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Universe Today has recently investigated a plethora of scientific disciplines, including impact craters, planetary surfaces, exoplanets, astrobiology, solar physics, comets, planetary atmospheres, planetary geophysics, cosmochemistry, meteorites, radio astronomy, extremophiles, organic chemistry, black holes, and cryovolcanism, while conveying their importance of how each of them continues to teach researchers and the public about our place in the vast universe.

Here, we investigate the field of planetary protection, which involves preventing Earth-born organisms from contaminating other worlds or interfering with scientific analyses on those worlds, along with preventing contamination to Earth from returned samples. To investigate this, we present a 2023 paper in Acta Astronautica with additional insights from the study’s lead author, Dr. Athena Coustenis, who serves as the Chair of the Committee on Space Research (COSPAR) Panel on Planetary Protection (PPP), regarding what planetary protection can teach us about finding life beyond Earth, exciting aspects about planetary protection, and advice for upcoming students who wish to study planetary protection.

The paper discusses the importance of planetary protection regarding space exploration, stating, “Planetary protection enables scientific return from solar system bodies investigations and at the same time protects life on Earth. As we continue to explore our solar system by landing machines and humans on other planets, we need to ascertain that we do not bring potentially dangerous material home to Earth or carry anything from Earth that may contaminate another planetary body and prevent scientific investigations.”

The paper discusses in greater detail the COSPAR PPP and its primary goals, including offering advice or guidance to government or private space-faring organizations and ensuring extraterrestrial samples returned from outer space do not contaminate the Earth, and specifically its biosphere. Additionally, the paper discusses recent policy actions taken by the PPP for the continued exploration of the Moon, Mars, and icy moons such as Europa, Enceladus, and Titan.

For the Moon, PPP recommended steps that need to be taken to prevent potential contamination of the permanently shadowed regions of the Moon, which are hypothesized to contain large quantities of water ice and are of significant interest for the upcoming Artemis missions. For Mars, the PPP focused on safeguarding more advanced scientific endeavors, including drilling, older areas of Mars that have yet to be explored, and sample return missions, to prevent contamination of potential scientific results and Earth’s biosphere, as well.

For icy moons, which the paper notes as being “possible habitable environments”, the PPP has already expressed concerns about exploring these worlds with the Planetary Protection of the Outer Solar System (PPOOS), which was led by the European Science Foundation and funded by the European Commission and is in the process of seeking additional insights in the future. Therefore, with these intriguing worlds being considered for exploration, what can planetary protection teach us about finding life beyond Earth?

Dr. Coustenis tells Universe Today, “Finding ways to preserve scientific research in our solar system helps the quest for finding life elsewhere and protecting our own biosphere during space exploration is essential for life on Earth. Working to that end with a large group of scientists, agency representatives and other expert stakeholders is one of the most rewarding activities in my career. The valuable outcome which represents thorough, long-term studies and reviews of knowledge is achieved through consensus and distributed to the large community. We are very excited to be able to offer such a service to the community via the COSPAR Panel on Planetary Protection.”

Along with serving as Chair of the COSPAR PPP, Dr. Coustenis has extensive research experience regarding planetary surfaces and atmospheres, specifically outer solar system objects like Europa, Ganymede, Titan, and Enceladus, as these worlds are targets for future astrobiology research. Additionally, Dr. Coustenis’ research extends far beyond the solar system as she has helped distinguish and characterize exoplanetary atmospheres, as well. Regarding planetary protection, some notable publications include being a co-author on a March 2024 paper discussing planetary protection for a future crewed Mars mission and a 2023 paper discussing COSPAR requirements for exploring Venus. Given her knowledge and experience regarding planetary protection, what are some of the most exciting aspects about planetary protection that Dr. Coustenis has encountered during her career?

Dr. Coustenis tells Universe Today, “We have recently worked on the Moon exploration requirements to preserve the poles and the regions where liquid water could be found at some periods of time and are currently working on the missions that will explore icy worlds, like the moons of our giant planets that harbor liquid water oceans underneath their surfaces, as well as organic chemistry and energy sources. These could be habitable environments that we need to explore with care.”

