Lifeboat Foundation

In recent decades, machine learning and deep learning algorithms have become increasingly advanced, so much so that they are now being introduced in a variety of real-world settings. In recent years, some computer scientists and electronics engineers have been exploring the development of an alternative type of artificial intelligence (AI) tools, known as diffractive optical neural networks.

Diffractive optical neural networks are deep neural networks based on diffractive optical technology (i.e., lenses or other components that can alter the phase of light propagating through them). While these networks have been found to achieve ultra-fast computing speeds and high energy efficiencies, typically they are very difficult to program and adapt to different use cases.

Researchers at Southeast University, Peking University and Pazhou Laboratory in China have recently developed a diffractive deep neural network that can be easily programmed to complete different tasks. Their network, introduced in a paper published in Nature Electronics, is based on a flexible and multi-layer metasurface array.

Atomic clocks are the best sensors mankind has ever built. Today, they can be found in national standards institutes or satellites of navigation systems. Scientists all over the world are working to further optimize the precision of these clocks. Now, a research group led by Peter Zoller, a theorist from Innsbruck, Austria, has developed a new concept that can be used to operate sensors with even greater precision irrespective of which technical platform is used to make the sensor. “We answer the question of how precise a sensor can be with existing control capabilities, and give a recipe for how this can be achieved,” explain Denis Vasilyev and Raphael Kaubrügger from Peter Zoller’s group at the Institute of Quantum Optics and Quantum Information at the Austrian Academy of Sciences in Innsbruck.

For this purpose, the physicists use a method from quantum information processing: Variational quantum algorithms describe a circuit of quantum gates that depends on free parameters. Through optimization routines, the sensor autonomously finds the best settings for an optimal result. “We applied this technique to a problem from metrology—the science of measurement,” Vasilyev and Kaubrügger explain. “This is exciting because historically advances in atomic physics were motivated by metrology, and in turn quantum information processing emerged from that. So, we’ve come full circle here,” Peter Zoller says. With the new approach, scientists can optimize quantum sensors to the point where they achieve the best possible precision technically permissible.

The algorithm estimates how weights will need to be altered on the forward pass, and the estimates perform comparably to backpropagation.

Artificial intelligence could help humanity solve many of the global issues in a positive way. See more about what AI-powered algorithms can do when influenced by human abuse.

A.I. is only beginning to show what it can do for modern medicine.

In today’s society, artificial intelligence (A.I.) is mostly used for good. But what if it was not?

Naive thinking “The thought had never previously struck us. We were vaguely aware of security concerns around work with pathogens or toxic chemicals, but that did not relate to us; we primarily operate in a virtual setting. Our work is rooted in building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery,” wrote the researchers in their paper. “We have spent decades using computers and A.I. to improve human health—not to degrade it. We were naive in thinking about the potential misuse of our trade, as our aim had always been to avoid molecular features that could interfere with the many different classes of proteins essential to human life.”

Continue reading “An artificial intelligence invents 40,000 chemical weapons in just 6 hours” »

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” »