10 Feb Entirely AI-formulated medicine to be tested on humans for the first time ever

A drug designed entirely by artificial intelligence is about to enter clinical human trials for the first time. The drug, which is intended to treat obsessive-compulsive disorder (OCD), was discovered using AI systems from Oxford-based biotech company Exscientia. While it would usually take around four and a half years to get a drug to this stage of development, Exscientia says that by using the AI tools it’s taken less than 12 months.

The drug, known as DSP-1181, was created by using algorithms to sift through potential compounds, checking them against a huge database of parameters, including a patient’s genetic factors. Speaking to the BBC, Exscientia chief executive Professor Andrew Hopkins described the trials as a "key milestone in drug discovery" and noted that there are "billions" of decisions needed to find the right molecules for a drug, making their eventual creation a "huge decision." With AI, however, "the beauty of the algorithm is that they are agnostic, so can be applied to any disease."

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10 Feb Food Poisoning Expert Reveals 6 Things He Refuses To Eat

Having spent over 20 years working on food-poisoning lawsuits, Bill Marler simply doesn’t eat certain foods anymore.
The attorney, who’s litigating suits a

Sourced through Scoop.it from: uk.businessinsider.com

07 Feb MIT and QCRI Researchers Build AI Model to Enrich Digital Maps

Researchers at Massachusetts Institute of Technology (MIT) and Qatar Computing Research Institute (QCRI) have developed a model powered by AI. The AI model is designed to tag road features in digital maps using satellite imagery. This AI-driven RoadTagger model combines a convolutional neural network (CNN) and a graph neural network (GNN) to automatically envisage the number of lanes and road types concealed by obstructions, improving GPS navigation, especially in countries with limited map data.

The model helps drivers in incorporating information about parking spots, while mapping bicycle lanes that can assist cyclists to negotiate busy city streets. Providing updated information on road conditions, the RoadTagger model can also improve planning for disaster relief.

Unlike other GPS navigation systems, RoadTagger makes use of an amalgamation of neural network architectures to automatically predict the number of lanes and road types, including residential or highway, even when roads can be blocked by trees or buildings.

Sam Madden, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in the Computer Science and AI Laboratory (CSAIL) says, “Most updated digital maps are from places that big companies care the most about. If you’re in places they don’t care about much, you’re at a disadvantage with respect to the quality of map. Our goal is to automate the process of generating high-quality digital maps, so they can be available in any country.”

When testing RoadTagger on occluded roads from digital maps of 20 US cities, the model reckoned lane numbers with 77 percent accuracy and inferred road types with 93 percent accuracy. Also, the researchers are planning to enable the model to foresee other features, such as parking spots and bike lanes.

The model relies on CNN and GNN, where GNNs form relationships between connected nodes in a graph, CNNs take as input raw satellite images of target roads. RoadTagger is based on an end-to-end model, meaning it is fed only raw data and automatically generates output, without human intervention. This combined architecture of CNN and GNN signifies a more human-like intuition, researchers noted.

Sourced through Scoop.it from: www.analyticsinsight.net

07 Feb Four Stunning AI Predictions And Warnings By Elon Musk

When it comes to AI, Elon Musk has a name in treating it like some aliens’ attack or God.

Sourced through Scoop.it from: autome.me

07 Feb Decoding the Brain Goes Global With the International Brain Initiative

By uniting efforts, the International Brain Initiative can help shape the future of neuroscience research at a global scale.

The initiative, at the time of writing, includes Japan’s Brain/Minds, Australian Brain Alliance, the EU’s Human Brain Project (HBP), Canadian Brain Research Strategy, the US’ BRAIN Initiative (BRAINI), the Korea Brain Initiative, and the China Brain Project.

Few times in history has mankind ever united to solve a single goal. Even the ultimate moonshot in history—putting a man on the moon—was driven by international competition rather than unification. So it’s perhaps fitting that mankind is now uniting to understand the organ that fundamentally makes us human: our brain. First envisioned in 2016 through a series of discussions on the “grand challenges” in neuroscience at Johns Hopkins University, the International Brain Initiative (IBI) “came out” this week in a forward-looking paper in Neuron.

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31 Jan Novel Coronavirus (2019-nCoV) Situation Summary (by CDC)

CDC is closely monitoring an outbreak of respiratory illness caused by a novel (new) coronavirus (named “2019-nCoV”) that was first detected in Wuhan City, Hubei Province, China and which continues to expand. Chinese health officials have reported thousands of infections with 2019-nCoV in China, with the virus reportedly spreading from person-to-person in many parts of that country. Infections with 2019-nCoV, most of them associated with travel from Wuhan, also are being reported in a growing number of international locations, including the United States. The United States reported the first confirmed instance of person-to-person spread with this virus on January 30, 2020.

Coronaviruses are a large family of RNA viruses that are common in many different species of animals, including camels, cattle, cats, and bats. Rarely, animal coronaviruses can infect people and then spread between people such as with MERS and SARS.

Laboratory testing of human suspected cases of novel coronavirus (nCoV) infection (PDF)

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30 Jan A quantum breakthrough brings a technique from astronomy to the nano-scale

Researchers at Columbia University and University of California, San Diego, have introduced a novel "multi-messenger" approach to quantum physics that signifies a technological leap in how scientists can explore quantum materials.

The findings appear in a recent article published in Nature Materials, led by A. S. McLeod, postdoctoral researcher, Columbia Nano Initiative, with co-authors Dmitri Basov and A. J. Millis at Columbia and R.A. Averitt at UC San Diego. "We have brought a technique from the inter-galactic scale down to the realm of the ultra-small," said Basov, Higgins Professor of Physics and Director of the Energy Frontier Research Center at Columbia. Equipped with multi-modal nanoscience tools we can now routinely go places no one thought would be possible as recently as five years ago."

Sourced through Scoop.it from: www.eurekalert.org

30 Jan You Don’t Need To Modify Gravity To Explain Dark Energy

One of the greatest unsolved puzzles in all of science is dark energy. The Universe isn’t just expanding, but the expansion rate that we infer for distant galaxies is accelerating: their recession velocity speeds up from our perspective as time goes on. This was a surprise when it was discovered empirically in the 1990s, and more than two decades later, we still don’t understand where this mysterious form of energy, the most abundant in all the Universe, comes from.

While you can explain dark energy in the context of General Relativity, it’s recently become fashionable to attempt to explain dark energy by modifying gravity instead. Recently, the award-winning theoretical work of Dr. Claudia de Rham has come into focus, leading The Guardian to ask, "Has physicist’s gravity theory solved ‘impossible’ dark energy riddle?" It’s a fascinating possibility, but one that demands an appropriate level of skepticism.

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30 Jan Artificial intelligence helps experts forecast icebergs

This year will see a relatively low number of icebergs drifting into busy shipping regions in the north-west Atlantic, according to a combination of control systems and artificial intelligence forecasting models developed by experts at the University of Sheffield.

A recently published control systems model has been used to predict that between 479 and 1,015 icebergs will reach waters south of 48°N—the area of greatest risk to shipping traveling between Europe and north-east North America—in 2020, compared with 1,515 observed there last year.

Sourced through Scoop.it from: phys.org