on one another? How can we ensure that AI is used only for good when nations are building it into their weapons systems? Then, there's the grandaddy of all technological threats, nuclear weapons. If we keep fighting wars, it's only a matter of time before one blows up.

The highly unacceptable level of nuclear risk highlights the need to look at the choices we make around critical decisions, including cyber security. We have to take into consideration all participants' needs for our strategies to be effective....

Your battle with the government to make private communication available to the general public in the digital age has the status of folklore. But, in your recent book (co-authored with your wife Dorothie [and freely available as a PDF]), you describe a meeting of minds with Admiral Bobby Ray Inman, former head of the NSA. Until I read your book, I saw the National Security Agency as bad and Diffie-Hellman as good, plain and simple. You describe how you came to see the NSA and its people as sincere actors rather than as a cynical cabal bent on repression. What changed your perspective?

Hellman: This is a great, real-life example of how taking a holistic view in a conflict, instead of just a one-sided one, resolved an apparently intractable impasse. Those insights were part of a major change in my approach to life. As we say in our book, "Get curious, not furious." These ideas are effective not just in highly visible conflicts like ours with the NSA, but in every aspect of life.
Hellman also had an interesting answer when asked if math, game theory, and software development teach any lessons applicable to issues like nuclear non-proliferation or national defense.

"The main thing to learn is that the narrative we (and other nations) tell ourselves is overly simplified and tends to make us look good and our adversaries bad."

Jennifer Ouellette reports via Ars Technica: A typical lawn sprinkler features various nozzles arranged at angles on a rotating wheel; when water is pumped in, they release jets that cause the wheel to rotate. But what would happen if the water were sucked into the sprinkler instead? In which direction would the wheel turn then, or would it even turn at all? That's the essence of the "reverse sprinkler" problem that physicists like Richard Feynman, among others, have grappled with since the 1940s. Now, applied mathematicians at New York University think they've cracked the conundrum, per a recent paper published in the journal Physical Review Letters -- and the answer challenges conventional wisdom on the matter. "Our study solves the problem by combining precision lab experiments with mathematical modeling that explains how a reverse sprinkler operates," said co-author Leif Ristroph of NYU's Courant Institute. "We found that the reverse sprinkler spins in the 'reverse' or opposite direction when taking in water as it does when ejecting it, and the cause is subtle and surprising." [...]

Enter Leif Ristroph and colleagues, who built their own custom sprinkler that incorporated ultra-low-friction rotary bearings so their device could spin freely. They immersed their sprinkler in water and used a special apparatus to either pump water in or pull it out at carefully controlled flow rates. Particularly key to the experiment was the fact that their custom sprinkler let the team observe and measure how water flowed inside, outside, and through the device. Adding dyes and microparticles to the water and illuminating them with lasers helped capture the flows on high-speed video. They ran their experiments for several hours at a time, the better to precisely map the fluid-flow patterns.

Ristroph et al. found that the reverse sprinkler rotates a good 50 times slower than a regular sprinkler, but it operates along similar mechanisms, which is surprising. "The regular or 'forward' sprinkler is similar to a rocket, since it propels itself by shooting out jets," said Ristroph. "But the reverse sprinkler is mysterious since the water being sucked in doesn't look at all like jets. We discovered that the secret is hidden inside the sprinkler, where there are indeed jets that explain the observed motions." A reverse sprinkler acts like an "inside-out rocket," per Ristroph, and although the internal jets collide, they don't do so head-on. "The jets aren't directed exactly at the center because of distortion of the flow as it passes through the curved arm," Ball wrote. "As the water flows around the bends in the arms, it is slung outward by centrifugal force, which gives rise to asymmetric flow profiles." It's admittedly a subtle effect, but their experimentally observed flow patterns are in excellent agreement with the group's mathematical models.

"According to one study, homeowners are three times more likely than renters to own an electric vehicle," writes the Washington Post. But others still have options: Drivers who park on the street have found novel ways to charge their vehicles, using extension cords running over the sidewalk or even into the branches of a nearby tree... [S]ome municipalities explicitly allow over-the-sidewalk charging as part of a broader strategy to cut transportation emissions... In some areas, homeowners can also hire an electrician to run power under the sidewalk to a curbside charging port. But homeowners should check local rules and permitting requirements for curbside charging. In some highly EV-friendly cities, local governments will cover the costs. In Seattle, a pilot program is installing faster curbside charging to residents who opt in to the program...

