Using machine learning, a team of Western computer scientists and biologists have identified an underlying genomic signature for 29 different COVID-19 DNA sequences.
This new data discovery tool will allow researchers to quickly and easily classify a deadly virus like COVID-19 in just minutes—a process and pace of high importance for strategic planning and mobilizing medical needs during a pandemic.
The study also supports the scientific hypothesis that COVID-19 (SARS-CoV-2) has its origin in bats as Sarbecovirus, a subgroup of Betacoronavirus.
Scientists believe the world will see it’s first working thermonuclear fusion reactor by the year 2025. That’s a tall order in short form, especially when you consider that fusion has been “almost here” for nearly a century.
Fusion reactors – not to be confused with common fission reactors – are the holiest of Grails when it comes to physics achievements. According to most experts, a successful fusion reactor would function as a near-unlimited source of energy.
In other words, if there’s a working demonstration of an actual fusion reactor by 2025, we could see an end to the global energy crisis within a few decades.
Can we study AI the same way we study lab rats? Researchers at DeepMind and Harvard University seem to think so. They built an AI-powered virtual rat that can carry out multiple complex tasks. Then, they used neuroscience techniques to understand how its artificial “brain” controls its movements.
Today’s most advanced AI is powered by artificial neural networks —machine learning algorithms made up of layers of interconnected components called “neurons” that are loosely inspired by the structure of the brain. While they operate in very different ways, a growing number of researchers believe drawing parallels between the two could both improve our understanding of neuroscience and make smarter AI.
Now the authors of a new paper due to be presented this week at the International Conference on Learning Representations have created a biologically accurate 3D model of a rat that can be controlled by a neural network in a simulated environment. They also showed that they could use neuroscience techniques for analyzing biological brain activity to understand how the neural net controlled the rat’s movements.
As an AI Consultant, I often have to evaluate AI startups to determine if their solutions would make sense for us and if they can become a real business partner over the long term. Our process for evaluating startups is more or less…
Built in about 24 hours, this robot is undergoing in-hospital testing for coronavirus disinfection.
UV disinfection is one of the few areas where autonomous robots can be immediately and uniquely helpful during the COVID pandemic. Unfortunately, there aren’t enough of these robots to fulfill demand right now, and although companies are working hard to build them, it takes a substantial amount of time to develop the hardware, software, operational knowledge, and integration experience required to make a robotic disinfection system work in a hospital.
Conor McGinn, an assistant professor of mechanical engineering at Trinity College in Dublin and co-leader of the Robotics and Innovation Lab (RAIL), has pulled together a small team of hardware and software engineers who’ve managed to get a UV disinfection robot into hospital testing within a matter of just a few weeks. They made it happen in such a short amount of time by building on previous research, collaborating with hospitals directly, and leveraging a development platform: the TurtleBot 2.
Over the last few years, RAIL has been researching mobile social robots for elder care applications, and during their pilot testing, they came to understand how big of a problem infection can be in environments like nursing homes. This was well before COVID-19, but it was (and still is) one of the leading causes of hospitalization for nursing home residents. Most places just wipe down surfaces with disinfectant sometimes, but these facilities have many surfaces (like fabrics) that aren’t as easy to clean, and with people coming in and out all the time, anyone with a compromised immune system is always at risk.
Software bugs have been a concern for programmers for nearly 75 years since the day programmer Grace Murray Hopper reported the cause of an error in an early Harvard Mark II computer: a moth stuck between relay contacts. Thus the term “bug” was born.
Bugs range from slight computer hiccups to catastrophes. In the Eighties, at least five patients died after a Therac-25 radiation therapy device malfunctioned due to an error by an inexperienced programmer. In 1962, NASA mission control destroyed the Mariner I space probe as it diverted from its intended path over the Atlantic Ocean; incorrectly transcribed handwritten code was blamed. In 1982, a software bug later alleged to have been implanted into the Soviet trans-Siberian gas pipeline by the CIA triggered one of the largest non–nuclear explosions in history.
According to data management firm Coralogix, programmers produce 70 bugs per 1,000 lines of code, with each bug solution demanding 30 times more hours than it took to write the code in the first place. The firm estimates the United States spends $113 billion a year identifying and remediating bugs.
After testing on public roads, Tesla is rolling out a new feature of its partially automated driving system designed to spot stop signs and traffic signals.
The update of the electric car company’s cruise control and auto-steer systems is a step toward CEO Elon Musk’s pledge to convert cars to fully self-driving vehicles later this year.
But it also runs contrary to recommendations from the U.S. National Transportation Safety Board that include limiting where Tesla’s Autopilot driving system can operate because it has failed to spot and react to hazards in at least three fatal crashes.
I got my rig in the back of my Beemer. Professional when I graze, I’m professional when I argue. 40 glass, I’m laughing at that s***, I’ma be roaring at that s***
The experiment also revealed which genres are hardest for AI songwriters to master.
The respondents struggled to spot which pop and country lyrics were written by an AI. And its rock song was so emo that they thought it was written by My Chemical Romance or Nirvana.
Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field.
Among the books, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, and MATLAB, etc.
This is the seventh in a series on the impact of the coronavirus on China’s technology sector.
China’s robotics market is forecast to reach US$103.6 billion by 2023, driven by manufacturing, consumer, retail, health care and resource applications.
Chinese robotics companies have seen a surge in demand since the coronavirus outbreak but some believe robot tech is not mature enough for widespread use.
