Climate change has intensified hurricane wind speeds by an average of 19 mph in 84% of North Atlantic hurricanes between 2019-2024, according to new research that links warming ocean temperatures to storm intensity for individual hurricanes.

This year, Hurricanes Helene and Milton slammed into Florida, breaking meteorological records and causing catastrophic damage. The study by Climate Central found that higher sea surface temperatures elevated most hurricanes by an entire category on the Saffir-Simpson scale, with three storms, including Hurricane Rafael, seeing wind speeds increase by 34 mph due to warming.

Researchers calculated storm intensity using models of pre-warming ocean temperatures. "It's really the evolution of our science on sea surface temperature attribution that has allowed this work to take place," said lead author Daniel Gilford, noting that hurricane damage increases exponentially with wind speed. For example, a storm with double the wind speed can cause 256 times as much damage. The methodology enables scientists to determine climate change impacts on hurricanes in near-real time.

Slashdot reader jjslash brings word that Casio "has reintroduced its iconic calculator watch featuring a retro design with green text on a negative LCD and a classic keypad layout."

TechSpot reports that the watch was based on the Casio Mini personal calculator first released in the early 1970s — even offering a keypad using the original fonts (with numbers separated by grid lines): Even the mode button, colored red, is a nod to the calculator's power indicator. The watches' calculator function can add, subtract, multiply, and divide up to eight digits. As for watch functions, you get dual time, an alarm, stopwatch functionality, and more...

Casio's original personal calculator debuted in 1972, and cost $59.95. It featured a six-digit display, was a quarter the size of its competitors, and cost just a third of rival products. The calculator was an instant hit for Casio, selling a million units in the first 10 months on the market and more than six million units over the span of the series.
Long-time Slashdot reader antdude says "I still wear one! Casio Data Bank 150 model...!"

Share your own vintage calculator memories in the comments...

Leading AI systems are solving less than 2% of problems in a new advanced mathematics benchmark, revealing significant limitations in their reasoning capabilities, research group Epoch AI reported this week.

The benchmark, called FrontierMath, consists of hundreds of original research-level mathematics problems developed in collaboration with over 60 mathematicians, including Fields Medalists Terence Tao and Timothy Gowers. While top AI models like GPT-4 and Gemini 1.5 Pro achieve over 90% accuracy on traditional math tests, they struggle with FrontierMath's problems, which span computational number theory to algebraic geometry and require complex reasoning.

"These are extremely challenging. [...] The only way to solve them is by a combination of a semi-expert like a graduate student in a related field, maybe paired with some combination of a modern AI and lots of other algebra packages," Tao said. The problems are designed to be "guessproof," with large numerical answers or complex mathematical objects as solutions, making it nearly impossible to solve without proper mathematical reasoning.

Further reading: New secret math benchmark stumps AI models and PhDs alike.

Australian mathematicians have proven the famous "infinite monkey theorem" impossible within the universe's lifespan. The theorem suggests monkeys typing randomly would eventually produce Shakespeare's complete works. Scientists Stephen Woodcock and Jay Falletta calculated that even 200,000 chimpanzees typing one character per second until the universe's heat death would fail to reproduce Shakespeare's writings.

A single chimp has only a 5% chance of typing "bananas" in its lifetime, with more complex phrases facing astronomically lower odds. "This finding places the theorem among other probability puzzles and paradoxes... where using the idea of infinite resources gives results that don't match up with what we get when we consider the constraints of our universe," Associate Prof Woodcock was quoted as saying by BBC.

Former Nvidia engineer Luke Durant, working with the Great Internet Mersenne Prime Search (GIMPS), recently discovered the largest known prime number: (2^136,279,841)-1 or M136279841 (where the number following the letter M represents the exponent). The achievement was detailed on Mersenne.org. Tom's Hardware reports: This is the largest prime number we've seen so far, with the last one, M82589933, being discovered six years prior. What makes this discovery particularly fascinating is that this is the first GIMPS discovery that used the power of data center GPUs. Mihai Preda was the first one to harness GPU muscle in 2017, says the GIMPS website, when he "wrote the GpuOwl program to test Mersenne numbers for primarilty, making his software available to all GIMPS users." When Luke joined GIMPS in 2023, they built the infrastructure needed to deploy Preda's software across several GPU servers available in the cloud.