If home charging simply isn't an option, some drivers rely on public charging — either using workplace chargers or charging occasionally on DC fast chargers, which can bring an EV battery from 0 to 80 percent in around 20 minutes. The problem is that public charging is more expensive than charging at home — although in most places, still less expensive than gas... For drivers who have access to Tesla superchargers, public charging might still be a solid option — but for non-Tesla drivers, it's still a challenge. Many fast chargers can be broken for days or weeks on end, or can be crowded with other drivers. The popular charging app PlugShare can help EV owners find available charging ports, but relying on public fast charging can quickly become a pain for drivers used to quickly filling up on gas. In those situations, a plug-in hybrid or regular hybrid car might be a better option.
And beyond that, "experts say that there are a key few steps that renters or condo owners can take to access charging," according to the article: The first is looking up local "right-to-charge" laws — regulations that require homeowners' associations or landlords to allow residents to install Level 1 or Level 2 charging. Ten states have "right-to-charge" laws on the books. In California and Colorado, for example, renters or homeowners have the right to install charging at their private parking space or, in some cases, in a public area at their apartment building. Other states, including Florida, Hawaii and New Jersey, have similar but limited laws. Residents can also reach out to landlords or property owners directly and make the case for installing charging infrastructure. All of this "puts a fair amount of onus on the driver," said Ben Prochazka, the executive director of the Electrification Coalition. But, he added, many EV advocacy groups are working on changing building codes in cities and states so that all multifamily homes with parking have to be "EV-ready."
Ingrid Malmgren, policy director at the EV advocacy group Plug In America, tells the newspaper that "communities all over the country are coming up with creative solutions. And it's just going to get easier and easier."

In response to new EU regulations, Apple on Thursday outlined plans to allow iOS developers to distribute apps outside the App Store starting in March, though developers must still submit apps for Apple's review and pay commissions. Now critics say the changes don't go far enough and Apple retains too much control.

Epic Games CEO Tim Sweeney: They are forcing developers to choose between App Store exclusivity and the store terms, which will be illegal under DMA (Digital Markets Act), or accept a new also-illegal anticompetitive scheme rife with new Junk Fees on downloads and new Apple taxes on payments they don't process. 37signals's David Heinemeier Hansson, who is also the creator of Ruby on Rails: Let's start with the extortion regime that'll befell any large developer who might be tempted to try hosting their app in one of these new alternative app stores that the EU forced Apple to allow. And let's take Meta as a good example. Their Instagram app alone is used by over 300 million people in Europe. Let's just say for easy math there's 250 million of those in the EU. In order to distribute Instagram on, say, a new Microsoft iOS App Store, Meta would have to pay Apple $11,277,174 PER MONTH(!!!) as a "Core Technology Fee." That's $135 MILLION DOLLARS per year. Just for the privilege of putting Instagram into a competing store. No fee if they stay in Apple's App Store exclusively.

Holy shakedown, batman! That might be the most blatant extortion attempt ever committed to public policy by any technology company ever. And Meta has many successful apps! WhatsApp is even more popular in Europe than Instagram, so that's another $135M+/year. Then they gotta pay for the Facebook app too. There's the Messenger app. You add a hundred million here and a hundred million there, and suddenly you're talking about real money! Even for a big corporation like Meta, it would be an insane expense to offer all their apps in these new alternative app stores.

Which, of course, is the entire point. Apple doesn't want Meta, or anyone, to actually use these alternative app stores. They want everything to stay exactly as it is, so they can continue with the rake undisturbed. This poison pill is therefore explicitly designed to ensure that no second-party app store ever takes off. Without any of the big apps, there will be no draw, and there'll be no stores. All of the EU's efforts to create competition in the digital markets will be for nothing. And Apple gets to send a clear signal: If you interrupt our tool-booth operation, we'll make you regret it, and we'll make you pay. Don't resist, just let it be. Let's hope the EU doesn't just let it be. Coalition of App Fairness, an industry body that represents over 70 firms including Tinder, Spotify, Proton, Tile, and News Media Europe: "Apple clearly has no intention to comply with the DMA. Apple is introducing new fees on direct downloads and payments they do nothing to process, which violates the law. This plan does not achieve the DMA's goal to increase competition and fairness in the digital market -- it is not fair, reasonable, nor non-discriminatory," said Rick VanMeter, Executive Director of the Coalition for App Fairness.

"Apple's proposal forces developers to choose between two anticompetitive and illegal options. Either stick with the terrible status quo or opt into a new convoluted set of terms that are bad for developers and consumers alike. This is yet another attempt to circumvent regulation, the likes of which we've seen in the United States, the Netherlands and South Korea. Apple's 'plan' is a shameless insult to the European Commission and the millions of European consumers they represent -- it must not stand and should be rejected by the Commission."

Dan Rockmore, the director of the Neukom Institute for Computational Sciences at Dartmouth College, writing for The New Yorker: Recently, statistical modelling has taken on a new kind of importance as the engine of artificial intelligence -- specifically in the form of the deep neural networks that power, among other things, large language models, such as OpenAI's G.P.T.s. These systems sift vast corpora of text to create a statistical model of written expression, realized as the likelihood of given words occurring in particular contexts. Rather than trying to encode a principled theory of how we produce writing, they are a vertiginous form of curve fitting; the largest models find the best ways to connect hundreds of thousands of simple mathematical neurons, using trillions of parameters.They create a vast data structure akin to a tangle of Christmas lights whose on-off patterns attempt to capture a chunk of historical word usage. The neurons derive from mathematical models of biological neurons originally formulated by Warren S. McCulloch and Walter Pitts, in a landmark 1943 paper, titled "A Logical Calculus of the Ideas Immanent in Nervous Activity." McCulloch and Pitts argued that brain activity could be reduced to a model of simple, interconnected processing units, receiving and sending zeros and ones among themselves based on relatively simple rules of activation and deactivation.

