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How to Read the Polls Ahead of the Election

Well, it’s that time again: Millions of Americans are stress-eating while clicking “Refresh” on 538’s presidential forecast, hoping beyond hope that the little red or blue line will have made a tiny tick upward. Some may be clutching themselves in the fetal position, chanting under their breath: “There’s a good new poll out of Pennsylvania.”

The stakes of this election are sky-high, and its outcome is not knowable in advance—a combination that most of us find deeply discomfiting. People crave certainty, and there’s just one place to look for it: in the data. Earlier humans might have turned to oracles or soothsayers; we have Nate Silver. But the truth is that polling—and the models that rely primarily on polling to forecast the election result—cannot confidently predict what will happen on November 5.

The widespread perception that polls and models are raw snapshots of public opinion is simply false. In fact, the data are significantly massaged based on possibly reasonable, but unavoidably idiosyncratic, judgments made by pollsters and forecasting sages, who interpret and adjust the numbers before presenting them to the public. They do this because random sampling has become very difficult in the digital age, for reasons I’ll get into; the numbers would not be representative without these corrections, but every one of them also introduces a margin for human error.

Most citizens see only the end product: a preposterously precise statistic, such as the notion that Donald Trump has a 50.2 percent—not 50.3 percent, mind you—chance of winning the presidency. (Why stop there? Why not go to three decimal points?) Such numerical precision gives the false impression of certainty where there is none.

[Read: The world is falling apart. Blame the flukes.]

Early American political polls were unscientific but seemingly effective. In the early 20th century, The Literary Digest, a popular magazine in its day, sent sample ballots to millions of its readers. By this method, the magazine correctly predicted the winner of every presidential election from 1916 until 1936. In that year, for the contest between Franklin D. Roosevelt and Alf Landon, the Digest sent out roughly 10 million sample ballots and received an astonishing 2.4 million back (a response rate of 24 percent would be off the charts by modern standards). Based on those responses, the Digest predicted that FDR would receive a drubbing, winning just 41 percent of the vote. Instead, he won 61 percent, carrying all but two states. Readers lost faith in the Digest (it went out of business two years later).

The conventional wisdom was that the poll failed because in addition to its readers, the Digest selected people from directories of automobile and telephone ownership, which skewed the sample toward the wealthy—particularly during the Great Depression, when cars and phones were luxuries. That is likely part of the explanation, but more recent analysis has pointed to a different problem: who responded to the poll and who didn’t. For whatever reason, Landon supporters were far more likely than FDR supporters to send back their sample ballots, making the poll not just useless, but wildly misleading. This high-profile error cleared the way for more “scientific” methods, such as those pioneered by George Gallup, among others.

The basic logic of the new, more scientific method was straightforward: If you can generate a truly random sample from the broader population you are studying—in which every person has an equally likely chance of being included in the poll—then you can derive astonishingly accurate results from a reasonably small number of people. When those assumptions are correct and the poll is based on a truly random sample, pollsters need only about 1,000 people to produce a result with a margin of error of plus or minus three percentage points.

To produce reasonably unbiased samples, pollsters would randomly select people from the telephone book and call them. But this method became problematic when some people began making their phone numbers unlisted; these people shared certain demographic characteristics, so their absence skewed the samples. Then cellphones began to replace landlines, and pollsters started using “random-digit dialing,” which ensured that every active line had an equal chance of being called. For a while, that helped.

But the matter of whom pollsters contacted was not the only difficulty. Another was how those people responded, and why. A distortion known as social-desirability bias is the tendency of respondents to lie to pollsters about their likely voting behavior. In America, that problem was particularly acute around race: If a campaign pitted a minority candidate against a white candidate, some white respondents might lie and say that they’d vote for the minority candidate to avoid being perceived as racist. This phenomenon, contested by some scholars, is known as the Bradley Effect, named after former Los Angeles Mayor Tom Bradley—a Black politician who was widely tipped to become governor of California based on pre-election polling, but narrowly lost instead. To deal with the Bradley Effect, many pollsters switched from live callers to robocalls, hoping that voters would be more honest with a computer than another person.

But representative sampling has continued to become more difficult. In an age of caller ID and smartphones, along with persistent junk and nuisance calls, few people answer when they see unfamiliar numbers. Most Americans spend much of their time online, but there are no reliable methods to get a truly random sample from the internet. (Consider, for example, how subscribers of The Atlantic differ from the overall American population, and it’s obvious why a digital poll on this site would be worthless at making predictions about the overall electorate.)

These shifts in technology and social behavior have created an enormous problem known as nonresponse bias. Some pollsters release not just findings but total numbers of attempted contacts. Take, for example, this 2018 New York Times poll within Michigan’s Eighth Congressional District. The Times reports that it called 53,590 people in order to get 501 responses. That’s a response rate lower than 1 percent, meaning that the Times pollsters had to call roughly 107 people just to get one person to answer their questions. What are the odds that those rare few who answered the phone are an unskewed, representative sample of likely voters? Zilch. As I often ask my undergraduate students: How often do you answer when you see an unknown number? Now, how often do you think a lonely elderly person in rural America answers their landline? If there’s any systematic difference in behavior, that creates a potential polling bias.