While it took a year of testing, Luke's efforts finally bore fruit when an A100 GPU in Dublin, Ireland gave the M136279841 result last October 11. This was then corroborated by an Nvidia H100 located in San Antonio, Texas, which confirmed its primality with the Lucas-Lehmer test.

Anthropic's Claude chatbot can now write and run JavaScript code. TechCrunch: Today, Anthropic launched a new analysis tool that helps Claude respond with what the company describes as "mathematically precise and reproducible answers." With the tool enabled -- it's currently in preview -- Claude can perform calculations and analyze data from files like spreadsheets and PDFs, rendering the results as interactive visualizations.

"Think of the analysis tool as a built-in code sandbox, where Claude can do complex math, analyze data, and iterate on different ideas before sharing an answer," Anthropic wrote in a blog post. "Instead of relying on abstract analysis alone, it can systematically process your data -- cleaning, exploring, and analyzing it step-by-step until it reaches the correct result." Anthropic gives a few examples of where this might be useful. For instance, a product manager could upload sales data and ask Claude for country-specific performance analysis, while an engineer could give Claude monthly financial data and have it create a dashboard highlighting key trends.

Theoretical Physicist Jacques Treiner, from the University of Paris Cite, explains why you should run in the rain: ... Let p represent the number of drops per unit volume, and let a denote their vertical velocity. We'll denote Sh as the horizontal surface area of the individual (e.g., the head and shoulders) and Sv as the vertical surface area (e.g., the body). When you're standing still, the rain only falls on the horizontal surface, Sh. This is the amount of water you'll receive on these areas. Even if the rain falls vertically, from the perspective of a walker moving at speed v, it appears to fall obliquely, with the angle of the drops' trajectory depending on your speed. During a time period T, a raindrop travels a distance of aT. Therefore, all raindrops within a shorter distance will reach the surface: these are the drops inside a cylinder with a base of Sh and a height of aT, which gives:
p.Sh.a.T.

As we have seen, as we move forward, the drops appear to be animated by an oblique velocity that results from the composition of velocity a and velocity v. The number of drops reaching Sh remains unchanged, since velocity v is horizontal and therefore parallel to Sh. However, the number of drops reaching surface Sv -- which was previously zero when the walker was stationary -- has now increased. This is equal to the number of drops contained within a horizontal cylinder with a base area of Sv and a length of v.T. This length represents the horizontal distance the drops travel during this time interval. In total, the walker receives a number of drops given by the expression:
p.(Sh.a + Sv.v). T

Now we need to take into account the time interval during which the walker is exposed to the rain. If you're covering a distance d at constant speed v, the time you spend walking is d/v. Plugging this into the equation, the total amount of water you encounter is:
p.(Sh.a + Sv.v). d/v = p.(Sh.a/v + Sv). d This equation proves that the faster you move, the less water hits your head and shoulders, but the amount of water hitting the vertical part of your body remains constant. To stay drier, it's best to move quickly and lean forward. However, you'll have to increase your speed to offset the exposed surface area caused by leaning.

Apple's latest iOS update has removed the "C" button from its Calculator app, replacing it with a backspace function. The change, part of iOS 18, has sparked debate among users accustomed to the traditional clear function. The removal of the "C" button represents a significant departure from decades-old calculator design conventions, The Atlantic writes. From the story: The "C" button's function is vestigial. Back when calculators were commercialized, starting in the mid-1960s, their electronics were designed to operate as efficiently as possible. If you opened up a desktop calculator in 1967, you might have found a dozen individual circuit boards to run and display its four basic mathematical functions. Among these would have been an input buffer or temporary register that could store an input value for calculation and display. The "C" button, which was sometimes labeled "CE" (Clear Entry) or "CI" (Clear Input), provided a direct interface to zero out -- or "clear" -- such a register. A second button, "AC" (All Clear), did the same thing, but for other parts of the circuit, including previously stored operations and pending calculations. (A traditional calculator's memory buttons -- "M+," "M-," "MC" -- would perform simple operations on a register.)

By 1971, Mostech and Texas Instruments had developed a "calculator on a chip," which condensed all of that into a single integrated circuit. Those chips retained the functions of their predecessors, including the ones that were engaged by "C" and "AC" buttons. And this design continued on into the era of pocket calculators, financial calculators, and even scientific calculators such as the ones you may have used in school. Some of the latter were, in essence, programmable pocket computers themselves, and they could have been configured with a backspace key. They were not.

chalsall writes: After more than six years of work since the last discovery, the Great Internet Mersenne Prime Search (GIMPS) has found the 52nd known Mersenne Prime number. This is also the largest prime number known to humans.

