Bill Gates took his 11th turn answering questions in Reddit's "Ask My Anything" forum this week — and occasionally looked back on his time at Microsoft: Is technology only functional for you nowadays, or is there a still hobby aspect to it? Do you for instance still do nerdy or geeky things in your spare time; e.g. write code?

Yes. I like to play around and code. The last time my code shipped in a Microsoft product was 1985 — so a long time ago. I can no longer threaten when I think a schedule is too long that "I will come in and code it over the weekend."


Mr Gates, with the benefit of hindsight regarding your years of involvement with Microsoft, what is the single biggest thing you wish you had done differently?

I was CEO until 2000. I certainly know a lot now that I didn't back then. Two areas I would change would be our work in phone Operating systems (Android won) and trying to settle the antitrust lawsuit sooner.
Gates posted all of his responses on his personal web site Gates Notes — and there were also some discussion about AI's coming role in our future. Asked for his opinion about generative AI, and how it will impact the world, Gates said "I am quite impressed with the rate of improvement in these AIs" I think they will have a huge impact. Thinking of it in the Gates Foundation context we want to have tutors that help kids learn math and stay interested. We want medical help for people in Africa who can't access a doctor. I still work with Microsoft some, so I am following this very closely.

Do you think that using technology to push teachers and doctors out of jobs will have a positive impact on our world? What about, instead, we use AI to give equitable access to education and training for more human teachers and doctors, without the $500,000 price tag. Do you think that might have a more positive impact on, ya know, humans?

I think we need more teachers and doctors, not less. In the Foundation's work, the shortage of doctors means that most people never see a doctor and they suffer because of that. We want class sizes to be smaller. Digital tools can help although their impact so far has been modest.


[W]hat are your views on OpenAI's ChatGPT?

It gives a glimpse of what is to come. I am impressed with this whole approach and the rate of innovation....


Many years ago, I think around 2000, I heard you say something on TV like, "people are vastly overestimating what the internet will be like in 5 years, and vastly underestimating what it will be like in 10 years." Is any mammoth technology shift at a similar stage right now? Any tech shift — not necessarily the Internet

AI is the big one. I don't think Web3 was that big or that metaverse stuff alone was revolutionary, but AI is quite revolutionary....


What are you excited about in the year ahead?

First being a grandfather. Second being a good friend and father. Third progress in health and climate innovation. Fourth helping to shape the AI advances in a positive way.
Gates also offered an update on the Terrapower molten salt Thorium reactors, shared his thoughts on veganism, and made predictions about climate change. "I still believe we can avoid a terrible outcome. The pace of innovation is really picking up even though we won't make the current timelines or avoid going over 1.5.... The key on climate is making the clean products as cheap as the dirty products in every area of emission — planes, concrete, meat etc."

Gates also revealed what kind of smartphone he uses (a foldable Samsung Fold 4), what he thought of the latest Avatar ("good"), and that his favorite bands include U2. "I loved Bono's recent book and he is a good friend."

And he said he believes that the very rich "should pay a lot more in taxes." But in addition, Gates said, "they should give away their wealth over time. It has been very fulfilling for me and is my full-time job."

U.K. Prime Minister Rishi Sunak will on Wednesday announce plans to force school pupils in England to study math up to the age of 18, according to a Downing Street briefing. The initiative attempts to tackle innumeracy and better equip young people for the workplace. CNBC reports: In his first speech of 2023, Sunak is expected to outline plans for math to be offered through alternative qualification routes. Comparatively, traditional A-Levels subject-based qualifications allow high school students in England to elect academic subjects to study between the ages of 16 and 18. [...] Sunak's education proposals would only affect pupils in England. Education is a devolved issue, with Welsh, Scottish and Northern Irish authorities managing their own systems.

School-based education in England is only compulsory up to the age of 16, after which children can choose to pursue further academic qualifications such as A-Levels or alternative qualifications, or vocational training. The prime minister is expected to say in his Wednesday speech that the issue of mandatory math is "personal" for him. "Every opportunity I've had in life began with the education I was so fortunate to receive. And it's the single most important reason why I came into politics: to give every child the highest possible standard of education," he will say.

Sunak attended prestigious fee-paying institutions -- the Stroud School and Winchester College -- before studying at Oxford University. He is expected to acknowledge that the planned overhaul will be challenging and time consuming, with work beginning during the current parliamentary term and finishing in the next.

