A study finds that people associate personality traits with faces.
People thought to have similar personalities were viewed as looking alike; people thought to look alike were viewed as having similar personalities.
The research holds a surprise for Vladimir Putin and Justin Bieber.
Humans are so good at identifying faces that we see them in places where they do not exist, such as on the moon or Mars or in combinations of circles, line segments, and dots. It is a particularly useful skill for a social animal. Yet, how exactly we recognize faces and process them is not exactly known. For instance, the Thatcher effect shows that our brains do not simply accept sensory input when deciding what a normal face looks like.
Now, a new study published in the journal Cognition shows that what we think of a person influences our perception of their facial features. In other words, we think people with similar personality traits look the same.
The social aspect of facial recognition
Image courtesy of NYU's Jonathan Freeman
The initial study, carried out with the help of roughly 200 volunteers, had famous faces placed next to each other above a test picture of one of them. Volunteers had to then move their cursor from the test picture to the image of the same person as quickly as possible. Subjects then rated the likelihood that each famous person in the study had particular personality traits.
The people used in the study, all white men for the sake of consistency, were Justin Bieber, George W. Bush, Bill Clinton, Jimmy Fallon, Ryan Gosling, Matthew McConaughey, Bill Murray, Bill Nye, Vladimir Putin, Keanu Reeves, John Travolta, and Mark Wahlberg, among others.
The results showed that the volunteers were inclined to think that people with similar traits looked more alike than those with differing traits. Three more studies followed to confirm the original findings. Two of them focused on showing that the effect works backward †that is, people with similar faces were thought to have similar traits.
The final test sealed the deal. Participants were shown faces that none of them had ever seen before. Once again, they reported that faces looked similar if they were told the people shared similar personality traits and vice versa.
Senior author Jonathan Freeman of New York University's Department of Psychology summarized the findings in apress release:
"Our findings show that the perception of facial identity is driven not only by facial features, such as the eyes and chin, but also distorted by the social knowledge we have learned about others, biasing it toward alternate identities despite the fact that those identities lack any physical resemblance."
Pootie-Poot and the Bieb
This study adds to the evidence for a "social-conceptual" approach to facial recognition. According to the authors, these models suggest that our ideas of a person are difficult to separate from how we view their faces. As they explain in the introduction of their study:
"[A]ccording [to] these models, after presented with a face, the processing of visual features begins activating identity representations… and these in turn begin activating social-conceptual representations, such as personality traits (e.g., bold, diligent, competent)."
Other studies have shown that setting is also important to our ability to recognize faces. Why volunteers think that Justin Bieber and Vladimir Putin look alike remains a bit of a mystery.
Is there an external reality? Is reality objective? Is the information your senses are feeding you an accurate depiction of reality? Most neuroscientists and scientific leaders believe that we can only comprehend a sliver of what is true reality.
Although we assume our senses are telling us the truth, they're actually fabricated to us. Considering senses are unique from person to person, and through our unique senses we can only intemperate a fraction of what is real, there is no all-encompassing and true perspective one individual can hold. Because of this, we need to take our perceptions seriously, but not literally.
Multiple perspectives have to be taken as each new perspective will hold some sliver of truth. Seeing partial truth in multiple perspectives is fundamental to navigating the world and making informed life decisions.
One of the popular memes in literature, movies and tech journalism is that man's creation will rise and destroy it.
Lately, this has taken the form of a fear of AI becoming omnipotent, rising up and annihilating mankind.
The economy has jumped on the AI bandwagon; for a certain period, if you did not have "AI" in your investor pitch, you could forget about funding. (Tip: If you are just using a Google service to tag some images, you are not doing AI.)
However, is there actually anything deserving of the term AI? I would like to make the point that there isn't, and that our current thinking is too focused on working on systems without thinking much about the humans using them, robbing us of the true benefits.
What companies currently employ in the wild are nearly exclusively statistical pattern recognition and replication engines. Basically, all those systems follow the "monkey see, monkey do" pattern: They get fed a certain amount of data and try to mimic some known (or fabricated) output as closely as possible.
When used to provide value, you give them some real-life input and read the predicted output. What if they encounter things never seen before? Well, you better hope that those "new" things are sufficiently similar to previous things, or your "intelligent" system will give quite stupid responses.
But there is not the slightest shred of understanding, reasoning and context in there, just simple re-creation of things seen before. An image recognition system trained to detect sheep in a picture does not have the slightest idea what "sheep" actually means. However, those systems have become so good at recreating the output, that they sometimes look like they know what they are doing.
