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.

Denis Shiryaev specializes in using AI-powered neural networks to upscale historical footage, and his latest work is this short clip of Moscow, Tverskaya Street in 1896 that was originally captured by Charles Moisson from the Lumière company. The neural networks were used upscale to 60FPS, boost image resolution with ESRGAN (general dataset), resort video sharpness, remove blur, remove compression artifacts, and colorization. Neural networks are basically networks of artificial neurons, or mathematical functions that transform a set of input values into an output value. The key feature is that they can be trained, so if you have lots of example inputs whose "correct" outputs are known, you can tune the parameters of the network to make it more likely to produce correct answers. The goal is that this training will generalize, in which once you've trained the neural networks to produce the right answer for inputs the network has seen before, it will produce good answers for inputs it hasn't seen as well.

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.

How Artificial Intelligence and Automation may reshape supply chain roles as we know them? Technological advancements throughout time have caused periods of huge change.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlight the increasing presence of AI in the healthcare industry, the assistance AI may provide in the future to workers' cognitive tasks, and the continuing acceleration in data production and dissemination. Of the $27 billion raised, U.S. startups accounted for $17 billion, up from $13.3 billion the previous year. Chinese AI startups, on the other hand, raised only $2.9 billion in 2019, down from $4.7 billion in 2018. Big companies wouldn't be investing billions [in AI] if it wasn't producing for them"--Geoffrey Hinton "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition"--Yuri Sebregts, chief technology officer, Shell

One-day research workshops on the application of AI approaches to a dedicated area of research (e.g. Workshops may be held in any Canadian city, but must include participants from multiple research institutions (universities, research institutes, research hospitals). The goal of the workshop should be to identify opportunities for the application of AI to the specific domain of interest, identify emerging research opportunities and foster the development of new collaborations. Up to $20,000 of funding is available and applicants will be asked to provide a complete budget. CIFAR will provide some logistical support to workshop organizers (e.g.

Over 80% of executives believe failing to get on board with artificial intelligence (AI) now will cost their organization for the next decade, according to Vanson Bourne. AI is driving revolutionary capabilities across the enterprise with departments like sales, marketing, HR and finance leveraging the technology for some of their most essential functions. As a result, organizations have increased their investment in AI by 23% over the last 12 months. But how are they investing? There are two paths to AI investment: building solutions in-house or buying from a third-party vendor.

The Defense Department has officially adopted a set of principles to ensure ethical artificial intelligence adoption, but much work is needed on the implementation front, senior DOD tech officials told reporters Feb. 24. The five principles [see sidebar], which are based on the recommendations of the Defense Innovation Board's 15-month study on the matter, represent a first step and generalized intentions around AI use and adoption including being responsible, equitable, traceable, reliable, and governable. DOD released the principles during a news briefing Feb. 24. Those AI ethical guidelines will likely be woven into a little bit of everything, like cyber, from data collection to testing, DOD CIO Dana Deasy told reporters. "We need to be very thoughtful about where that data is coming from, what was the genesis of that data, how was that data previously being used and you can end up in a state of [unintentional] bias and therefore create an algorithmic outcome that is different than what you're actually intending," Deasy said.

Clearview AI, a start-up with a database of more than three billion photographs from Facebook, YouTube and Twitter, has been hacked. The attack allowed hackers to gain access to its client list but it said its servers had not been breached. Most of its clients are US law enforcement agencies who use its facial-recognition software to identify suspects. Its use of images scraped from the internet has raised privacy concerns. The company told BBC News: "Security is Clearview's top priority. "Unfortunately, data breaches are part of life in the 21st Century.