The McCulloch-Pitts model was intended as a foundational step in a larger project, spearheaded by McCulloch, to uncover a biological foundation of psychiatry. McCulloch and Pitts never imagined that their cartoon neurons could be trained, using data, so that their on-off states linked to certain properties in that data. But others saw this possibility, and early machine-learning researchers experimented with small networks of mathematical neurons, effectively creating mathematical models of the neural architecture of simple brains, not to do psychiatry but to categorize data. The results were a good deal less than astonishing. It wasn't until vast amounts of good data -- like text -- became readily available that computer scientists discovered how powerful their models could be when implemented on vast scales. The predictive and generative abilities of these models in many contexts is beyond remarkable. Unfortunately, it comes at the expense of understanding just how they do what they do. A new field, called interpretability (or X-A.I., for "explainable" A.I.), is effectively the neuroscience of artificial neural networks.

This is an instructive origin story for a field of research. The field begins with a focus on a basic and well-defined underlying mechanism -- the activity of a single neuron. Then, as the technology scales, it grows in opacity; as the scope of the field's success widens, so does the ambition of its claims. The contrast with climate modelling is telling. Climate models have expanded in scale and reach, but at each step the models must hew to a ground truth of historical, measurable fact. Even models of covid or elections need to be measured against external data. The success of deep learning is different. Trillions of parameters are fine-tuned on larger and larger corpora that uncover more and more correlations across a range of phenomena. The success of this data-driven approach isn't without danger. We run the risk of conflating success on well-defined tasks with an understanding of the underlying phenomenon -- thought -- that motivated the models in the first place.

Part of the problem is that, in many cases, we actually want to use models as replacements for thinking. That's the raison detre of modelling -- substitution. It's useful to recall the story of Icarus. If only he had just done his flying well below the sun. The fact that his wings worked near sea level didn't mean they were a good design for the upper atmosphere. If we don't understand how a model works, then we aren't in a good position to know its limitations until something goes wrong. By then it might be too late. Eugene Wigner, the physicist who noted the "unreasonable effectiveness of mathematics," restricted his awe and wonder to its ability to describe the inanimate world. Mathematics proceeds according to its own internal logic, and so it's striking that its conclusions apply to the physical universe; at the same time, how they play out varies more the further that we stray from physics. Math can help us shine a light on dark worlds, but we should look critically, always asking why the math is so effective, recognizing where it isn't, and pushing on the places in between.

An anonymous reader quotes a report from MIT Technology Review: Google DeepMind has created an AI system that can solve complex geometry problems. It's a significant step towards machines with more human-like reasoning skills, experts say. Geometry, and mathematics more broadly, have challenged AI researchers for some time. Compared with text-based AI models, there is significantly less training data for mathematics because it is symbol driven and domain specific, says Thang Wang, a coauthor of the research, which is published in Nature today. Solving mathematics problems requires logical reasoning, something that most current AI models aren't great at. This demand for reasoning is why mathematics serves as an important benchmark to gauge progress in AI intelligence, says Wang.

DeepMind's program, named AlphaGeometry, combines a language model with a type of AI called a symbolic engine, which uses symbols and logical rules to make deductions. Language models excel at recognizing patterns and predicting subsequent steps in a process. However, their reasoning lacks the rigor required for mathematical problem-solving. The symbolic engine, on the other hand, is based purely on formal logic and strict rules, which allows it to guide the language model toward rational decisions. These two approaches, responsible for creative thinking and logical reasoning respectively, work together to solve difficult mathematical problems. This closely mimics how humans work through geometry problems, combining their existing understanding with explorative experimentation.

DeepMind says it tested AlphaGeometry on 30 geometry problems at the same level of difficulty found at the International Mathematical Olympiad, a competition for top high school mathematics students. It completed 25 within the time limit. The previous state-of-the-art system, developed by the Chinese mathematician Wen-Tsun Wu in 1978, completed only 10. "This is a really impressive result," says Floris van Doorn, a mathematics professor at the University of Bonn, who was not involved in the research. "I expected this to still be multiple years away." DeepMind says this system demonstrates AI's ability to reason and discover new mathematical knowledge. "This is another example that reinforces how AI can help us advance science and better understand the underlying processes that determine how the world works," said Quoc V. Le, a scientist at Google DeepMind and one of the authors of the research, at a press conference.

"Sal Kahn, the founder and CEO of Khan Academy predicted last year that AI tutoring bots would soon revolutionize education," writes long-time Slashdot reader theodp: theodp writes: His vision of tutoring bots tapped into a decades-old Silicon Valley dream: automated teaching platforms that instantly customize lessons for each student. Proponents argue that developing such systems would help close achievement gaps in schools by delivering relevant, individualized instruction to children faster and more efficiently than human teachers ever could. But some education researchers say schools should be wary of the hype around AI-assisted instruction, warning that generative AI tools may turn out to have harmful or "degenerative" effects on student learning.
A ChatGPT-powered tutoring bot was tested last spring at the Khan Academy — and Bill Gates is enthusiastic about that bot and AI education in general (as well as the Khan Academy and AI-related school curriculums). From the original submission: Explaining his AI vision in November, Bill Gates wrote, "If a tutoring agent knows that a kid likes [Microsoft] Minecraft and Taylor Swift, it will use Minecraft to teach them about calculating the volume and area of shapes, and Taylor's lyrics to teach them about storytelling and rhyme schemes. The experience will be far richer—with graphics and sound, for example—and more personalized than today's text-based tutors."

