Amazing Science

OpenAI today announced the general availability of GPT-4, its latest text-generating model, through its API. Starting this afternoon, all existing OpenAI API developers “with a history of successful payments” can access GPT-4. The company plans to open up access to new developers by the end of this month, and then start raising availability limits after that “depending on compute availability.

"Millions of developers have requested access to the GPT-4 API since March, and the range of innovative products leveraging GPT-4 is growing every day,” OpenAI wrote in a blog post. “We envision a future where chat-based models can support any use case.” GPT-4 can generate text (including code) and accept image and text inputs — an improvement over GPT-3.5, its predecessor, which only accepted text — and performs at “human level” on various professional and academic benchmarks. Like previous GPT models from OpenAI, GPT-4 was trained using publicly available data, including from public webpages, as well as data that OpenAI licensed. The image understanding capability isn’t available to all OpenAI customers just yet. OpenAI’s testing it with a single partner, Be My Eyes, to start with. But it hasn’t indicated when it’ll open it up to the wider customer base.

It’s worth noting that, as with even the best generative AI models today, GPT-4 isn’t perfect. It “hallucinates” facts and makes reasoning errors, sometimes with confidence. And it doesn’t learn from its experience, failing at hard problems such as introducing security vulnerabilities into code it generates. In the future, OpenAI says that it’ll allow developers to fine-tune GPT-3 and GPT-3.5 Turbo, one of its other recent but less capable text-generating models, with their own data, as has long been possible with OpenAI’s other text-generating models. That capability should arrive later this year, according to OpenAI.

GPT-3.5 Turbo, DALL·E and Whisper APIs are also generally available, and we are releasing a deprecation plan for older models of the Completions API, which will retire at the beginning of 2024.

The BMW i Vision Dee concept arrived at CES with an E Ink-powered color-changing technology that was much improved over 2022’s monochromatic display.

Engineers at Radiant announced last year that they had received two provisional patents for its portable nuclear reactor technology. One of these was for a technology that reduces the cost and the time needed to refuel their reactor, while the other improves efficiency in heat transference from the reactor core. The microreactor will use an advanced particle fuel that does not melt down and is capable of withstanding higher temperatures than traditional nuclear fuels. Helium coolant, meanwhile, reduces the corrosion and contamination risks associated with traditional water coolant. Radiant has signed a contract with Battelle Energy Alliance to test its portable microreactor technology at its Idaho National Laboratory (INL).

"In some areas of the world, reliance on diesel fuel is untenable, and solar and wind power are either unavailable or impractical," said Jess Gehin, Ph.D., Chief Scientist, Nuclear Science & Technology Directorate at INL. "Clean, safe nuclear microreactors are emerging as the best alternative for these environments." 

Radiant's microreactor can be used in remote locations, such as arctic villages and isolated military encampments that would otherwise typically rely on fossil fuel-powered generators. Not only is the portable microreactor better for the environment, but it is also more practical as it doesn't rely on constant shipments of fuel. Instead, the clean fuel used for Radiant's microreactors can last more than 4 years. If all goes well with Radiant's test campaign, nuclear power might soon hit the road. In doing so it will help to power countless remote communities, and will further bolster the resurgence of nuclear power in a world that needs clean energy solutions more than ever.

Training a large language model like OpenAI’s GPT-4 involves feeding vast amounts of curated text from books, webpages, and other sources into machine learning software known as a transformer. It uses the patterns in that training data to become proficient at predicting the letters and words that should follow a piece of text, a simple mechanism that proves strikingly powerful at answering questions and generating text or code.

An important additional step in making ChatGPT and similarly capable language models is using reinforcement learning based on feedback from humans on an AI model’s answers to finesse its performance. DeepMind’s deep experience with reinforcement learning could allow its researchers to give Gemini novel capabilities.

Hassabis and his team might also try to enhance large language model technology with ideas from other areas of AI. DeepMind researchers work in areas ranging from robotics to neuroscience, and earlier this week the company demonstrated an algorithm capable of learning to perform manipulation tasks with a wide range of different robot arms.

