In May of last year, two brothers in their 20s were arrested for what the Justice Department at the time called “attacking the Ethereum blockchain and stealing $25 million.” Attacking the blockchain does sound like a cool, sci-fi crime, but the brothers maintained that they were just aggressive traders, not criminals, and yesterday, their prosecution culminated in what sounds like a very stressful mistrial.
The prosecution’s case was that Anton Peraire-Bueno and James Pepaire-Bueno set a trap that amounted to fraud. Prosecutors said they preyed upon crypto trading bots that moved digital money around on behalf of, apparently, three entities tied to actual human beings—although only one, David Yakira, ever came forward as an alleged victim. The trading bots were targeted because they were performing what are known as “sandwich transactions,” and were allegedly lured into situations that caused them to glitch out and release valuable tokens in exchange for, well, shitcoins.
Then the brothers allegedly tried to launder their winnings.
Performing digital muggings (allegedly!) on bots that perform sandwich transactions required extreme sophistication, and the ability to spot an exploit that wasn’t expressly forbidden in the Wild West universe that is crypto land.
The nature of the scheme also seems like a bid for a Robin Hood-type vigilante reputation. Sandwich transactions are legal, but are perceived as parasitic arbitrage plays, or at the very least extremely irritating—essentially just gaming unsuspecting people’s transactions to set the price where the, if you will, sandwich artisan wants it in order to make a quick buck at the expense of a sucker with no recourse. In other words, it appears the brothers correctly predicted the rather nasty behavior of some bots, slipped in some sketchy code, and came away with $25 million.
So were these brothers grifters, or just aggressive traders with what their lawyer called a very good “trading strategy”?
According to Business Insider, the Pepaire-Bueno brothers faced a Manhattan jury specifically chosen to pry apart these fuzzy distinctions, with half of them holding masters degrees of one sort or another. “Almost all,” Business Insider noted, were either middle-aged or retirement-aged.
Welp, in the course of a three week trial, that ambiguity was apparently not resolved to the unanimous satisfaction of the jury, and things sound like they got intense for this unhappy group of 12 people.
According to Bloomberg’s account of the mistrial declaration, while an anonymous juror later explained that the facts of the case were not in dispute, at some point on Friday, the jury pleaded with the judge for help coming to a resolution. Some had lost “multiple nights” of sleep. Then later in the day, a note from the jury said that coming to a decision was placing them under an “emotional burden” and that half of the jurors had “spontaneously broken down in tears” while they were deliberating.
So U.S. District Judge Jessica Clarke went ahead and declared a mistrial Friday.
To be clear, a deadlocked jury doesn’t necessarily free the Peraire-Bueno brothers, but it is unwelcome news for prosecutors, who will naturally want to retry the brothers in the hopes of getting a conviction. But they do so with the burden of having already fought to a stalemate, which can’t be any better for morale than the fact that deliberating on the details of this highly technical case made a jury cry.
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#Crypto #Bros #Mistrial #Emotional #Burden #Deadlocked #Jurors #Cried
![In Harvard study, AI offered more accurate emergency room diagnoses than two human doctors | TechCrunch
A new study examines how large language models perform in a variety of medical contexts, including real emergency room cases — where at least one model seemed to be more accurate than human doctors.
The study was published this week in Science and comes from a research team led by physicians and computer scientists at Harvard Medical School and Beth Israel Deaconess Medical Center. The researchers said they conducted a variety of experiments to measure how OpenAI’s models compared to human physicians.
In one experiment, researchers focused on 76 patients who came into the Beth Israel emergency room, comparing the diagnoses offered by two internal medicine attending physicians to those generated by OpenAI’s o1 and 4o models. These diagnoses were assessed by two other attending physicians, who did not know which ones came from humans and which came from AI.
“At each diagnostic touchpoint, o1 either performed nominally better than or on par with the two attending physicians and 4o,” the study said, adding that the differences “were especially pronounced at the first diagnostic touchpoint (initial ER triage), where there is the least information available about the patient and the most urgency to make the correct decision.”
In Harvard Medical School’s press release about the study, the researchers emphasized that they did not “pre-process the data at all” — the AI models were presented with the same information that was available in the electronic medical records at the time of each diagnosis.
With that information, the o1 model managed to offer “the exact or very close diagnosis” in 67% of triage cases, compared to one physician who had the exact or close diagnosis 55% of the time, and to the other who hit the mark 50% of the time.
“We tested the AI model against virtually every benchmark, and it eclipsed both prior models and our physician baselines,” said Arjun Manrai, who heads an AI lab at Harvard Medical School and is one of the study’s lead authors, in the press release.
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San Francisco, CA
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October 13-15, 2026
To be clear, the study didn’t claim that AI is ready to make real life-or-death decisions in the emergency room. Instead, it said the findings show an “urgent need for prospective trials to evaluate these technologies in real-world patient care settings.”