The New York Times article notes that similar enthusiasm greeted automated teaching tools in the 1960s, but predictions that that the mechanical and electronic "teaching machines' — which were programmed to ask students questions on topics like spelling or math — would revolutionize education didn't pan out.

So, is this time different?

OpenAI has launched an online store where people can share customized versions of the company's popular ChatGPT chatbot, after initially delaying the rollout because of leadership upheaval last year. From a report: The new store, which rolled out Wednesday to paid ChatGPT users, will corral the chatbots that users create for a variety of tasks, for example a version of ChatGPT that can teach math to a child or come up with colorful cocktail recipes. The product, called the GPT Store, will include chatbots that users have chosen to share publicly. It will eventually introduce ways for people to make money from their creations -- much as they might through the app stores of Apple or Alphabet's Google.

Similar to those app stores, OpenAI's GPT Store will let users see the most popular and trending chatbots on a leaderboard and search for them by category. In a blog post announcing the rollout, OpenAI said that people have made 3 million custom chatbots thus far, though it was not clear how many were available through its store at launch. The store's launch comes as OpenAI works to build out its ecosystem of services and find new sources of revenue. On Wednesday, OpenAI also announced a new paid ChatGPT tier for companies with smaller teams that starts at $25 a month per user. OpenAI first launched a corporate version of ChatGPT with added features and privacy safeguards in August.

The chief content officer for New York's public radio station WNYC predicts an "AI-fueled shift to niche community and authored excellence."

And ironically, it will be fueled by "Greedy publishers and malicious propagandists... flooding the web with fake or just mediocre AI-generated 'content'" which will "spotlight and boost the value of authored creativity." And it may help give birth to a new generation of independent media. Robots will make the internet more human.

First, it will speed up our migration off of big social platforms to niche communities where we can be better versions of ourselves. We're already exhausted by feeds that amplify our anxiety and algorithms that incentivize cruelty. AI will take the arms race of digital publishing shaped by algorithmic curation to its natural conclusion: big feed-based social platforms will become unending streams of noise. When we've left those sites for good, we'll miss the (mostly inaccurate) sense that we were seeing or participating in a grand, democratic town hall. But as we find places to convene where good faith participation is expected, abuse and harassment aren't, and quality is valued over quantity, we'll be happy to have traded a perception of scale influence for the experience of real connection.

Second, this flood of authorless "content" will help truly authored creativity shine in contrast... "Could a robot have done this?" will be a question we ask to push ourselves to be funnier, weirder, more vulnerable, and more creative. And for the funniest, the weirdest, the most vulnerable, and most creative: the gap between what they do and everything else will be huge. Finally, these AI-accelerated shifts will combine with the current moment in media economics to fuel a new era of independent media.
For a few years he's seen the rise of independent community-funded journalists, and "the list of thriving small enterprises is getting longer." He sees more growth in community-funding platforms (with subscription/membership features like on Substack and Patreon) which "continue to tilt the risk/reward math for audience-facing talent....

"And the amount of audience-facing, world-class talent that left institutional media in 2023 (by choice or otherwise) is unlike anything I've seen in more than 15 years in journalism... [I]f we're lucky, we'll see the creation of a new generation of independent media businesses whose work is as funny, weird, vulnerable and creative as its creators want it to be. And those businesses will be built on truly stable ground: a direct financial relationship with people who care.

"Thank the robots."

Longtime Slashdot reader fahrbot-bot shares a report from ScienceAlert: According to a new study [published in Nature Human Behavior], the human brain has two separate ways of processing numbers of things: one system for quantities of four or fewer, and another system for five and up. Presented with four or fewer objects, humans can usually identify the sum at first glance, without counting. And we're almost always right. This ability is known as "subitizing," a term coined by psychologists last century, and it's different from both counting and estimating. It refers to an uncanny sense of immediately knowing how many things you're looking at, with no tallying or guessing required.

While we can easily subitize quantities up to four, however, the ability disappears when we're looking at five or more things. If asked to instantly quantify a group of seven apples, for example, we tend to hesitate and estimate, taking slightly longer to respond and still providing less precise answers. Since our subitizing skills vanish so abruptly for quantities larger than four, some researchers have suspected our brains use two distinct processing methods, specialized for either small or large quantities. "However, this idea has been disputed up to now," says co-author Florian Mormann, a cognitive neurophysiologist from the Department of Epileptology at the University Hospital Bonn. "It could also be that our brain always makes an estimate but the error rates for smaller numbers of things are so low that they simply go unnoticed."