Remember when MIT researchers did fMRI brain scans measuring the blood flow through brains to determine which parts were engaged when programmers evaluated code? MIT now says that a new paper (by many of the same authors) delves even deeper: Whereas the previous study looked at 20 to 30 people to determine which brain systems, on average, are relied upon to comprehend code, the new research looks at the brain activity of individual programmers as they process specific elements of a computer program. Suppose, for instance, that there's a one-line piece of code that involves word manipulation and a separate piece of code that entails a mathematical operation. "Can I go from the activity we see in the brains, the actual brain signals, to try to reverse-engineer and figure out what, specifically, the programmer was looking at?" asks Shashank Srikant, a PhD student in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "This would reveal what information pertaining to programs is uniquely encoded in our brains." To neuroscientists, he notes, a physical property is considered "encoded" if they can infer that property by looking at someone's brain signals.

Take, for instance, a loop — an instruction within a program to repeat a specific operation until the desired result is achieved — or a branch, a different type of programming instruction than can cause the computer to switch from one operation to another. Based on the patterns of brain activity that were observed, the group could tell whether someone was evaluating a piece of code involving a loop or a branch. The researchers could also tell whether the code related to words or mathematical symbols, and whether someone was reading actual code or merely a written description of that code.....

The team carried out a second set of experiments, which incorporated machine learning models called neural networks that were specifically trained on computer programs. These models have been successful, in recent years, in helping programmers complete pieces of code. What the group wanted to find out was whether the brain signals seen in their study when participants were examining pieces of code resembled the patterns of activation observed when neural networks analyzed the same piece of code. And the answer they arrived at was a qualified yes. "If you put a piece of code into the neural network, it produces a list of numbers that tells you, in some way, what the program is all about," Srikant says. Brain scans of people studying computer programs similarly produce a list of numbers. When a program is dominated by branching, for example, "you see a distinct pattern of brain activity," he adds, "and you see a similar pattern when the machine learning model tries to understand that same snippet."
But where will it all lead? They don't yet know what these recently-gleaned insights can tell us about how people carry out more elaborate plans in the real world.... Creating models of code composition, says O'Reilly, a principal research scientist at CSAIL, "is beyond our grasp at the moment." Lipkin, a BCS PhD student, considers this the next logical step — figuring out how to "combine simple operations to build complex programs and use those strategies to effectively address general reasoning tasks." He further believes that some of the progress toward that goal achieved by the team so far owes to its interdisciplinary makeup. "We were able to draw from individual experiences with program analysis and neural signal processing, as well as combined work on machine learning and natural language processing," Lipkin says. "These types of collaborations are becoming increasingly common as neuro- and computer scientists join forces on the quest towards understanding and building general intelligence."

MPs and peers tasked with completing a year 6 Sats exam have scored lower results on average than the country's 10-year-olds. From a report: MPs including Commons education select committee chair Robin Walker took part in the exams, invigilated by 11-year-olds, at a Westminster event organised by More Than A Score, who campaign for the tests to be scrapped. Only 44% of the cross-party group of parliamentarians dubbed the Westminster Class of 2022 achieved the expected standard in maths and just 50% had achieved the expected standard in spelling, punctuation and grammar.

Across the country, 59% of pupils aged 10 and 11 reached the expected standard in the Sats tests of maths, reading and writing this year, down from 65% in 2019, the previous time the tests were taken. Detailed figures published by the Department for Education in the summer revealed disadvantaged children had a steeper fall than their better-off peers. Walker took part in the Big SATS Sit-In Westminster alongside his Conservative colleagues Flick Drummond and Gagan Mohindra; Labour MPs Ian Byrne and Emma Lewell-Buck with the Green party's Lady Bennett to experience the high-stakes nature of the exams. More Than A Score hope the politicians will take the high-pressured experience away with them and realise that "the exams only judge schools but do not help children's learning" at that age.

"Maintenance of the software that's used for the hardest physics calculations rests almost entirely with a retiree," reports Quanta magazine, saying the situation "reveals the problematic incentive structure of academia." Particle physicists use some of the longest equations in all of science. To look for signs of new elementary particles in collisions at the Large Hadron Collider, for example, they draw thousands of pictures called Feynman diagrams that depict possible collision outcomes, each one encoding a complicated formula that can be millions of terms long. Summing formulas like these with pen and paper is impossible; even adding them with computers is a challenge. The algebra rules we learn in school are fast enough for homework, but for particle physics they are woefully inefficient.