Isn't that good enough, you may ask? Well, for some limited cases, it is. But it is not "intelligent", as it lacks any ability to reason and needs informed users to identify less obvious outliers with possibly harmful downstream effects.
The ladder of thinking has three rungs, pictured in the graph below:
Imitation: You imitate what you have been shown. For this, you do not need any understanding, just correlations. You are able to remember and replicate the past. Lab mice or current AI systems are on this rung.
Intervention: You understand causal connections and are able to figure out what would happen if you now would do this, based on what you learned about the world in the past. This requires a mental model of the part of the world you want to influence and the most relevant of its downstream dependencies. You are able to imagine a different future. You meet dogs and small children on that rung, so it is not a bad place to be.
Counterfactual reasoning: The highest rung, where you wonder what would have happened, had you done this or that in the past. This requires a full world model and a way to simulate the world in your head. You are able to imagine multiple pasts and futures. You meet crows, dolphins and adult humans here.
In order to ascend from one rung to the next, you need to develop a completely new set of skills. You can't just make an imitation system larger and expect it to suddenly be able to reason. Yet this is what we are currently doing with our ever-increasing deep learning models: We think that by giving them more power to imitate, they will at some point magically develop the ability to think. Apart from self-delusional hope and selling nice stories to investors and newspapers, there is little reason to believe that.
And we haven't even touched the topic of computational complexity and economical and ecological impact of ever-growing models. We might simply not be able to grow our models to the size needed, even if the method worked (which it doesn't, so far).
Whatever those systems create is the mere semblance of intelligence and in pursuing the goal of generating artificial intelligence by imitation, we are following a cargo cult.
Instead, we should get comfortable with the fact that the current ways will not achieve real AI, and we should stop calling it that. Machine learning (ML) is a perfectly fitting term for a tool with awesome capabilities in the narrow fields where it can be applied. And with any tool, you should not try to make the entire world your nail, but instead find out where to use it and where not.
Machines are strong when it comes to quickly and repeatedly performing a task with minimal uncertainty. They are the ruling class of the first rung.
Humans are strong when it comes to context, understanding and making sense with very little data at hand and high uncertainties. They are the ruling class of the second and third rung.
So what if we focus our efforts away from the current obsession with removing the human element from everything and thought about combining both strengths? There is an enormous potential in giving machine learning systems the optimal, human-centric shape, in finding the right human-machine interface, so that both can shine. The ML system prepares the data, does some automatable tasks and then hands the results to the human, who further handles them according to context.
ML can become something like good staff to a CEO, a workhorse to a farmer or a good user interface to an app user: empowering, saving time, reducing mistakes.
Building a ML system for a given task is rather easy and will become ever easier. But finding a robust, working integration of the data and the pre-processed results of the data with the decision-maker (i.e. human) is a hard task. There is a reason why most ML projects fail at the stage of adoption/integration with the organization seeking to use them.
Solving this is a creative task: It is about domain understanding, product design and communication. Instead of going ever bigger to serve, say, more targetted ads, the true prize is in connecting data and humans in clever ways to make better decisions and be able to solve tougher and more important problems.
Republished with permission of the World Economic Forum. Read the original article.
Many small animals grow their teeth, claws and other “tools" out of materials that are filled with zinc, bromine and manganese, reaching up to 20% of the material's weight.
My colleagues and I call these “heavy element biomaterials," and in a new paper, we suggest that these materials make it possible for animals to grow scalpel-sharp and precisely shaped tools that are resistant to breaking, deformation and wear.
We examined ant mandible teeth and found that they are a smooth mix of proteins and zinc, with single zinc atoms attached to about a quarter of the amino acid units that make up the proteins forming the teeth. In contrast, calcified tools – like human teeth – are made of relatively large chunks of calcium minerals. We think the lack of chunkiness in heavy element biomaterials makes them better than calcified materials at forming smooth, precisely shaped and extremely sharp tools.
To evaluate the advantages of heavy element biomaterials, we estimated the force, energy and muscle size required for cutting with tools made of different materials. Compared with other hard materials grown by these animals, the wear-resistant zinc material enables heavily used tools to puncture stiff substances using only one-fifth of the force. The estimated advantage is even greater relative to calcified materials that – since they can't be nearly as sharp as heavy element biomaterials - can require more than 100 times as much force.
