Via Jeffrey Goldberg at The Atlantic:
U.S. national-security leaders included me in a group chat about upcoming military strikes in Yemen. I didn’t think it could be real. Then the bombs started falling.
This level of incompetence would be impressive if it weren’t so terrifying.
Via The Bulwark:
The American people have made a disastrous choice. And they have done so decisively, and with their eyes wide open.
…After everything…the American people liked what they saw. At a minimum, they were willing to accept what they saw.
…So: We can lament our situation. We can analyze how we got here. We can try to learn lessons from what has happened. We have to do all these things.
But we can’t only do those things. As Churchill put it: “In Defeat: Defiance.” We’ll have to keep our nerve and our principles against all the pressure to abandon them. We’ll have to fight politically and to resist lawfully. We’ll have to do our best to limit the damage from Trump. And we’ll have to lay the groundwork for future recovery.
Henry Kissinger has, at long last, died:
The infamy of Nixon’s foreign-policy architect sits, eternally, beside that of history’s worst mass murderers. A deeper shame attaches to the country that celebrates him.
Physics has long sought to find a material that could serve practically as a room-temperature superconductor to carry electricity with zero resistance. A physics pre-print was posted on July 31st, and updated August 3rd, to arxive.org claiming that a material, LK-99, was a room-temperature superconducting material.
It became an internet and social media sensation overnight. And rightfully so. Labs were able to replicate parts of the experiment adding to the evidence that this was a breakthrough. Such a discovery would have a huge impact on many fields.
However, scientists were skeptical after trying to replicate other parts of the experiment. By August 16th, enough scientists had attempted replication to determine that LK-99 was, in fact, not a room-temperature superconductor.
So what happened? Dan Garisto, writing for Nature, compiled the evidence and explains how this happened:
Researchers seem to have solved the puzzle of LK-99. Scientific detective work has unearthed evidence that the material is not a superconductor, and clarified its actual properties.
The conclusion dashes hopes that LK-99 — a compound of copper, lead, phosphorus and oxygen — marked the discovery of the first superconductor that works at room temperature and ambient pressure. Instead, studies have shown that impurities in the material — in particular, copper sulfide — were responsible for the sharp drops in electrical resistivity and partial levitation over a magnet, which looked similar to properties exhibited by superconductors.
Essentially, impure samples created properties that resulted in ferromagnetism. This ferromagnetism mimicked some of the expected properties of a room-temperature superconductor. The ferromagnetism was the key to concluding that the initial result was not valid.
Large language model use continues to pop up in surprising and interesting ways. The New York Times reports on ways doctors have been using these models.
In one survey, 85 percent of patients reported that a doctor’s compassion was more important than waiting time or cost. In another survey, nearly three-quarters of respondents said they had gone to doctors who were not compassionate. And a study of doctors’ conversations with the families of dying patients found that many were not empathetic.
Enter chatbots, which doctors are using to find words to break bad news and express concerns about a patient’s suffering, or to just more clearly explain medical recommendations.
Breaking bad news to patients, and explaining things to them in a way that is empathetic and that they can digest easily, is difficult. The harder the news, the harder I imagine coming up with the right words would be. This feels like a perfect use-case for large language models. As an aid and supplement to communication. Not as a replacement for anything or as a tool to generate more revenue.
In this brilliant piece, Ted Chiang argues that artificial intelligence (
Is there a way for
A.I. to do something other than sharpen the knife blade of capitalism? Just to be clear, when I refer to capitalism, I’m not talking about the exchange of goods or services for prices determined by a market, which is a property of many economic systems. When I refer to capitalism, I’m talking about a specific relationship between capital and labor, in which private individuals who have money are able to profit off the effort of others. So, in the context of this discussion, whenever I criticize capitalism, I’m not criticizing the idea of selling things; I’m criticizing the idea that people who have lots of money get to wield power over people who actually work. And, more specifically, I’m criticizing the ever-growing concentration of wealth among an ever-smaller number of people, which may or may not be an intrinsic property of capitalism but which absolutely characterizes capitalism as it is practiced today.As it is currently deployed,
A.I. often amounts to an effort to analyze a task that human beings perform and figure out a way to replace the human being. Coincidentally, this is exactly the type of problem that management wants solved. As a result,A.I. assists capital at the expense of labor. There isn’t really anything like a labor-consulting firm that furthers the interests of workers. Is it possible forA.I. to take on that role? CanA.I. do anything to assist workers instead of management?Some might say that it’s not the job of
A.I. to oppose capitalism. That may be true, but it’s not the job ofA.I. to strengthen capitalism, either. Yet that is what it currently does. If we cannot come up with ways forA.I. to reduce the concentration of wealth, then I’d say it’s hard to argue thatA.I. is a neutral technology, let alone a beneficial one.
It seems that
The twin Voyager probes launched from Cape Canaveral in August (Voyager 2) and September (Voyager 1) of 1977. The original mission was designed to explore Jupiter and Saturn with the hope the probes would last the four years it would take to get there. They did. So the mission was extended to send Voyager 2 to Uranus and Neptune while sending Voyager 1 into interstellar space. Voyager 2 encountered Neptune August 25, 1989. The probes chugged along. In fact, they are still chugging along as of today.
There are five teams at
However, engineers continue to find ways to eke more power from the technologically ancient craft. This time they turned to a backup power supply that helps maintain a consistent voltage. This adds increased risk of damaging the instruments but the benefit far outweighs the risk. They are running out of options to keep all the instruments running so they will eventually begin shutting them down, one-by-one. Until then, they will continue providing valuable scientific data about the universe.