The number is 2^136,279,841-1, which is 41,024,320 decimal digits long.

Luke Durant, a researcher from San Jose, CA, found it after contributing a fantastic amount of compute to the GIMPS project.

In a rather critical analysis of the 2024 Economics Nobel, commentator Noah Smith has questioned the prize's shift back to "big-think" theories. He argues that Acemoglu, Johnson, and Robinson's (the winner of the 2024 Economics Nobel) influential work on institutions and development, while intriguing, lacks robust empirical validation. From his blog: The science prizes rely very heavily on external validity to determine who gets the prize -- your theory or your invention has to work, basically. If it doesn't, you can be the biggest genius in the world, but you'll never get a Nobel. The physicist Ed Witten won a Fields Medal, which is even harder to get than a Nobel, for the math he invented for string theory. But he'll almost certainly never get a Physics Nobel, because string theory can't be empirically tested.

The Econ Nobel is different. Traditionally, it's given to economists whose ideas are most influential within the economics profession. If a whole bunch of other economists do research that follows up on your research, or which uses theoretical or empirical techniques you pioneered, you get an Econ Nobel. Your theory doesn't have to be validated, your specific empirical findings can already have been overturned by the time the prize is awarded, but if you were influential, you get the prize.

You could argue that this is appropriate for what Thomas Kuhn would call a "pre-paradigmatic" science -- a field that's still looking for a set of basic concepts and tools. But it's been 55 years since they started giving the prize, and that seems like an awfully long time for a field to still be tooling up. Meanwhile, making "influence within the economics profession" the criterion for successful research seems a little too much like a popularity contest. It's how you end up with prizes like the one in 2004, which was given to some macroeconomic theorists whose theory said that recessions are caused by technological slowdowns and that mass unemployment is a voluntary vacation.

In recent years, that looked like it might be changing. Often, the prize was given to empirical economists associated with the so-called "credibility revolution" -- basically, quasi-experiments. Those cases include Goldin in 2023, Card/Angrist/Imbens in 2021, and Banerjee/Duflo/Kremer in 2019. And when it was given to theorists, they tended to be game theorists whose theories are very predictive of real-world outcomes -- Milgrom/Wilson in 2020, Hart/Holmstrom in 2016, Tirole in 2014, and Roth/Shapley in 2012. Even when the prize was given to macro -- a field where validity is much harder to establish -- it was given to economists whose theories have seen immediate application to pressing problems of the day, such as Bernanke/Diamond/Dybvig in 2022 and Nordhaus in 2018. In other words, the recent Nobels have made it seem like economics might be becoming more like a natural science, where practical applications and external validity are the ultimate arbiter of the value of research, rather than cultural influence within the economics profession. But this year's prize seems like a step away from that, and back toward the sort of big-think that used to be more popular in the prize's early years.

Slashdot reader Rick Schumann shared this report from the blog AppleInsider: A new paper from Apple's artificial intelligence scientists has found that engines based on large language models, such as those from Meta and OpenAI, still lack basic reasoning skills.

The group has proposed a new benchmark, GSM-Symbolic, to help others measure the reasoning capabilities of various large language models (LLMs). Their initial testing reveals that slight changes in the wording of queries can result in significantly different answers, undermining the reliability of the models. The group investigated the "fragility" of mathematical reasoning by adding contextual information to their queries that a human could understand, but which should not affect the fundamental mathematics of the solution. This resulted in varying answers, which shouldn't happen...

The study found that adding even a single sentence that appears to offer relevant information to a given math question can reduce the accuracy of the final answer by up to 65 percent. "There is just no way you can build reliable agents on this foundation, where changing a word or two in irrelevant ways or adding a few bit of irrelevant info can give you a different answer," the study concluded... "We found no evidence of formal reasoning in language models," the new study concluded. The behavior of LLMS "is better explained by sophisticated pattern matching" which the study found to be "so fragile, in fact, that [simply] changing names can alter results."