As noted in the Acta Astronautica paper, the field of planetary protection requires international collaboration not only from a multitude of scientists, but also engineers, as they are the individuals responsible for building the spacecraft that are sent to far-off worlds for scientific exploration. Other disciplines that contribute to planetary protection include geology, physics, geophysics, biotechnology, astrobiology, biomedical, planetary science. It is through this constant collaboration of scientists, engineers, and medical professionals that planetary protection has successfully prevented contamination of planetary bodies outside the Earth, but also preventing contamination of the Earth from returned samples. Therefore, what advice can Dr. Coustenis offer to upcoming students who wish to pursue a career in planetary protection?

Dr. Coustenis tells Universe Today, “Planetary protection offers the possibility to contribute coming from many different fields, scientific, engineering, economic or legal. We need all these varied points of view in order to accomplish adequate characterizations of space missions and related requirements and also to establish the real value of planetary protection, the enabling capacity of this tool and to spread the word about what we do and how others can contribute, in particular the younger generations. So, we encourage students and early-career space aficionados to join COSPAR and learn more about our work and that of other commissions and panels within its structure so as to be able also to position themselves and engage with the space community.”

How will planetary protection teach us about our place in the cosmos in the coming years and decades? Only time will tell, and this is why we science!

As always, keep doing science & keep looking up!

The post Planetary Protection: Why study it? What can it teach us about finding life beyond Earth? appeared first on Universe Today.

The Large Binocular Telescope (LBT), located on Mount Graham in Arizona and run by the University of Arizona, is part of the next generation of extremely large telescopes (ELTs). With two primary mirrors measuring 8.4 m (~27.5 ft), it has a collecting area slightly greater than that of a 30-meter (98.4 ft) telescope. With their resolution, adaptive optics, and sophisticated instruments, these telescopes are expected to probe deeper into the Universe and provide stunning images of everything from distant galaxies to objects in our Solar System.

An international team led by the University of Arizona recently acquired images of Jupiter’s moon Io that were the highest-resolution pictures ever taken by a ground-based telescope. The images revealed surface features measuring just 80 km (50 mi) across, a spatial resolution previously reserved for spacecraft. This includes NASA’s Juno mission, which has captured some of the most stunning images of Io’s volcanoes. These images were made possible by the LBT’s new SHARK-VIS instrument and the telescope’s adaptive optics system.

The team was led by Al Conrad, an Associate Staff Scientist with the University of Arizona’s Department of Astronomy, the Stewart Observatory, and the Large Binocular Telescope Observatory (LBTO). He was joined by researchers from the University of California, Berkeley, the California Institute of Technology, and NASA’s Jet Propulsion Laboratory. Their paper, “Observation of Io’s Resurfacing via Plume Deposition Using Ground-Based Adaptive Optics at Visible Wavelengths With LBT SHARK-VIS (GRL),” and the LBT images are set to be published in the Geophysical Research Letters.

The Large Binocular Telescope, showing the two imaging mirrors. Credit: NASA

SHARK-VIS is a high-contrast optical coronagraphic imaging instrument designed and built at INAF-Osservatorio Astronomico di Roma. The instrument is fed by the refurbished LBT extreme Adaptive Optics system, called the Single conjugated adaptive Optics Upgrade for LBT (SOUL). It was installed in 2023 on the LBT along with the near-infrared instrument, SHARK-NIR, to take advantage of the telescope’s outstanding adaptive optics system. The key to the instrument is its fast, ultra-low-noise “fast imaging” camera that captures slow-motion footage that freezes the optical distortions caused by atmospheric interference.

Gianluca Li Causi, the data processing manager for SHARK-VIS at the Italian National Institute for Astrophysics, explained how it works in a recent University of Arizona News release:

“We process our data on the computer to remove any trace of the sensor’s electronic footprint. We then select the best frames and combine them using a highly efficient software package called Kraken, developed by our colleagues Douglas Hope and Stuart Jefferies from Georgia State University. Kraken allows us to remove atmospheric effects, revealing Io in incredible sharpness.”

