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Supercomputers map large-scale quantum systems

Scientists at Paderborn University have found ways to solve very complex math problems related to quantum photonics using high-performance computing (HPC).

Quantum photonics is about studying and using photons in quantum systems. HPC is about super powerful computers that can do calculations much faster than regular computers.

The scientists have described their methods and results in a paper published in Quantum Science and Technology.

The scientists focused on quantum tomography. This is a method to figure out the full details of a quantum state, like mapping it out completely. They used this on a special kind of detector that measures individual photons.

There’s a huge amount of data involved. Analyzing this data while keeping the quantum state intact is tough.

The scientists developed new open source HPC algorithms. With these, they managed to perform quantum tomography on a very large scale. This had never been done before at this level because regular computer methods couldn’t handle the scale or speed needed.

Faster computers to model larger quantum systems

“By developing customised open-source algorithms using high-performance computing, we have carried out quantum tomography on a photonic quantum detector on a mega-scale,” says researcher Timon Schapeler in a Paderborn University press release.

“The results open up completely new possibilities in the field of scalable quantum photonics in terms of the size of the systems to be analysed,” continues Schapeler. “This also has implications for the characterisation of photonic quantum computer hardware, for example.”

The significance of this work is that it allows for much larger quantum systems to be analyzed quickly. For example, they could describe a photon detector in just a few minutes, which was faster than ever done before. This breakthrough means scientists can now work on bigger quantum systems, potentially leading to advancements in quantum computers and other quantum technologies.

This research not only pushes the boundaries of what we can compute but also how we can apply quantum technology in real-world applications like better measurement tools, enhanced data processing, and secure communication systems. It also shows how basic research in quantum physics can lead to practical future technologies.

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AI-designed DNA switches for personalized medicine

Researchers at The Jackson Laboratory, with teams from the Broad Institute of MIT and Harvard, and Yale University, have used Artificial Intelligence (AI) to create new DNA switches.

These switches help control when and where genes in our body are turned on or off, which is crucial for understanding and potentially treating various diseases.

DNA switches, known as cis-regulatory elements (CREs), are parts of the DNA that manage how genes are expressed, meaning they decide if a gene is active or not. Think of genes like light bulbs and CREs like dimmer switches; they can brighten, dim, or turn off the light entirely.

DNA switches work in a complex language that scientists are still decoding. Using deep learning, the research team trained a computer model with hundreds of thousands of DNA sequences. They specifically looked at how these sequences acted in blood, liver, and brain cells. This training helped the AI understand the “language” of these DNA switches.

After learning, the AI was used to design thousands of new, synthetic CREs through a platform named CODA (Computational Optimization of DNA Activity). These new CREs are special because they can be tailored to activate or deactivate genes in very specific types of cells. For example, a CRE could turn on a gene in liver cells but keep it turned off in blood or brain cells.

“This creates the opportunity for us to turn the expression of a gene up or down in just one tissue without affecting the rest of the body,” says research co-leader Ryan Tewhey in a Jackson Laboratory press release.

The researchers describe the methods and results of this study in a paper published in Nature.

Applications to personalized medicine

This approach makes it possible to control genes with high precision and introduces the possibility of creating treatments that target specific cells or conditions without affecting others. This could be particularly useful in gene therapy, where the goal is to fix or replace faulty genes causing diseases.

The researchers found that these AI-designed CREs worked better than natural ones because they combined elements that activated genes in the desired cells and suppressed them in others. This research marks a step forward in personalized medicine, where treatments could be designed specifically for an individual’s genetic makeup, potentially leading to more effective therapies for complex diseases.

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Chemical language model generates creative pharmaceutical solutions

Researchers at the University of Bonn have developed an Artificial Intelligence (AI) that works like a “chemical ChatGPT” to help create new medicines.

This AI works as a chemical language model. The researchers trained the chemical language model to predict chemical compounds, or molecules, that can interact with two specific proteins in the body at the same time.

This chemical ChatGPT generates the structures of chemical compounds that could potentially fight diseases more effectively by affecting multiple targets within the body.

This dual action is valuable in pharmaceuticals because it means a single drug could handle multiple tasks, like fighting cancer by blocking different processes that help cancer grow.

“Because compounds with desirable multi-target activity influence several intracellular processes and signaling pathways at the same time, they are often particularly effective – such as in the fight against cancer,” says research leader Jürgen Bajorath in a University of Bonn press release.

The researchers describe the methods and results of the study in a paper published in Cell Reports Physical Science.

The process involves teaching the AI with SMILES strings, which are like sentences but for chemistry, describing the structure of molecules.

