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Space-based solar power for Iceland

A British company called Space Solar and an Icelandic company called Transition Labs are planning to give Iceland electricity from space. They plan to build a space-based solar power (SBSP) plant, which means they’ll put solar panels in space to catch sunlight.

Solar panels turn sunlight into electricity. When these panels are in space, they can work all the time because there’s no night or clouds to block the sun. This is different from solar panels on Earth, which only work when it’s sunny.

Space Solar and Transition Labs are working with Iceland’s electricity company Reykjavik Energy. They’ve agreed to start sending power down to Iceland by the year 2030. This would be the first operational SBSP plant to send energy from space to use on Earth.

The initial SBSP plant would generate 30 megawatts (MW) of power. A megawatt is a lot of electricity, enough to power many homes. But the companies wouldn’t stopping there; they want to make even bigger plants by 2036 that could send back gigawatts (GW) of power. A gigawatt is a thousand megawatts, so that’s a lot more electricity.

To get the electricity from space to Earth, the SBSP plant would send high-frequency radio waves down to special antennas on Earth. These antennas would catch the waves and turn their energy back into electricity that we can use.

The promise of SBSP

John Mankins covers SBSP concepts and technologies in detail in his book “The Case for Space Solar Power” (2014).

This idea of getting power from space might sound like science fiction, but it is an actual possibility. It could give us a way to get energy without needing fossil fuels like coal or oil, which are bad for the environment. Of course there are big challenges, first and foremost the fact that sending equipment into space is still expensive. But the costs are going down.

People are excited about this because it could change how we get our energy, making it cleaner and possibly cheaper over time and providing yet one more compelling argument in support of space operations.

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Philip Rosedale returns to Second Life as Linden Lab CTO

Philip Rosedale, the visionary behind Second Life, has returned to the platform he founded, New World Notes reports.

Second Life, a pioneering virtual world launched in the early 2000s, is a Virtual Reality (VR) world built and programmed by its users with realtime, in-world tools. Today Second Life doesn’t often make news headlines as it did in the late 2000s. But it is still there and it has stood the test of time.

After leaving Second Life, Rosedale explored the frontiers of VR with High Fidelity, a project aimed at creating immersive, high-quality VR experiences.

Rosedale is returning to Second Life with a conviction that this platform still holds untapped potential. He envisions a revitalized Second Life that not only keeps up with but also sets the pace for emerging digital trends.

Rosedale will be Chief Technology Officer (CTO) of Second Life’s parent company Linden Lab.

One of the key areas Rosedale is focusing on is the expansion of Second Life’s user base. Currently, the platform boasts a user engagement of around half a million monthly active users. His strategy includes the development of a mobile application, recognizing the shift in digital consumption towards mobile devices. This move could potentially democratize access to Second Life, making it more appealing to a broader audience who might not engage with virtual worlds on traditional computing platforms.

Moreover, Rosedale has expressed concerns about the direction of other virtual platforms, particularly regarding the limitations they impose on user creativity and economic freedom.

Rosedale is using X to engage directly with the Second Life community.

The future of Second Life

Rosedale’s return to Second Life is not just a nostalgic journey but a strategic move to leverage his experience and vision for the future of virtual spaces. His renewed involvement signals a new chapter for Second Life, potentially setting it on a path to recapture its pioneering spirit in the ever-evolving landscape of VR and digital interaction.

In particular, Rosedale wants to more Artificial Intelligence (AI)-related features to Second Life. He refers to “many experimental projects underway… that are related to AI in different ways.”

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A landscape of theories of consciousness

Robert Lawrence Kuhn, the producer and host of the PBS program Closer to Truth, has analyzed theories of consciousness – how physical matter in the brain could produce subjective experiences, or what it “feels like” to be someone.

This puzzle, often called “the hard problem” of consciousness research, is foundational in philosophy and neuroscience.

Kuhn has created a structured “landscape” or taxonomy of various theories that try to explain consciousness.

“I have discussed consciousness with over 200 scientists and philosophers,” says Kuhn in a press release issued by the Foundational Questions Institute (FQxI). “Landscape is the product of a lifetime.”

Kuhn is a member of FQxI’s scientific advisory council.

Kuhn has published his monumental 175,000 words taxonomy in Progress in Biophysics and Molecular Biology.

Kuhn’s taxonomy divides theories into categories: materialism, quantum theories, panpsychism, dualism, idealism, and other frameworks that mix different perspectives.