Programs called computer algebra systems strive to handle these tasks. And if you want to solve the biggest equations in the world, for 33 years one program has stood out: FORM. Developed by the Dutch particle physicist Jos Vermaseren, FORM is a key part of the infrastructure of particle physics, necessary for the hardest calculations. However, as with surprisingly many essential pieces of digital infrastructure, FORM's maintenance rests largely on one person: Vermaseren himself. And at 73, Vermaseren has begun to step back from FORM development. Due to the incentive structure of academia, which prizes published papers, not software tools, no successor has emerged. If the situation does not change, particle physics may be forced to slow down dramatically...

Without ongoing development, FORM will get less and less usable — only able to interact with older computer code, and not aligned with how today's students learn to program. Experienced users will stick with it, but younger researchers will adopt alternative computer algebra programs like Mathematica that are more user-friendly but orders of magnitude slower. In practice, many of these physicists will decide that certain problems are off-limits — too difficult to handle. So particle physics will stall, with only a few people able to work on the hardest calculations.

In April, Vermaseren is holding a summit of FORM users to plan for the future. They will discuss how to keep FORM alive: how to maintain and extend it, and how to show a new generation of students just how much it can do. With luck, hard work and funding, they may preserve one of the most powerful tools in physics.
Thanks to long-time Slashdot reader g01d4 for submitting the story.

Amazon will be shutting down Amazon Academy, an online learning platform it launched in India for high-school students last year. From a report: The retailer says it will wind down the edtech service in the country in a phased manner starting August 2023. Those who signed up for the current academic batch will receive a full refund, it said. Amazon officially launched Academy, previously called JEE Ready, early last year, but had been testing the platform since mid-2019. Academy sought to help students prepare for entry into the nation's prestigious engineering colleges. The service offered curated learning material, live lectures, mock tests and comprehensive assessments to help students learn and practice math, physics and chemistry and prepare for the Joint Entrance Examinations (JEE), a government-backed engineering entrance assessment conducted in India for admission to various engineering colleges.

On November 15 Meta unveiled a new large language model called Galactica, designed to assist scientists. But instead of landing with the big bang Meta hoped for, Galactica has died with a whimper after three days of intense criticism. Yesterday the company took down the public demo that it had encouraged everyone to try out. From a report: Meta's misstep -- and its hubris -- show once again that Big Tech has a blind spot about the severe limitations of large language models. There is a large body of research that highlights the flaws of this technology, including its tendencies to reproduce prejudice and assert falsehoods as facts.

Galactica is a large language model for science, trained on 48 million examples of scientific articles, websites, textbooks, lecture notes, and encyclopedias. Meta promoted its model as a shortcut for researchers and students. In the company's words, Galactica "can summarize academic papers, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more." But the shiny veneer wore through fast. Like all language models, Galactica is a mindless bot that cannot tell fact from fiction. Within hours, scientists were sharing its biased and incorrect results on social media.

The CTO of a software company argues the software industry's current trajectory "is toward increasing complexity, longer product-development times, and greater fragility of production systems" — not to mention nightmarish problems maintaining code.

"To address such issues, companies usually just throw more people at the problem: more developers, more testers, and more technicians who intervene when systems fail. Surely there must be a better way," they write in IEEE Spectrum. "I'm part of a growing group of developers who think the answer could be functional programming...." Today, we have a slew of dangerous practices that compromise the robustness and maintainability of software. Nearly all modern programming languages have some form of null references, shared global state, and functions with side effects — things that are far worse than the GOTO ever was. How can those flaws be eliminated? It turns out that the answer has been around for decades: purely functional programming languages....

Indeed, software based on pure functions is particularly well suited to modern multicore CPUs. That's because pure functions operate only on their input parameters, making it impossible to have any interactions between different functions. This allows the compiler to be optimized to produce code that runs on multiple cores efficiently and easily....

