Lifeboat Foundation

Rice University researchers have tested a tiny lensless microscope called Bio-FlatScope, capable of producing high levels of detail in living samples. The team imaged plants, hydra, and, to a limited extent, a human.

A previous iteration of the technology, FlatCam, was a lensless device that channeled light through a mask and directly onto a camera sensor, aimed primarily outward at the world at large. The raw images looked like static, but a custom algorithm translated the raw data into focused images.

The device described in current research looks inward to image micron-scale targets such as cells and blood vessels inside the body, and even through skin. The technology combines a sophisticated phase mask to generate patterns of light that fall directly onto the chip, the researchers said. The mask in the original FlatCam looked like a barcode and limited the amount of light that passes through to the sensor.

The researchers simulated the molecules H4, molecular nitrogen, and solid diamond. These involved as many as 120 orbitals, the patterns of electron density formed in atoms or molecules by one or more electrons. These are the largest chemistry simulations performed to date with the help of quantum computers.

A classical computer actually handles most of this fermionic quantum Monte Carlo simulation. The quantum computer steps in during the last, most computationally complex step—calculating the differences between the estimates of the ground state made by the quantum computer and the classical computer.

The prior record for chemical simulations with quantum computing employed 12 qubits and a kind of hybrid algorithm known as a variational quantum eigensolver (VQE). However, VQEs possess a number of limitations compared with this new hybrid approach. For example, when one wants a very precise answer from a VQE, even a small amount of noise in the quantum circuitry “can cause enough of an error in our estimate of the energy or other properties that’s too large,” says study coauthor William Huggins, a quantum physicist at Google Quantum AI in Mountain View, Calif.

Developments in artificial intelligence and human enhancement technologies have the potential to remake American society in the coming decades. A new Pew Research Center survey finds that Americans see promise in the ways these technologies could improve daily life and human abilities. Yet public views are also defined by the context of how these technologies would be used, what constraints would be in place and who would stand to benefit – or lose – if these advances become widespread.

Fundamentally, caution runs through public views of artificial intelligence (AI) and human enhancement applications, often centered around concerns about autonomy, unintended consequences and the amount of change these developments might mean for humans and society. People think economic disparities might worsen as some advances emerge and that technologies, like facial recognition software, could lead to more surveillance of Black or Hispanic Americans.

This survey looks at a broad arc of scientific and technological developments – some in use now, some still emerging. It concentrates on public views about six developments that are widely discussed among futurists, ethicists and policy advocates. Three are part of the burgeoning array of AI applications: the use of facial recognition technology by police, the use of algorithms by social media companies to find false information on their sites and the development of driverless passenger vehicles.

What can heal can also be used to destroy?

MegaSyn is built to generate drug candidates with the lowest toxicity for patients. That got Urbina thinking. He retrained the model using data to drive the software toward generating lethal compounds, like nerve gas, and flipped the code so that it ranked its output from high-to-low toxicity. In effect, the software was told to come up with the most deadly stuff possible.

He ran the model and left it overnight to create new molecules.

Continue reading “AI drug algorithms can be flipped to generate bioweapons” »

Steadicopter, a leader in the Rotary Unmanned Aerial Systems (RUAS) industry, and Smart Shooter, a world-class designer, developer, and manufacturer of innovative fire control systems that significantly increase the accuracy and lethality of small arms, have unveiled the Golden Eagle — the first-ever unmanned helicopter with precise hit capabilities. The two companies will present the solution at the ISDEF exhibition in Tel Aviv.

Based on the combat-proven Black Eagle 50E platform, the Golden Eagle incorporates AI-based technology and Smart Shooter’s SMASH Dragon system. The AI-based technology enables superior situational awareness and autonomous multi-target classification and tracking. The SMASH Dragon, a remotely-operated robotic weaponry payload, locks on the target, tracks it and ensures precise target hit. SMASH Dragon integrates a unique stabilization concept with proprietary target acquisition, tracking algorithms and sophisticated computer vision capabilities that allow accurate hitting of static and moving targets while mounted onto the Golden Eagle.