The researchers trained the AI with over 70,000 pairs of SMILES strings.

One string described a molecule that targets only one protein, and the other string described a molecule that targets two. Through this training, the AI learned to understand the chemical differences between single-target and dual-target compounds.

The AI generates original, out of the box solutions

After the initial learning, the researchers fine-tuned the AI with specific examples to predict molecules that could target different types of proteins.

The results were promising. After fine-tuning, the AI was able to suggest molecules that are known to work against the desired pair of protein targets, showing that this method has practical applications.

The AI “often suggests chemical structures that most chemists would not even think of right away,” notes Bajorath. “To a certain extent, it triggers ‘out of the box’ ideas and comes up with original solutions that can lead to new design hypotheses and approaches.”

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Researchers suggest that AI is part of a new human cognitive system

Researchers led by Giuseppe Riva from Università Cattolica del Sacro Cuore have studied how Artificial Intelligence (AI) interacts with human thought processes, enhancing human cognitive abilities externally, much like an external hard drive enhances a computer’s storage.

In a paper published in Nature Human Behaviour., the researchers argue that AI is part of a new human cognitive system, which they call System 0.

System 0 sits alongside existing human cognitive models: System 1, which is fast and intuitive, and System 2, which involves deliberate and analytical thinking. System 0, however, uses AI to process vast amounts of data and provide outputs or decisions based on algorithms, but it doesn’t inherently understand or assign meaning to this information. Instead, humans must interpret and give meaning to AI output.

The addition of System 0 to the existing System 1 and 2 brings both opportunities and risks.

On one hand, System 0 can handle complex data processing beyond human capabilities, potentially aiding in scientific research, data analysis, and social system management.

On the other hand, there’s a risk of humans becoming overly reliant on AI, potentially losing cognitive autonomy or blindly trusting AI outputs without critical analysis. This reliance might also lead to issues with transparency, bias in AI systems, and a distorted perception of reality through synthetic data.

The researchers highlight that we must engage with AI critically, maintaining control over decision-making processes.

Benefits and risks of AI

The researchers conclude that, while System 0 could revolutionize how we approach complex problems, it’s crucial to navigate its integration with caution, ensuring that it enhances rather than diminishes human cognitive and decision-making abilities. The future of human thought, the researchers believe, will depend on how well we manage this interaction, keeping human judgment at the forefront while leveraging AI’s vast computational power.

“The risk,” emphasize the researchers in a press release from Università Cattolica del Sacro Cuore, “is relying too much on System 0 without exercising critical thinking. If we passively accept the solutions offered by AI, we might lose our ability to think autonomously and develop innovative ideas. In an increasingly automated world, it is crucial that humans continue to question and challenge the results generated by AI.”

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Electron fluid research could open the door to better electronics

Scientists at National University of Singapore are advancing toward new electronic technology involving fluid-like electrons.

Normally, electrons move in a somewhat random manner inside materials. However, in certain conditions, electrons can start to flow more like a fluid, similar to how water flows. This behavior is unusual because electrons typically bounce off atoms and other electrons, but in this fluid state, they move more freely and collectively.

The scientists have discovered materials where electrons can exhibit this fluid-like behavior at room temperature or under specific conditions. This discovery opens up new technological possibilities.

The scientists have described the methods and results of their study in a paper published in Nature Nanotechnology. The scientists have found that, in graphene exposed to electromagnetic radiation of terahertz frequencies, the electron fluid heats up and its viscosity is drastically reduced, resulting in lower electrical resistance.

The ability to detect Terahertz (THz) waves, situated between microwaves and infrared light, could unlock advances in technologies.

A conceptual image shows a beam of terahertz radiation heating electron fluid and causing a change in viscosity (Credit: National University of Singapore).
A conceptual image shows a beam of terahertz radiation heating electron fluid and causing a change in viscosity (Credit: National University of Singapore).

New electronic devices based on this research could use less power because fluid-like electron movement reduces resistance, which usually causes energy loss as heat. These new electronic devices could be much faster since electrons moving more freely can carry information or electricity quicker.

The flow of fluid-like electrons can be very sensitive to changes in environment (like temperature or magnetic fields). Therefore, this research could permit the development of new, highly sensitive sensors.

Materials with fluid-like electrons could manage heat better, which is crucial for cooling electronic devices or even in harnessing waste heat for energy. Less energy wasted as heat means more efficient devices, which is good for battery life in gadgets and for reducing energy consumption overall.

Applications of viscous electronics

This research project might not immediately change everyday electronic gadgets, but it sets the groundwork for future technologies.

New devices could be smarter, more energy-efficient, thinner, more flexible, and capable of new functions.