Learning from physical experience of the world, as humans and animals do, is widely expected to be important to making AI more capable. The fact that language models learn about the world indirectly, through text, is seen by some AI experts as a major limitation.

Hassabis is tasked with accelerating Google’s AI efforts while also managing unknown and potentially grave risks. The recent, rapid advancements in language models have made many AI experts—including some building the algorithms—worried about whether the technology will be put to malevolent uses or become difficult to control. Some tech insiders have even called for a pause on the development of more powerful algorithms to avoid creating something dangerous.

Hassabis says the extraordinary potential benefits of AI—such as for scientific discovery in areas like health or climate—make it imperative that humanity does not stop developing the technology. He also believes that mandating a pause is impractical, as it would be near impossible to enforce. “If done correctly, it will be the most beneficial technology for humanity ever,” he says of AI. “We’ve got to boldly and bravely go after those things.”

That doesn’t mean Hassabis advocates AI development proceeds in a headlong rush. DeepMind has been exploring the potential risks of AI since before ChatGPT appeared, and Shane Legg, one of the company’s cofounders, has led an “AI safety” group within the company for years. Hassabis joined other high-profile AI figures last month in signing a statement warning that AI might someday pose a risk comparable to nuclear war or a pandemic.

One of the biggest challenges right now, Hassabis says, is to determine what the risks of more capable AI are likely to be. “I think more research by the field needs to be done—very urgently—on things like evaluation tests,” he says, to determine how capable and controllable new AI models are. To that end, he says, DeepMind may make its systems more accessible to outside scientists. “I would love to see academia have early access to these frontier models,” he says—a sentiment that if followed through could help address concerns that experts outside big companies are becoming shut out of the newest AI research.

How worried should you be? Hassabis says that no one really knows for sure that AI will become a major danger. But he is certain that if progress continues at its current pace, there isn’t much time to develop safeguards. “I can see the kinds of things we're building into the Gemini series right, and we have no reason to believe that they won't work,” he says.

Detecting nuclear reactors is important for nonproliferation of nuclear arms and nuclear power in general. The nearly 100 researchers in the SNO+ Collaboration working at SNOLAB science

demonstrated that a large, water-filled detector can identify neutrinos from a reactor hundreds of kilometers away.

Neutrinos are subatomic particles that interact with matter extremely weakly. They are produced in many types of radioactive decays, including in the core of the Sun and in nuclear reactors. Neutrinos are also impossible to block—they easily travel from the core of a nuclear reactor to a detector far away, and even through the Earth itself. Detecting the tiny signals from neutrinos therefore requires huge devices that are extremely sensitive. The SNO+ experiment has just shown that a detector filled with simple water can still detect reactor neutrinos, even though the neutrinos create only tiny signals in the detector.

The SNO+ measurement shows that distant nuclear reactors can be observed and monitored with something as simple and inexpensive as water. Reactors cannot shield the neutrinos they produce. This means SNO+’s measurement is a proof of the idea that such water detectors could play a role in ensuring nuclear non-proliferation. Like SNO+, such detectors would still need to be very clean of any radioactivity, large (SNO+ contains 1,000 tons of water), and able to detect the tiny amount of light that the neutrinos produce. The use of water, however, means that very large detectors are possible and a real option for “seeing” even very distant reactors.

Scientists long thought that the tiny signals (just 10-20 photons) created by reactor neutrinos in a water detector would make it impossible to detect those neutrinos, particularly when the detector was far away from the reactor and the rate of these signals was very low. By ensuring that the detector was clean from even trace amounts of radioactivity, and by having an energy threshold lower than any water detector ever built, SNO+ was able to see these signals and show that they came from nuclear reactors at least 240 kilometers (150 miles) away. The measurement was still quite difficult, as backgrounds (fake events) from residual radioactivity, and from neutrinos created in the atmosphere by cosmic rays, needed to be identified and removed.