Researchers at BitEnergy AI, Inc. have developed Linear-Complexity Multiplication (L-Mul), a technique that reduces AI model power consumption by up to 95% by replacing energy-intensive floating-point multiplications with simpler integer additions. This method promises significant energy savings without compromising accuracy, but it requires specialized hardware to fully realize its benefits. Decrypt reports: L-Mul tackles the AI energy problem head-on by reimagining how AI models handle calculations. Instead of complex floating-point multiplications, L-Mul approximates these operations using integer additions. So, for example, instead of multiplying 123.45 by 67.89, L-Mul breaks it down into smaller, easier steps using addition. This makes the calculations faster and uses less energy, while still maintaining accuracy. The results seem promising. "Applying the L-Mul operation in tensor processing hardware can potentially reduce 95% energy cost by element wise floating point tensor multiplications and 80% energy cost of dot products," the researchers claim. Without getting overly complicated, what that means is simply this: If a model used this technique, it would require 95% less energy to think, and 80% less energy to come up with new ideas, according to this research.

The algorithm's impact extends beyond energy savings. L-Mul outperforms current 8-bit standards in some cases, achieving higher precision while using significantly less bit-level computation. Tests across natural language processing, vision tasks, and symbolic reasoning showed an average performance drop of just 0.07% -- a negligible tradeoff for the potential energy savings. Transformer-based models, the backbone of large language models like GPT, could benefit greatly from L-Mul. The algorithm seamlessly integrates into the attention mechanism, a computationally intensive part of these models. Tests on popular models such as Llama, Mistral, and Gemma even revealed some accuracy gain on certain vision tasks.

At an operational level, L-Mul's advantages become even clearer. The research shows that multiplying two float8 numbers (the way AI models would operate today) requires 325 operations, while L-Mul uses only 157 -- less than half. "To summarize the error and complexity analysis, L-Mul is both more efficient and more accurate than fp8 multiplication," the study concludes. But nothing is perfect and this technique has a major achilles heel: It requires a special type of hardware, so the current hardware isn't optimized to take full advantage of it. Plans for specialized hardware that natively supports L-Mul calculations may be already in motion. "To unlock the full potential of our proposed method, we will implement the L-Mul and L-Matmul kernel algorithms on hardware level and develop programming APIs for high-level model design," the researchers say.

"Even black holes have edge cases," writes Astronomy magazine contributing editor Steve Nadis, in an article in Quanta magazine (republished today by Wired): Black holes rotate in space. As matter falls into them, they start to spin faster; if that matter has charge, they also become electrically charged. In principle, a black hole can reach a point where it has as much charge or spin as it possibly can, given its mass. Such a black hole is called "extremal" — the extreme of the extremes. These black holes have some bizarre properties. In particular, the so-called surface gravity at the boundary, or event horizon, of such a black hole is zero. "It is a black hole whose surface doesn't attract things anymore," said Carsten Gundlach, a mathematical physicist at the University of Southampton. But if you were to nudge a particle slightly toward the black hole's center, it would be unable to escape.

In 1973, the prominent physicists Stephen Hawking, James Bardeen and Brandon Carter asserted that extremal black holes can't exist in the real world — that there is simply no plausible way that they can form. Nevertheless, for the past 50 years, extremal black holes have served as useful models in theoretical physics. "They have nice symmetries that make it easier to calculate things," said Gaurav Khanna of the University of Rhode Island, and this allows physicists to test theories about the mysterious relationship between quantum mechanics and gravity. Now two mathematicians have proved Hawking and his colleagues wrong. The new work — contained in a pair of recent papers by Christoph Kehle of the Massachusetts Institute of Technology and Ryan Unger of Stanford University and the University of California, Berkeley — demonstrates that there is nothing in our known laws of physics to prevent the formation of an extremal black hole.

Their mathematical proof is "beautiful, technically innovative and physically surprising," said Mihalis Dafermos, a mathematician at Princeton University (and Kehle's and Unger's doctoral adviser). It hints at a potentially richer and more varied universe in which "extremal black holes could be out there astrophysically," he added. That doesn't mean they are. "Just because a mathematical solution exists that has nice properties doesn't necessarily mean that nature will make use of it," Khanna said. "But if we somehow find one, that would really [make] us think about what we are missing." Such a discovery, he noted, has the potential to raise "some pretty radical kinds of questions." Before Kehle and Unger's proof, there was good reason to believe that extremal black holes couldn't exist.
Hawking, Bardeen, and Carter believed there was no way an extremal black hole could form, according to the article, and "in 1986, a physicist named Werner Israel seemed to put the issue to rest."