Materialist theories propose that physical processes in the brain generate consciousness. Non-reductive physicalism suggests that while consciousness is rooted in the brain, physical processes in the brain alone cannot fully explain it.

Quantum theories propose that consciousness might involve quantum mechanics, and panpsychism suggests that some form of consciousness might be a basic property of all matter.

Dualism suggests that the mind and body are separate but interacting substances, and idealism proposes that consciousness or the mind is the primary substance of reality.

Understanding consciousness

Understanding consciousness could change our views on free will, whether AI could ever be conscious, and if consciousness might continue after death.

Kuhn does not aim to solve these issues but seeks to collect and organize perspectives for deeper insight.

“In my Landscape review, I did not want to defend, or even to offer, my own view because it might skew perceptions of the entire enterprise,” says Kuhn in an essay published by FQxI last week. “I try to present each theory as accurately and persuasively as I can, usually with the words of its creator.”

Kuhn adds that he himself leans toward a mix of dualism and idealism, with some interest in quantum consciousness.

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Better 3D printable concrete mixed with graphene

Researchers at the University of Virginia have made a step forward in 3D printing technology for concrete.

The researchers have created a new kind of concrete that is stronger, lasts longer, and is better for the environment. This concrete is mixed with graphene, a super thin and strong material made from carbon, along with limestone and calcined clay cement.

3D printing in construction means using a machine to build things layer by layer (additive manufacturing), just like how a regular 3D printer works but on a much bigger scale. Instead of the plastic materials used in regular 3D printers, 3D printers for construction use concrete.

The new concrete mix helps to cut down on the carbon emissions that come from making regular concrete.

The researchers looked at how well the concrete could be printed and how strong it was. They found that this new concrete mix not only prints well but also holds up better over time. This means buildings made with this concrete could last longer without needing repairs.

The researchers describe the methods and results of this study in a paper published in the Journal of Building Engineering.

Innovation for the future of construction

A life cycle assessment of the overall environmental impact showed that the process is much kinder to the environment than previous methods.

Traditional concrete making contributes a lot to carbon emissions globally. “Our goal was to design a printable concrete that performs better and is more eco-friendly,” says professor Osman Ozbulut in a University of Virginia press release. “This kind of innovation is essential for the future of construction,” adds research co-leader Tuğba Baytak.

The press release and the paper focus on the low environmental impact of the new 3D printable concrete, but the economic advantages of construction materials that last longer also come to mind. The researchers have studied preliminary applications to transportation infrastructure to showcase the real-world potential of the work. Future applications could extend the advantages of 3D printing to the full range of construction works.

In related news, university team in Chile has built a large 3D printed concrete home, Reuters reports.

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Stephen Wolfram’s new physics could actually work

Physicist and science communicator Sabine Hossenfelder has described the new approach to fundamental physics proposed by maverick scientist Stephen Wolfram as a Theory of Everything that could actually work.

Hossenfelder explains that Wolfram’s theory “is basically an attempt to put the simulation hypothesis on a solid mathematical  basis.” Wolfram is looking for code, Hossenfelder says, “that will produce fundamental physics as we know and like it, with gravity and the particles in the standard model.”

Wolfram outlined his then preliminary ideas on fundamental physics in a chapter of his book “A New Kind of Science” (2002). Then he collected further thoughts in an essay titled “What Is Spacetime, Really?” (2015).

It’s worth noting that the late lamented, recently departed mathematician Ralph Abraham had anticipated some of Wolfram’s ideas in a 2010 book.

Hossenfelder also establishes parallels with Rafael Sorkin’s causal set theory.

Wolfram’s book “A Project to Find the Fundamental Theory of Physics” (2020) includes his 2015 essay and the chapter on fundamental physics in “A New Kind of Science.” The book also includes an introduction written like a press release, titled “Finally We May Have a Path to the Fundamental Theory of Physics… and It’s Beautiful.”

“This is probably part of the reason why physicists mostly ignore Wolfram,” says Hossenfelder. “He doesn’t follow standard procedure.” The standard procedure would be “just publishing a paper like normal people.”

The Wolfram Physics Project

Wolfram’s 2020 book is the bible of the Wolfram Physics Project to derive fundamental physics from the discrete mathematics of hypergraphs. Hypergraphs, Hossenfelder explains, are sets of graphs, where graphs are sets of points connected by links. In Wolfram’s Physics Project, Wolfram and his collaborators are investigating how space, time, matter, end everything else including quantum behavior, could emerge from these hypergraphs.