Water detectors have several advantages. They are inexpensive and can be very large, making them useful for monitoring reactors across international borders. Improvements to such monitoring, including using water-based liquid scintillator or “loading” the water with gadolinium, both of which would boost the signal size, are being tested by other collaborations. This work is from the SNO+ Collaboration, an international collaboration of roughly 100 scientists from the United States (the University of Pennsylvania, the University of California at Berkeley and Lawrence Berkeley National Laboratory, the University of California at Davis, Brookhaven National Laboratory, Boston University, and the University of Chicago), Canada, the United Kingdom, Portugal, Germany, China, and Mexico. SNO+ is located in SNOLAB, the Canadian underground laboratory.

The black hole, located about 656 million light-years away from Earth, has a powerful jet streaming from its core. This jet, which is blasted out at light-like speeds, has “drastically” changed directions. The black hole just changed its direction in a way that is perpendicular to our line of sight, and it now faces Earth. This is a very exceptional case of jet reorientation and has led to the redefinition of the galaxy that houses this black hole.

The Peculiar Properties of PBC J2333.9-2343

PBC J2333.9-2343 is a distant galaxy that spans four million light-years. It shines strongly in radio wavelengths, making it a radio galaxy. The core of this galaxy had blasted jets in the past but had long gone quiet. In the latest research, astronomers found that the core had once again stirred the jets back to life, and one of them had changed its direction.

Given the sharp change in the jet’s orientation, astronomers have redefined the giant radio galaxy into one with a blazar at its center. Blazars are galaxies whose cores have jets aimed directly at Earth. This is the first time astronomers have observed a galaxy transform from one group to another, marking a significant milestone in our understanding of these celestial objects.

The Mystery of Jet Reorientation

While the black hole just changed its direction, the reasons behind such changes are not very well understood. Some astronomers speculate that mergers of galaxies or black holes contribute to the intermittent bursts of jet activity, and that the directions of the jets change between bursts. This erratic behavior of jets is also observed in bright but rare X-shaped galaxies.

Conclusion: A Cosmic First and Its Implications

The black hole just changed its direction, marking a cosmic first that is reshaping our understanding of black holes and their behavior. As scientists continue to study PBC J2333.9-2343 and other celestial objects, we can expect to gain more insights into the nature of black holes and their jets. These insights will not only help us understand these celestial objects better but also shed light on the mysteries of the cosmos.

Astronomers have detected, a critical life component, phosphate on Saturn's moon Enceladus, marking a significant step forward in the search for extraterrestrial life.

Phosphate is vital for all life on Earth. It forms the backbone of DNA and RNA, the genetic material that carries the instructions for life. It is also a key component of ATP (adenosine triphosphate), the molecule that provides energy for cellular processes, and phospholipids, which form the membranes of cells. The discovery of phosphates on Saturn’s moon Enceladus is the first to report direct evidence of phosphorus on an extraterrestrial ocean world, making it a significant milestone in astrobiology.

The detection of phosphates on Saturn’s moon Enceladus was made possible by data from NASA’s Cassini mission. Launched in 1997, Cassini spent 13 years exploring Saturn, its rings, and its moons. One of its most significant discoveries was the detection of a subsurface ocean on Enceladus, which was confirmed by the spacecraft’s observations of geysers erupting from the moon’s south pole. These geysers spew water ice particles into space, some of which supply Saturn’s E ring.

The Cassini spacecraft was equipped with the Cosmic Dust Analyzer, which analyzed individual ice grains emitted from Enceladus and sent those measurements back to Earth. In the new study, researchers used data collected from Cassini’s Cosmic Dust Analyzer. They found sodium phosphate molecules in 9 of 345 ice grains Cassini analyzed as it passed through Saturn’s outer E ring.

The detection of phosphates on Saturn’s moon Enceladus is a stunning result for astrobiology and a major step forward in the search for life beyond Earth. The high concentration of phosphates in Enceladus’ ocean satisfies one of the strictest requirements in establishing whether celestial bodies are habitable.

The discovery of phosphate on Saturn’s moon Enceladus reinforces the idea of the potential habitability of ice-covered ocean worlds across the solar system, beyond surface ocean worlds like Earth. Worlds with an interior ocean like Enceladus can occur over a much wider range of distances, greatly expanding the number of habitable worlds likely to exist across the galaxy.