But the two mathematicians, studying the formation of electrically charged black holes, stumbled into a counterexample — and along the way "also constructed two other solutions to Einstein's equations of general relativity that involved different ways of adding charge to a black hole. Having disproved Bardeen, Carter and Hawking's hypothesis in three different contexts, the work should leave no doubt, Unger said... "This is a beautiful example of math giving back to physics," said Elena Giorgi, a mathematician at Columbia University....

In the meantime, a better understanding of extremal black holes can provide further insights into near-extremal black holes, which are thought to be plentiful in the universe. "Einstein didn't think that black holes could be real [because] they're just too weird," Khanna said. "But now we know the universe is teeming with black holes."

For similar reasons, he added, "we shouldn't give up on extremal black holes. I just don't want to put limits on nature's creativity."

OpenAI has launched a new AI model, named "o1", designed for improved reasoning and problem-solving skills. o1, part of a new series of models and available in ChatGPT and the API, can tackle complex tasks in science, coding, and math more effectively than their predecessors. Notably, o1 models have shown promising results in standardized tests and coding competitions. While o1 models represent a significant advancement in AI capabilities, they currently lack features like web browsing and file uploading. The Verge adds: But it's also more expensive and slower to use than GPT-4o. OpenAI is calling this release of o1 a "preview" to emphasize how nascent it is.

ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today, while Enterprise and Edu users will get access early next week. OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn't set a release date yet. Developer access to o1 is really expensive: In the API, o1-preview is $15 per 1 million input tokens, or chunks of text parsed by the model, and $60 per 1 million output tokens. For comparison, GPT-4o costs $5 per 1 million input tokens and $15 per 1 million output tokens.

The training behind o1 is fundamentally different from its predecessors, OpenAI's research lead, Jerry Tworek, tells me, though the company is being vague about the exact details. He says o1 "has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it."

When most students get As, grading loses all meaning as a way to encourage exceptional work and recognize excellence. From a report: Grade inflation at American universities is out of control. The statistics speak for themselves. In 1950, the average GPA at Harvard was estimated at 2.6 out of 4. By 2003, it had risen to 3.4. Today, it stands at 3.8. The more elite the college, the more lenient the standards. At Yale, for example, 80% of grades awarded in 2023 were As or A minuses. But the problem is also prevalent at less selective colleges. Across all four-year colleges in the U.S., the most commonly awarded grade is now an A. Some professors and departments, especially in STEM disciplines, have managed to uphold more stringent criteria. A few advanced courses attract such a self-selecting cohort of students that virtually all of them deserve recognition for genuinely excellent work. But for the most part, the grading scheme at many institutions has effectively become useless. An A has stopped being a mark of special academic achievement.

If everyone outside hard-core engineering, math or pre-med courses can easily get an A, the whole system loses meaning. It fails to make distinctions between different levels of achievement or to motivate students to work hard on their academic pursuits. All the while, it allows students to pretend -- to themselves and to others -- that they are performing exceptionally well. Worse, this system creates perverse incentives. To name but one, it actively punishes those who take risks by enrolling in truly challenging courses. All of this contributes to the strikingly poor record of American colleges in actually educating their students. As Richard Arum and Josipa Roksa showed in their 2011 book "Academically Adrift," the time that the average full-time college student spent studying dropped by half in the five decades after 1960, falling to about a dozen hours a week. A clear majority of college students "showed no significant progress on tests of critical thinking, complex reasoning and writing," with about half failing to make any improvements at all in their first two years of higher education.

An anonymous reader quotes a report from Wired: Jazmin Jones knowswhat she did. "If you're online, there's this idea of trolling," Jones, the director behindSeeking Mavis Beacon, said during a recent panel for her new documentary. "For this project, some things we're taking incredibly seriously ... and other things we're trolling. We're trolling this idea of a detective because we're also, like,ACAB." Her trolling, though, was for a good reason. Jones and fellow filmmaker Olivia Mckayla Ross did it in hopes of finding the woman behind Mavis Beacon Teaches Typing. The popular teaching tool was released in 1987 by The Software Toolworks, a video game and software company based in California that produced educational chess, reading, and math games. Mavis, essentially the "mascot" of the game, is a Black woman donned in professional clothes and a slicked-back bun. Though Mavis Beacon was not an actual person, Jones and Ross say that she is one of the first examples of Black representation they witnessed in tech. Seeking Mavis Beacon, which opened in New York City on August 30 and is rolling out to other cities in September, is their attempt to uncover the story behind the face, which appeared on the tool's packaging and later as part of its interface.