Functional programming also has a solution to Hoare's "billion-dollar mistake," null references. It addresses that problem by disallowing nulls. Instead, there is a construct usually called Maybe (or Option in some languages). A Maybe can be Nothing or Just some value. Working with Maybe s forces developers to always consider both cases. They have no choice in the matter. They must handle the Nothing case every single time they encounter a Maybe. Doing so eliminates the many bugs that null references can spawn.

Functional programming also requires that data be immutable, meaning that once you set a variable to some value, it is forever that value. Variables are more like variables in math...

Pure functional programming solves many of our industry's biggest problems by removing dangerous features from the language, making it harder for developers to shoot themselves in the foot.... I anticipate that the adoption of pure functional languages will improve the quality and robustness of the whole software industry while greatly reducing time wasted on bugs that are simply impossible to generate with functional programming. It's not magic, but sometimes it feels like that, and I'm reminded of how good I have it every time I'm forced to work with a non-functional codebase.

Far from being an abstract mathematical curiosity, knot theory has driven many findings in math and beyond. Quanta magazine: Knot theory began as an attempt to understand the fundamental makeup of the universe. In 1867, when scientists were eagerly trying to figure out what could possibly account for all the different kinds of matter, the Scottish mathematician and physicist Peter Guthrie Tait showed his friend and compatriot Sir William Thomson his device for generating smoke rings. Thomson -- later to become Lord Kelvin (namesake of the temperature scale) -- was captivated by the rings' beguiling shapes, their stability and their interactions. His inspiration led him in a surprising direction: Perhaps, he thought, just as the smoke rings were vortices in the air, atoms were knotted vortex rings in the luminiferous ether, an invisible medium through which, physicists believed, light propagated.

Although this Victorian-era idea may now sound ridiculous, it was not a frivolous investigation. This vortex theory had a lot to recommend it: The sheer diversity of knots, each slightly different, seemed to mirror the different properties of the many chemical elements. The stability of vortex rings might also provide the permanence that atoms required. Vortex theory gained traction in the scientific community and inspired Tait to begin tabulating all knots, creating what he hoped would be equivalent to a table of elements. Of course, atoms are not knots, and there is no ether. By the late 1880s Thomson was gradually abandoning his vortex theory, but by then Tait was captivated by the mathematical elegance of his knots, and he continued his tabulation project. In the process, he established the mathematical field of knot theory.

We are all familiar with knots -- they keep shoes on our feet, boats secured to docks, and mountain climbers off the rocks below. But those knots are not exactly what mathematicians (including Tait) would call a knot. Although a tangled extension cord may appear knotted, it's always possible to disentangle it. To get a mathematical knot, you must plug together the free ends of the cord to form a closed loop. Because the strands of a knot are flexible like string, mathematicians view knot theory as a subfield of topology, the study of malleable shapes. Sometimes it is possible to untangle a knot so it becomes a simple circle, which we call the "unknot." But more often, untangling a knot is impossible.

U.S. students in most states and across almost all demographic groups have experienced troubling setbacks in both math and reading, according to an authoritative national exam released on Monday, offering the most definitive indictment yet of the pandemic's impact on millions of schoolchildren. The New York Times reports: In math, the results were especially devastating, representing the steepest declines ever recorded on the National Assessment of Educational Progress, known as the nation's report card, which tests a broad sampling of fourth and eighth graders and dates to the early 1990s. In the test's first results since the pandemic began, math scores for eighth graders fell in nearly every state. A meager 26 percent of eighth graders were proficient, down from 34 percent in 2019. Fourth graders fared only slightly better, with declines in 41 states. Just 36 percent of fourth graders were proficient in math, down from 41 percent.

Reading scores also declined in more than half the states, continuing a downward trend that had begun even before the pandemic. No state showed sizable improvement in reading. And only about one in three students met proficiency standards, a designation that means students have demonstrated competency and are on track for future success. And for the country's most vulnerable students, the pandemic has left them even further behind. The drops in their test scores were often more pronounced, and their climbs to proficiency are now that much more daunting.

It's the tale of a company manufacuring precision card-shuffling machines for casinos — and a gang of hustlers who used a hidden video camera to film the shuffler's insides. "The images, transmitted to an accomplice outside in the casino parking lot, were played back in slow motion to figure out the sequence of cards in the deck," remembers the BBC, "which was then communicated back to the gamblers inside. The casino lost millions of dollars before the gang were finally caught."

