Lifeboat Foundation

Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities’ worth of people. Imagine that all authority for tilling, planting, fertilizing, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up.

A new risk analysis, published today in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers and food security that are poorly understood and under-appreciated.

“The idea of intelligent machines running farms is not science fiction. Large companies are already pioneering the next generation of autonomous ag-bots and decision support systems that will replace humans in the field,” said Dr. Asaf Tzachor in the University of Cambridge’s Center for the Study of Existential Risk (CSER), first author of the paper.

With the advent of Big Data, current computational architectures are proving to be insufficient. Difficulties in decreasing transistors’ size, large power consumption and limited operating speeds make neuromorphic computing a promising alternative.

Neuromorphic computing, a new brain-inspired computation paradigm, reproduces the activity of biological synapses by using artificial neural networks. Such devices work as a system of switches, so that the ON position corresponds to the information retention or “learning,” while the OFF position corresponds to the information deletion or “forgetting.”

In a recent publication, scientists from the Universitat Autònoma de Barcelona (UAB), the CNR-SPIN (Italy), the Catalan Institute of Nanoscience and Nanotechnology (ICN2), the Institute of Micro and Nanotechnology (IMN-CNM-CSIC) and the ALBA Synchrotron have explored the emulation of artificial synapses using new advanced material devices. The project was led by Serra Húnter Fellow Enric Menéndez and ICREA researcher Jordi Sort, both at the Department of Physics of the UAB, and is part of Sofia Martins Ph.D. thesis.

In the field of optogenetics, scientists investigate the activity of neurons in the brain using light. A team led by Prof. Dr. Ilka Diester and Dr. David Eriksson from the Optophysiology Laboratory at the University of Freiburg has developed a new method to simultaneously conduct laminar recordings, multifiber stimulations, 3D optogenetic stimulation, connectivity inference, and behavioral quantification on brains. Their results are presented in Nature Communications. “Our work paves the way for large-scale photo-recording and controlled interrogation of fast neural communication in any combination of brain areas,” Diester explains. “This can help us unravel the rapid and multilayered dialogs between neurons that maintain brain function.”

The research group, in collaboration with Dr. Patrick Ruther of the Department of Microsystems Engineering (IMTEK) at the University of Freiburg, is developing a new method for the controlled interrogation and recording of neuronal activity in the brain. To do this, the team is taking advantage of thin, cell-sized optical fibers for minimally invasive optogenetic implantation. “We combine side-emitting fibers with silicon probes to achieve high-quality recordings and ultrafast, multichannel optogenetic control.”

They call the system Fused Fiber Light Emission and eXtracellular Recording, or FFLEXR. In addition to optical fibers that can be attached to any silicon probe, the team uses linear depth-resolved stimulation, a lightweight fiber matrix connector, a flexible multifiber ribbon cable, an optical commutator for efficient multichannel stimulation, a general-purpose patch cable, and an algorithm to manage the photovoltaic response.

Bipartisan hostility toward China means US lawmakers are unlikely to cite Chinese regulations as inspiration. But Beijing’s manoeuvres could perhaps have a subtle effect. In the UK, some lawmakers have called for online companies to shield young people from harmful content in an approach that some have likened to China’s proposals. “These ideas could ripple out,” says Matt Sheehan, a fellow at the Carnegie Endowment for International Peace who researches China’s AI ecosystem. “What’s interesting in China is that they’re going to be able to run experiments at a very large scale on what it actually means to implement these ideas.”

Sweeping rules will cover algorithms that set prices, control search results, recommend videos, and filter content.

When video chatting with colleagues, coworkers, or family, many of us have grown accustomed to using virtual backgrounds and background filters. It has been shown to offer more control over the surroundings, allowing fewer distractions, preserving the privacy of those around us, and even liven up our virtual presentations and get-togethers. However, Background filters don’t always work as expected or perform well for everyone.

Image segmentation is a computer vision process of separating the different components of a photo or video. It has been widely used to improve backdrop blurring, virtual backgrounds, and other augmented reality (AR) effects. Despite advanced algorithms, achieving highly accurate person segmentation seems challenging.

The model used for image segmentation tasks must be incredibly consistent and lag-free. Inefficient algorithms may result in bad experiences for the users. For instance, during a video conference, artifacts generated by erroneous segmentation output might easily confuse persons utilizing virtual background programs. More importantly, segmentation problems may result in unwanted exposure to people’s physical environments when applying backdrop effects.

The Google-backed AI firm taught a reinforcement learning algorithm to control the fiery plasma inside a tokamak nuclear fusion reactor.

We construct quantum algorithms to compute physical observables of nonlinear PDEs with M initial data. Based on an exact mapping between nonlinear and linear PDEs using the level set method, these new quantum algorithms for nonlinear Hamilton-Jacobi and scalar hyperbolic PDEs can be performed with a computational cost that is independent of M, for arbitrary nonlinearity. Depending on the details of the initial data, it can also display up to exponential advantage in both the dimension of the PDE and the error in computing its observables. For general nonlinear PDEs, quantum advantage with respect to M is possible in the large M limit.

Instead of relying on a fixed catalogue of available materials or undergoing trial-and-error attempts to come up with new ones, engineers can turn to algorithms running in supercomputers to design unique materials, based on a “materials genome,” with properties tailored to specific needs. Among the new classes of emerging materials are “transient” electronics and bioelectronics that portend applications and industries comparable to the scale that followed the advent of silicon-based electronics.

In each of the three technological spheres, we find the Cloud increasingly woven into the fabric of innovation. The Cloud itself is, synergistically, evolving and expanding from the advances in new materials and machines, creating a virtuous circle of self-amplifying progress. It is a unique feature of our emerging century that constitutes a catalyst for innovation and productivity, the likes of which the world has never seen.

Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning. Now they can reduce quantum computing errors using custom machine learning algorithms.