To cope, pollsters have adopted new methodologies. As the Pew Research Center notes, 61 percent of major national pollsters used different approaches in 2022 than they did in 2016. This means that when Americans talk about “the polls” being off in past years, we’re not comparing apples with apples. One new polling method is to send text messages with links to digital surveys. (Consider how often you’d click a link from an unknown number to understand just how problematic that method is.) Many pollsters rely on a mix of approaches. Some have started using online “opt-in” methods, in which respondents choose to take a survey and are typically paid a small amount for participating. This technique, too, has raised reasonable questions about accuracy: One of my colleagues at University College London, Thomas Gift, tested opt-in methods and found that nearly 82 percent of participants in his survey likely lied about themselves in order to qualify for the poll and get paid. Pew further found that online opt-in polls do a poor job of capturing the attitudes of young people and Hispanic Americans.

No matter the method, a pure, random sample is now an unattainable ideal—even the aspiration is a relic of the past. To compensate, some pollsters try to design samples representative of known demographics. One common approach, stratification, is to divide the electorate into subgroups by gender, race, age, etc., and ensure that the sample includes enough of each “type” of voter. Another involves weighting some categories of respondents differently from others, to match presumptions about the broader electorate. For example, if a polling sample had 56 percent women, but the pollster believed that the eventual electorate would be 52 percent women, they might weigh male respondents slightly more heavily in the adjusted results.

[Read: The asterisk on Kamala Harris’s poll numbers]

The problem, of course, is that nobody knows who will actually show up to vote on November 5. So these adjustments may be justified, but they are inherently subjective, introducing another possible source of human bias. If women come out to vote in historically high numbers in the aftermath of the Supreme Court’s Dobbs decision, for example, the weighting could be badly off, causing a major polling error.

The bottom line is that modern pollsters are trying to correct for known forms of possible bias in their samples by making subjective adjustments to the data. If their judgments are correct, then their polls might be accurate. But there’s no way to know beforehand whether their assumptions about, say, turnout by demographic group are wise or not.

Forecasters then take that massaged polling data and feed it into a model that’s curated by a person—or team of people—who makes further subjective assessments. For example, the 538 model adjusts its forecasts based on polls plus what some in the field call “the fundamentals,” such as historical trends around convention polling bounces, or underlying economic data. Most forecasters also weight data based on how particular pollsters performed in earlier elections. Each adjustment is an educated guess based on past patterns. But nobody knows for sure whether past patterns are predictive of future results. Enough is extraordinary about this race to suspect that they may not be.

More bad news: Modern polling often misses the mark even when trying to convey uncertainty, because pollsters grossly underestimate their margins of error. Most polls report a plus or minus margin of, say, 3 percent, with a 95 percent confidence interval. This means that if a poll reports that Trump has the support of 47 percent of the electorate, then the reported margin of error suggests that the “real” number likely lies between 44 percent (minus three) and 50 percent (plus three). If the confidence interval is correct, that spread of 44 to 50 should capture the actual result of the election about 95 percent of the time. But the reality is less reassuring.

In a 2022 research paper titled “Election Polls Are 95 Percent Confident but Only 60 Percent Accurate,” Aditya Kotak and Don Moore of UC Berkeley analyzed 6,000 polls from 2008 through 2020. They found that even with just one week to go before Election Day, only about six in 10 polls captured the end result within their stated margin of error. Four in 10 times, the polling data fell outside that window. The authors conclude that to justify a 95 percent confidence interval, pollsters should “at least double” their reported margins of error—a move that would be statistically wise but render polling virtually meaningless in close elections. After all, if a margin of error doubled to six percentage points, then a poll finding that Harris had 50 percent support would indicate that the “true” number was somewhere between 44 percent (a Trump landslide) and 56 percent (a Harris landslide).

Alas, the uncertainty doesn’t end there. Unlike many other forms of measurement, polls can change what they’re measuring. Sticking a thermometer outside doesn’t make the weather hotter or colder. But poll numbers can and do shift voting behavior. For example, studies have shown that perceived poll momentum can make people more likely to vote for the surging party or candidate in a “bandwagon” effect. Take the 2012 Republican primaries, when social conservatives sought an alternative to Mitt Romney and were split among candidates. A CNN poll conducted the night before the Iowa caucus showed Rick Santorum in third place. Santorum went on to win the caucus, likely because voters concluded from the poll that he was the most electable challenger.