This discovery has profound implications for our understanding of where life might exist beyond Earth. It suggests that the conditions necessary for life as we know it could exist on moons and planets far from the warmth of their parent star, in the cold depths of subsurface oceans. With the discovery of phosphates on Saturn’s moon Enceladus, the next step is clear – we need to go back to Enceladus to see if the habitable ocean is actually inhabited. This finding has made the search for extraterrestrial life in our solar system more exciting and promising. Future missions to Enceladus could involve landers or even submarines designed to probe its subsurface ocean. Such missions could collect and analyze samples directly from the ocean, potentially detecting signs of life such as microorganisms or organic molecules.

For more than a century, biologists have wondered what the earliest animals were like when they first arose in the ancient oceans over half a billion years ago. Searching among today’s most primitive-looking animals for the earliest branch of the animal tree of life, scientists gradually narrowed the possibilities down to two groups: sponges, which spend their entire adult lives in one spot, filtering food from seawater; and comb jellies, voracious predators that oar their way through the world’s oceans in search of food.

In a new study published this week in the journal Nature, researchers use a novel approach based on chromosome structure to come up with a definitive answer: Comb jellies, or ctenophores (teen’-a-fores), were the first lineage to branch off from the animal tree. Sponges were next, followed by the diversification of all other animals, including the lineage leading to humans. Although the researchers determined that the ctenophore lineage branched off before sponges, both groups of animals have continued to evolve from their common ancestor. Nevertheless, evolutionary biologists believe that these groups still share characteristics with the earliest animals, and that studying these early branches of the animal tree of life can shed light on how animals arose and evolved to the diversity of species we see around us today.

“The most recent common ancestor of all animals probably lived 600 or 700 million years ago. It’s hard to know what they were like because they were soft-bodied animals and didn’t leave a direct fossil record. But we can use comparisons across living animals to learn about our common ancestors,” said Daniel Rokhsar, University of California, Berkeley professor of molecular and cell biology and co-corresponding author of the paper along with Darrin Schultz and Oleg Simakov of the University of Vienna. “It’s exciting — we’re looking back deep in time where we have no hope of getting fossils, but by comparing genomes, we’re learning things about these very early ancestors.”

Understanding the relationships among animal lineages will help scientists understand how key features of animal biology, such as the nervous system, muscles and digestive tract, evolved over time, the researchers say. “We developed a new way to take one of the deepest glimpses possible into the origins of animal life,” said Schultz, the lead author and a former UC Santa Cruz graduate student and researcher at the Monterey Bay Aquarium Research Institute (MBARI) who is now a postdoctoral researcher at the University of Vienna. “This finding will lay the foundation for the scientific community to begin to develop a better understanding of how animals have evolved.”

What is an animal?
Most familiar animals, including worms, flies, mollusks, sea stars and vertebrates — and including humans — have a head with a centralized brain, a gut running from mouth to anus, muscles and other shared features that had already evolved by the time of the famed “Cambrian Explosion” around 500 million years ago. Together, these animals are called bilaterians.

Other bona fide animals, however, such as jellyfish, sea anemones, sponges and ctenophores, have simpler body plans. These creatures lack many bilaterian features — for example, they lack a defined brain and may not even have a nervous system or muscles — but still share the hallmarks of animal life, notably the development of multicellular bodies from a fertilized egg.

The evolutionary relationships among these diverse creatures — specifically, the order in which each of the lineages branched off from the main trunk of the animal tree of life — has been controversial. With the rise of DNA sequencing, biologists were able to compare the sequences of genes shared by animals to construct a family tree that illustrates how animals and their genes evolved over time since the earliest animals arose in the Precambrian Period. But these phylogenetic methods based on gene sequences failed to resolve the controversy over whether sponges or comb jellies were the earliest branch of the animal tree, in part because of the deep antiquity of their divergence, Rokhsar said. “The results of sophisticated sequence-based studies were basically split,” he said. “Some researchers did well-designed analyses and found that sponges branched first. Others did equally complex and justifiable studies and got ctenophores. There hasn’t really been any convergence to a definitive answer.”

