Massachusetts Institute of Technology (MIT) researchers have discovered a powerful antibiotic compound using their machine-learning algorithm to counter many of the world's deadliest bacteria, including some strains that are immune to all known antibiotics. It prevented infections in two different mouse models, according to MIT official release. An advanced computer model that can screen more than a hundred million chemical compounds was used to design potential antibiotics that can kill dangerous bacteria. Speaking about the discovery, James Collins, the Termeer Professor of Medical Engineering and Science at MIT stated in a press release: "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery. He added that the researchers at MIT revealed this "amazing" molecule which is arguably one of the most potent antibiotics that has ever been discovered.
But while the manufacturing industry currently leads the way in robotics adoption, other industries are also being attracted by the clear productivity gains on offer. The three industries that follow manufacturing in terms of spending on robotics and drones are resources, consumer, and health care. It's the latter of these three that is likely to have the biggest impact on our own lives.
People's Google searches, social media posts and even chatbot questions are being used by artificial intelligence to try and predict where the novel coronavirus is going to pop up next. The technology, which has been fine-tuned over the last 15 years, is already feeding information to major health agencies like the World Health Organisation to help them decide where they should focus their efforts. One system, called HealthMap, uses publicly available data from across the internet as well as user-submitted information, according to one of its developers, John Brownstein, a professor at Harvard Medical School. "We work in this hybrid of data mining as well as crowdsourcing," he told the ABC's news podcast The Signal. "What's really phenomenal here is we're seeing incredible international collaboration and a huge amount of data sharing."
Chris Smith and Phil Sansom delve into the world of artificial Intelligence (AI) to find out how this emerging technology is changing the way we practise medicine... Mike - I think this is an area where AI stands a really good chance of making quite dramatic improvements to very large numbers of people's lives. Carolyn - Save lives and reduce medical complications. Beth - That's a concern - when machine-learning algorithms learn the wrong things. Andrew - Frankly revolutionary productivity that we are now starting to see from these AI approaches in drug design. Lee - It will replace all manual labor in all research laboratories. And then suddenly everyone can collaborate. Phil - But what was previously sci-fi is now closer to reality. AI technology exists, and there's a brand new frontier where it's being applied to the world of healthcare. Chris - But this isn't the AI you see in the movies.
Artificial intelligence (AI) has proved to be a useful ally in the battle against antibiotic resistance. A powerful antibiotic that's even able to kill superbugs has been discovered thanks to a machine-learning algorithm Researchers from MIT used a novel computer algorithm to sift through a vast digital archive of over 100 million chemical compounds and spot those that were able to kill bacteria using different mechanisms from existing drugs. Reported in the journal Cell, this method highlighted a molecule that appeared to possess some truly remarkable antibiotic properties. The team named the molecule halicin, a hat tip to the sentient AI system "Hal" from Stanley Kubrick's film 2001: A Space Odyssey. When tested in mice, halicin was able to effectively treat tuberculosis and drug-resistant Enterobacteriaceae, the family of bacteria that includes E. coli and Salmonella.
CTA, in collaboration with more than 50 companies and organizations, has developed the first ANSI-accredited standard for defining terms related to artificial intelligence in health care. "As health systems and providers use AI tools such as machine learning to diagnose, treat and manage disease, there's an urgent need to understand and agree on AI concepts for consistent use," said CTA's Rene Quashie.
Far more people in the US may have died from opioids in the past two decades than previously reported, according to a new analysis of unclassified drug deaths carried out using machine-learning algorithms. Elaine Hill and her colleagues at the University of Rochester, New York, were examining data on drug overdose deaths when they realised that 22 per cent of such cases reported between 1999 and 2016 were listed on death certificates as overdoses without specifying the substance involved. "We found that remarkable, given the scale of the issue," says team member Andrew Boslett. The team tried to estimate what percentage of these unclassified deaths were due to opioids by analysing the coroners' and medical reports from opioid overdoses and unclassified overdoses. First, the researchers used machine-learning algorithms to analyse deaths that had been recorded as being due to opioid overdose.
Years before smart homes became a thing, I replaced all the switches in our house with computerized switches. At first, it was just a way to add wall switches without pulling new wire. Over time, I got more ambitious. The system runs a timer routine when it detects no one is home, turns on the basement light when you open the door, and lights up rooms in succession on well-worn paths such as bedroom to kitchen. Other members of the family are less enthusiastic. A light might fail to turn on or might go out for lack of motion, or maybe for lack of any discernible reason. The house seems to have a mind of its own.
The nature of consciousness seems to be unique among scientific puzzles. Not only do neuroscientists have no fundamental explanation for how it arises from physical states of the brain, we are not even sure whether we ever will. Astronomers wonder what dark matter is, geologists seek the origins of life, and biologists try to understand cancer--all difficult problems, of course, yet at least we have some idea of how to go about investigating them and rough conceptions of what their solutions could look like. Our first-person experience, on the other hand, lies beyond the traditional methods of science. Following the philosopher David Chalmers, we call it the hard problem of consciousness. But perhaps consciousness is not uniquely troublesome. Going back to Gottfried Leibniz and Immanuel Kant, philosophers of science have struggled with a lesser known, but equally hard, problem of matter. What is physical matter in and of itself, behind the mathematical structure described by physics?