The truth is that even after election results are announced, we may not really know which forecasters were “correct.” Just as The Literary Digest accurately predicted the winner of presidential races with a deeply flawed methodology, sometimes a bad approach is just lucky, creating the illusion of accuracy. And neither polling nor electoral dynamics are stable over time. Polling methodology has shifted radically since 2008; voting patterns and demographics are ever-changing too. Heck, Barack Obama won Indiana in 2008; recent polls suggest that Harris is losing there by as much as 17 points. National turnout was 55 percent in 2016 and 63 percent in 2020. Polls are trying to hit a moving target with instruments that are themselves constantly changing. For all of these reasons, a pollster who was perfectly accurate in 2008 could be wildly off in 2024.

In other words, presidential elections are rare, contingent, one-off events. Predicting their outcome does not yield enough comparable data points to support any pollster’s claim to exceptional foresight, rather than luck. Trying to evaluate whether a forecasting model is “good” just from judging its performance on the past four presidential elections is a bit like trying to figure out whether a coin is “fair” or “rigged” from just four coin flips. It’s impossible.

[Read: The man who’s sure that Harris will win]

The social scientists Justin Grimmer, Dean Knox, and Sean Westwood recently published research supporting this conclusion. They write: “We demonstrate that scientists and voters are decades to millennia away from assessing whether probabilistic forecasting provides reliable insights into election outcomes.” (Their research has sparked fierce debate among scholars about the wisdom of using probabilistic forecasting to measure rare and idiosyncratic events such as presidential elections.)

Probabilistic presidential forecasts are effectively unfalsifiable in close elections, meaning that they can’t be proved wrong. Nate Silver’s model in 2016 suggested that Hillary Clinton had a 71.4 percent chance of victory. That wasn’t necessarily “wrong” when she lost: After all, as Silver pointed out to the Harvard Gazette, events with a 28.6 percent probability routinely happen—more frequently than one in four times. So was his 2016 presidential model “wrong”? Or was it bang-on accurate, but an unusual, lower-probability event took place? There’s no way of knowing for sure.

The pollsters and forecasters who are studying the 2024 election are not fools. They are skilled analysts attempting some nearly impossible wizardry by making subjective adjustments to control for possible bias while forecasting an uncertain future. Their data suggest that the race is a nail-biter—and that may well be the truth. But nobody—not you, not me, not the betting markets, not Nate Silver—knows what’s going to happen on November 5.