Biomaterials that incorporate zinc (red) and manganese (orange) are located in the important cutting and piercing edges of ant mandibles, worm jaws and other 'tools.' (Robert Schofield, CC BY-ND)
Why it matters
It's not surprising that materials that could make sharp tools would evolve in small animals. A tick and a wolf both need to puncture the same elk skin, but the wolf has vastly stronger muscles. The tick can make up for its tiny muscles by using sharper tools that focus force onto smaller regions.
But, like a sharp pencil tip, sharper tool tips break more easily. The danger of fracture is made even worse by the tendency for small animals to extend their reach using long thin tools – like those pictured above. And a chipped claw or tooth may be fatal for a small animal that doesn't have the strength to cut with blunted tools.
From an evolutionary perspective, these materials allow smaller animals to consume tougher foods. And the energy saved by using less force during cutting can be important for any animal. These advantages may explain the widespread use of heavy element biomaterials in nature – most ants, many other insects, spiders and their relatives, marine worms, crustaceans and many other types of organisms use them.
What still isn't known
While my team's research has clarified the advantages of heavy element biomaterials, we still don't know exactly how zinc and manganese harden and protect the tools.
One possibility is that a small fraction of the zinc, for example, forms bridges between proteins, and these cross-links stiffen the material – like crossbeams stiffen a building. We also think that when a fang bangs into something hard, these zinc cross-links may break first, absorbing energy to keep the fang itself from chipping.
We speculate that the abundance of extra zinc is a ready supply for healing the material by quickly reestablishing the broken zinc-histidine cross-links between proteins.
What's next?
The potential that these materials are self-healing makes them even more interesting, and our team's next step is to test this hypothesis. Eventually we may find that self-healing or other features of heavy element biomaterials could lead to improved materials for things like small medical devices.
Mass psychogenic illness, also known as mass hysteria, is when a group of people manifest physical symptoms from imagined threats.
History is littered with outbreaks of mass hysteria.
Recently, alleged cases of Tourette's syndrome appeared all over the world. Was it real or mass psychogenic illness?
While the term is often avoided for fear of ridiculing something more serious,mass psychogenic illness (MPI) †also known as mass sociogenic illness (MSI) or mass hysteria †is a real occurrence that can cause a variety of physical symptoms to manifest in groups of people despite the lack of any physical cause. Often compared toconversion disorder, in which emotional issues are "converted" into physical problems, MPI tends to occur among people who share anxieties, fears, and a sense of community. In the right group of people, it can spread like a virus.
A curious case of the condition related to TikTok videos shows both how imagined conditions can spread and how our modern media landscape presents new problems never even dreamt of in a time before the internet.
TikTok tics
In 2019, a strange slew ofnew Tourette's cases made its way into hospitals all over the world. Oddly, these were suddenly occurring in children well over the age of six, the age of typical onset. Most peculiar of all, many of the patients were exhibiting identical symptoms and tics. While many cases of Tourette's are similar, these symptoms were precisely the same.
As it turned out, the tics were also identical to those exhibited by one Jan Zimmermann, a 23-year-old YouTuber from Germany with Tourette's. On his channel,Gewitter im Kopf, he documents his daily life with the condition. All of the patients who suddenly claimed to have tics were fans of his or of similar channels on YouTube and TikTok.
There was nothing physically wrong with the large number of people who suddenly came down with Tourette's-like symptoms, and most of them recovered immediately after being told that they did not have Tourette's syndrome. Others recovered after brief psychologicalinterventions. The spread of the condition across a social group despite the lack of a physical cause all pointed toward an MPI event.
Historical cases of mass hysteria
Of course, humans do not need social media to develop symptoms of a disease that they do not have. Several strange cases of what appears to have been mass hysteria exist throughout history. While some argue for a physical cause in each case, the consensus is that the ultimate cause was psychological.
The dancing plagues of the Middle Ages, in which hundreds of people began to dance until they were utterly exhausted despite apparently wishing to stop, are thought to have been examples of mass madness. Some cases also involved screaming, laughing, having violent reactions to the color red, and lewd behavior. Attempts to calm the groups by providing musicians just made the problem worse, as people joined in to dance to the music. By the time the dancing plague of1518 ended, several people had died of exhaustion or injuries sustained during their dance marathon.