So the company turned for help to a mathematician/magician: The executives were determined not to be hacked again. They had developed a prototype of a sophisticated new shuffling machine, this time enclosed in an opaque box. Their engineers assured them that the machine would sufficiently randomise a deck of cards with one pass through the device, reducing the time between hands while also beating card-counters and crooked dealers. But they needed to be sure that their machine properly shuffled the deck. They needed Persi Diaconis.

Diaconis, a magician-turned-mathematician at Stanford University, is regarded as the world's foremost expert on the mathematics of card shuffling. Throughout the surprisingly large scholarly literature on the topic, his name keeps popping up like the ace of spades in a magician's sleight-of-hand trick. So, when the company executives contacted him and offered to let him see the inner workings of their machine — a literal "black box" — he couldn't believe his luck. With his collaborator Susan Holmes, a statistician at Stanford, Diaconis travelled to the company's Las Vegas showroom to examine a prototype of their new machine.

The pair soon discovered a flaw. Although the mechanical shuffling action appeared random, the mathematicians noticed that the resulting deck still had rising and falling sequences, which meant that they could make predictions about the card order. To prove this to the company executives, Diaconis and Holmes devised a simple technique for guessing which card would be turned over next. If the first card flipped was the five of hearts, say, they guessed that the next card was the six of hearts, on the assumption that the sequence was rising. If the next card was actually lower — a four of hearts, for instance — this meant they were in a falling sequence, and their next guess was the three of hearts. With this simple strategy, the mathematicians were able to correctly guess nine or 10 cards per deck — one-fifth of the total — enough to double or triple the advantage of a competent card-counter....

The executives were horrified. "We are not pleased with your conclusions," they wrote to Diaconis, "but we believe them and that's what we hired you for." The company quietly shelved the prototype and switched to a different machine.
The article also explains why seven shuffles "is just as close to random as can be" — rendering further shuffling largely ineffective.

The National Science Foundation announced Thursday that it will not rebuild a renowned radio telescope in Puerto Rico, which was one of the world's largest until it collapsed nearly two years ago. The Associated Press reports: Instead, the agency issued a solicitation for the creation of a $5 million education center at the site that would promote programs and partnerships related to science, technology, engineering and math. It also seeks the implementation of a research and workforce development program, with the center slated to open next year in the northern mountain town of Arecibo where the telescope was once located. The solicitation does not include operational support for current infrastructure at the site that is still in use, including a 12-meter radio telescope or the Lidar facility, which is used to study the upper atmosphere and ionosphere to analyze cloud cover and precipitation data.

The decision was mourned by scientists around the world who used the telescope at the Arecibo Observatory for years to search for asteroids, planets and extraterrestrial life. The 1,000-foot-wide (305-meter-wide) dish also was featured in the Jodie Foster film "Contact" and the James Bond movie "GoldenEye." The reflector dish and the 900-ton platform hanging 450 feet above it previously allowed scientists to track asteroids headed to Earth, conduct research that led to a Nobel Prize and determine if a planet is potentially habitable. The Arecibo Observatory collapsed in on itself in December 2020, after the telescope suffered two major cable malfunctions in the two months prior. The National Science Foundation released shocking footage of the moment when support cables snapped, causing the massive 900-ton structure suspended above Arecibo to fall onto the observatory's iconic 1,000-foot-wide dish.

Last week, DeepMind announced it discovered a more efficient way to perform matrix multiplication, conquering a 50-year-old record. This week, two Austrian researchers at Johannes Kepler University Linz claim they have bested that new record by one step. Ars Technica reports: In 1969, a German mathematician named Volker Strassen discovered the previous-best algorithm for multiplying 4x4 matrices, which reduces the number of steps necessary to perform a matrix calculation. For example, multiplying two 4x4 matrices together using a traditional schoolroom method would take 64 multiplications, while Strassen's algorithm can perform the same feat in 49 multiplications. Using a neural network called AlphaTensor, DeepMind discovered a way to reduce that count to 47 multiplications, and its researchers published a paper about the achievement in Nature last week.