Hossenfelder notes that it is one of Wolfram’s collaborators, Jonathan Gorard, “who did most of this work.”

Gorard posted to X to thank Hossenfelder. “Spending the last 5 years watching Stephen take sole credit for ideas, insights, developments, and discoveries that were the products of our collaboration,” he said, “has been a uniquely exhausting experience.”

Despite whatever bad things other physicists (who may be just envious of Wolfram’s fame and money) may say about Wolfram, it will be very interesting to see how this project develops.

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OpenAI denies rumors but confirms plans for new AI technology

Last week The Verge reported rumors that OpenAI would launch Orion, its next Large Language Model (LLM), by December.

According to the rumors, Orion would be up to 100 times more powerful than GPT-4. Over time, OpenAI would combine it with other Artificial Intelligence (AI) tools, likely including the o1 reasoning model that OpenAI released in September. Eventually, OpenAI would “create an even more capable model that could eventually be called artificial general intelligence, or AGI.”

Alongside anonymous sources, The Verge cited a September X post reporting that “Tadao Nagasaki of OpenAI Japan unveiled plans for ‘GPT Next,’ promising an Orders of Magnitude (OOMs) leap.” According to the post, the new AI model “aims for 100x more computational volume than GPT-4, using similar resources but with improved architecture and efficiency. Trained on a compact Strawberry version, it’s set for release later this year.”

Strawberry is a code name for OpenAI’s o1 reasoning model.

OpenAI CEO San Altman posted to X to deny the rumors, calling them “fake news out of control.”

The editors of The Verge have changed the original story to add that “OpenAI spokesperson Niko Felix told The Verge that the company doesn’t ‘have plans to release a model code-named Orion this year’ but that ‘we do plan to release a lot of other great technology.'”

OpenAI previously told TechCrunch that The Verge’s report wasn’t accurate, but declined to elaborate further. However, TechCrunch notes that OpenAI’s statement leaves substantial wiggle room and conclude that “At this point, it’s anyone’s guess.”

The march of OpenAI

This is a developing story that AI researchers and enthusiasts will want to follow closely. Felix confirmed that OpenAI plans to release “great technology” soon.

OpenAI has been in the top technology and business news in the last few years. It has launched what is arguably the most impactful technology trend of this decade, and its valuation has skyrocketed as a result.

It seems plausible that OpenAI could continue to generate game-changers in the fast-developing world of AI research and applications.

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Tiny robots controlled by magnetic fields for targeted drug delivery

Scientists at Nanyang Technological University (NTU) have developed grain-sized robots that could have significant medical and industrial uses.

These tiny soft robots, around the size of a rice grain, are made from biocompatible materials that are safe to use inside the body. The scientists describe the methods and results of this study in a paper published in Advanced Materials.

Each robot is embedded with magnetic particles, allowing it to be controlled by magnetic fields. Using an external electromagnetic coil system and a computer, researchers can manipulate the robots’ movements with high precision in six different directions: forward, backward, left, right, up, and down, along with rotational motion on each axis. This flexibility, known as “six degrees of freedom,” enables the robots to perform delicate tasks and reach areas that larger instruments cannot access.

The NTU team created various prototypes to demonstrate potential uses. One version of the robot, inspired by jellyfish, swims through water and can squeeze through tight spaces. This ability could allow it to reach delicate areas in the human body, like the brain, for minimally invasive procedures.

Another prototype functions as a “gripper,” able to pick up and assemble tiny structures, making it promising for building small-scale devices in “micro-factories” where intricate assembly is required. In lab experiments, these robots performed tasks faster and with more precision than previous models.

Applications and future developments

The researchers emphasize that the key to their success was a deep understanding of the physics of magnetic fields, allowing them to optimize the robots’ movement and control.

The development has broad applications, especially in the field of targeted drug delivery, where such tiny robots could deliver medicine directly to hard-to-reach parts of the body. Beyond medicine, the technology might also be used in manufacturing for the assembly of micro-scale products. Though this research shows promise, the team plans further tests to ensure these robots are ready for practical use, especially in clinical settings. Future developments may include making the robots even smaller, potentially on the scale of a few hundred micrometers, and increasing their autonomy, which means they could operate without constant external control.

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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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