Can Technology Help Reduce Drunk-Driving Deaths?

An anonymous reader shared this report from the Wall Street Journal:
Drunken-driving deaths in the U.S. have risen to levels not seen in nearly two decades, federal data show, a major setback to long-running road-safety efforts. At the same time, arrests for driving under the influence have plummeted, as police grapple with challenges like hiring woes and heightened concern around traffic stops… About 13,500 people died in alcohol impairment-related crashes in 2022, according to data released in April by the National Highway Traffic Safety Administration. That is 33% above 2019’s toll and on par with 2021’s. The last time so many people died as a result of accidents involving intoxicated drivers was in 2006.
That’s still down from the early 1980s, when America was seeing over 20,000 drunk-driving deaths a year, according to the article. “By 2010, that number had fallen to around 10,000 thanks to high-profile public-education campaigns by groups like MADD, tougher laws, and aggressive enforcement that included sobriety checkpoints and typically yielded well over a million DUI arrests annually.”
But some hope to solve the problem using technology:
Many activists and policymakers are banking on the promise of built-in devices to prevent a car from starting if the driver is intoxicated, either by analyzing a driver’s exhaled breath or using skin sensors to gauge the blood-alcohol level. NHTSA issued a notice in December that it said lays the groundwork for potential alcohol-impairment detection technology standards in all new cars “when the technology is mature.”
And Glenn Davis, who manages Colorado’s highway-safety office, “pointed to Colorado’s extensive use of ignition interlock systems that require people convicted of DUI to blow into a tube to verify they are sober in order for their car to start. He said the office promotes nondriving options such as Lyft and Uber.”

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AI-Powered ‘HorseGPT’ Fails to Predict This Year’s Kentucky Derby Winner

In 2016, an online “swarm intelligence” platform generated a correct prediction for the Kentucky Derby — naming all four top finishers, in order. (But the next year their predictions weren’t even close, with TechRepublic suggesting 2016’s race had an unusual cluster of just a few top racehorses.)

So this year Decrypt.co tried crafting their own system “that can be called up when the next Kentucky Derby draws near.
There are a variety of ways to enlist artificial intelligence in horse racing. You could process reams of data based on your own methodology, trust a third-party pre-trained model, or even build a bespoke solution from the ground up. We decided to build a GPT we named HorseGPT to crunch the numbers and make the picks for us…

We carefully curated prompts to instill HorseGPT with expertise in data science specific to horse racing: how weather affects times, the role of jockeys and riding styles, the importance of post positions, and so on. We then fed it a mix of research papers and blogs covering the theoretical aspects of wagering, and layered on practical knowledge: how to read racing forms, what the statistics mean, which factors are most predictive, expert betting strategies, and more. Finally, we gave HorseGPT a wealth of historical Kentucky Derby data, arming it with the raw information needed to put its freshly imparted skills to use.
We unleashed HorseGPT on official racing forms for this year’s Derby. We asked HorseGPT to carefully analyze each race’s form, identify the top contenders, and recommend wager types and strategies based on deep background knowledge derived from race statistics.

HorseGPT picked two horses to win — both of which failed to do so. (Sierra Leone did finish second — in a rare photo finish. But Fierceness finished… 15th.) It also recommended the same two horses if you were trying to pick the top two finishers in the correct order — a losing bet, since, again, Fierceness finished 15th.

But even worse, HorseGPT recommended betting on Just a Touch to finish in either first or second place. When the race was over, that horse finished dead last. (And when asked to pick the top three finishers in correct order, HorseGPT stuck with its choices for the top two — which finished #2 and #15 — and, again, Just a Touch, who came in last.)

When Google Gemini was asked to pick the winner by The Athletic, it first chose Catching Freedom (who finished 4th). But it then gave an entirely different answer when asked to predict the winner “with an Italian accent.”

“The winner of the Kentucky Derby will be… Just a Touch! Si, that’s-a right, the underdog! There will be much-a celebrating in the piazzas, thatta-a I guarantee!”

Again, Just a Touch came in last.

Decrypt noticed the same thing. “Interestingly enough, our HorseGPT AI agent and the other out-of-the-box chatbots seemed to agree with each other,” the site notes, “and with many experts analysts cited by the official Kentucky Derby website.”

But there was one glimmer of insight into the 20-horse race. When asked to choose the top four finishers in order, HorseGPT repeated those same losing picks — which finished #2, #15, and #20. But then it added two more underdogs for fourth place finishers, “based on their potential to outperform expectations under muddy conditions.”
One of those two horses — Domestic Product — finished in 13th place.

But the other of the two horses was Mystik Dan — who came in first.

Mystik Dan appeared in only one of the six “Top 10 Finishers” lists (created by humans) at the official Kentucky Derby site… in the #10 position.