Previous research involving some of the new study's authors showed that human brains have neurons responsible for each number, with certain nerve cells firing selectively in response to certain quantities. Some neurons fire mainly when a person sees two of something, they found, while others show a similar affinity for their own number of visual elements. Yet many of these neurons also fire in response to slightly smaller or larger numbers, the researchers note, with a weaker reaction for quantities further removed from their numerical focus. "A brain cell for a number of 'seven' elements thus also fires for six and eight elements but more weakly," says neurobiologist Andreas Nieder from the University of Tubingen. "The same cell is still activated but even less so for five or nine elements."

This kind of "numerical distance effect" also occurs in monkeys, as Nieder has shown in previous research. Among humans, however, it typically happens only when we see five or more things, hinting at some undiscovered difference in the way we identify smaller numbers. "There seems to be an additional mechanism for numbers of around less than five elements that makes these neurons more precise," Nieder says. Neurons responsible for lower numbers are able to inhibit other neurons responsible for adjacent numbers, the study's authors report, thus limiting any mixed signals about the quantity in question. When a trio-specializing neuron fires, for example, it also inhibits the neurons that typically fire in response to groups of two or four things. Neurons for the number five and beyond apparently lack this mechanism.

"The events of 2023 showed that A.I. doesn't need to be that good in order to do damage," argues novelist Lincoln Michel in the New Republic: This March, news broke that the latest artificial intelligence models could pass the LSAT, SAT, and AP exams. It sparked another round of A.I. panic. The machines, it seemed, were already at peak human ability. Around that time, I conducted my own, more modest test. I asked a couple of A.I. programs to "write a six-word story about baby shoes," riffing on the famous (if apocryphal) Hemingway story. They failed but not in the way I expected. Bard gave me five words, and ChatGPT produced eight. I tried again, specifying "exactly six words," and received eight and then four words. What did it mean that A.I. could best top-tier lawyers yet fail preschool math?

A year since the launch of ChatGPT, I wonder if the answer isn't just what it seems: A.I. is simultaneously impressive and pretty dumb. Maybe not as dumb as the NFT apes or Zuckerberg's Metaverse cubicle simulator, which Silicon Valley also promised would revolutionize all aspects of life. But at least half-dumb. One day A.I. passes the bar exam, and the next, lawyers are being fined for citing A.I.-invented laws. One second it's "the end of writing," the next it's recommending recipes for "mosquito-repellant roast potatoes." At best, A.I. is a mixed bag. (Since "artificial intelligence" is an intentionally vague term, I should specify I'm discussing "generative A.I." programs like ChatGPT and MidJourney that create text, images, and audio. Credit where credit is due: Branding unthinking, error-prone algorithms as "artificial intelligence" was a brilliant marketing coup)....

The legal questions will be settled in court, and the discourse tends to get bogged down in semantic debates about "plagiarism" and "originality," but the essential truth of A.I. is clear: The largest corporations on earth ripped off generations of artists without permission or compensation to produce programs meant to rip us off even more. I believe A.I. defenders know this is unethical, which is why they distract us with fan fiction about the future. If A.I. is the key to a gleaming utopia or else robot-induced extinction, what does it matter if a few poets and painters got bilked along the way? It's possible a souped-up Microsoft Clippy will morph into SkyNet in a couple of years. It's also possible the technology plateaus, like how self-driving cars are perpetually a few years away from taking over our roads. Even if the technology advances, A.I. costs lots of money, and once investors stop subsidizing its use, A.I. — or at least quality A.I. — may prove cost-prohibitive for most tasks....

A year into ChatGPT, I'm less concerned A.I. will replace human artists anytime soon. Some enjoy using A.I. themselves, but I'm not sure many want to consume (much less pay for) A.I. "art" generated by others. The much-hyped A.I.-authored books have been flops, and few readers are flocking to websites that pivoted to A.I. Last month, Sports Illustrated was so embarrassed by a report they published A.I. articles that they apologized and promised to investigate. Say what you want about NFTs, but at least people were willing to pay for them.
"A.I. can write book reviews no one reads of A.I. novels no one buys, generate playlists no one listens to of A.I. songs no one hears, and create A.I. images no one looks at for websites no one visits.

"This seems to be the future A.I. promises. Endless content generated by robots, enjoyed by no one, clogging up everything, and wasting everyone's time."

Following policy adjustments by the U.S. Citizenship and Immigration Services (USCIS) in January, more foreign-born workers in science, technology, engineering, and math (STEM) fields are able to live and work permanently in the United States. "The jump comes after USCIS in January 2022 tweaked its guidance criteria relating to two visa categories available to STEM workers," reports Science Magazine. "One is the O-1A, a temporary visa for 'aliens of extraordinary ability' that often paves the way to a green card. The second, which bestows a green card on those with advanced STEM degrees, governs a subset of an EB-2 (employment-based) visa." From the report: The USCIS data, reported exclusively by ScienceInsider, show that the number of O-1A visas awarded in the first year of the revised guidance jumped by almost 30%, to 4570, and held steady in fiscal year 2023, which ended on 30 September. Similarly, the number of STEM EB-2 visas approved in 2022 after a "national interest" waiver shot up by 55% over 2021, to 70,240, and stayed at that level this year. "I'm seeing more aspiring and early-stage startup founders believe there's a way forward for them," says Silicon Valley immigration attorney Sophie Alcorn. She predicts the policy changes will result in "new technology startups that would not have otherwise been created."