The SHARK-VIS image was so rich in detail that it allowed the researchers to identify a major resurfacing event around Pele, one of Io’s largest volcanoes located in the southern hemisphere near the equator (and named after the Hawaiin deity associated with fire and volcanoes). The image shows a plume deposit around Pele covered by eruption deposits from Pillan Patera, a neighboring volcano. NASA’s Galileo spacecraft observed a similar eruption sequence while exploring the Jupiter system between 1995 and 2003. However, this was the first time an Earth-based observatory took such detailed images.

An artist’s concept of the interior of Io. Credit: Kelvinsong/Wikimedia

“We interpret the changes as dark lava deposits and white sulfur dioxide deposits originating from an eruption at Pillan Patera, which partially cover Pele’s red, sulfur-rich plume deposit,” said co-author Ashley Davies, a principal scientist at NASA’s Jet Propulsion Laboratory. “Before SHARK-VIS, such resurfacing events were impossible to observe from Earth.” Io is the innermost of Jupiter’s largest moons (aka. Galilean moons), which include Europa, Ganymede, and Callisto. Since NASA’s Voyager 1 spacecraft flew through the Jupiter system in 1979, scientists have been fascinated by Io and its volcanic features.

Along with Europa and Ganymede, Io is locked in a 1:2:4 orbital resonance, where Europa makes two orbits for every orbit made by Ganymede, and Io makes four. Between its interaction with these moons and Jupiter’s powerful gravity, Io’s interior is constantly flexing, producing hot lava that erupts through the surface. While telescopes have taken infrared images that revealed hot spots caused by eruptions, they are not sharp enough to reveal surface details or identify the locations of the eruptions. By monitoring the eruptions on Io’s surface, scientists hope to gain insights into the tidal heating mechanism responsible for Io’s intense volcanism.

“Io, therefore, presents a unique opportunity to learn about the mighty eruptions that helped shape the surfaces of the Earth and the moon in their distant pasts,” said Conrad. Studies like this one, he added, will help researchers understand why some planets have active volcanoes while others do not. For instance, while Venus is thought to still be volcanically active, Mars is home to the largest volcanoes in the Solar System but is inactive. These studies may also shed light on volcanic exoplanets someday, helping astronomers to identify geological activity on distant planets (a possible indication of habitability).

SHARK-VIS instrument scientist Simone Antoniucci anticipates that it will enable new observations of objects throughout the Solar System with similar sharpness, revealing all manner of features that would otherwise require spacecraft.”The keen vision of SHARK-VIS is particularly suited to observing the surfaces of many solar system bodies, not only the moons of giant planets but also asteroids,” he said. “We have already observed some of those, with the data currently being analyzed, and are planning to observe more.”

Further Reading: University of Arizona

The post New Telescope Images of Io are so Good, it Looks like a Spacecraft Took Them appeared first on Universe Today.

It is truly wonderful to see so many nations aspiring to space exploration and trips to the Moon. Earlier this week on the 27th May, South Korea innaugurated its new space agency, the Korea AeroSpace Administration otherwise known as KASA. The group is headed up by former professor of aerospace engineering Yoon Young-bin. Whilst the group has yet to announce detailed plans for their upcoming missions Young-bin has stated they hope to land on the Moon by 2032 and to get to Mars by 2045.

The President of Korea, Yoon Suk-yeol, had confirmed that the government was committed to the space sector. To that end, they intend to secure investments of billions of dollars to fund the project. In March this year Korean Space Agency was formed in a ceremony that took place in March this year. Suk-yeol pleduged to facilitate 1,000 space companies and he hoped that 10 of the companies would become top-tier space firms. They would work hard to increase Korea’s share of the space market, aiming to hit 10% instead of the existing 1%. and create over 100,000 jobs. 

The Korean goverment has for sometime been keen to expand the space industry, Young-bin also prioritised support for the private sector. “The establishment of KASA will be an important stepping stone that guides the way for Korea to become a powerhouse in space economy by setting up the private-led space ecosystem,” Young-bin said. 