It was also common for nunneries to get outbreaks of what was then considered demonic possession but what now appears to be MPI. In many well recorded cases, young nuns †often cast into a life of poverty and severe discipline with little to say about it †suddenly found themselves "possessed" and began behaving in extremely un-nunlikefashion. These instances often spread to other members of the convent and required intervention by exorcists to resolve.
A more recent example might be the curious story of theMad Gasser of Mattoon. During WWII in the small town of Mattoon, Illinois, 33 people awoke in the middle of the night to a "sweet smell" in their homes followed by symptoms such as nausea, vomiting, and paralysis. Many claimed to see a figure outside their rooms fleeing the scene. Claims of gassings rapidly followed the initial cases, and the police department was swamped with reports that amounted to nothing. The cases ended after the sheriff threatened to arrest anyone submitting a report of being gassed without agreeing to a medicalreview.
Each of these cases exhibits the generally agreed upon conditions for MPI: the people involved were a cohesive group, they all agreed on the same threats existing, and they were enduring stressful and emotional conditions that later manifested as physical symptoms. Additionally, the symptoms appeared suddenly and spread by sight and communication among the affected individuals.
Social diseases for a social media age
One point upon which most sources on MPI agree is the tendency of the outbreaks to occur among cohesive groups whose members are in regular contact. This is easy to see in the above examples: nuns live together in small convents, medieval peasants did not travel much, and the residents of Mattoon were in a small community.
This makes the more recent case that relies on the internet all the more interesting. And it's not the only one. Another MPI centered around a school in New York in2011.
As a result, a team of German researchers has put forth the idea of a new version of MPI for the modern age: "mass social media-induced illness." It is similar to MPI but differs in that it is explicitly for cases driven by social media, in which people suffering from the same imagined symptoms never actually come into direct contact with one another.
Of course, these researchers are not the first to consider the problem in a digital context. Dr. Robert Bartholomew described the aforementioned New York case in a paper published in the Journal of the Royal Society of Medicine.
All this seems to imply that our online interactions can affect us in much the same ways as direct communication has for ages past and that the social groups we form online can be cohesive enough to cause identical symptoms in people who have never met. Therefore, we likely have not seen the last of "mass social media-induced illness."
By studying the characteristics of stars, like their temperature and luminosity, astrophysicists figured out how stars evolve over time.
This amazing insight is the primary lesson of the Hertzsprung-Russell (HR) diagram.
Human beings, as the species Homo sapiens, have been around for about 300,000 years. That turns out to be about 100 million nights during which somebody, somewhere looked up at the dark sky and asked, "What are those twinkly lights?"
Given all those nights and all those people asking pretty much the same question, it is pretty remarkable that we happen to live in one of the first generations that actually knows the answer. Here in the 21st century, we know for sure what stars are, and a key reason we have that knowledge is because of a little something called the HR diagram. Over the summer, I wrote two other posts on what I called the "most important graph in astrophysics." Today, I want to finish the series by explaining how the HR diagram shows us how stars age and evolve.
Stellar evolution: a star's life cycle
You can read the first and second posts here and here, respectively. But for completeness, let's restate that the HR diagram is a plot with stellar luminosity (L for energy output) on the vertical axis and stellar surface temperature (T for temperature) on the horizonal axis. In the previous posts, we learned that when you measure L and T for a bunch of stars and then drop them onto this kind of plot, you find the majority of the points fall on a thick diagonal band running from high stellar luminosity and temperature (high L and T) to low stellar luminosity and temperature (low L and T). That band is what astronomers call the Main Sequence, and its discovery in the HR diagram was key to understanding what stars were and how they shined.
What the Main Sequence revealed were stars in their long middle age. Middle-aged stars (meaning stars in between their relatively short birth and death phases) support themselves against their own crushing, titanic gravity by releasing energy through fusion reactions in their hot, dense cores. Hydrogen nuclei are fused into helium nuclei, giving up a little energy along the way through good ol' E = mc2.
As long as there is hydrogen to burn in the core, a star is stable, happy, and free to shine its brilliance into the dark night of space. Luckily stars have lots of hydrogen to burn. A star like the sun contains about a billion billion billion tons of hydrogen gas. That translates into about 10 billion years of life on the Main Sequence. But a billion billion billion tons of gas is not infinite. Eventually, the hydrogen fusion party must end. The star will run out of fuel in the core, and that is when it stops being middle-aged.