President Joe Biden has long sought to make it easier for foreign-born STEM workers to remain in the country and use their talent to spur the U.S. economy. But under the terms of a 1990 law, only 140,000 employment-based green cards may be issued annually, and no more than 7% of those can go to citizens of any one country. The ceiling is well below the demand. And the country quotas have created decades-long queues for scientists and high-tech entrepreneurs born in India and China. The 2022 guidance doesn't alter those limits on employment-based green cards but clarifies the visa process for foreign-born scientists pending any significant changes to the 1990 law. The O-1A work visa, which can be renewed indefinitely, was designed to accelerate the path to a green card for foreign-born high-tech entrepreneurs.

Although there is no cap on the number of O-1A visas awarded, foreign-born scientists have largely ignored this option because it wasn't clear what metrics USCIS would use to assess their application. The 2022 guidance on O-1As removed that uncertainty by listing eight criteria -- including awards, peer-reviewed publications, and reviewing the work of other scientistsâ"and stipulating that applicants need to satisfy at least three of them. The second visa policy change affects those with advanced STEM degrees seeking the national interest waiver for an EB-2. Under the normal process of obtaining such a visa, the Department of Labor requires employers to first satisfy rules meant to protect U.S. workers from foreign competition, for example, by showing that the company has failed to find a qualified domestic worker and that the job will pay the prevailing wage. That time-consuming exercise can be waived if visa applicants can prove they are doing "exceptional" work of "substantial merit and national importance." But once again, the standard for determining whether the labor-force requirements can be waived was vague, so relatively few STEM workers chose that route. The 2022 USCIS guidance not only specifies criteria, which closely track those for the nonimmigrant, O-1A visa, but also allows scientists to sponsor themselves.

"It's like visiting an old friend for the holidays," according to this article: Approaching his 86th birthday, Donald Knuth — Stanford's beloved computer science guru — honored what's become a long-standing tradition. He gave a December "Christmas lecture" that's also streamed online for all of his fans...

More than 60 years ago, back in 1962, a 24-year-old Donald Knuth first started writing The Art of Computer Programming — a comprehensive analysis of algorithms which, here in 2023, he's still trying to finish. And 30 years ago Knuth also began making rare live appearances each December in front of audiences of Stanford students...

Recently Stanford uploaded several decades of Knuth's past Christmas lectures, along with a series of 22 videos of Knuth from 1985 titled "the 'Aha' Sessions'" (courses in mathematical problem-solving). There are also two different sets of five videos from 1981 showing Knuth introducing his newly-created typesetting system TeX. There are even 12 videos from 1982 of what Knuth calls "an intensive course about the internal details."

And on Dec. 6, wearing his traditional brown holiday sweater, Knuth gave yet another live demonstration of the beautifully clear precision that's made him famous.

The New Yorker looks at 41-year-old Amabel Holland, an autistic board-game designer who "thinks about the world in terms of systems," and realized you could make a board game about almost anything, "and, when you did, its rules could both mirror and analyze the subject on which it was based."

They've since designed more than 60 games, and the article notes that Holland's work, "which is part of a larger turn toward complexity in the industry, often tackles historical and social subjects — death, religion, misinformation — using surprising 'mechanics,' or building blocks of game play, to immerse players in an experience." "With every game, you build a certain model of the world," Reiner Knizia, a former mathematician who's designed more than eight hundred games, told me. Several of his games illustrate market forces: in Modern Art, for instance, you play as auctioneers and buyers, hoping to buy low and sell high. Knizia is a traditional game designer inasmuch as he aims to "bring enjoyment to the people." But Amabel sometimes aims for the opposite of enjoyment... This Guilty Land, from 2018, is about the struggle to end slavery."
Holland says their games are "meant to evoke frustration" — specifically to communicate how difficult it can be to actually achieve political progress.

Thanks to Slashdot reader silverjacket for sharing the article.

Long-time Slashdot reader schwit1 shared this article from the Atlantic: For the past few years, parents, researchers, and the news media have paid closer attention to the relationship between teenagers' phone use and their mental health. Researchers such as Jonathan Haidt and Jean Twenge have shown that various measures of student well-being began a sharp decline around 2012 throughout the West, just as smartphones and social media emerged as the attentional centerpiece of teenage life. Some have even suggested that smartphone use is so corrosive, it's systematically reducing student achievement. I hadn't quite believed that last argument — until now.

The Program for International Student Assessment, conducted by the Organization for Economic Co-operation and Development in almost 80 countries every three years, tests 15-year-olds est scores have been falling for years — even before the pandemic. Across the OECD, science scores peaked in 2009, and reading scores peaked in 2012. Since then, developed countries have as a whole performed "increasingly poorly" on average. "No single country showed an increasingly positive trend in any subject," PISA reported, and "many countries showed increasingly poor performance in at least one subject." Even in famously high-performing countries, such as Finland, Sweden, and South Korea, PISA grades in one or several subjects have been declining for a while.