To discover more efficient matrix math algorithms, DeepMind set up the problem like a single-player game. The company wrote about the process in more detail in a blog post last week. DeepMind then trained AlphaTensor using reinforcement learning to play this fictional math game -- similar to how AlphaGo learned to play Go -- and it gradually improved over time. Eventually, it rediscovered Strassen's work and those of other human mathematicians, then it surpassed them, according to DeepMind. In a more complicated example, AlphaTensor discovered a new way to perform 5x5 matrix multiplication in 96 steps (versus 98 for the older method).

This week, Manuel Kauers and Jakob Moosbauer of Johannes Kepler University in Linz, Austria, published a paper claiming they have reduced that count by one, down to 95 multiplications. It's no coincidence that this apparently record-breaking new algorithm came so quickly because it built off of DeepMind's work. In their paper, Kauers and Moosbauer write, "This solution was obtained from the scheme of [DeepMind's researchers] by applying a sequence of transformations leading to a scheme from which one multiplication could be eliminated."

"After 433 gamblers won a lottery drawing in the Philippines last weekend, people across the country debated a thorny question," reports the New York Times. "At what point does randomness begin to look a little too much like a racket?" Some Filipinos accused the state-owned company behind the roughly $4 million prize drawing of fraud, a charge that was swiftly denied. Lawmakers said that they planned to investigate the winning draw as a way of securing the lottery's integrity. How was it possible, skeptics asked, that 433 people had all picked the same winning combination of six numbers — 09-45-36-27-18-54? Or that all six figures turned out to be multiples of nine?

Others said that the outcome was a simple case of good luck. (The winning numbers could be in any order.) Statisticians noted that it was not mathematically impossible for 433 winners to strike it big....

A few [critics] noted that some officials from the Philippine Charity Sweepstakes Office, which sold nearly $443 million in tickets in the first half of this year, have been convicted of bribery and other charges over the past decade, including one case in which they pocketed prize money.... Lawmakers in both the House and Senate said this week that they planned to investigate the contentious draw. One of those legislators, Aquilino Pimentel III, the minority leader of the Senate, told The Times in a text message on Wednesday that while the result was "not impossible," it seemed "highly improbable...."

Professor Chua Tin Chiu, a statistician at the National University of Singapore, said the criticism was an example of humans misunderstanding the nature of randomness. "Some time ago, there was news about a person that struck the jackpot more than once in his lifetime," he said. "Would that be possible? Yes. Are the chances very low? Yes. Is it going to happen to someone? Yes."

An anonymous reader quotes a report from MIT Technology Review: DeepMind has used its board-game playing AI AlphaZero to discover a faster way to solve a fundamental math problem in computer science, beating a record that has stood for more than 50 years. A year after it took biologists by surprise, AlphaFold has changed how researchers work and set DeepMind on a new course. The problem, matrix multiplication, is a crucial type of calculation at the heart of many different applications, from displaying images on a screen to simulating complex physics. It is also fundamental to machine learning itself. Speeding up this calculation could have a big impact on thousands of everyday computer tasks, cutting costs and saving energy.

Despite the calculation's ubiquity, it is still not well understood. A matrix is simply a grid of numbers, representing anything you want. Multiplying two matrices together typically involves multiplying the rows of one with the columns of the other. The basic technique for solving the problem is taught in high school. But things get complicated when you try to find a faster method. This is because there are more ways to multiply two matrices together than there are atoms in the universe (10 to the power of 33, for some of the cases the researchers looked at).

The trick was to turn the problem into a kind of three-dimensional board game, called TensorGame. The board represents the multiplication problem to be solved, and each move represents the next step in solving that problem. The series of moves made in a game therefore represents an algorithm. The researchers trained a new version of AlphaZero, called AlphaTensor, to play this game. Instead of learning the best series of moves to make in Go or chess, AlphaTensor learned the best series of steps to make when multiplying matrices. It was rewarded for winning the game in as few moves as possible. [...] The researchers describe their work in a paper published in Nature today. The headline result is that AlphaTensor discovered a way to multiply together two four-by-four matrices that is faster than a method devised in 1969 by the German mathematician Volker Strassen, which nobody had been able to improve on since. The basic high school method takes 64 steps; Strassen's takes 49 steps. AlphaTensor found a way to do it in 47 steps. "Overall, AlphaTensor beat the best existing algorithms for more than 70 different sizes of matrix," concludes the report. "It reduced the number of steps needed to multiply two nine-by-nine matrices from 511 to 498, and the number required for multiplying two 11-by-11 matrices from 919 to 896. In many other cases, AlphaTensor rediscovered the best existing algorithm."