What happens next is also revealed by the HR diagram, which once again, is why it is the most important graph in astrophysics. When astronomers first started dropping their stars onto the diagram more than 100 years ago, they saw not only the Main Sequence but also stars clustered in other places. There were lots of moderately bright stars with low temperatures (high L and low T). There were also lots of really, really bright stars with even lower temperatures (very high L and lower T). Using the laws of physics associated with hot glowing matter, astronomers could derive the sizes of these bright cool stars and found that they were much bigger than the sun. They identified giant stars (the bright ones), which were 10 times the size of the sun, and supergiants (the really, really bright ones), which were 100 times the size of the sun.
These various kinds of giant stars on the HR diagram were the all-important evidence for the evolution of stars. Stellar properties were not static. They aged and changed just like we did. Astrophysicists eventually saw that the evolution of a star on the HR diagram was driven by the evolution of nuclear burning in its core. As researchers got better at modeling what happens within stars as they age, they came to see that after the hydrogen fuel runs out in the core, gravity begins to crush what is left: inert helium "ash."
Eventually, the gravitational squeeze drives temperatures and densities in the core high enough to ignite the helium ash, allowing the helium nuclei to fuse into carbon nuclei. These internal changes rearrange the outer layers of the star, making them swell and bloat †first into the giants, and then into the supergiants. The details of why they get so large are complicated and require lots of detailed calculations (done with computers). What matters for us is that what comes out of those calculations are evolutionary tracks across the HR diagram. The tracks are predictions, telling astronomers how changes in a star's nuclear burning history will manifest in it its luminosity and temperature which, in turn, translates into how it will move across the HR diagram over time.
The changes for actual stars are too slow to watch over a human lifetime. But by taking measurements of lots of random stars (meaning they are at random points in their evolution), we can find the older ones in their giant or supergiant phases. Then, via some statistics, astronomers can then see if their theoretical evolutionary tracks match what they see in the HR diagram. The answer is a resounding yes.
So not only do we know what stars are (big balls of mostly hydrogen gas with a fusion furnace in the core), but we also know exactly how those luminous spheres evolve across billions of years of cosmic history †including lighting up the nights for a remarkable planet that is home to some remarkable hairless monkeys.
However, it's still relatively expensive to store energy. And since renewable energy generation isn't available all the time – it happens when the wind blows or the sun shines – storage is essential.
Here are three emerging technologies that could help make this happen.
Longer charges
From alkaline batteries for small electronics to lithium-ion batteries for cars and laptops, most people already use batteries in many aspects of their daily lives. But there is still lots of room for growth.
For example, high-capacity batteries with long discharge times – up to 10 hours – could be valuable for storing solar power at night or increasing the range of electric vehicles. Right now there are very few such batteries in use. However, according to recent projections, upwards of 100 gigawatts' worth of these batteries will likely be installed by 2050. For comparison, that's 50 times the generating capacity of Hoover Dam. This could have a major impact on the viability of renewable energy.
Batteries work by creating a chemical reaction that produces a flow of electrical current.
One of the biggest obstacles is limited supplies of lithium and cobalt, which currently are essential for making lightweight, powerful batteries. According to some estimates, around 10% of the world's lithium and nearly all of the world's cobalt reserves will be depleted by 2050.
Furthermore, nearly 70% of the world's cobalt is mined in the Congo, under conditions that have long been documented as inhumane.
Scientists are working to develop techniques for recycling lithium and cobalt batteries, and to design batteries based on other materials. Tesla plans to produce cobalt-free batteries within the next few years. Others aim to replace lithium with sodium, which has properties very similar to lithium's but is much more abundant.
Safer batteries
Another priority is to make batteries safer. One area for improvement is electrolytes – the medium, often liquid, that allows an electric charge to flow from the battery's anode, or negative terminal, to the cathode, or positive terminal.
When a battery is in use, charged particles in the electrolyte move around to balance out the charge of the electricity flowing out of the battery. Electrolytes often contain flammable materials. If they leak, the battery can overheat and catch fire or melt.
Scientists are developing solid electrolytes, which would make batteries more robust. It is much harder for particles to move around through solids than through liquids, but encouraging lab-scale results suggest that these batteries could be ready for use in electric vehicles in the coming years, with target dates for commercialization as early as 2026.
While solid-state batteries would be well suited for consumer electronics and electric vehicles, for large-scale energy storage, scientists are pursuing all-liquid designs called flow batteries.
