Lifeboat Foundation

Researchers at CSIRO & Queensland University of Technology have recently carried out a study aimed at automatically evolving the physical structure of robots to enhance their performance in different environments. This project, funded by CSIRO’s Active Integrated Matter Future Science Platform, was conceived by David Howard, research scientist at Data61’s Robotics and Autonomous Systems Group (RASG).

“RASG focuses on field robotics, which means we need our robots to go out into remote places and conduct missions in adverse, difficult environmental conditions,” David Howard told TechXplore. “The research came about through an identified opportunity, as RASG makes extensive use of 3D printing to build and customise our robots. This research demonstrates a design algorithm that can automatically generate 3D printable components so that our robots are better equipped to function in different environments.”

The main objective of the study was to generate components automatically that can improve a robot’s environment-specific performance, with minimal constraints on what these components look like. The researchers particularly focused on the legs of a hexapod (6-legged) robot, which can be deployed in a variety of environments, including industrial settings, rainforests, and beaches.

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That is changing. This month fast.ai, an education non-profit based in San Francisco, kicked off the third year of its course in deep learning. Since its inception it has attracted more than 100,000 students, scattered around the globe from India to Nigeria. The course and others like it come with a simple proposition: there is no need to spend years obtaining a phd in order to practise deep learning. Creating software that learns can be taught as a craft, not as a high intellectual pursuit to be undertaken only in an ivory tower. Fast.ai’s course can be completed in just seven weeks.

Treating it like a craft is paying dividends.

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Cinemagraphs are all the rage.

Researchers from the Moscow Institute of Physics and Technology (MIPT), Aalto University in Finland, and ETH Zurich have demonstrated a prototype device that uses quantum effects and machine learning to measure magnetic fields more accurately than its classical analogues. Such measurements are needed to seek mineral deposits, discover distant astronomical objects, diagnose brain disorders, and create better radars.

“When you study nature, whether you investigate the human brain or a supernova explosion, you always deal with some sort of electromagnetic signals,” explains Andrey Lebedev, a co-author of the paper describing the new device in npj Quantum Information. “So measuring magnetic fields is necessary across diverse areas of science and technology, and one would want to do this as accurately as possible.”

The results of the poll are in.

Despite the simplicity of their visual system, fruit flies are able to reliably distinguish between individuals based on sight alone. This is a task that even humans who spend their whole lives studying Drosophila melanogaster struggle with. Researchers have now built a neural network that mimics the fruit fly’s visual system and can distinguish and re-identify flies. This may allow the thousands of labs worldwide that use fruit flies as a model organism to do more longitudinal work, looking at how individual flies change over time. It also provides evidence that the humble fruit fly’s vision is clearer than previously thought.

In an interdisciplinary project, researchers at Guelph University and the University of Toronto, Mississauga combined expertise in fruit fly biology with machine learning to build a biologically-based algorithm that churns through low-resolution videos of fruit flies in order to test whether it is physically possible for a system with such constraints to accomplish such a difficult task.

Fruit flies have small compound eyes that take in a limited amount of visual information, an estimated 29 units squared (Fig. 1A). The traditional view has been that once the image is processed by a fruit fly, it is only able to distinguish very broad features (Fig. 1B). But a recent discovery that fruit flies can boost their effective resolution with subtle biological tricks (Fig. 1C) has led researchers to believe that vision could contribute significantly to the social lives of flies. This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?”

Whether it’s left there or right here… the tactics and destination look pretty much the same to me…

China is the world leader in facial recognition technology. Discover how the country is using it to develop a vast hyper-surveillance system able to monitor and target its ethnic minorities, including the Muslim Uighur population.

Continue reading “China: facial recognition and state control | The Economist” »

Quantum computers could solve complex tasks that are beyond the capabilities of conventional computers. However, the quantum states are extremely sensitive to constant interference from their environment. The plan is to combat this using active protection based on quantum error correction. Florian Marquardt, Director at the Max Planck Institute for the Science of Light, and his team have now presented a quantum error correction system that is capable of learning thanks to artificial intelligence.

A “portrait” that is the first piece of artificial-intelligence art sold by a major auction house shattered estimates, selling for 45 times what was expected.

“Portrait of Edmond de Belamy” was sold Thursday at Christies in New York for $432,500. It had been expected to go for $7,000 to $10,000. The buyer was not revealed.

The painting is one of 11 portraits of a fictional family created so far by the Paris-based art collective Obvious.

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