Young-bin is no stranger to space exploration since he had been researching space propulsion at the time of his appointment. His research chiefly focuses on liquid rocket engine. He has also been a serving director of the Institute of Advanced Aerospace Technology. 

Mid to long term goals and visions for space development are important next steps along the journey. To achieve those, KASA are striving for active cooperation from public, private and academic sectors. All of this is of course subject to securing the necessary funding. 

The framework for operations of KASA have been established and will be implemented with a maximum of 293 employees. Currenly only 110 are in place which includes a number of officials who were originally part of the Science Ministry in Korea. With the establishment of KASA, the Ministry of Science and ICT have been reorganised to align to their reduced scope of work but to find the remaining employees KASA will continue to search at home and abroad for the right people.

Along with their plans to explore the Moon and Mars, KASA is also planning to explore the Lagrangian Point known as L4. These regions in space lie along the Earth’s orbit and usually a little ahead or a litle behind but at these points, the gravitational force of the Earth and that of the Sun balance out against each other making for a highly efficient location for a probe. No country has acehived this yet so it will really put KASA on the international space exploration map.

They also plan to restore the Apophis mission which had been scrapped some years ago. The asteroid will pass close by Earth in 2029. The plan is for this to become an international mission, calling upon international co-operation. Other projects include participation in the Event Horizon Telescope and black hole imaging from one of NASA’s solar coronagraph.

Source : Korea ushers in new space era with KASA launch

The post South Korea is Planning to Send a Mission to Mars by 2045 appeared first on Universe Today.

Mars has a distinct structure in its mantle and crust with discernible reservoirs, and this is known thanks to meteorites that scientists have analyzed. These results are important for understanding not only how Mars formed and evolved, but also for providing precise data that can inform recent NASA missions like Insight and Perseverance and the Mars Sample Return.

If matter falls into one end of a wormhole, it could heat up in a tornado of plasma hot enough to initiate nuclear fusion – and come blasting out the other end

A simple statistical test can quickly guide humanitarian efforts in areas like Gaza and Ukraine impacted by war – and it could perform as well as more expensive, AI-powered methods

Machine-based decision-making is an interesting vision for the future: Humanity, crippled by its own cognitive deformations, tries to improve its lot by opting to outsource its decisions to adaptive machines—a kind of mental prosthetic.

For most of the twentieth century, artificial intelligence was based on representing explicit sets of rules in software and having the computer “reason” based on these rules—the machine’s “intelligence” involved applying the rules to a particular situation. Because the rules were explicit, the machine could also “explain” its reasoning by listing the rules that prompted its decision. Even if AI had the ring of going beyond the obvious in reasoning and decisionmaking, traditional AI depended on our ability to make explicit all relevant rules and to translate them into some machine-digestible representation. It was transparent and explainable, but it was also static—in this way, it did not differ fundamentally from other forms of decisional guardrails such as standard operating procedures (SOPs) or checklists. The progress of this kind of AI stalled because in many everyday areas of human activity and decisionmaking, it is exceptionally hard to make rules explicit.

In recent decades, however, AI has been used as a label for something quite different. The new kind of AI analyzes training data in sophisticated ways to uncover patterns that represent knowledge implicit in the data. The AI does not turn this hidden knowledge into explicit and comprehensible rules, but instead represents it as a huge and complex set of abstract links and dependencies within a network of nodes, a bit like neurons in a brain. It then “decides” how to respond to new data by applying the patterns from the training data. For example, the training data may consist of medical images of suspected tumors, and information about whether or not they in fact proved to be cancerous. When shown a new image, the AI estimates how likely that image is to be of a cancer. Because the system is learning from training data, the process is referred to as “machine learning.”

Such data-driven AI offers two important advantages over conventional AI. First, humans no longer have to make rules explicit to feed into the system. Instead, rules emerge from the training data. Alex Davies, author of the book Driven on machine learning and self-driving cars, puts it succinctly: in this new paradigm “the computer gets lessons, not laws.” That means we can use such AI for the kind of everyday knowledge that’s so difficult to capture with explicit rules.