The system, named TOI-2096, consists of two planets orbiting a cool star in a synchronized dance at approximately 150 light-years from Earth.

A study led by researchers of the University of Liège and the CSIC -- using observations from NASA's TESS telescope -- presents the detection of a system of two planets slightly larger than Earth orbiting a cold star in a synchronized dance. Named TOI-2096, the system is located 150 light-years from Earth.

The discovery is the result of a close collaboration between European and American universities and was made possible by the US space mission TESS (Transiting Exoplanet Survey Satellite), which aims to find planets orbiting nearby bright stars. "TESS is conducting an all-sky survey using the transit method, that is, monitoring the stellar brightness of thousands of stars in the search for a slight dimming, which could be caused by a planet passing between the star and the observer. However, despite its power to detect new worlds, the TESS mission needs support from ground-based telescopes to confirm the planetary nature of the detected signals," explains Francisco J. Pozuelos, astrophysicist, first author of the paper, former member of the ExoTIC laboratory at the Univeristy of Liège, and who has now joined the Spanish National Research Council (IAA-CSIC).

The planets TOI-2096 b and TOI-2096 c were observed with an international network of ground-based telescopes, allowing their confirmation and characterization. The majority of the transits were obtained with telescopes of the TRAPPIST and SPECULOOS projects led by the University of Liège. "Making an exhaustive analysis of the data, we found that the two planets were in resonant orbits: for each orbit of the outer planet, the inner planet orbits the star twice," says Mathilde Timmermans, a doctoral student and second author of the scientific paper. Their periods are therefore very close to being a multiple of each other with about 3.12 days for planet b and about 6.38 days for planet c. This is a very particular configuration, and it causes a strong gravitational interaction between the planets. This interaction delays or accelerates the passage of the planets in front of their star and could lead to the measurement of the planetary masses using larger telescopes in the near future."

The researchers behind the discovery estimate that the radius of planet b -- the closest to its star -- is 1.2 times that of Earth, hence the name 'super-Earth'. Its properties could be similar to Earth's: a planet with a mostly rocky composition, possibly surrounded by a thin atmosphere. Similarly, the radius of planet c is 1.9 times the radius of the Earth and 55% that of Neptune, which could place the planet in the category of 'mini-Neptunes', planets composed of a rocky and icy core surrounded by extended hydrogen- or water-rich atmospheres, such as Uranus and Neptune in our Solar System. These sizes are very interesting because the number of planets with a radius between 1.5 and 2.5 Earth radii is smaller than what theoretical models predict, making these planets a rarity. These planets are of crucial importance given their sizes," notes Mathilde Timmermans, "the formation of super-Earths and mini-Neptunes remains a mystery today. There are several formation models trying to explain it, but none fits the observations perfectly. TOI-2096 is the only system found to date that has a super-Earth and a mini-Neptune precisely at the sizes where the models contradict each other. In other words, TOI-2096 may be the system we've been looking for to understand how these planetary systems have formed."

"Furthermore, these planets are among the best in their category to study their possible atmospheres," explains Francisco J. Pozuelos. Thanks to the relative sizes of the planets with respect to the host star, as well as the brightness of the star, we find that this system is one of the best candidates for a detailed study of their atmosphere with the JWST space telescope. We hope to be able to do this quickly by coordinating with other universities and research centers. These studies will help confirm the presence of an atmosphere, extensive or not, around planets b and c and thus give us clues as to their formation mechanism."

A team of engineers at the University of Colorado Boulder has designed a new class of tiny, self-propelled robots that can zip through liquid at incredible speeds -- and may one day even deliver prescription drugs to hard-to-reach places inside the human body.

The Singularity, often referred to as the Technological Singularity, is a hypothetical point in the near future at which artificial intelligence (AI) becomes so incredibly advanced that it surpasses human intelligence, leading to rapid, unforeseeable changes in society and technology, life as we know it. The term was popularized by mathematician and computer scientist Vernor Vinge and futurist Ray Kurzweil.