The researchers also noted that they only studied how models performed when provided with text-based information, and that “existing studies suggest that current foundation models are more limited in reasoning over nontext inputs.”
Adam Rodman, a Beth Israel doctor who’s also one of the study’s lead authors, warned the Guardian that there’s “no formal framework right now for accountability” around AI diagnoses, and that patients still “want humans to guide them through life or death decisions [and] to guide them through challenging treatment decisions.”
In a post about the study, Kristen Panthagani, an emergency physician, said this is an “an interesting AI study that has led to some very overhyped headlines,” especially since it was comparing AI diagnoses to those from internal medicine physicians, not ER physicians.
“If we’re going to compare AI tools to physicians’ clinical ability, we should start by comparing to physicians who actually practice that specialty,” Panthagani said. “I would not be surprised if a LLM could beat a dermatologist at an neurosurgery board exam, [but] that’s not a particularly helpful thing to know.”
She also argued, “As an ER doctor seeing a patient for a first time, my primary goal is not to guess your ultimate diagnosis. My primary goal is to determine if you have a condition that could kill you.”
This post and headline have been updated to reflect the fact that the diagnoses in the study came from internal medicine attending physicians, and to include commentary from Kristen Panthagani.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Harvard #study #offered #accurate #emergency #room #diagnoses #human #doctors #TechCrunchbeth israel,harvard medical school,OpenAI](https://i2.wp.com/techcrunch.com/wp-content/uploads/2024/01/GettyImages-104509077.jpg?resize=1200%2C800&ssl=1 "In Harvard study, AI offered more accurate emergency room diagnoses than two human doctors | TechCrunch
A new study examines how large language models perform in a variety of medical contexts, including real emergency room cases — where at least one model seemed to be more accurate than human doctors.
The study was published this week in Science and comes from a research team led by physicians and computer scientists at Harvard Medical School and Beth Israel Deaconess Medical Center. The researchers said they conducted a variety of experiments to measure how OpenAI’s models compared to human physicians.
In one experiment, researchers focused on 76 patients who came into the Beth Israel emergency room, comparing the diagnoses offered by two internal medicine attending physicians to those generated by OpenAI’s o1 and 4o models. These diagnoses were assessed by two other attending physicians, who did not know which ones came from humans and which came from AI.
“At each diagnostic touchpoint, o1 either performed nominally better than or on par with the two attending physicians and 4o,” the study said, adding that the differences “were especially pronounced at the first diagnostic touchpoint (initial ER triage), where there is the least information available about the patient and the most urgency to make the correct decision.”
In Harvard Medical School’s press release about the study, the researchers emphasized that they did not “pre-process the data at all” — the AI models were presented with the same information that was available in the electronic medical records at the time of each diagnosis.
With that information, the o1 model managed to offer “the exact or very close diagnosis” in 67% of triage cases, compared to one physician who had the exact or close diagnosis 55% of the time, and to the other who hit the mark 50% of the time.
“We tested the AI model against virtually every benchmark, and it eclipsed both prior models and our physician baselines,” said Arjun Manrai, who heads an AI lab at Harvard Medical School and is one of the study’s lead authors, in the press release.
Techcrunch event
San Francisco, CA
|
October 13-15, 2026
To be clear, the study didn’t claim that AI is ready to make real life-or-death decisions in the emergency room. Instead, it said the findings show an “urgent need for prospective trials to evaluate these technologies in real-world patient care settings.”
The researchers also noted that they only studied how models performed when provided with text-based information, and that “existing studies suggest that current foundation models are more limited in reasoning over nontext inputs.”
Adam Rodman, a Beth Israel doctor who’s also one of the study’s lead authors, warned the Guardian that there’s “no formal framework right now for accountability” around AI diagnoses, and that patients still “want humans to guide them through life or death decisions [and] to guide them through challenging treatment decisions.”
In a post about the study, Kristen Panthagani, an emergency physician, said this is an “an interesting AI study that has led to some very overhyped headlines,” especially since it was comparing AI diagnoses to those from internal medicine physicians, not ER physicians.
“If we’re going to compare AI tools to physicians’ clinical ability, we should start by comparing to physicians who actually practice that specialty,” Panthagani said. “I would not be surprised if a LLM could beat a dermatologist at an neurosurgery board exam, [but] that’s not a particularly helpful thing to know.”
She also argued, “As an ER doctor seeing a patient for a first time, my primary goal is not to guess your ultimate diagnosis. My primary goal is to determine if you have a condition that could kill you.”
This post and headline have been updated to reflect the fact that the diagnoses in the study came from internal medicine attending physicians, and to include commentary from Kristen Panthagani.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Harvard #study #offered #accurate #emergency #room #diagnoses #human #doctors #TechCrunchbeth israel,harvard medical school,OpenAI")
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