Lifeboat Foundation

A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning.

Researchers fed the robot nearly 5,000 complete songs — from Beethoven to the Beatles to Lady Gaga to Miles Davis — and more than 2 million motifs, riffs and licks of music. Aside from giving the machine a seed, or the first four measures to use as a starting point, no humans are involved in either the composition or the performance of the music.

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Spotted on my feed.

“The new burger chef makes $3 an hour and never goes home.” — LA Times.

Flippy is the world’s first autonomous robotic kitchen assistant that can learn from its surroundings and acquire new skills over time. Specifically designed to operate in an existing commercial kitchen layout and to serve alongside kitchen staff to safely and efficiently fulfill a variety of cooking tasks. Miso is working with major QSR locations to integrate Flippy as an overhead rail system. The overhead rail system will reduce the cost to produce Flippy by 50% and requires ZERO real estate footprint.

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Using machine learning three groups, including researchers at IBM and DeepMind, have simulated atoms and small molecules more accurately than existing quantum chemistry methods. In separate papers on the arXiv preprint server the teams each use neural networks to represent wave functions of electrons that surround the molecules’ atoms. This wave function is the mathematical solution of the Schrödinger equation, which describes the probabilities of where electrons can be found around molecules. It offers the tantalising hope of ‘solving chemistry’ altogether, simulating reactions with complete accuracy. Normally that goal would require impractically large amounts of computing power. The new studies now offer a compromise of relatively high accuracy at a reasonable amount of processing power.

Each group only simulates simple systems, with ethene among the most complex, and they all emphasise that the approaches are at their very earliest stages. ‘If we’re able to understand how materials work at the most fundamental, atomic level, we could better design everything from photovoltaics to drug molecules,’ says James Spencer from DeepMind in London, UK. ‘While this work doesn’t achieve that quite yet, we think it’s a step in that direction.’

Two approaches appeared on arXiv just a few days apart in September 2019, both combining deep machine learning and Quantum Monte Carlo (QMC) methods. Researchers at DeepMind, part of the Alphabet group of companies that owns Google, and Imperial College London call theirs Fermi Net. They posted an updated preprint paper describing it in early March 2020.¹ Frank Noé’s team at the Free University of Berlin, Germany, calls its approach, which directly incorporates physical knowledge about wave functions, PauliNet.²

An artificial neural network can reveal patterns in huge amounts of gene expression data, and discover groups of disease-related genes. This has been shown by a new study led by researchers at Linköping University, published in Nature Communications. The scientists hope that the method can eventually be applied within precision medicine and individualised treatment.

It’s common when using social media that the platform suggests people whom you may want to add as friends. The suggestion is based on you and the other person having common contacts, which indicates that you may know each other. In a similar manner, scientists are creating maps of biological networks based on how different proteins or genes interact with each other. The researchers behind a new study have used artificial intelligence, AI, to investigate whether it is possible to discover biological networks using deep learning, in which entities known as “artificial neural networks” are trained by experimental data. Since artificial neural networks are excellent at learning how to find patterns in enormous amounts of complex data, they are used in applications such as image recognition. However, this machine learning method has until now seldom been used in biological research.

“We have for the first time used deep learning to find disease-related genes. This is a very powerful method in the analysis of huge amounts of biological information, or ‘big data’,” says Sanjiv Dwivedi, postdoc in the Department of Physics, Chemistry and Biology (IFM) at Linköping University.

Imagine you had a robotic dog, and you wanted to try to program it to just do something basic, like walking.

Wingcopter and UPS Flight Forward (UPSFF) are collaborating to develop package delivery drones.

The companies will work toward earning regulatory certification for a Wingcopter unmanned aircraft to make commercial delivery flights in the United States, according to a news release. This partnership represents “a critical step toward building a diverse fleet of drones with varying capabilities to meet even more potential customer needs.”

The Wingcopter drone is capable of vertical takeoff and landing in tight spaces and transitioning to high-speed horizontal flight. Its patented tilt-rotor mechanism enables a seamless transition between the two drone modes—multicopter for hovering and fixed-wing for low-noise forward flight. The aerodynamic drone provides stability even in harsh weather conditions.

Last March, Chinese researchers announced an ingenious and potentially devastating attack against one of America’s most prized technological assets—a Tesla electric car.

The team, from the security lab of the Chinese tech giant Tencent, demonstrated several ways to fool the AI algorithms on Tesla’s car. By subtly altering the data fed to the car’s sensors, the researchers were able to bamboozle and bewilder the artificial intelligence that runs the vehicle.

Researchers from Carnegie Mellon and the University of Pittsburgh today published research showing how they’d solved a frustrating problem for people who use a brain-computer interface (BCI) to control prosthetic devices with their thoughts.

While the research itself is interesting – they created an algorithm that keeps the devices from constantly needing to be re-calibrated to handle the human brain’s fluctuating neuronal activity – the real takeaway here is how close we are to a universal BCI.

BCIs have been around for decades in one form or another, but they’re costly to maintain and difficult to keep working properly. Currently they only make sense for narrow use – specifically, in the case of those who’ve lost limbs. Because they’re already used to using their brain to control an appendage, it’s easier for scientists and researchers to harness those brainwaves to control prosthetic devices.

What police would do with the information has yet to be determined. The head of WMP told New Scientist they won’t be preemptively arresting anyone; instead, the idea would be to use the information to provide early intervention from social or health workers to help keep potential offenders on the straight and narrow or protect potential victims.

But data ethics experts have voiced concerns that the police are stepping into an ethical minefield they may not be fully prepared for. Last year, WMP asked researchers at the Alan Turing Institute’s Data Ethics Group to assess a redacted version of the proposal, and last week they released an ethics advisory in conjunction with the Independent Digital Ethics Panel for Policing.

While the authors applaud the force for attempting to develop an ethically sound and legally compliant approach to predictive policing, they warn that the ethical principles in the proposal are not developed enough to deal with the broad challenges this kind of technology could throw up, and that “frequently the details are insufficiently fleshed out and important issues are not fully recognized.”