Read full article on: theatlantic.com
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But nonprofit to for-profit conversions are rare, and misinformation has swirled about what, exactly, “OpenAI becoming a for-profit company” even means.  Elon Musk, who co-founded OpenAI but left after a leadership dispute, paints the for-profit transition as a naked power grab, arguing in a recent lawsuit that Altman and his associates “systematically drained the non-profit of its valuable technology and personnel” in a scheme to get rich off a company that had been founded as a charity. (OpenAI has moved to dismiss Musk’s lawsuit, arguing that it is an “increasingly blusterous campaign to harass OpenAI for his own competitive advantage”). While Musk — who has his own reasons to be competitive with OpenAI — is among the more vocal critics, many people seem to be under the impression that the company could just slap on a new “for-profit” label and call it a day. Can you really do that? 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Nonprofit law might seem abstruse, which is why most coverage of OpenAI’s transition hasn’t dug into any of the messy details. But those messy details involve tens of billions of dollars, all of which appear to be up for negotiation. The results will dramatically affect how much sway Microsoft has with OpenAI going forward and how much of the company’s value is still tied to its founding mission.  This might seem like something that only matters for OpenAI shareholders, but the company is one of the few that may just have a chance of creating world-changing artificial intelligence. If the public wants a transparent and open process from OpenAI, they have to understand what the law actually allows and who is responsible for following it so we can be sure that OpenAI pursues this transition in a transparent and accountable way. How OpenAI went from nonprofit to megacorp In 2015, OpenAI was a nonprofit research organization. It told the IRS in a filing for nonprofit status that its mission was to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”  Understanding OpenAI’s expansive reach OpenAI, the maker of ChatGPT, is one of the most important companies in artificial intelligence and one of the most controversial. I’ve been covering the ins and outs of OpenAI for years; here are some highlights: What it means that new AIs can “reason” Leaked OpenAI documents reveal aggressive tactics toward former employees Four different ways of understanding AI — and its risks Have questions or comments? Email me at kelsey.piper@vox.com. By 2019, that idealistic nonprofit model was running into some trouble. OpenAI had attracted an incredible staff and published some very impressive research. But it was becoming clear that the lofty goal the company had set itself — building general artificial intelligence, machines that can do everything humans can do — was going to be very expensive. It was naturally hard to raise billions of dollars for an effort that was meant to be nonprofit. “We realized that we’d reached the limits of our fundraising ability as a pure nonprofit,” co-founder Ilya Sutskever (who has since departed the company) told me at the time. The company would attempt to split the difference with a hybrid structure: a nonprofit board controlling a for-profit company. An additional twist: Investors in the for-profit company’s returns were capped at 100x their original investments so that, if world-altering superintelligence was achieved as the OpenAI leadership believed it might, the benefits would accrue to all humanity and not just investors. After all, investors needed to be enticed to invest, but if the company truly ended material scarcity and built a God on Earth, as they essentially said they wanted to, the hope was that more than just the investors would come out ahead. The nonprofit, therefore, was still supposed to be preeminent. “It would be wise to view any investment in OpenAI Global, LLC in the spirit of a donation,” an enormous black-and-pink disclaimer box on OpenAI’s website alerts would-be investors, “with the understanding that it may be difficult to know what role money will play in a post-AGI world. The Company exists to advance OpenAl, Inc.’s mission of ensuring that safe artificial general intelligence is developed and benefits all of humanity. The Company’s duty to this mission and the principles advanced in the OpenAl, Inc. Charter take precedence over any obligation to generate a profit.” One might expect that a prominent disclaimer like that would give commercial investors pause. You would be mistaken. OpenAI had Altman, a fantastic fundraiser, at the helm; its flagship product, ChatGPT, was the fastest app to 100 million users. The company was a gamble, but it was the kind of gamble investors can’t wait to get in on. But that was then, and this is now. In 2023, in an unexpected and disastrously under-explained move, the nonprofit board fired OpenAI CEO Sam Altman. The board had that authority, of course — it was preeminent — but the execution was shockingly clumsy. The timing of the firing looked likely to disrupt an opportunity for employees to sell millions of dollars of stock in the company. The board gave a few examples of underhanded, bizarre, and dishonest behavior by Altman, including being “not consistently candid” with the board. (One board member later expanded the allegations, saying that Altman had lied to board members about private conversations with other board members, but provided nothing as clear as confused and frustrated employees hoped.) Employees threatened to resign en masse. Microsoft offered to hire them all and reconstitute the company. Sutskever, who was among the board members who’d voted for Altman’s removal, suddenly changed his mind and voted for Altman to stay. That meant the members who had fired Altman were suddenly in the minority. Two of the board members who had opposed Altman resigned, and the once and future CEO returned to the helm.  Many people concluded that it had been a serious mistake to try to run a company worth 11 figures as a nonprofit instead of as the decidedly for-profit company it was clearly operating as, whatever its bylaws might say. So it’s not surprising that ever since the aborted Altman coup, rumors swirled that OpenAI meant to transition to a fully for-profit entity.   In the last few weeks, those rumors have gotten much more concrete. OpenAI’s latest funding round has been reported to include commitments that the nonprofit-to-for-profit transition will get done in the next two years on pain of the more than $6 billion raised being paid back to those investors. Microsoft and OpenAI — both of whom have enormous amounts to gain in the wrangling over who owns the resulting for-profit company — have hired dueling investment banks to negotiate the details.  