These findings might also help in creating components for quantum computers, where there is a need to control electron states very precisely.

Beyond their fundamental implications, note the scientists in the Nature Nanotechnology paper, these findings underscore the practicality of electron hydrodynamics in designing ultra-fast THz sensors and electron thermometers.

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First patient: Neuralink is a new world, a new frontier

In January, Noland Arbaugh was the first person to receive a Neuralink brain implant. This implant is designed to help people who are paralyzed use computers and other devices with just their thoughts.

Arbaugh, who was paralyzed from the shoulders down, received the brain implant in January. The device uses tiny wires with electrodes to read brain activity, which then allows Arbaugh to control a computer cursor.

After the surgery, Arbaugh experienced some ups and downs. Initially, he was excited about the potential of regaining some independence. For example, he could play chess and video games just by thinking. However, some of the implant’s threads that connect to the brain got displaced, reducing the effectiveness of the implant.

But Neuralink managed to improve the device’s performance through software updates, allowing Arbaugh to continue using it to control digital devices.

Arbaugh has recently spoken with LBC. The interview is online on YouTube.

Arbaugh is hopeful about the technology’s future and suggested the idea of controlling a robot with his mind, which could assist him daily.

The road ahead

Neuralink announced its second human trial in August.

“It really is just a whole new world that we’re exploring here,” Arbaugh says, “a new frontier.”

Arbaugh’s journey with Neuralink highlights a step towards merging human cognitive functions with computers. This could restore autonomy to those who’ve lost it due to paralysis.

But there are more futuristic and awesome, world-changing implications and applications down the road. In his biography of Elon Musk (2023), Walter Isaacson notes that Musk started Neuralink to develop futuristic interfaces between minds and machines.

In her book “Artificial You: AI and the Future of Your Mind” (2019), Susan Schneider notes that Neuralink would facilitate “some sort of merger of biological intelligence and machine intelligence.” This would achieve the full fusion of humanity and technology that Ray Kurzweil envisions in “The Singularity Is Nearer: When We Merge with AI” (2024).

Meanwhile, Brain-Computer Interfacing (BCI) technology continues to advance step by step. Neuralink co-founder Max Hodak has launched a company to develop a tech stack for neural engineering. This “is going to be bigger than a lot of people think,” Hodak told Bloomberg. Here is an unpaywalled copy of the Bloomberg story.

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Simple synthetic cells produce and transfer energy like natural cells

Scientists at the University of Groningen are trying to create synthetic cells that are simplified versions of living cells and work like living cells.

The scientists have successfully replicated the natural process of energy production and transfer in living cells.

Mitochondria, the “energy factories” of cells, produce energy in the form of a molecule called ATP (Adenosine Triphosphate), which is the main energy carrier in cells.

ATP stores and supplies the energy needed for many cellular processes. The ATP molecule becomes ADP (Adenosine Diphosphate) after releasing its energy. ADP can be converted back into ATP.

The scientists have used only five components to produce ATP, compared to the hundreds used by natural mitochondria. They placed the simplified system inside vesicles, small sacs that can carry substances within a cell or between cells.

These vesicles can absorb ADP and an amino acid called arginine from their surroundings. Burning arginine provides energy and converts ADP back into ATP.

Another type of vesicle can absorb the ATP produced and use it for energy-consuming reactions, turning ATP back into ADP, which can then be reused by the first vesicle. This cycle mimics the natural process of energy production and use in cells.

The scientists have described their research methods and results in two research papers.

In the first paper, published in Nature Communications in September, they have described a system for energy conversion and cross-feeding of products of this reaction between synthetic cells.

In the second paper, just published in Nature Nanotechnology, they have described a system for concentrating and converting nutrients in cells.

Applications and future research

Understanding how to create synthetic cells can help scientists learn more about the fundamental processes of life. It can also lead to new technologies in medicine and biotechnology.

“Ultimately, this would give us a blueprint for life, something that is currently lacking in biology,” says research leader Bert Poolman. “This may eventually have all kinds of applications, but will also help us to better understand what life is.”

This seems a small step to replicate some important cellular processes in synthetic cells. But eventually, research on synthetic cells could lead to the ability to engineer entire living beings.

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    Cloud of axions around neutron stars could explain dark matter

    Researchers from the universities of Amsterdam, Princeton, and Oxford have proposed that neutron stars might be surrounded by clouds of particles called axions. These axions are very light and hard to detect, but they could help explain dark matter, which makes up most of the universe’s matter but is invisible to us.