Saul Kripke, a math prodigy and pioneering logician whose revolutionary theories on language qualified him as one of the 20th century's greatest philosophers, died on Sept. 15 in Plainsboro, N.J. He was 81. The New York Times reports: His death, at Penn Medicine Princeton Medical Center, was caused by pancreatic cancer, according to Romina Padro, director of the Saul Kripke Center at the City University of New York, where Professor Kripke had been a distinguished professor of philosophy and computer science since 2003 and had capped a career exploring how people communicate. Professor Kripke's classic work, "Naming and Necessity," first published in 1972 and drawn from three lectures he delivered at Princeton University in 1970 before he was 30, was considered one of the century's most evocative philosophical books.

"Kripke challenged the notion that anyone who uses terms, especially proper names, must be able to correctly identify what the terms refer to," said Michael Devitt, a distinguished professor of philosophy who recruited Professor Kripke to the City University Graduate Center in Manhattan. "Rather, people can use terms like 'Einstein,' 'springbok,' perhaps even 'computer,' despite being too ignorant or wrong to provide identifying descriptions of their referents," Professor Devitt said. "We can use terms successfully not because we know much about the referent but because we're linked to the referent by a great social chain of communication."

The Pulitzer Prize-winning historian Taylor Branch, writing in The New York Times Magazine in 1977, said Professor Kripke had "introduced ways to distinguish kinds of true statements -- between statements that are 'possibly' true and those that are 'necessarily' true." "In Professor Kripke's analysis," he continued, "a statement is possibly true if and only if it is true in some possible world -- for example, 'The sky is blue' is a possible truth, because there is some world in which the sky could be red. A statement is necessarily true if it is true in all possible worlds, as in 'The bachelor is an unmarried man.'"

Ben Nowack, a 26-year old inventor and CEO of Tons of Mirrors, is trying to use satellite-mounted reflective surfaces to redirect sunlight to earthbound solar panels at night. In an interview with Motherboard, Nowack explains what inspired this idea and how he can turn his concept into reality. Here's an excerpt from the report: What was the initial idea? I had an interesting way to solve the real issue with solar power. It's this unstoppable force. Everybody's installing so many solar panels everywhere. It's really a great candidate to power humanity. But sunlight turns off, it's called nighttime. If you solve that fundamental problem, you fix solar everywhere.

Where did the idea come from? I was watching a YouTube video called The Problem with Solar Energy in Africa. It was basically saying that you need three times as many solar panels in Germany as you do in the Sahara Desert and you can't get the power from the Sahara to Germany in an easy way. I thought, what if you could beam the sunlight and then reflect it with mirrors, and put that light into laser beam vacuum tubes that zigzag around the curvature of the Earth. It could be this beam that comes in just like power companies, this tube full of infinite light. That was the initial idea. But the approach was completely economically unworkable. I was like, this is not going to compete with solar in 10 years. I should just completely give up and do something else. Then I was on a run two days later and thought what if I put that thing that turns sunlight into a beam in orbit then you don't have to build a vacuum tube anymore. And it's so much more valuable because you can shine sunlight on solar farms that already exist. Then I developed several more technologies which I know for a fact no one else is working on. That made the model even more economical.

Are these just like regular household mirrors, but fixed to a satellite? If you did that, the light would go to too many places. The sun is a certain size. It's not a point, it has a distance across. The light from one side of the sun would bounce off your mirror, and the light from the other side would also bounces off your mirror. If you used a perfectly flat mirror, every single microscopic piece would have this angle of diverging light coming from it. By the time the reflection hit Earth, you'd get a 3.6 kilometer diameter spot, which is gigantic. There are only 10 solar farms that big. So I did the math, and figured out that if I could hit a 500-meter spot instead of a 3,600-meter spot, then I'd be able to hit 44 times more solar sites per orbit.