We are moving into a new era for OpenAI, and it remains to be seen what that will mean for the humble nonprofit that has ended up owning tens of billions of dollars of the company’s assets. How do you turn a charity into a for-profit? If OpenAI were really just taking the nonprofit organization’s assets and declaring them “converted” into a for-profit — as if they were playing a game of tag and suddenly decided a tree was “base” — that would absolutely be illegal. The takeaway, though, shouldn’t be that a crime is happening in plain sight, but that something much more complicated is being negotiated. Nonprofit law experts I talked to said that the situation was being widely and comprehensively misunderstood.  Here are the rules. First off, assets accumulated by a nonprofit cannot be used for private benefit. “It’s the job of the board first, and then the regulators and the court, to ensure that the promise that was made to the public to pursue the charitable interest is kept,” UCLA law professor Jill Horwitz told Reuters.  If it looks as though a nonprofit isn’t pursuing its charitable interest, and especially if it appears to be handing some of its board members bargain-bin deals on billion-dollar assets during a transition to for-profit status? That will have the IRS investigating, along with the state’s Attorney General.  But a nonprofit can sell anything it owns. If a nonprofit owns a piece of land, for example, and it wants to sell that land so that it has more money to spend on its mission, it’s all good. If the nonprofit sold the land for well below market value to the director’s nephew, it would be a clear crime, and the IRS or the state’s Attorney General might well investigate. The nonprofit has to sell the land at a fair market price, take the money, and keep using the money for its nonprofit work. At a much larger scale, that is exactly what is at stake in the OpenAI transition. The nonprofit owns some assets: control over the for-profit company, a lot of AI IP from OpenAI’s proprietary research, and all future returns from the for-profit company once they exceed the 100x cap set up by the capped profit company — which, should the company achieve its goals, could well be limitless. If the new OpenAI wants to extract all of its assets from the nonprofit, it has to pay the full market price. And the nonprofit has to continue to exist and to use the money it has earned in that transfer for its mission of ensuring that AI benefits all of humanity. There have been a few other cases in corporate legal history of a nonprofit making the transition to a for-profit company, most prominently the credit card company Mastercard, which was founded as a nonprofit collaboration among banks. When that situation happens, the nonprofit’s assets still belong to the nonprofit.  Mastercard, in the course of transitioning to a public company, ended up founding the now-$47 billion Mastercard Foundation, one of the world’s wealthiest private foundations. Far from the for-profit walking away with all the nonprofit’s assets, the for-profit emerges as an independent company and the nonprofit emerges not only still extant but very rich. OpenAI’s board has indicated that this is exactly what they are doing. “Any potential restructuring would ensure the nonprofit continues to exist and thrive, and receives full value for its current stake in the OpenAI for-profit with an enhanced ability to pursue its mission.” OpenAI board chairman Bret Taylor, a technologist and CEO, told me in a statement.  (What counts as “full value”? We’ll come back to that.)Outside actors, too, expect to be applying oversight to make sure that the nonprofit gets a fair deal. A spokesperson for the California Attorney General’s office told the Information that their office is “committed to protecting charitable assets for their intended purpose.” OpenAI is registered in Delaware, but the company operates primarily in California, and California’s AG is much less deferential to business than Delaware’s. So, the OpenAI entity will definitely owe the nonprofit mind-boggling amounts of money. Depending who you ask, it could be between $37 billion and $80 billion. The OpenAI for-profit entity does not have that kind of money on hand — don’t forget that OpenAI is projected to lose tens of billions of dollars in the years ahead — so the plans in the works are reportedly for the for-profit to make the nonprofit a major shareholder in the for-profit. The Information reported last week that “the nonprofit is expected to own at least a 25% stake in the for-profit — which on paper would be worth at least $37 billion.” In other words, rather than buying the assets from the non-profit with cash, OpenAI will trade equity.  That’s a lot of money. But many experts I spoke to thought it was actually much too low.  What’s a fair price for control of a mega company? Everyone agrees that the OpenAI board is required to negotiate and receive a fair price for everything the OpenAI nonprofit owns that the for-profit is purchasing. But what counts as a fair price? That’s an open question, one that people stand to earn or lose tens of billions of dollars by getting answered in their favor.  But first: What does the OpenAI nonprofit own?  It owns a lot of OpenAI’s IP. How much exactly is highly confidential, but some experts speculate that the $37 billion number is probably a reflection of the easily measured, straightforward assets of the nonprofit, like its IP and business agreements.  Secondly, and most crucially, it owns full control over the OpenAI for-profit. As part of this deal, it is definitely going to give that up, either becoming a minority shareholder or ending up with nonvoting shares entirely. That is, substantially, the whole point of the nonprofit-to-for-profit conversion: After Altman’s ouster, the Wall Street Journal reported, “[I]nvestors began pushing OpenAI to turn into a more typical company.” Investors throwing around billions of dollars don’t want a nonprofit board to be able to fire the CEO because they’re worried he’s too dishonest to make good decisions around powerful new technology. Investors want a normal board that will fire the CEO for normal reasons, like that he’s not maximizing shareholder value.  Control is generally worth a lot more, in for-profit companies, than shares that come without control — often something like 40 percent more. So if the nonprofit is getting a fair deal, it should get some substantive compensation in exchange for giving up control of the company.  