A typical flow battery consists of two tanks of liquids that are pumped past a membrane held between two electrodes. ( Qi and Koenig, 2017, CC BY)
In these devices both the electrolyte and the electrodes are liquids. This allows for super-fast charging and makes it easy to make really big batteries. Currently these systems are very expensive, but research continues to bring down the price.
Storing sunlight as heat
Other renewable energy storage solutions cost less than batteries in some cases. For example, concentrated solar power plants use mirrors to concentrate sunlight, which heats up hundreds or thousands of tons of salt until it melts. This molten salt then is used to drive an electric generator, much as coal or nuclear power is used to heat steam and drive a generator in traditional plants.
These heated materials can also be stored to produce electricity when it is cloudy, or even at night. This approach allows concentrated solar power to work around the clock.
This idea could be adapted for use with nonsolar power generation technologies. For example, electricity made with wind power could be used to heat salt for use later when it isn't windy.
Concentrating solar power is still relatively expensive. To compete with other forms of energy generation and storage, it needs to become more efficient. One way to achieve this is to increase the temperature the salt is heated to, enabling more efficient electricity production. Unfortunately, the salts currently in use aren't stable at high temperatures. Researchers are working to develop new salts or other materials that can withstand temperatures as high as 1,300 degrees Fahrenheit (705 C).
One leading idea for how to reach higher temperature involves heating up sand instead of salt, which can withstand the higher temperature. The sand would then be moved with conveyor belts from the heating point to storage. The Department of Energy recently announced funding for a pilot concentrated solar power plant based on this concept.
Advanced renewable fuels
Batteries are useful for short-term energy storage, and concentrated solar power plants could help stabilize the electric grid. However, utilities also need to store a lot of energy for indefinite amounts of time. This is a role for renewable fuels like hydrogen and ammonia. Utilities would store energy in these fuels by producing them with surplus power, when wind turbines and solar panels are generating more electricity than the utilities' customers need.
Today these fuels are mostly made from natural gas or other nonrenewable fossil fuels via extremely inefficient reactions. While we think of it as a green fuel, most hydrogen gas today is made from natural gas.
Scientists are looking for ways to produce hydrogen and other fuels using renewable electricity. For example, it is possible to make hydrogen fuel by splitting water molecules using electricity. The key challenge is optimizing the process to make it efficient and economical. The potential payoff is enormous: inexhaustible, completely renewable energy.
BBC: ‘Climate change could make beer taste worse’ https://www.bbc.com/news/science-environment-67078674 Global warming is changing the quality and taste of beer, scientists have warned. A new study reveals that the quantity of European hops, which gives beer its distinctive bitter taste, is declining. Hotter, longer and drier summers are predicted to worsen the situation, and could lead to beer becoming more expensive. The authors warned […] Wed, 11 Oct 2023 17:28:11 +0000
EV’s suddenly become uninsurable (unless you are rich) https://joannenova.com.au/2023/10/evs-suddenly-become-uninsurable-unless-you-are-rich/ By Jo Nova Remember how we predicted insurance costs would rise when people realized that almost brand spanking new EV’s were being written off for scratches, because no one could test their battery and be sure it would not ignite? And then there was the news that after an accident, electric cars need to social distance, […] Fri, 06 Oct 2023 20:27:44 +0000
Repeated gunshots fired on live TV as ex-lawmaker shot by assassins Atiq Ahmed, a former lawmaker in India's parliament, convicted of kidnapping, was shot dead along with his brother while police were escorting them for a medical check-up in a slaying caught on live television on Saturday. CNN's Vedika Sud reports. Tue, 18 Apr 2023 09:41:42 GMT
Michael Jordan's 1998 NBA Finals sneakers sell for a record $2.2 million In 1998, Michael Jordan laced up a pair of his iconic black and red Air Jordan 13s to bring home a Bulls victory during Game 2 of his final NBA championship †and now they are the most expensive sneakers ever to sell at auction.
The game-winning sneakers sold for $2.2 million at Sotheby's in New York on Tuesday, smashing the sneaker auction record of $1.47 million, set in 2021 by a pair of Nike Air Ships that Jordan wore earlier in his career. Wed, 12 Apr 2023 03:17:57 GMT
The International Court of Justice in The Hague has handed down a momentous judgement that says Britain's colonial authority over the Chagos Islands is no longer legal. John Pilger, whose 2004 film, Stealing a Nation, alerted much of the world to the plight of the islanders, tells their story here.