So what's driving down student scores around the world? The PISA report offers three reasons to suspect that phones are a major culprit. First, PISA finds that students who spend less than one hour of "leisure" time on digital devices a day at school scored about 50 points higher in math than students whose eyes are glued to their screens more than five hours a day. This gap held even after adjusting for socioeconomic factors... Second, screens seem to create a general distraction throughout school, even for students who aren't always looking at them.... Finally, nearly half of students across the OECD said that they felt "nervous" or "anxious" when they didn't have their digital devices near them. (On average, these students also said they were less satisfied with life.) This phone anxiety was negatively correlated with math scores.

In sum, students who spend more time staring at their phone do worse in school, distract other students around them, and feel worse about their life.

According to researchers from a startup called Patronus AI, ChatGPT and other chatbots that rely on large language models frequently fail to answer questions derived from Securities and Exchange Commission filings. CNBC reports: Even the best-performing artificial intelligence model configuration they tested, OpenAI's GPT-4-Turbo, when armed with the ability to read nearly an entire filing alongside the question, only got 79% of answers right on Patronus AI's new test, the company's founders told CNBC. Oftentimes, the so-called large language models would refuse to answer, or would "hallucinate" figures and facts that weren't in the SEC filings. "That type of performance rate is just absolutely unacceptable," Patronus AI co-founder Anand Kannappan said. "It has to be much much higher for it to really work in an automated and production-ready way." [...]

Patronus AI worked to write a set of more than 10,000 questions and answers drawn from SEC filings from major publicly traded companies, which it calls FinanceBench. The dataset includes the correct answers, and also where exactly in any given filing to find them. Not all of the answers can be pulled directly from the text, and some questions require light math or reasoning. Qian and Kannappan say it's a test that gives a "minimum performance standard" for language AI in the financial sector. Patronus AI tested four language models: OpenAI's GPT-4 and GPT-4-Turbo, Anthropic's Claude 2 and Meta's Llama 2, using a subset of 150 of the questions it had produced. It also tested different configurations and prompts, such as one setting where the OpenAI models were given the exact relevant source text in the question, which it called "Oracle" mode. In other tests, the models were told where the underlying SEC documents would be stored, or given "long context," which meant including nearly an entire SEC filing alongside the question in the prompt.

GPT-4-Turbo failed at the startup's "closed book" test, where it wasn't given access to any SEC source document. It failed to answer 88% of the 150 questions it was asked, and only produced a correct answer 14 times. It was able to improve significantly when given access to the underlying filings. In "Oracle" mode, where it was pointed to the exact text for the answer, GPT-4-Turbo answered the question correctly 85% of the time, but still produced an incorrect answer 15% of the time. But that's an unrealistic test because it requires human input to find the exact pertinent place in the filing -- the exact task that many hope that language models can address. Llama 2, an open-source AI model developed by Meta, had some of the worst "hallucinations," producing wrong answers as much as 70% of the time, and correct answers only 19% of the time, when given access to an array of underlying documents. Anthropic's Claude 2 performed well when given "long context," where nearly the entire relevant SEC filing was included along with the question. It could answer 75% of the questions it was posed, gave the wrong answer for 21%, and failed to answer only 3%. GPT-4-Turbo also did well with long context, answering 79% of the questions correctly, and giving the wrong answer for 17% of them.

India consumes more groundwater. That's testing India's ability to feed itself and much of the world. From a report: The South Asian nation is already the world's largest guzzler of groundwater. Cheap power has encouraged routine overreliance on finite riches. India overwhelmingly grows some of the thirstiest crops: rice, wheat and sugar cane. Over the last half century, farm productivity has leapt forward, but so, too, has water usage -- up 500% over that period, according to the World Bank. Erratic monsoons and brutal heat waves are only making the problem more acute. Farmers are digging deeper wells because existing ones are no longer refilling. Some regions may run out of groundwater entirely -- Punjab, a major wheat producer, could go dry within the next 15 or so years, according to a former state official. States in southern India are battling over water rights in areas where rampant urban development has drained thousands of lakes.

The government is not blind to the crisis. But with a national election on the horizon next year, there's little to gain in pushing actively for change among farmers, one of the most important voting blocs in the country. Any long-term solution will involve tinkering with farm subsidies or the minimum price set for water-intensive crops. Prime Minister Narendra Modi's ruling party is all too aware that farmers from India's grain-growing northern regions dominated months of protests against proposed agrarian reforms from late 2020. Modi was forced to withdraw the proposals. For now, it's clear the water math does not add up.

Modi has promised piped water to all Indian households by 2024. Yet nearly half of India's 1.4 billion residents already face high-to-extreme water stress, and the world's most populous nation is expected to add more than 200 million more people by 2050. Agriculture, meanwhile, accounts for 90% of water use, helping to explain why Indian officials say the clearest strategy for preserving supplies is modernizing the industry. The government has tried to convince farmers to adopt different irrigation technologies, return to traditional rain harvesting and plant less thirsty crops like millets, pulses and oilseeds. Nothing has yet made a substantial difference, in a country where subsidies supporting wheat and rice persist, and farming is dominated by smallholders.