Thirdly, investors in OpenAI under its old business model agreed to a “capped profit” model. For most investors, that cap was set at 100x their original investment, so if they invested $1 million, they would get a maximum of $100 million in return. Above that cap, all returns would go to the nonprofit. The logic for this setup was that, under most circumstances, it’s the same as investing in a normal company. Investments don’t usually produce 100x returns, after all, with the exception of early investments in massively successful tech companies like Google or Amazon. The capped profit setup would be most significant in the unlikely world where OpenAI attained its ambitious goals and built an AI that fundamentally transformed the world economy. (How likely is that? Experts disagree, rather heatedly, but we shouldn’t discount it altogether.) If that does happen, its value will be nearly unfathomably huge. “OpenAI’s value is mostly in the extreme upside,” AI analyst Zvi Mowshowitz wrote in an analysis of the valuation question.  The company might fail entirely; it might muddle along as a midsized company. But it also might be worth trillions of dollars, or more than that, and most investors are investing on the premise it might be worth trillions of dollars. That means the share of profits owned by the nonprofit would also be worth trillions of dollars. “Most future profits still likely flow to the nonprofit,” Mowshowitz concludes. “OpenAI is shooting for the stars. As every VC in this spot knows, it is the extreme upside that matters. That is what the nonprofit is selling. They shouldn’t sell it cheap.” So what would be an appropriate valuation? $60 billion? $100 billion? Mowshowitz’s analysis is that a fair price would involve the nonprofit still owning a majority of shares in the for-profit, which is to say at least $80 billion. (Presumably these would be nonvoting shares.)  The only people with full information are the ones with access to the company’s confidential balance sheets, and they aren’t talking. OpenAI and Microsoft will be negotiating the answer to the question, but it’s not clear that either of them particularly wants the nonprofit to get a valuation that reflects, for example, the expected value of the profits in excess of the cap because there’s more money for everyone else who wants a piece of the pie if the nonprofit gets less.  There are two forces working toward the nonprofit getting fair compensation: the nonprofit board — whose members are capable people, but also people handpicked by Altman not to get ideas and get in the way of his control of the company — and the law. Experts I spoke with were a bit cynical about the board’s willingness to hold out for a good deal in what is an extremely awkward circumstance for it. “We have kind of already seen what’s going on with the OpenAI board,” Ogden told me.  “I think the common understanding is they’re friendly to Sam Altman, and the ones who were trying to slow things down or protect the nonprofit purpose have left,” Rose Chan Loui, the director of UCLA Law’s nonprofit program, observed to the Transformer.  If the board is inclined to go with the flow, the Delaware Attorney General or the IRS could object. These are fundamentally complicated questions about the valuation of a private company, and the law isn’t always good at consistent and principled enforcement in cases like this one. “When you’re talking about numbers like $150 billion,” UCLA law professor Jill Horwitz warned, “the law has a way of getting weak.” Does that mean that Elon Musk’s allegation — that we’re witnessing a bait-and-switch before our eyes, a massive theft of resources that were originally dedicated to the common good — is right after all? I’m not inclined to grant him that much.  Firstly, having spoken to OpenAI leadership and OpenAI employees over the six years I’ve been reporting on the company, I genuinely come away with the impression that the bait-and-switch, to the extent it happened, was completely unintentional.  In 2015, the involved parties really were — including in private emails leaked in Musk’s lawsuits — convinced that a research organization serving the public was the way to achieve their mission. And then over the next few years, as the power of big machine learning models became apparent, they became sincerely convinced they needed to find clever ways to raise money for their research. In 2019, when I spoke with Brockman and Sutskever, they were enthusiastic about their capped profit structure and saw it as a model for how a company could raise money but ensure most of its benefits if it succeeded went to humanity as a whole.  Altman has a habit of being all things to all people, even when that may require being less than truthful. His detractors say he’s “deceptive, manipulative, and worse”, and even his supporters will say he’s “extremely good at becoming powerful,” which VCs might consider more of a compliment than the general public does.  But I don’t think Altman was aiming for this predicament. OpenAI did not inflict its current legal headache on itself out of cunning chicanery, but out of a desire to satisfy a number of different early stakeholders, many of them true believers. It was due chiefly to understandable failures of foresight about how much power corporate governance law would really have once employees had millions riding on the company’s continued fundraising and once investors had billions riding on its ability to make a profit.  Secondly, I think it’s far too soon to call this a bait-and-switch. The nonprofit’s control of OpenAI was meant to give it the power to stop the company from putting profits before the mission. But it turns out that being on a nonprofit board does not come with enough access to the company, or enough real power, to productively turn OpenAI away from the brink, as we discovered last November.  It seems entirely possible that a massive and highly capitalized nonprofit foundation with the aim of ensuring AI benefits humanity is a better approach than a corporate governance agreement with power on paper and none in practice. If the nonprofit gets massively undervalued in the conversion and shooed away with a quarter of the company when more careful estimates suggest it currently controls a majority of the company’s value, then we can call it a bait-and-switch. But that hasn’t happened. The correct attitude is to wait and see, to demand transparency, to hold the board to account for getting the valuation it is legally obligated to pursue, and to pursue OpenAI to the full extent of the law if it ends up convincing the board to give up its extraordinary bequest at bargain-basement prices. 
1 h
vox.com
Michael Bay’s 2000s-era Slasher Remakes Were Quickly Buried By Critics — But Are Now Seeing Second Life On Letterboxd
Bay's company remade Friday the 13th, Nightmare on Elm Street, and The Texas Chainsaw Massacre with music-video aesthetics. Has time been kind to these fan-non-favorites?