The film shows the duo setting up a detective room, conversing over FaceTime, running up to people on the street, and even tracking down a relative connected to the ever-elusive Mavis. But the journey of their search turned up a different question they didn't initially expect: What are the impacts of sexism, racism, privacy, and exploitation in a world where you can present yourself any way you want to? Using shots from computer screens, deep dives through archival footage, and sit-down interviews, the noir-style documentary reveals that Mavis Beacon is actually Renee L'Esperance, a Black model from Haiti who was paid $500 for her likeness with no royalties, despite the program selling millions of copies. [...]

In a world where anyone can create images of folks of any race, gender, or sexual orientation without having to fully compensate the real people who inspired them, Jones and Ross are working to preserve not only the data behind Mavis Beacon but also the humanity behind the software. On the panel, hosted by Black Girls in Media, Ross stated that the film's social media has a form where users of Mavis Beacon can share what the game has meant to them, for archival purposes. "On some level, Olivia and I are trolling ideas of worlds that we never felt safe in or protected by," Jones said during the panel. "And in other ways, we are honoring this legacy of cyber feminism, historians, and care workers that we are very seriously indebted to." You can watch the trailer for "Seeking Mavis Beacon" on YouTube.

Research by the Federal Reserve Bank of New York, published on Tuesday, indicates that professional economic forecasters tend to overestimate their accuracy in long-term predictions while underestimating their short-term precision.

The study, which analyzed data from the Survey of Professional Forecasters from 1982 to 2022, revealed that for forecasts two to four quarters ahead, actual errors were two to four times greater than the forecasters' estimated uncertainty ranges for both GDP growth and inflation. In contrast, for predictions less than three months out, forecasters typically overestimated potential errors.

The study's author, Marco Del Negro, highlighted significant differences in uncertainty estimates among individual forecasters, suggesting that these findings challenge the rational expectations theory. Del Negro proposed that these discrepancies might stem from an over-reliance on varying models or priors in making longer-term forecasts.

Bruce66423 shares a report from the Los Angeles Times: In a milestone breakthrough, more than half of Caltech's incoming undergraduate class this fall will be women (source paywalled; alternative source) for the first time in its 133-year history. The class of 113 women and 109 men comes 50 years after Caltech graduated its first class of undergraduate women, who were admitted in 1970. "What this means for young women is that we are a place that can be representative of them and their experiences ... where they can grow and thrive and excel and become really impressive, extraordinary scientists and engineers and go on to make a difference in this really research-heavy profession," said Ashley Pallie, dean of admissions

Gloria L. Blackwell, chief executive of the American Assn. of University Women, lauded Caltech's achievement as critical progress in reducing the substantial gap of women in science, technology, engineering and math. Although women hold about 60% of degrees in biological sciences, they represent only about 18% in computer science and 20% in engineering, Blackwell said. Research has shown that boys are not better at math and science than girls, but a persistent message in society says otherwise -- and especially discourages Latinas and Black girls from pursuing the fields because they face discrimination and have less access to role models, resources and opportunities, the AAUW says. The report notes that Caltech isn't the first educational institution to reach gender parity in STEM. Harvey Mudd College, a small private institution in Claremont, "enrolled more women than men in 2010 for the first time in its history and in 2014 graduated more women than men in engineering," reports the LA Times. "Today, women make up 52.8% of majors in computer science, 50.5% in engineering and 68.2% in mathematical and computational biology."

UC Berkeley is another powerful producer of STEM graduates, with "nearly half of students majoring in those fields [identifying] as women or nonbinary." However, the report notes that the field they enter varies significantly. "They make up more than two-thirds of students in biological and biomedical sciences, but about one-third in engineering, computer and informational sciences, and mathematics and statistics."