An anonymous reader quotes a report from MIT Technology Review: In a paper published in Nature today, the researchers say it is the first time a large language model has been used to discover a solution to a long-standing scientific puzzle -- producing verifiable and valuable new information that did not previously exist. "It's not in the training data -- it wasn't even known," says coauthor Pushmeet Kohli, vice president of research at Google DeepMind. Large language models have a reputation for making things up, not for providing new facts. Google DeepMind's new tool, called FunSearch, could change that. It shows that they can indeed make discoveries -- if they are coaxed just so, and if you throw out the majority of what they come up with.

FunSearch (so called because it searches for mathematical functions, not because it's fun) continues a streak of discoveries in fundamental math and computer science that DeepMind has made using AI. First Alpha Tensor found a way to speed up a calculation at the heart of many different kinds of code, beating a 50-year record. Then AlphaDev found ways to make key algorithms used trillions of times a day run faster. Yet those tools did not use large language models. Built on top of DeepMind's game-playing AI AlphaZero, both solved math problems by treating them as if they were puzzles in Go or chess. The trouble is that they are stuck in their lanes, says Bernardino Romera-Paredes, a researcher at the company who worked on both AlphaTensor and FunSearch: "AlphaTensor is great at matrix multiplication, but basically nothing else." FunSearch takes a different tack. It combines a large language model called Codey, a version of Google's PaLM 2 that isfine-tuned on computer code, with other systems that reject incorrect or nonsensical answers and plug good ones back in.

The researchers started by sketching out the problem they wanted to solve in Python, a popular programming language. But they left out the lines in the program that would specify how to solve it. That is where FunSearch comes in. It gets Codey to fill in the blanks -- in effect, to suggest code that will solve the problem. A second algorithm then checks and scores what Codey comes up with. The best suggestions -- even if not yet correct -- are saved and given back to Codey, which tries to complete the program again. After a couple of million suggestions and a few dozen repetitions of the overall process -- which took a few days -- FunSearch was able to come up with code that produced a correct and previously unknown solution to the cap set problem, which involves finding the largest size of a certain type of set. Imagine plotting dots on graph paper. [...] To test its versatility, the researchers used FunSearch to approach another hard problem in math: the bin packing problem, which involves trying to pack items into as few bins as possible. This is important for a range of applications in computer science, from data center management to e-commerce. FunSearch came up with a way to solve it that's faster than human-devised ones.

Ivana Saric reports via Axios: U.S. students lag behind their peers in many industrialized countries when it comes to math, according to the results of a global exam released Tuesday. U.S. students saw a 13-point drop in their 2022 Program for International Student Assessment (PISA) math results when compared to the 2018 exam. The 2022 math score was not only lower than it was in 2012 but it was "among the lowest ever measured by PISA in mathematics" for the U.S., per the Organisation for Economic Co-operation and Development (OECD) country note. The 2018 PISA assessment found that U.S. students straggled behind their peers in East Asia and Europe, per the Washington Post.

PISA examines the proficiency of 15-year-olds in reading, mathematics, and science worldwide. The 2022 PISA edition is the first to take place since the pandemic and compares the test results of nearly 700,000 students across 81 OECD member states and partner economies. The exam, coordinated by the OECD, was first administered in 2000 and is conducted every three years. Due to the COVID-19 pandemic, the 2021 test was delayed until 2022. What about the rest of the world? According to Axios, a total of 31 countries and economies "maintained or improved upon their 2018 math scores, including Switzerland and Japan."

"10 countries and economies -- Canada, Denmark, Finland, Hong Kong, Ireland, Japan, Korea, Latvia, Macao and the U.K. -- saw their students score proficiently in all three domains and had 'high levels of socio-economic fairness,'" the report adds.

An anonymous reader writes: People spend one-third of their lives asleep. What if employees could work during that time ... in their dreams? Prophetic, a venture-backed startup founded earlier this year, wants to help workers do just that. Using a headpiece the company calls the "Halo," Prophetic says consumers can induce a lucid dream state, which occurs when the person having a dream is aware they are sleeping. The goal is to give people control over their dreams, so they can use that time productively. A CEO could practice for an upcoming board meeting, an athlete could run through plays, a web designer could create new templates -- "the limiting factor is your imagination," founder and CEO Eric Wollberg told Fortune.

Consumer devices claiming to induce lucid dream states aren't new. Headbands, eye masks, and boxes with electrodes that stick to the forehead all populate the market. Even some supplements claim to do the trick. But there's still an appetite for new technologies, since the potential for creativity and problem-solving is so great and since many on the market don't work to the extent they promise, a dreaming expert told Fortune. The potential of lucid dreaming is less about conquering specific problems and more about finding new, creative ways to approach topics that a sleeper couldn't previously fathom. For example, a mathematician might not reach a specific, numerical answer to a math problem while asleep, but the lucid dream allows them to explore new strategies to tackle the equation while awake. Halos will cost around $1,500 to $2,000 each.