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nypost.com
I was paralyzed a decade ago — here’s how I run the NYC marathon
Michael Ring is preparing to toe the line at Sunday's TCS New York City Marathon, a 26.2-mile course he knows all too well but takes him twice as long to run.
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nypost.com
Baseball’s Next Great Analytical Frontier
Mariano Rivera was never secretive about the grip on his signature pitch. He’d show it to teammates, coaches, even reporters. He placed his index and middle fingers together along the seams. He pulled down with his middle finger upon release. The ball would whiz arrow-straight before veering sharply a few inches from where the hitter expected it.When teaching pitchers how it should feel coming out of their hand, however, Rivera could be frustratingly vague. Put pressure on the middle finger, he would say. This can be a moneymaker for you. Even now, nobody can make a fastball move quite like Mo’s. “It is as if it dropped straight from the heavens,” he wrote in his 2014 memoir. “How can I explain it any other way?”Eleven years after Rivera’s retirement, a wrist brace with claws could strip any last intimation of divinity out of pitching. A pitcher’s fingers slide into its four rubber rings, attached to metal straws that are fastened by a Velcro strap around the wrist. This device, the FlexPro Grip, measures exactly how quickly each of a pitcher’s fingers exert pressure on a ball. But the point of the gadget isn’t just to register finger forces. It’s to transform the art of pitching into a science.One afternoon last year, at a training facility called VeloU, I watched as Aidan Dolinsky, a pitcher for New York University, slipped on the FlexPro Grip and awaited instructions from Adam Moreau, the device’s co-creator. “I want you to squeeze with your two fingers”—the index and middle—“but only at about 50 percent of your maximum pressure,” Moreau said. “Hold it there for a few seconds. Hold, hold. And then instantly—boom—ramp up to your max force.”As Dolinsky squeezed, Moreau began peppering him with numbers. “Get to 69,” he said, glancing at the app in front of them, “and then when you see that little green dot there, slam on it … Okay, hold, hold, go!”The young pitcher needed a few tries before he mastered the proper sequence of acceleration. “I realized I was squeezing too hard, so then I backed off too much,” Dolinsky said.“That’s quantifying feel!” Moreau cried. Imagine, he said, standing on the mound, and knowing exactly how much force to put on each key finger, and exactly how to peak them at the same time. “What would that do to your spin?”Today’s professional pitchers throw harder than ever, but their art is still largely dictated by speculative notions of feel. Pitchers have forever been licking their fingers and clutching rosin bags to help with grip; these days, camera technology and data analysis have put a premium on players who can also impart enough spin to make the ball run, ride, cut, carry, sink, tunnel, and bore along a split-second flight path. It’s not enough to be blessed with a golden arm. You need to have it work in conjunction with your fingers, too.Only recently, though, has anyone tried to understand exactly how those fingers work in pitching. In 2017, Glenn Fleisig, an expert in biomechanics, led a cohort of researchers looking at how elite pitchers apply finger pressure while throwing. By stuffing a regulation baseball with sensors, the researchers found that the force of the middle and index finger on the ball spiked twice, the last coming roughly six to seven milliseconds before release—in essence, the instant the ball leaves the hand. The force of that final peak averaged 185 Newtons, exerted through two fingers kissing the seams of a five-ounce baseball. It’s enough force to heave a bowling ball about 90 miles an hour.When I spoke with Fleisig, he recalled that the primary motivation around the study was injury prevention. Elbow tears are collectively a billion-dollar problem for Major League Baseball each year, and “knowing how hard someone grips has implications about what’s happening in your elbow,” he said. What he found, though, also unlocked a mystery about pitching. Fleisig had previously reported that the angular velocity achievable by a pitcher’s shoulder maxes out at about 90 miles an hour, but pitchers can throw faster than that. Something else had to be providing that extra oomph—the fingers. “A huge thing that separates a good pitcher from a great pitcher,” Fleisig said, “is their ability to do that last push.”[Devin Gordon: Arms are flying off their hinges]Fleisig’s work is emblematic of a recent and long-overdue boom in touch research. “We’re now catching up to where we’ve been for many decades in the auditory and visual fields,” David Ginty, a neuroscientist at Harvard Medical School, told me. When Ginty started his somatosensory research lab in the mid-1990s, the field was small and quirky, dominated by a few labs producing a handful of papers a year. Today, the IEEE World Haptics conference, the top symposium where touch researchers share their findings, is a sprawling, festival-like event, sponsored by a subsidiary of Meta. Advancements in molecular-genetic techniques have enabled labs like Ginty’s to see how individual nerve cells respond to certain stimuli. It’s given researchers the best picture yet of the basic biology of touch, and it’s jump-started investigations into new treatments for chronic pain, anemia, irritable bowel syndrome, traumatic brain injury, and even low bone density. A stream of studies in recent years has also highlighted the psychological, cognitive, and creative benefits of doing things by hand.In science, the closer anyone looks at touch, the more its influence becomes apparent. In baseball, it could revolutionize how teams look for the next Mariano Rivera with the magic feel.For Connor Lunn’s entire baseball career, “feel” was waved off as something subjective and abstract, mostly because it couldn’t be measured. Eventually, Lunn, a recently retired minor-league pitcher, realized that people weren’t even trying. “We have every other metric out there—how hard you’re throwing, all the spin rates, the tail axis, everything,” Lunn told me. “But there was nothing out there on where you’re gripping the ball.” Learning how to throw a new pitch was like getting a prescription for eyeglasses based on what somebody else is telling you looks clear for them. In April, shortly before being signed as a free agent by the Tampa Bay Rays, Lunn was co-awarded the patent on a design for a baseball wrapped in a pressure-sensing fabric.Alex Fast, a data analyst and writer for PitchingList.com, also thought the role of pressure was being overlooked. In March 2023, he gave a talk at the MIT Sloan Analytics Conference in Boston about measuring finger pressure in baseball. Using sensors and other supplies bought from Amazon, he built a feedback device that was tiny and flexible enough to be worn underneath a piece of tape on the fingertip and that could transmit force data to a microcontroller, worn inside a fanny pack on the pitcher’s lower back. “When I first got into analytics, I remember thinking that they’ve quantified everything,” Fast told me. But so many people that he spoke with after the conference shared his hunch about finger force, Fast told me later, that he began to think, This could be pitching’s next great analytical frontier.