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Humans Now Share the Web Equally With Bots, Report Warns

An anonymous reader quotes a report from The Independent, published last month: Humans now share the web equally with bots, according to a major new report — as some fear that the internet is dying. In recent months, the so-called “dead internet theory” has gained new popularity. It suggests that much of the content online is in fact automatically generated, and that the number of humans on the web is dwindling in comparison with bot accounts. Now a new report from cyber security company Imperva suggests that it is increasingly becoming true. Nearly half, 49.6 per cent, of all internet traffic came from bots last year, its “Bad Bot Report” indicates. That is up 2 percent in comparison with last year, and is the highest number ever seen since the report began in 2013. In some countries, the picture is worse. In Ireland, 71 per cent of internet traffic is automated, it said.

Some of that rise is the result of the adoption of generative artificial intelligence and large language models. Companies that build those systems use bots scrape the internet and gather data that can then be used to train them. Some of those bots are becoming increasingly sophisticated, Imperva warned. More and more of them come from residential internet connections, which makes them look more legitimate. “Automated bots will soon surpass the proportion of internet traffic coming from humans, changing the way that organizations approach building and protecting their websites and applications,” said Nanhi Singh, general manager for application security at Imperva. “As more AI-enabled tools are introduced, bots will become omnipresent.”

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Apple Announces Largest-Ever $110 Billion Share Buyback As iPhone Sales Drop

Apple reported fiscal second-quarter earnings that topped estimates, despite a 10% drop in iPhone sales. The company also announced that its board had authorized $110 billion in share repurchases, “a 22% increase over last year’s $90 billion authorization,” notes CNBC. “It’s the largest buyback in history, ahead of Apple’s previous repurchases.” From the report: Apple did not provide formal guidance, but Apple CEO Tim Cook told CNBC’s Steve Kovach that overall sales would grow in the “low single digits” during the June quarter. Apple posted $81.8 billion in revenue during the year-ago June quarter and LSEG analysts were looking for a forecast of $83.23 billion. On an earnings call with analysts, Apple finance chief Luca Maestri said the company expected the current quarter will deliver double-digit year-over-year percentage growth in iPad sales. What’s more, he said the Services division is forecast to continue growing at about the current high rate it’s achieved during the past two quarters.

Apple reported net income of $23.64 billion, or $1.53 per share, down 2% from $24.16 billion, or $1.52 per share, in the year-earlier period. Cook told CNBC that sales in the fiscal second quarter suffered from a difficult comparison to the year-earlier period, when the company realized $5 billion in delayed iPhone 14 sales from Covid-based supply issues. “If you remove that $5 billion from last year’s results, we would have grown this quarter on a year-over-year basis,” Cook said. “And so that’s how we look at it internally from how the company is performing.”

Apple said iPhone sales fell nearly 10% to $45.96 billion, suggesting weak demand for the current generation of smartphones, which were released in September. The sales were in line with analyst estimates, and Cook said that without last year’s increased sales, iPhone revenue would have been flat. Mac sales were up 4% to $7.45 billion, but they are still below the segment’s high-water mark set in 2022. Cook said sales were driven by the company’s new MacBook Air models which were released with an upgraded M3 chip in March. Other Products, which is how Apple reports sales of its Apple Watch and AirPods headphones, was down 10% year over year to $7.9 billion.

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Nurses Say Hospital Adoption of Half-Cooked ‘AI’ Is Reckless

An anonymous reader quotes a report from Techdirt: Last week, hundreds of nurses protested the implementation of sloppy AI into hospital systems in front of Kaiser Permanente. Their primary concern: that systems incapable of empathy are being integrated into an already dysfunctional sector without much thought toward patient care: “No computer, no AI can replace a human touch,” said Amy Grewal, a registered nurse. “It cannot hold your loved one’s hand. You cannot teach a computer how to have empathy.”

There are certainly roles automation can play in easing strain on a sector full of burnout after COVID, particularly when it comes to administrative tasks. The concern, as with other industries dominated by executives with poor judgement, is that this is being used as a justification by for-profit hospital systems to cut corners further. From a National Nurses United blog post (spotted by 404 Media): “Nurses are not against scientific or technological advancement, but we will not accept algorithms replacing the expertise, experience, holistic, and hands-on approach we bring to patient care,” they added.

Kaiser Permanente, for its part, insists it’s simply leveraging “state-of-the-art tools and technologies that support our mission of providing high-quality, affordable health care to best meet our members’ and patients’ needs.” The company claims its “Advance Alert” AI monitoring system — which algorithmically analyzes patient data every hour — has the potential to save upwards of 500 lives a year. The problem is that healthcare giants’ primary obligation no longer appears to reside with patients, but with their financial results. And, that’s even true in non-profit healthcare providers. That is seen in the form of cut corners, worse service, and an assault on already over-taxed labor via lower pay and higher workload (curiously, it never seems to impact outsized high-level executive compensation).

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