The COVID-19 pandemic has accelerated technological advances and the automation of many routine tasks – from contactless cashiers to robots delivering packages. In this environment, many are concerned that artificial intelligence (AI) will drive significant automation and destroy jobs in the coming decades. Just a few decades ago, the internet created similar concerns as it grew. Despite skepticism, the technology created millions of jobs and now comprises 10% of US GDP. Today, AI is poised to create even greater growth in the US and global economies.
How is Machine Learning helping to develop TB drugs? Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology.
With the current year coming to an end, the definition of how businesses leverage technology has changed much due to the pandemic. With disruptive technologies driving global discussion, sustainability is emerging as a new investment. Business leaders are now looking to run their companies in an environmentally sustainable manner, so less harm is done on the planet. Therefore there is a growing emphasis on how technology can be employed for improving a company's environmental performance and the bottom line. From incorporating sustainable practices into business operations to encouraging consumers, employees to embrace sustainability to using AI and quantum computing to find alternate energy-efficient fuels, most of the top enterprises are already doing their part to ensure a greener future.
Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.
Currently, the world is facing the most challenging time and going through economic turmoil. One of the important priorities of many companies is to recover quickly from the current scenario and be operational as quickly as possible. The coronavirus has impacted many companies and this economic hit is very fast throughout the world. Companies around the globe are looking for stabilizing their business and the recovery. According to the Organization for Economic Co-operation and Development of the reports released on 14th April, consumer expenditure has dropped more than 25% in Canada, France, and Germany in many majority economies, thus causing the slowdown between 20-25%. The Machine Learning (ML) and Artificial Intelligence (AI) can play a major role in the business recovery after and during the COVID 19 pandemic.
MY 85-year-old uncle was hospitalised due to ageing issues at a private hospital in Kolkata, India. In the intensive care unit, he contracted Covid-19 from another patient whose infection the hospital was not even aware of. It took the hospital days to figure out that many of its ICU patients, doctors and other professionals were already infected. The pandemic shows us the inequality of healthcare access. This collective global experience will invariably lead to demands of massive upscaling of healthcare.
Marblehead High School will return to all-remote learning for at least two weeks after police broke up a "large" student party over the weekend where revelers were gathered "without social distancing or face coverings, sharing drinks," school officials said. "In choosing to ignore the rules set down by the Governor and our community in the pandemic, however, we are not just endangering individuals… we are also potentially harming the community at large," Superintendent John J. Buckey wrote in a letter to families. Many students "scattered" when police responded to break up the party on Friday, making it impossible to identify and quarantine individuals involved, Buckey said. Buckey pointed to a "troubling pattern of behavior in play" in Marblehead and other school districts and said the Friday party was not a singular event. "We all know this is not a new thing for teenagers. However, these are not ordinary times," Buckey said in the letter.
TORONTO – As makeshift tent cities spring up across Canada to house rough sleepers who fear using shelters due to COVID-19, one city is leveraging artificial intelligence (AI) to predict which residents risk becoming homeless. Computer programmers working for the city of London, Ontario, 170km southwest of the provincial capital Toronto, say the new system is the first of its kind anywhere – and it could offer insights for other regions grappling with homelessness. "Shelters are just packed to the brim across the country right now," said Jonathan Rivard, London's Homeless Prevention Manager, who works on the AI system. "We need to do a better job of providing resources to individuals before they hit rock bottom, not once they do," he told the Thomson Reuters Foundation. Canada is seeing a second wave of coronavirus cases, with Ontario's government warning the province could experience "worst-case scenarios seen in northern Italy and New York City" if trends continue.
Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology. "It could also prove useful in protein design and many other fields of biology," says the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) Bonnie Berger.