[From the July/August 2023 issue: Moneyball broke baseball]Part of what’s so notable about the attention being paid to touch in baseball circles is its contrast with how most of us navigate the world. I can point to one tool I reliably touch in my daily life: my iPhone, with its flat, smooth surface. I tap, scroll, and occasionally pinch it; calling it a touchscreen is an insult to the various forms of touch humans once used to manipulate pens, books, Rolodexes, keys, cash, coins, camcorders, calculators, discs, tapes, and credit cards. In households around the world, voice assistants and smart devices already respond nimbly to vocal commands to turn on lights, play songs, set temperatures, and change television channels. Hands-free fixtures fill the bathroom. Telehealth visits replace physical exams. Virtual reality has barely any use for the hands or feet.That our grip on the physical world is slipping has real consequences: A long history of medical study has connected hand strength to overall physical health and longevity, for reasons that still aren’t entirely clear. Christy Isbell, a pediatric occupational therapist at East Tennessee State University, said she sees some kids as old as 4 or 5 years who have never held a pencil or a crayon. The absence of that tactile experience may change how they learn to read and write, she told me, and limit them in other ways. Healthy young adults who spend lots of time on their smartphones have weaker grips, duller fingers, and higher rates of hand and wrist injuries than their peers who use their phones less frequently. Professors at medical schools are raising alarms about the diminishing dexterity of surgical students.Pitchers are an outlier. Unlike the rest of us, they must be attuned to precisely how their fingertips interact with the world every time they take the mound. And simply paying a little more attention to that interaction appears to make a great difference. According to research by the company that manufactures the FlexPro Grip, pitchers who use the device have been able to increase the rate of spin on their fastball by about 4 percent. A higher spin rate on a fastball can produce a “rising” effect that makes it harder for hitters to square up.[Read: The scourge of ‘win probability’ in sports]Even if the rest of us never get our finger pressure measured, the research is clear that we can benefit emotionally, cognitively, and physically by doing more with our hands—by jotting down notes, knitting, or taking a pottery class. With that effort, and the help of a few committed baseball buffs, perhaps we can arrest our collective drift into a hands-free world.
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theatlantic.com
NYC hip hop producer DJ Clark Kent dead at 58 after battle with cancer
“Clark quietly and valiantly fought a three year battle with Colon Cancer, while continuing to share his gifts with the world.
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nypost.com
X-rated Dem campaign ad claims GOP wants to ‘ban porn nationwide’
A new advertisement by a group supporting Democratic candidates shows a man involved in a solitary sex act being interrupted by a fictional Republican, who informs the man that the GOP has banned porn nationwide.
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nypost.com
World "miles short' of emissions goals to curb climate change, U.N. says
Greenhouse gas concentrations in the atmosphere reached record highs in 2023, the U.N. warned, with countries falling "miles short" of what is needed to curb devastating global warming.
2 h
cbsnews.com
How the Jets season went from life support to DOA
The 2024 Jets season died Sunday night in Foxborough, Mass., succumbing to a battle with under-performance after it was left weakened by over-expectations. It was just eight games old. Time of death was called at 4:02 p.m., when a 25-22 loss to the Patriots was sealed and a familiar bones collector appeared. Pat Patriot —...
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nypost.com
Political cartoons of the day
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foxnews.com
Singer Loomis apologizes, begs for forgiveness after botching National Anthem: ‘Lesson learned’
"I’m taking this as a lesson learned, and I can’t wait to come back even stronger," Loomis said on Instagram.
2 h
nypost.com
Trump, Harris nearly tied in battleground Wisconsin 8 days from Election Day, poll finds
Former President Trump and Vice President Harris are neck and neck in Wisconsin, with just eight days until Election Day, according to a new poll.
2 h
foxnews.com
Harris mocked for unveiling ‘new accent’ at Philadelphia event: ‘Everything about this woman is fake’
"BREAKING: Kamala Harris unveils a new accent at a black Philadelphia church," popular conservative X account "End Wokeness" posted.
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nypost.com
Pro photographer dies in horror accident backing into plane propeller
A photographer was killed in a freak accident over the weekend after backing into an active airplane propeller while taking photos at a Kanas airfield.
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nypost.com
2 music icons put political beliefs aside for common cause -- the Detroit Lions
The Detroit Lions' winning ways appear to be bringing those on opposite sides of the political aisle together in Kid Rock and Eminem. The Lions won Sunday over the Tennessee Titans.
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foxnews.com
Matthew Perry was ‘giggling between takes’ of the 2021 ‘Friends’ reunion: ‘Being his usually silly self’
Matthew Perry was his "sweet" and "silly" self behind the scenes of the "Friends" reunion.
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nypost.com
The Sports Report: Justin Herbert looks like his old self as Chargers win
Chargers quarterback Justin Herbert's ankle looks great as he leads team to victory over the New Orleans Saints.
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latimes.com