At the Singularity, AI systems would possess the ability to autonomously learn, self-improve, and create new AI systems with very advanced capabilities and more intelligent than themselves, resulting in an exponential increase in intelligence. This self-improvement cycle could lead to AI systems that are vastly more capable and sophisticated than human minds and anything humans can even imagine.

The Singularity is often associated with the development of artificial general intelligence (AGI), which is an AI system that can perform any intellectual task that a human being can do. AGI is distinct from narrow AI, which is designed for specific tasks and lacks the broad cognitive abilities and adaptability of human intelligence.

The implications of the Singularity are the subject of much speculation and debate today. Most people who have thought about it agree that the Singularity would be a decisive turning point in human history. Some view this with awe and trepidation, almost like a religious event that will lead to everlasting life and happiness. Many believe it will bring about tremendous breakthroughs in medicine, energy, and space exploration. Others are more cautious, expressing concerns about the ethical, societal, and existential risks associated with super-intelligent AI systems. Some predict doom and gloom. Others think it is all but impossible.

Predicting the Singularity Start Date

The general caveat to this question given by all experts is that predicting when the Singularity might occur is highly uncertain, as it depends on the pace of AI research, breakthroughs in understanding human cognition, and the development of advanced hardware and software systems. Still, ChatGPT-4 has predicted a 5%-10% chance that the singularity will happen within five years, 2028.  Chat GPT-4 did so without knowledge of what has happened since September 2021, when it was last fed data.

We know what has happened since September 2021, we know the pace of change, even though the ChatGPT model does not. We know that when ChatGPT-3.5 was released in November 2022, it scored in the bottom 10% of the multi-state Bar exam. But just five months later, when 4.0 was released in March 2023, it scored in the top 10%. Let that sink in and remember no new data was provided. There was only improvement in reasoning ability. As lawyers who have all taken this test, we know better than anyone the significance of a jump from the bottom to the top 10% of this challenging test. Yes, it is getting pretty smart, fast. How much longer until its intelligence exceeds our own?

Opportunities and Challenges Presented by the Singularity

The three opportunities listed here, all good, are generally thought to be possible in a time of technological Singularity, a time where AI intelligence and capacities accelerate at an exponential rate.

1. Accelerated Technological Progress: The Singularity could lead to rapid advancements in various fields, as super-intelligent AI systems develop new technologies and solve complex problems that were previously beyond human reach.
2. Economic Growth: With Ai systems handling tasks more efficiently and effectively than humans, productivity and economic growth could increase dramatically, potentially leading to higher standards of living and reduced global inequality.
3. Scientific Discoveries: The Singularity might enable breakthroughs in areas such as healthcare, environmental sustainability, and space exploration, as Ai systems analyze vast amounts of data, simulate complex phenomena, and generate innovative solutions to pressing challenges.

Conversely, as usual, their is a flip side to opportunities, the generally accepted challenges, some might say threats, presented by the Singularity:

1. Ethical Concerns: As Ai systems surpass human intelligence, ethical considerations become paramount. Ensuring that Ai systems behave ethically, adhere to human values, and respect individual rights will be crucial.
2. Job Displacement: The widespread adoption of super-intelligent Ai systems could result in job displacement across various industries, as human workers are replaced by more efficient Ai. This raises concerns about unemployment, workforce retraining, and social safety nets.
3. Existential Risks: The Singularity poses potential existential risks, as super-intelligent Ai might act in ways that are harmful to humanity, either intentionally or unintentionally. Ensuring Ai safety and robustness is essential to mitigate these risks.

While generative AI is a relatively new household term, drug discovery company Insilico Medicine has been using it for years to develop new therapies for debilitating diseases.

The company’s early bet on deep learning is bearing fruit — a drug candidate discovered using its AI platform is now entering Phase 2 clinical trials to treat idiopathic pulmonary fibrosis, a relatively rare respiratory disease that causes progressive decline in lung function. Insilico used generative AI for each step of the preclinical drug discovery process: to identify a molecule that a drug compound could target, generate novel drug candidates, gauge how well these candidates would bind with the target, and even predict the outcome of clinical trials.

