In every hospital labor and delivery department, a resource nurse decides which patients go to which room, which nurses care for which patients, and much more. “It’s a lot to keep in your head,” says Kristen Jerrier, a resource nurse in the labor and delivery department at Beth Israel Deaconess Medical Center in Boston.
The era of telecommunications systems designed solely by humans is coming to an end. From here on, artificial intelligence will play a pivotal role in the design and operation of these systems. The reason is simple: rapidly escalating complexity.
Each new generation of communications system strives to improve coverage areas, bit rates, number of users, and power consumption. But at the same time, the engineering challenges grow more difficult. To keep innovating, engineers have to navigate an increasingly tangled web of technological trade-offs made during previous generations.
In telecommunications, a major source of complexity comes from what we’ll call impairments. Impairments include anything that deteriorates or otherwise interferes with a communications system’s ability to deliver information from point A to point B. Radio hardware itself, for example, impairs signals when it sends or receives them by adding noise. The paths, or channels, that signals travel over to reach their destinations also impair signals. This is true for a wired channel, where a nearby electrical line can cause nasty interference. It’s equally true for wireless channels, where, for example, signals bouncing off and around buildings in an urban area create a noisy, distortive environment.
In a paper, Google researchers reveal that they’ve developed an AI framework that can produce chip designs in less than six hours.
Boston Dynamic’s robotic dog Spot is helping healthcare workers treat coronavirus patents remotely. Spot is fitted with an iPad and two-way radio so both can communicate in real-time.
Elon Musk has unveiled plans to implant computer chips in people’s brains that the US billionaire says will treat brain diseases and enable superhuman intelligence.
Neuralink, a secretive company set up by Mr Musk two years ago, has said it plans to begin tests of its “brain-computer interface” technology on humans in the next year.
Mr Musk, 48, the chief executive of Tesla and SpaceX, said the technology will help “solve brain disorders of all kinds” and allow humans to merge with artificial intelligence.
Three score and ten is so 1970s. Today, the average baby born in the UK will live long enough to see the beginning of the 22nd century. Increasingly we also hear claims of longevity breakthroughs that could propel those children – and maybe even their parents – into triple digits and beyond. Is eternal life something we want outside of science fiction? And how will society cope if it is?
“The first ten million years were the worst,” said Marvin. “The second ten million years, they were the worst, too. The third ten million years I didn’t enjoy at all. After that I went into a bit of a decline.”
So opines Marvin, Douglas Adams’ paranoid android, who follows the protagonists of ‘The Hitchhiker’s Guide to the Galaxy’ around like a bumbling, grumbling storm cloud. Functionally immortal (and cursed with a “brain the size of a planet”), Marvin is the hubristic dream of eternal life printed and stamped in circuitry. While his human shipmates stumble from one disaster to another, devoting their limited talents to avoiding death at all costs, Marvin plods glumly along, bemoaning the pointlessness of an infinite existence in which there is nothing new to learn, no challenge to his intellect and in which everyone – even his closest friend, a rat that nested for a time in his foot – dies. Except him.
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.
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.