    Neutron stars are incredibly dense remnants of massive stars that have exploded. The researchers found that axions can get trapped by the strong gravity of neutron stars, forming a cloud around them. Some of these axions can turn into light (photons) when they interact with the star’s magnetic field. This light can be detected by telescopes on Earth, making it possible to observe these axion clouds.

    Axions are hypothetical particles, first proposed in the 1970s to solve theoretical problems in particle physics. Axions would be very light and interact weakly with other particles, making them difficult to detect. Although axions have not been directly observed yet, they are a leading candidate for dark matter, the unseen mass that influences the behavior of galaxies and the large scale structure of the universe.

    The intersections of astronomy and fundamental physics

    Unlike normal matter, dark matter doesn’t emit, absorb, or reflect light, making it invisible and detectable only through its gravitational effects. Scientists believe dark matter is crucial for explaining the structure and behavior of galaxies. Despite extensive research, dark matter has not been directly observed, and its exact nature remains one of the biggest puzzles in modern astrophysics.

    An axion cloud around a neutron star (Credit: University of Amsterdam).

    The researchers describe their methods and results in a paper published in Physical Review X, building on their previous research. Earlier, they looked at axions that escape from neutron stars. Now, they focus on those that stay behind and form clouds. These axion clouds could be a new way to study dark matter and learn more about the universe.

    This research could help scientists understand dark matter better. It also opens up new possibilities for observing these elusive particles, highlighting the intersections of astronomical observation and open issues in fundamental physics.

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    Game shows that humans feel empathy for AI

    A study from Imperial College London shows that people feel sorry for Artificial Intelligence (AI) bots and try to include them in games if they see the bots being left out.

    The Imperial College researchers used a game called “Cyberball” where players throw a virtual ball to each other. They had 244 people play this game with an AI bot involved.

    The researchers describe the methods and results of the study in a paper published in Human Behavior and Emerging Technologies. When the AI bot was excluded, the human players often tried to throw the ball to the bot more to make up for it.

    This behavior is similar to what people do when they see other persons being left out: they try to include them more.

    This reaction shows that humans can treat AI like social beings. Even though the participants knew they were playing with a bot, they still wanted to be fair to it.

    Social AI design

    This tendency to sympathize with AI could be important for designing future AI systems, suggesting that as AI becomes more common in daily life, people might interact with them as they would with other humans.

    As AI technology grows, and bots or virtual agents become more involved in human activities (like work or social interactions), understanding this human behavior can help in making these interactions smoother and more natural. If humans naturally want to include and be fair to AI, this could lead to better collaboration between humans and machines.

    “This is a unique insight into how humans interact with AI, with exciting implications for their design and our psychology,” says researcher in a press release issued by Imperial College.

    The researchers believe that we should think about how we design AI to not just perform tasks but also to engage socially in a way that feels right to humans. They’re looking into more ways to study this, like having face-to-face interactions with AI in different settings.

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    Soft robots steered by light swim in viscous fluids

    Researchers at Tampere University and Anhui Jianzhu University have developed a new type of soft robot that can swim by itself in thick liquids. The researchers consider this a breakthrough in soft robotics.

    This soft toroidal micro-robot, shaped like a doughnut and made with flexible materials, can move and adapt to its surroundings. Unlike hard robots, soft robots can squeeze through small spaces and handle delicate tasks.

    The main goal of this micro-robot is to navigate through viscous liquids. Viscous liquids are thick and sticky, like honey or syrup. Many industries need to work with such liquids, and having a robot that can move in them can make processes easier and safer.

    The liquid crystalline elastomeric toroidal submarines can start swimming in honey when laser beams are directed at them (Credit: Hao Zeng, Tampere University).

    The researchers describe the new micro-robot in a paper published in Nature Materials. The robot pushes itself forward by moving its body under constant light illumination.

    The researchers used a synthetic material known as liquid crystalline elastomer, which reacts to stimuli like lasers.

    The micro-robot moves spontaneously under constant exposure to light or heat. Exploiting dynamic friction or drag forces makes it possible to obtain light-steerable motion in a variety of fluid environments.

    Applications and future research

    “The implications of this research extend beyond robotics, potentially impacting fields such as medicine and environmental monitoring,” says researcher Zixuan Deng in a Tampere University press release.

    Deng adds that the device could transport drugs through physiological mucus and unblock blood vessels.

    Deng believes that future research will explore the interactions and collective dynamics of multiple robots, potentially leading to new methods of communication between intelligent microrobots.

    There is, indeed, a project to explore “lifelike material structures that communicate with each other via physical contact, fluidic media or optical beams.”

    The project’s goal is “to design soft robots that sustain their own movement, make their own decisions and adapt to their environmental conditions, without human control.”

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