As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models. It's so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want. "That's it," says Alex Renda, a PhD student at MIT. "The standard things people do to prune their models are crazy complicated." Renda discussed the technique when the International Conference of Learning Representations (ICLR) convened remotely this month.

Could a computer program write engaging news stories? In a recent Technology Trends and Predictions report from Reuters, 78% of the 200 digital leaders, editors, and CEOs surveyed said investing in artificial intelligence (AI) technologies would help secure the future of journalism (Newman, 2018). Exploring these new methods of reporting, however, has introduced a wide array of unforeseen ethical concerns for those already struggling to understand the complex dynamics between human journalists and computational work. Implementing automated storytelling into the newsrooms presents journalists with questions of how to preserve and encourage accuracy and fairness in their reporting and transparency with the audiences they serve. Artificial intelligence in the newsroom has progressed from an idea to a reality.

Artificial intelligence (AI), "big data," block-chain and edge computing are all ways of collecting, storing and analyzing information that, if leveraged effectively, have the potential to rapidly speed up progress in healthcare. AI, with its ability to discern patterns, correlations and trends in huge volumes of data, is among a swathe of new technologies that experts expect to have deep, revolutionary impacts across numerous sectors. Already, ophthalmologists have shown that AI algorithms can provide objective metrics, from simple photographs and optical coherence tomography (OCT), as well as quantify the amount of optic nerve damage in glaucoma. Given the speed at which AI is able to accurately work, many experts predict it will help alleviate time and resource pressures against the backdrop of an aging population – a particularly pertinent issue, given the shortage of ophthalmologists. Though research and integration of AI in healthcare is ongoing, it has the potential to transform a number of areas – including processing and analyzing biomedical, clinical and patient data; medical imaging and diagnostics; drug discovery; biomarker research; personal AI assistants; and genomics. There is also a wealth of AI research underway in retinal disease, notably the Moorfields and Deepmind collaboration – a project that is investigating the use of AI to read complex eye scans and detect more than 50 eye conditions, and identify patients who require urgent treatment. Anthony Khawaja, a Consultant Ophthalmologist at Moorfields Eye Hospital explains how the project came about.

The helmet sounds an alarm if anyone has a fever. If you think that something is missing, you're right. We haven't mentioned AI -- …

TOKYO (Reuters) - Coronavirus patients with light symptoms arriving to stay at several Tokyo hotels are likely to get a lift from a pleasant surprise - a robot greeter in the lobby. Japan is now using hotels to house patients who have tested positive for the coronavirus but whose symptoms are too light to need hospitalisation, and several in the capital of Tokyo just opened on Friday feature robots to help lighten the burden on nurses. In one, a big-eyed robot named "Pepper" - appropriately wearing a protective mask - stood waiting to welcome visitors. "Please, wear a mask inside," it said in a perky voice. "I hope you recover as quickly as possible."

Members get 60 days free site access, $6.95/article thereafter. In 2017, the Chinese government announced plans to "lead the world" in artificial intelligence by 2030. The announcement has fed considerable uneasiness in the United States and elsewhere about the scope of China's aspirations and the extent to which the country might use AI to tighten control over its citizens and develop more sophisticated military capabilities. However, the anxiety over China's plans for AI may be overblown, says Jeffrey Ding, an economics and technology researcher at the University of Oxford's Future of Humanity Institute. Yes, there are many signs that China is making huge investments in AI, and it leads the world in AI-related patent filings and publications -- in 2017 alone, it won some 900 patents related to facial recognition, compared with fewer than 150 in the United States.

I have been a data analytics professional for the past twelve years. Throughout my career, I have seen a steady spike in the use of data across the industry, be it engineering, education, healthcare or financial services. It was in 2017 when I read about the Economist article "The world's most valuable resource is no longer oil, but data" an idea which was first coined by Clive Humby, UK Mathematician and architect of Tesco's Clubcard in 2006. Many prominent personalities like Meglena Kuneva, European Consumer Commissioner, 2009 [1] later reiterated this. I could see everyone talking about the infinite potential of data and how to use it in a million ways.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved -- in some cases, down to low triple digits.

Japan is now using hotels to house patients who have tested positive for the coronavirus but whose symptoms are too light to need hospitalisation, and several in the capital of Tokyo just opened on Friday feature robots to help lighten the burden on nurses.