Doing this using traditional methods would have cost more than $400 million and taken up to six years. But with generative AI, Insilico accomplished them for one-tenth of the cost and one-third of the time — reaching the first phase of clinical trials just two and a half years after beginning the project. “This first drug candidate that’s going to Phase 2 is a true highlight of our end-to-end approach to bridge biology and chemistry with deep learning,” said Alex Zhavoronkov, CEO of Insilico Medicine. “This is a significant milestone not only for us, but for everyone in the field of AI-accelerated drug discovery.”

Insilico is a premier member of NVIDIA Inception, a free program that provides cutting-edge startups with technical training, go-to-market support and AI platform guidance. The company uses NVIDIA Tensor Core GPUs in its generative AI drug design engine, Chemistry42, to generate novel molecular structures — and was one of the first adopters of an early precursor to NVIDIA DGX systems in 2015.

AI Enables End-to-End Preclinical Drug Discovery

Insilico’s Pharma.AI platform includes multiple AI models trained on millions of data samples for a range of tasks. One AI tool, PandaOmics, rapidly identifies and prioritizes targets that play a significant role in a disease’s effectiveness — like the infamous spike protein on the virus that causes COVID-19.

The Chemistry42 engine can design within days new potential drug compounds that target the protein identified using PandaOmics. The generative chemistry tool uses deep learning to come up with drug-like molecular structures from scratch.

“Typically, AI companies in drug discovery focus either on biology or on chemistry,” said Petrina Kamya, head of AI platforms at Insilico. “From the start, Insilico has been applying the same deep learning approach to both fields, using AI both to discover drug targets and generate chemical structures of small molecules.”

Over the years, the Insilico team has adopted different kinds of deep neural networks for drug discovery, including generative adversarial networks and transformer models. They’re now using NVIDIA BioNeMo to accelerate the early drug discovery process with generative AI.

By treating molecules as geometric tessellations, scientists devised a new way to forecast how 2D materials might self-assemble.

Researchers are working out ways to ignore the noise and leverage the power of qubits to advance quantum computing.

Scientists have used cesium atoms on a solid-state semiconductor to create artificial atoms and create synthetic molecules.

The nuclei of atoms are made up of protons and neutrons, collectively referred to as nucleons. Nucleons in turn consist of quarks and gluons. Understanding how those inner building blocks are distributed within nuclei can reveal how large protons and neutrons appear when probed at high energy. This work used comparisons between model calculations and new precision data from collisions of heavy ions (containing many protons and neutrons) to access the distribution of gluons and predict the size of the proton. Researchers now at Brookhaven Laboratory

found that gluons were less smoothly distributed than they expected.

Identifying and precisely measuring factors that are sensitive to nucleon size will help physicists more accurately describe the quark-gluon plasma (QGP). This is a hot, dense form of nuclear matter created when individual protons and neutrons “melt” in heavy ion collisions, mimicking the conditions of the early universe. This knowledge can eliminate significant uncertainties about the initial state of the produced QGP. Knowing more about the initial state of QGP provides input for the model calculations that scientists use to infer the viscosity and other properties of the QGP. The results also add to measurements of proton size based on the distribution of quarks inside the proton.

It may be hard to imagine that the debris of violent heavy ion collisions—which dissolve the boundaries of protons and neutrons and produce thousands of new particles—can be used to gain detailed insight into the properties of nucleons. New advances in experimental methods along with improved theoretical modeling have made it possible. Based on a state-of-the-art model for the colliding nuclei and the hydrodynamic evolution of the quark-gluon plasma produced in the collision, this work demonstrates that specific observables are strongly sensitive to the size of the protons and neutrons inside the colliding nuclei.

Comparison of the model with data from experiments also indicates that the gluon distribution inside protons and neutrons is rather lumpy—not as smooth and spherical as modeled using naïve assumptions. Current and future measurements using collisions of different nuclei at the Relativistic Heavy Ion Collider (RHIC), a Department of Energy (DOE) user facility at Brookhaven National Laboratory, and the Large Hadron Collider (LHC) at CERN, along with a sophisticated theoretical program, will provide more detailed insight into the gluon distribution inside protons and neutrons, in and outside of heavy nuclei, and how it behaves with changing collision energy. This fundamentally important information will be explored with even higher precision at the Electron-Ion Collider to be built at Brookhaven.