An anonymous reader shared this story from the blog Decrypt: Michael Lewis, author of Going Infinite, an account of the rise and fall of Sam Bankman-Fried, has argued that the disgraced FTX founder didn't have "the character of a thief" in a new The Washington Post article. "His crime was of a piece with his character. The character wasn't the character of a thief. It was the character of a person numb to risk." Lewis explained in the final paragraphs of a 4,500 word essay adapted from a new introduction to his book. "Unable to feel risk himself, he can't really imagine other people feeling much at all about the risk he has subjected them to...."

Lewis doubled down on previous claims that Bankman-Fried wasn't running a Ponzi scheme, arguing that "The crime was unnecessary to the business in a way that, say, Bernie Madoff's was not," and that "The crime made no sense." The collapse of FTX, he added, "might have been avoided and FTX might have survived."

"That doesn't mean I think that Sam Bankman-Fried is innocent. It merely informs how I feel about him," Lewis explained. "I think the truth is closer to 'young person with an intellectually defensible but socially unacceptable moral code makes a huge mistake in trying to live by it' than "criminal on the loose in the financial system.'"
From from The Daily Beast: Lewis also pointed to bankruptcy court filings from FTX in the weeks after Bankman-Fried's sentencing showing that "against the $8.7 billion in missing customer deposits, FTX was now sitting on something like $14.5 to $16.3 billion." "Whatever the exact sum, it was enough to repay all depositors and various other creditors at least 118 cents on the dollar — that is, everyone who imagined they had lost money back in November 2022 would get their money back, with interest," Lewis writes.
Michael Lewis's article offers some vivid details: Inside of three years, he'd gone from socially and emotionally isolated 25-year-old with an upper-middle-class bank account to leader of a small army of math nerds and (according to Forbes magazine) not merely the world's richest person under 30 but maybe the fastest creator of wealth in recorded history... He'd gone from having no friends as a child to having too many as an adult without ever developing a capacity for friendship....

The prosecutors didn't need Sam's help. Sam helped them anyway by ignoring the counsel of his lawyers and testifying on his own behalf... As Lewis Kaplan, the federal judge who presided over the case, said later: "When he wasn't outright lying, he was often evasive, hairsplitting, dodging questions and trying to get the prosecutor to reword questions in ways that he could answer in ways he thought less harmful than a truthful answer to the question that was posed would have been. I've been doing this job for close to 30 years. I've never seen a performance quite like that...." [T]he judge ordered Sam to rise so that he might address him directly. Two hours or so earlier, Sam had shuffled into the courtroom in prison khakis with his head down and his hands oddly clasped behind his back. Just before he'd entered, his guards had told him he was meant to be wearing handcuffs and asked if he could create the impression that he was doing so...

"There is a risk that this man will be in a position to do something very bad in the future, and it's not a trivial risk, not a trivial risk at all," said the judge. "So, in part, my sentence will be for the purpose of disabling him." He then sentenced Sam to 25 years in prison, with no possibility of parole.

A few minutes later, Sam dutifully clasped his hands behind his back and shuffled out of the courtroom.
Lewis adapted his 4,500-word article from the upcoming (updated) paperback edition of his book — which was originally published in 2023 on the same day jurors were selected for Bankman-Fried's trial...

America's top colleges and finance-industry recruiters have long had their eye on teenage whiz-kids who compete in a prestigious high-school math contest. Now, allegations of cheating are threatening to disrupt it. WSJ: Online leaks of tests for the country's best-known math contest -- the 74-year-old American Mathematics Competition -- are upsetting students who have spent years preparing for the exams. Ahead of the coming school year and test season, angry parents and math coaches have pushed the contest's administrator to tighten controls. The incident is the latest byproduct of a high-pressure college-admissions race that can lead students to look for any edge to get ahead.

[...] As early as elementary school, students interested in flexing their math knowledge beyond what is taught in school can participate in math clubs and competitions. Each year, more than 300,000 students through high school participate in the AMC's first round of multiple-choice tests. Several thousand top performers are invited to sit for a higher-level test, and from there, around 600 compete in national "math olympiads." The top six math students in the nation then represent the U.S. internationally; the U.S. won its ninth International Mathematical Olympiad title this summer.

Murmurs about cheating in the AMC have circulated for a few years, participants say, but reached critical levels during the past school year. The entirety of exams at each level of the competition were available online hours or days before students sat for the tests, a spokeswoman for the Mathematical Association of America confirmed. Testing sites in the U.S. and abroad receive the questions online early to give proctors time to print them out for the in-person exams.