The second advantage—which is even greater, in this context—is that because rules are derived from training data, they don’t have to be fixed. Instead, they can be adapted as more (and newer) training data is used. This should prevent the stiffening that lessens the effectiveness of many decisional guardrails as times change. It enables looking at patterns not only from the past but also from the present to deduce rules that can be applied to decisions in the future. It has, in other words, a built-in mechanism of updating rules.

Advocates suggest that we should incentivize the use of machine learning in an ever-increasing number of contexts, and even mandate it—much like collision warning systems have become obligatory in commercial aviation. While this might sound dramatic, the change may actually be more gradual. In many instances in our daily lives, we already have machines making decisions for us, from the relatively simple—such as an airbag deploying in a car crash—to the more sophisticated, such as Siri selecting music on our smartphone. And we profit from it: Machines aren’t as easily derailed by human biases; they perform consistently, irrespective of their emotional state. They also act efficiently—capable of doing so within a split second and at relatively low cost.

The central idea of data-driven decision guidance is that past experiences can be employed to decide well in the present. That works when the world doesn’t change—not the circumstances in which we must decide, nor the goals we want to attain through our decisions. Hard-coded rules are a poor fit for times of change; in theory, this is where data-driven AI should be able to shine. If a situation changes, we should be able to add more training data that reflect the new situation. However, there is a flaw in this line of reasoning.

Autonomous driving company Waymo illustrates the argument—and the flaw. For years, Waymo has had hundreds of cars roam the roads in the United States, collecting enormous heaps of data on roads, signage, conditions, weather, and the behavior of drivers. The data were used to train Waymo’s AI system, which then could drive autonomously. These cars were the guinea pigs for the Waymo system. Mistakes observed (including by their own drivers) in turn help the Waymo system to learn to avoid them. To identify the best driving behavior for any given circumstance, such a system needs not only data about a wide variety of situations, but also data about the outcomes of many different decisions made by drivers in each situation. Learning is richest when there is sufficient variability in the training data, so the system can deduce what works best in which conditions. To get diverse training data, Waymo needs to capture drivers making a variety of choices.

The more we use data-driven machine learning to make decisions, the more it will take the variability of decisions out of the data and shed its ability to progress.

Because Waymo never stopped collecting training data, even small changes in circumstances—such as in driving laws and resulting driving behavior—were reflected in the data collected and eventually embedded in the Waymo system. It was a machine that not only learned once, but never stopped learning.

However, let’s imagine a world in which we increasingly rely on machines when making decisions. The more machines shape our choices, the more these decisions will become the only source of training data for ongoing machine learning. The problem is that data-driven machine learning does not experiment; it acts based on the best practice it has deduced from data about previous decisions. If machines begin to learn more from choices we made based on their recommendations, they will amplify their own, conservative solutions.

Over time, this will narrow and drown out behavioral diversity in the training data. There will not be enough experimentation represented in it to enable the machines to adjust to new situations. This means data-driven machine learning will lose its single most important advantage over explicit rule-based systems. We will end up with a decisional monoculture that’s unable to evolve; we are back to fixed decisional rules.

The flaw is even bigger and more consequential than not being able to adjust to changed circumstances. Even if reality doesn’t change, we may miss opportunities to improve our decision-making in the future. Many innovations that end up becoming successful are less useful than existing choices in their initial form. But any new decision options emerging from the training data will likely only be adopted if they yield better results than existing choices straight away. This closes off any opportunity to experiment with promising new ideas.

For example, the first steam engines used far more energy than they could translate into motion and power. If a machine had compared them to the existing solution of using horses for power, it would have discarded the idea of steam power right away. The only reason the steam engine succeeded is because stubborn humans thought that they could improve the invention in the long run and stuck with it. These tinkerers had no data to support their confidence. They just imagined—and kept tinkering.