The finding contradicts previous assumptions about the role of mobile plate tectonics in the development of life on Earth.

Scientists have taken a journey back in time to unlock the mysteries of Earth's early history, using tiny mineral crystals called zircons to study plate tectonics billions of years ago. The research sheds light on the conditions that existed in early Earth, revealing a complex interplay between Earth's crust, core, and the emergence of life.

Plate tectonics allows heat from Earth's interior to escape to the surface, forming continents and other geological features necessary for life to emerge. Accordingly, "there has been the assumption that plate tectonics is necessary for life," says John Tarduno, who teaches in the Department of Earth and Environmental Sciences at the University of Rochester. But new research casts doubt on that assumption.

Tarduno, the William R. Kenan, Jr. Professor, is lead author of a paper published in Nature examining plate tectonics from a time 3.9 billion years ago, when scientists believe the first traces of life appeared on Earth. The researchers found that mobile plate tectonics was not occurring during this time. Instead, they discovered, Earth was releasing heat through what is known as a stagnant lid regime. The results indicate that although plate tectonics is a key factor for sustaining life on Earth, it is not a requirement for life to originate on a terrestrial-like planet.

"We found there wasn't plate tectonics when life is first thought to originate, and that there wasn't plate tectonics for hundreds of millions of years after," says Tarduno. "Our data suggests that when we're looking for exoplanets that harbor life, the planets do not necessarily need to have plate tectonics."

A new study documenting Webb’s recent observations of a rocky planet around 26 light-years away has caused a waterlogged mystery that’s left researchers uncertain whether they made a groundbreaking discovery or not.

Original article is here

Large language models are all the rage these days and new ones are popping up every other day. Most of these linguistic behemoths, including OpenAI’s ChatGPT and Google’s Bard, are trained on text data from all over the internet – websites, articles, books, you name it. This means that their output is a mixed bag of genius. But what if instead of the web, LLMs were trained on the dark web? Researchers have done just that with DarkBERT to some surprising results. Let’s take a look.

What is DarkBERT?

Penn State researchers created the first protein-based nano-computing agent that functions as a circuit, putting them closer to developing cell-based therapies that can treat diseases like diabetes and cancer.

The absolute internal quantum efficiency (IQE) of indium gallium nitride (InGaN) based blue light-emitting diodes (LEDs) at low temperatures is often assumed to be 100%. However, a new study from University of Illinois Urbana-Champaign Electrical and Computer Engineering researchers has found that the assumption of always perfect IQE is wrong: the IQE of an LED can be as low as 27.5%. This new research, "Low temperature absolute internal quantum efficiency of InGaN-based light-emitting diodes," was recently published in Applied Physics Letters.

As professor Can Bayram puts it, LEDs are the ultimate lighting source. Since their invention, they have become increasingly popular due to their energy efficiency and cost-effectiveness. An LED is a semiconductor that emits light when current flows through the device. It generates photons through the recombination of electrons and holes (carriers), releasing energy in the form of photons. The color of the light emitted corresponds to the energy of the photon.

InGaN-based blue LEDs enable bright and energy-saving white lighting. The transition to solid-state lighting sources has significantly reduced energy needs and greenhouse gas emissions, but continual efficiency improvements are necessary to hit energy savings goals in the long term. The U.S. Department of Energy's 2035 roadmap calls for blue LED efficiency to increase from 70% to 90% and furthering energy savings by 450 terawatt hours (TWh) and CO2 emission savings by 150 million metric tons.

Bayram says, "The question is, how can we push this ultimate lighting source further? The answer is by understanding its absolute efficiency, not relative efficiency." Relative efficiency benchmarks a device with itself, while absolute efficiency allows for comparison across different devices by measuring the efficiency on a commonly shared scale.