“Using artificial intelligence, the new system provides a field combat solution for the modern battlefield. Forces on the ground can now send a helicopter for autonomous intelligence gathering into the relevant area and, having identified and classified the targets, send in another helicopter with precise attack capabilities.”

Continue reading “Teadicopter and Smart Shooter unveil the Golden Eagle — a groundbreaking RUAS with precision hit capabilities utilizing the SMASH technology” »

Quantum computers are getting bigger, but there are still few practical ways to take advantage of their extra computing power. To get over this hurdle, researchers are designing algorithms to ease the transition from classical to quantum computers. In a new study in Nature, researchers unveil an algorithm that reduces the statistical errors, or noise, produced by quantum bits, or qubits, in crunching chemistry equations.

Developed by Columbia chemistry professor David Reichman and postdoc Joonho Lee with researchers at Google Quantum AI, the algorithm uses up to 16 qubits on Sycamore, Google’s 53-qubit computer, to calculate ground state energy, the lowest energy state of a molecule. “These are the largest quantum chemistry calculations that have ever been done on a real quantum device,” Reichman said.

Continue reading “Toward a quantum computer that calculates molecular energy” »

And going forward, we’ll do this with far more knowledge of what we’re doing, and more control over the genes of our progeny. We can already screen ourselves and embryos for genetic diseases. We could potentially choose embryos for desirable genes, as we do with crops. Direct editing of the DNA of a human embryo has been proven to be possible — but seems morally abhorrent, effectively turning children into subjects of medical experimentation. And yet, if such technologies were proven safe, I could imagine a future where you’d be a bad parent not to give your children the best genes possible.

Computers also provide an entirely new selective pressure. As more and more matches are made on smartphones, we are delegating decisions about what the next generation looks like to computer algorithms, who recommend our potential matches. Digital code now helps choose what genetic code passed on to future generations, just like it shapes what you stream or buy online. This might sound like dark science fiction, but it’s already happening. Our genes are being curated by computer, just like our playlists. It’s hard to know where this leads, but I wonder if it’s entirely wise to turn over the future of our species to iPhones, the internet and the companies behind them.

Discussions of human evolution are usually backward looking, as if the greatest triumphs and challenges were in the distant past. But as technology and culture enter a period of accelerating change, our genes will too. Arguably, the most interesting parts of evolution aren’t life’s origins, dinosaurs, or Neanderthals, but what’s happening right now, our present – and our future.

Artificial intelligence advances how scientists explore materials. Researchers from Ames Laboratory and Texas A&M University trained a machine-learning (ML) model to assess the stability of rare-earth compounds. This work was supported by Laboratory Directed Research and Development Program (LDRD) program at Ames Laboratory. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.

Ames Lab has been a leader in rare-earths research since the middle of the 20th century. Rare earth elements have a wide range of uses including clean energy technologies, energy storage, and permanent magnets. Discovery of new rare-earth compounds is part of a larger effort by scientists to expand access to these materials.

The present approach is based on machine learning (ML), a form of artificial intelligence (AI), which is driven by computer algorithms that improve through data usage and experience. Researchers used the upgraded Ames Laboratory Rare Earth database (RIC 2.0) and high-throughput density-functional theory (DFT) to build the foundation for their ML model.

Russian scientists have proposed a new algorithm for automatic decoding and interpreting the decoder weights, which can be used both in brain-computer interfaces and in fundamental research. The results of the study were published in the Journal of Neural Engineering.

Brain-computer interfaces are needed to create robotic prostheses and neuroimplants, rehabilitation simulators, and devices that can be controlled by the power of thought. These devices help people who have suffered a stroke or physical injury to move (in the case of a robotic chair or prostheses), communicate, use a computer, and operate household appliances. In addition, in combination with machine learning methods, neural interfaces help researchers understand how the human brain works.

Most frequently brain-computer interfaces use electrical activity of neurons, measured, for example, with electro-or magnetoencephalography. However, a special decoder is needed in order to translate neuronal signals into commands. Traditional methods of signal processing require painstaking work on identifying informative features—signal characteristics that, from a researcher’s point of view, appear to be most important for the decoding task.