Of course, most such would-be innovators fail over time. The path of progress is paved with epitaphs to dogged tinkerers following crazy ideas. Occasionally, though, small changes accumulate and lead to a breakthrough—a far more optimal decision option. Modern societies have permitted tinkering to persist, though it is almost always unproductive, even destructive, in the short term—because of the slight chance of a big payoff sometime in the future.

Data-driven machine learning, if widely utilized, would discard initially suboptimal inventions. But in doing so, it would forego the possibility of long-term breakthroughs. Machines can learn only from what already exists. Humans can imagine what does not yet exist but could. Where humans invented steam power, data-driven machine learning would instead have found more and more efficient ways to use horse power.

Human dreaming can go far beyond technical novelties. Our ancestors once dreamed of a world in which slavery is abolished; women can vote; and people can choose for themselves whom to marry and whether to have children. They imagined a world in which smallpox is extinct and we vaccinate against polio. And they worked to make those dreams come true. If they had looked only at data from their past and present, none of these dreams would have been realized.

Decisional guidelines, from SOPs to nudges, emphasize constancy. Traditional education, too, often aims to perpetuate—suggesting there is a right answer for decisions much like for math problems. But decisional guidelines are just that—suggestions that can be disobeyed if one is willing to take the risk (and shoulder the responsibility). For eons, young people have frequently revolted against their parents and teachers, pushed back against the old, the conventional and predictable, and embraced instead not just the original and novel, but the still only imagined. Humans continue to dream—of a world, for example, that will warm by less than two degrees, or in which people have enough to eat without depleting the planet.

In contrast to humans, machine decision-making is optimized toward consistency across time. Even if data-driven machine learning has access to the very latest data, it will still limit our option space. It will always choose a more efficient way to travel along our current path, rather than try to forge a new one. The more we use it to make decisions, the more it will take the variability of decisions out of the data and shed its ability to progress. It will lead us into vulnerability, rigidity, and an inability to adapt and evolve. In this sense, data-driven machine learning is an adulation of immutability, the anathema of imagination.

This article appeared in Skeptic magazine 29.1
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No technological adjustment can remedy this easily. If we want to increase diversity in the data, we will need variability in machine decisions. By definition, this means machines that make suboptimal choices. But the entire argument for using more AI in our decision-making is premised on AI’s ability to suggest better choices consistently across space and time. In many instances, it would not be societally palatable to deliberately introduce variation into what options a machine picks, thereby increasing the near-term risk of bad decisions in the hope of long-term benefits. And even if it were, it would not necessarily produce the experimentation we hope for. Very often, the theoretical decision space is immense. Randomly iterating through decision options to generate the diverse data necessary would take a very long time—far too long in most instances to help in timely decision-making. Even when iterations are non-random and can be done purely digitally, it would require massive computing resources.

In contrast, when humans experiment, they rarely decide randomly; instead, they use mental models to imagine outcomes. Done correctly, this can dramatically narrow the decision space. It’s that filtering based on cognitive modeling that differentiates human experimentation in decision contexts from the random walk that the machine, in the absence of a mental model, has to employ. And if machines were to use a particular mental model, the resulting data would be constrained again by the limitations of that model. A diverse set of humans experimenting using diverse mental models is simply very hard to beat.

This essay was excerpted and adapted by the authors from their book Guardrails: Guiding Human Decisions in the Age of AI. Copyright © 2024 by Princeton University Press.

About the Author

Urs Gasser is professor of public policy, governance, and innovative technology and dean of the School of Social Sciences and Technology at the Technical University of Munich. He is the author of Born Digital: How Children Grow Up in a Digital Age.

Viktor Mayer-Schönberger is professor of internet governance and regulation at the University of Oxford. He is the author of Delete: The Virtue of Forgetting in the Digital Age. This essay was excerpted and adapted by the authors from their book Guardrails: Guiding Human Decisions in the Age of AI.

A novel, human-inspired approach to training artificial intelligence (AI) systems to identify objects and navigate their surroundings could set the stage for the development of more advanced AI systems to explore extreme environments or distant worlds, according to new research.