How do we, as a society, end up with a tiny handful of ultra-rich individuals while the rest live in poverty? Better yet, why do societies allow this to happen? Mathematicians have something called the Yard-sale model that explains why.
This is the crux of the Yard-sale model. In a free market, one person ends up with all of the wealth – completely by chance.
Spoiler, the solution to this problem is wealth redistribution.
Via kottke.org
On sleeping beauties, evolutionary and cultural innovations that quietly survive for long periods before achieving explosive success once conditions are right for thriving.
Why do some innovations remain dormant for long periods of time before, seemingly, experience explosive success? This article, from The Guardian, discusses some evolutionary biology principles that may help us better understand human innovations.
Excerpted from Sleeping Beauties: the Mystery of Dormant Innovations in Nature and Culture by Andreas Wagner.
Art is generally created with the intent of it being around indefinitely for people to enjoy. Whether it has actual staying power, or not, is irrelevant. It is created with the intent of it being around for a while. On the other hand, “ephemeral art has in its genesis a component of transience, of fleeting object or expression in time.”
Social media has become the ultimate manifestation of ephemerality. Something is posted, it makes the rounds, and then is tossed aside. Sure, it may still be there, but it was created for a specific moment in time and, generally, loses relevance quickly.
Ahoy, a YouTube channel that covers a wide range of topics, but primarily video games, recently posted a video by Stuart Brown, about some early computer art (1986) that was created to show off the power of the new Amiga program Graphicraft.
It was created before the program had a save function, so it was always intended to be ephemeral. At least in its original digital format. It was printed in the user manual and in magazines but, as soon as the machine turned off, the original artwork was gone forever.
Stuart set out to recreate this artwork, in its original format, as faithfully as possible. It is not the same piece of artwork. It is plainly, and admittedly, a sophisticated reproduction of the original.
In any case, if you enjoy thinking philosophically about ephemeral art, or enjoy learning about computer graphics history, or just enjoy watching someone creating something just for the passion of creating, then this video is for you.
Tesla has been under investigation for crashes involving Autopilot. There is super villain behavior and then there’s what Tesla has programmed their vehicles to do, endangering thousands of people’s lives:
On Thursday [9 June 2022],
NHTSA said it had discovered in 16 separate instances when this occurred that Autopilot “aborted vehicle control less than one second prior to the first impact,” suggesting the driver was not prepared to assume full control over the vehicle.
They can report that there are virtually zero accidents when Autopilot is engaged because they program it to turn off moments before the accident thus shifting all blame to the driver.
Digital Rights Management (
Without
[…]
DRM in wheelchairs prevents wheelchair users and independent technicians from diagnosing routine problems with the chairs’ electronics. It also stops wheelchair users from making routine adjustments to their wheelchairs, as when “a wheelchair user with a balky wheel or failing motor may need to adjust the power wheelchair’s speed damping setting, which is accomplished using the administrative software” or when “a wheelchair user who installs a different tire on their chair for navigating inclement weather may want to access administrative software features to adjust the chair’s grip parameters.”
The fact is,
Maciej Cegłowski spoke to the
Instead of relying on algorithms, which we can be accused of manipulating for our benefit, we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It’s a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don’t lie.
In another talk, this time to the Library of Congress, he talked about a food cart vendor who was shut down for never changing the oil in which he fried everything. This, it turned out, resulted in the unique flavor that kept customers returning for more. You could fry anything and it was delicious. He then asks a question about how we use and analyze data:
So what’s your data being fried in? These algorithms train on large collections that you know nothing about. Sites like Google operate on a scale hundreds of times bigger than anything in the humanities. Any irregularities in that training data end up infused into…the classifier.
The world is full of data. It is increasingly difficult to justify collecting even more data. It is even more difficult to use this data in a way that doesn’t reinforce the status quo or harm at-risk communities.
There are no clear-cut answers to the problem of what to do with all the data. But thinking critically about it is essential. These two talks are a great starting point for people, and organizations, to begin thinking carefully about ethical data collection, usage, and analysis.
Uvalde Consolidated Independent School District Police Chief, Pete Arredondo, made the following statement regarding the Uvalde school mass shooting:
Arredondo told the Texas Tribune’s James Barragan and Zach Despart that he dropped his police and campus radios outside the school seconds after arriving at the northeast entrance of Robb Elementary, stating that he believed the tools would slow him down while responding to the shooter.[…]
The Tribune’s report includes claims from the police chief that he had no idea he was in charge of the police response inside the school.
There are only a couple of reasons the chief of police wouldn’t know he was in charge of the police response to a mass shooting. None involve honesty or competence.
If you don’t watch closely, it might be hard to miss all the indicators that this was treason. If you do watch closely, it would be impossible to see this as nothing short of a failed coup d’état. Were I this Michael Pence guy, I would be deeply disturbed by the threats directed against me that day.
Nick Heer on the sorry state of privacy policies:
As long as it is left up to companies to negotiate individually with users and nominally inform them about their privacy sacrifices, this problem will never go away. There is just too much money encouraging the worst behaviour. The only thing stopping this is to require this industry to rebuild, from scratch, with privacy at the core — and the only way that happens is to destroy, with law, any incentives for collecting, merging, and sharing incidental or behavioural user data. Only then can we consider ending the ridiculous practice of requiring a separate, wildly permissive contract for every digital product and service we use.
John Oliver on why police in schools does not solve the problem, unique to the United States, of mass shootings in schools.