Long-time Slashdot reader theodp writes: A new study on the Impact of Programming on Primary Mathematics Learning (abstract only, full article $24.95 on ScienceDirect) is generating some buzz on Twitter amongst K-12 CS educator types. It concluded that:

1. Programming did not benefit mathematics learning compared to traditional activities
2. There's a negative though small effect of programming on mathematics learning
3. Mindful "high-road transfer" from programming to mathematics is not self-evident
4. Visual programming languages might distract students from mathematics activities

From the Abstract: "The aim of this study is to investigate whether a programming activity might serve as a learning vehicle for mathematics acquisition in grades four and five.... Classes were randomly assigned to the programming (with Scratch) and control conditions. Multilevel analyses indicate negative effects (effect size range 0.16 to 0.21) of the programming condition for the three mathematical notions.

"A potential explanation of these results is the difficulties in the transfer of learning from programming to mathematics."

The findings of the new study come 4+ years after preliminary results were released from the $1.5M 2015-2019 NSF-funded study Time4CS, a "partnership between Broward County Public Schools (FL), researchers at the University of Chicago, and [tech-bankrolled] Code.org," which explored whether learning CS using Code.org's CS Fundamentals curriculum may be linked to improved learning in math at the grade 3-5 level. Time4CS researchers concluded that the "quasi-experimental" study showed that "No significant differences in Florida State Assessment mathematics scores resulted between treatment and comparison groups."

SiFive is the first company to produce a chip implementing the RISC-V ISA.

They've now been selected to provide the core CPU for NASA's next generation High-Performance Spaceflight Computing processor (or HSPC), according to a SiFive announcement: HPSC is expected to be used in virtually every future space mission, from planetary exploration to lunar and Mars surface missions.

HPSC will utilize an 8-core, SiFive® Intelligence X280 RISC-V vector core, as well as four additional SiFive RISC-V cores, to deliver 100x the computational capability of today's space computers. This massive increase in computing performance will help usher in new possibilities for a variety of mission elements such as autonomous rovers, vision processing, space flight, guidance systems, communications, and other applications....

The SiFive X280 is a multi-core capable RISC-V processor with vector extensions and SiFive Intelligence Extensions and is optimized for AI/ML compute at the edge. The X280 is ideal for applications requiring high-throughput, single-thread performance while under significant power constraints. The X280 has demonstrated a 100x increase in compute capabilities compared to today's space computers..

In scientific and space workloads, the X280 provides several orders of magnitude improvement compared to competitive CPU solutions.
A business development executive at SiFive says their X280 core "demonstrates orders of magnitude performance gains over competing processor technology," adding that the company's IP "allows NASA to take advantage of the support, flexibility, and long-term viability of the fast-growing global RISC-V ecosystem.

"We've always said that with SiFive the future has no limits, and we're excited to see the impact of our innovations extend well beyond our planet."

And their announcement stresses that open hardware is a win for everybody: The open and collaborative nature of RISC-V will allow the broad academic and scientific software development community to contribute and develop scientific applications and algorithms, as well optimizing the many math functions, filters, transforms, neural net libraries, and other software libraries, as part of a robust and long-term software ecosystem.

Chinese authorities have punished 27 people over the publication of a maths textbook that went viral over its "tragically ugly" illustrations. The Guardian reports: A months-long investigation by a ministry of education working group found the books were "not beautiful," and some illustrations were "quite ugly" and did not "properly reflect the sunny image of China's children." The mathematics books were published by the People's Education Press almost 10 years ago, and were reportedly used in elementary schools across the country. But they went viral in May after a teacher published photos of the illustrations inside, including people with distorted faces and bulging pants, boys pictures grabbing girls' skirts and at least one child with an apparent leg tattoo.

Social media users were largely amused by the illustrations, but many also criticized them as bringing disrepute and "cultural annihilation" to China, speculating they were the deliberate work of western infiltrators in the education sector. Related hashtags were viewed billions of times, embarrassing the Communist party and education authorities who announced a review of all textbooks "to ensure that the textbooks adhere to the correct political direction and value orientation."

In a lengthy statement released on Monday, the education authorities said 27 individuals were found to have "neglected their duties and responsibilities" and were punished, including the president of the publishing house, who was given formal demerits, which can affect a party member's standing and future employment. The editor-in-chief and the head of the maths department editing office were also given demerits and dismissed from their roles. The statement said the illustrators and designers were "dealt with accordingly" but did not give details. They and their studios would no longer be engaged to work on textbook design or related work, it said. The highly critical statement found a litany of issues with the books, including critiquing the size, quantity and quality of illustrations, some of which had "scientific and normative problems."