Converting home heating systems from natural gas furnaces to electric heat pumps is seen as a way to address climate change by reducing greenhouse gas emissions.

MRI scans are commonly used to diagnose a variety of conditions, anything from liver disease to brain tumors. But, as anyone who has been through one knows, patients must remain completely still to avoid blurring the images and requiring a new scan. A prototype device could change that. The self-powered sensor detects movement and shuts down an MRI scan in real time, improving the process for patients and technicians.

MRI scans are commonly used to diagnose a variety of conditions, anything from liver disease to brain tumors. But, as anyone who has been through one knows, patients must remain completely still to avoid blurring the images and requiring a new scan. A prototype device could change that. The self-powered sensor detects movement and shuts down an MRI scan in real time, improving the process for patients and technicians.

Here’s the last video of the day, as well as the last live performance of Otis Redding, who died with his band in a plane crash the day after this video was recorded on December 9, 1967.  He was only 26. This song, along with “Dock of the Bay”, are Redding’s best recordings, but “Dock of the Bay” was largely written by him, while this song, “Try a Little Tenderness“, recorded on the Stax label, was actually written by three white men in 1932. And it was recorded by, among others, Bing Crosby and Frank Sinatra.  (Redding’s released recording, from 1966, is here.)

NPR’s “Fresh Air” did a documentary on Stax Records that’s still up, and well worth listening to (it’s only 46 minutes long and has tons of music, including some good stuff from Booker T., who, with the M.G.s, backed Redding on the recorded version of “Tenderness”.). Since Redding recorded for Stax, I revisited this song and found this live version.  If you listen to the recordings by Crosby or Sinatra, you’ll see that Redding’s soul version is infinitely better. The difference between the performance below and the earlier versions shows you the very essence of soul music.

And you can also get an inkling of Redding’s talent—talent cut off way too early.

(“Dock of the Bay,” by the way, was released posthumously, and became the first Top 100 pop single to top the charts after the performer’s death.)

Ladies and gentlemen, brothers and sisters, comrades, here’s Otis Redding, giving James Brown a run for his money as “the hardest-working man in show business.”

Queen Asian hornets found in East Sussex this year are a genetic match to a 2023 nest, suggesting the invasive species is becoming established in the UK

The true nature of time has eluded physicists for centuries, but a new theoretical model suggests it may only exist due to entanglement between quantum objects

For centuries, civilizations have used naturally occurring, inorganic materials for their perceived healing properties. Egyptians thought green copper ore helped eye inflammation, the Chinese used cinnabar for heartburn, and Native Americans used clay to reduce soreness and inflammation. Flash forward to today, and researchers are still discovering ways that inorganic materials can be used for healing. A new article explains that cellular pathways for bone and cartilage formation can be activated in stem cells using inorganic ions. Another recent article explores the usage of mineral-based nanomaterials, specifically 2D nanosilicates, to aid musculoskeletal regeneration.

To overcome issues associated with real-life testing, researchers successfully demonstrated the use of digital twin technology within robot simulation software in assessing a robot's suitability for deployment in simulated built environments.

As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalized prices, and thus minimize both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.

As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalized prices, and thus minimize both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.

A simulation-generated image reveals how charge distributions and gas densities vary in the plasma that floats across our universe

Here’s another good talk, though not as good as the preceding one.  But it does get better in the last third.

‘Yes, Douglas Murray is a conservative, and yes, the Manhattan Institute is a generally conservative think tank, but Murray is eloquent also sensible on many issues, including the war and (in this case), the courage of Israelis, and it’s worth listening to his 24-minute acceptance speech from May 6, when he was given the Alexander Hamilton Award from the Manhattan Institute for his “unwavering defense of Western values.”  I hate to have to qualify things this way, but yes, I disagree with Murray on several issues, the main one being his consistent opposition to widespread immigration into Britain. (I’m sure many of you will agree with him, though.)

In some ways, including his memory and his eloquence, Murray resembles Hitchens. (When he makes a crack about “Queers for Palestine,” remember that Murray is gay.)

The transcript of this speech is at The Free Press.