A team of chemistry, life science, and AI researchers are using graph neural networks to identify molecules and predict smells. Models made by researchers outperform current state-of-the-art approaches and the top-performing model from the DREAM Olfaction Prediction Challenge, a competition for mapping the chemical properties of odors. The work was created by researchers from Google, Canadian Institute for Advanced Research, Vector Institute for Artificial Intelligence, University of Toronto, and Arizona State University. The researchers believe progress in machine learning application of molecule identification can help deliver machine intelligence that's able to predict smell similar to the way AI that can imitate other senses like vision and hearing has advanced in recent years. With grasping challenges, researchers are also trying to help robotic hands tackle the human sense of touch as well.

A University of Texas at Austin computer science team, in partnership with researchers from Cornell Tech, Georgia Tech and Facebook AI Research, has developed an artificial intelligence system that can look at a photo of an outfit and suggest helpful tips to make it more fashionable. Suggestions may include tweaks such as selecting a sleeveless top or a longer jacket. "We thought of it like a friend giving you feedback," said Kristen Grauman, a professor of computer science whose previous research has largely focused on visual recognition for artificial intelligence. "It's also motivated by a practical idea: that we can work with a given outfit to make small changes so it's just a bit better." The tool, named Fashion, uses visual recognition systems to analyze the color, pattern, texture and shape of garments in an image.

According IoT Analytics, there are over 17 Billion connected devices in the world as of 2018, with over 7 Billion of these "internet of things" (IoT) devices. The Internet of Things is the collection of those various sensors, devices, and other technologies that aren't meant to directly interact with consumers, like phones or computers. Rather, IoT devices help provide information, control, and analytics to connect a world of hardware devices to each other and the greater internet. With the advent of cheap sensors and low cost connectivity, IoT devices are proliferating. From 1 to 5 April, everything at Hannover Messe will revolve around networking, learning machines and the Internet of Things.

Facial recognition has been used for a wide range of hi-tech applications in China – from airport security clearance to crime prevention. Now it is being used to solve a more down-to-earth problem. On Shamian island, a popular historical tourist attraction in Guangzhou, facial recognition for toilet paper dispensing has been introduced in some cubicles, according to a report in the Guangzhou-based Information Times. Users can remove 90cm of toilet paper after their face is recognized. If the system detects the same face twice within 10 minutes, no further paper will be dispensed.

Arnold Schwarzenegger means it when he says: "I'll be back," but not everyone is thrilled there's a new Terminator film out this week. In labs at the University of Cambridge, Facebook and Amazon, researchers fear Terminator: Dark Fate could mislead the public on the actual dangers of artificial intelligence (AI). AI pioneer Yoshua Bengio told BBC News he didn't like the Terminator films for several reasons. "They paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future," says Prof Bengio, who is sometimes called one of the "godfathers of AI" for his work on deep learning in the 1990s and 2000s. "We are very far from super-intelligent AI systems and there may even be fundamental obstacles to get much beyond human intelligence."

Unilever has claimed it is saving hundreds of thousands of pounds a year by replacing human recruiters with an artificial intelligence system, amid warnings of a populist backlash against the spread of machine learning. The multinational told the Guardian it had saved 100,000 hours of human recruitment time in the last year by deploying software to analyse video interviews. The system scans graduate candidates' facial expressions, body language and word choice and checks them against traits that are considered to be predictive of job success. Vodafone, Singapore Airlines and Intel are among other companies to have used similar systems. Polling commissioned by the Royal Society of Arts and released on Friday suggests 60% of the public are opposed to the use of automated decision-making in recruitment as well as in criminal justice.

Federal data analytics and automation efforts are still getting organized around a larger new paradigm, Gil Alterovitz, director of artificial intelligence at the Department of Veterans Affairs, said at ATARC's Oct. 24 Federal Artificial Intelligence And Data Analytics Summit. The VA is testing a number of artificial intelligence-driven projects to help patients, including programs to reduce waiting times at its facilities, predict potential suicides and monitor customer service. Todd Myers, automation lead at the National Geospatial-Intelligence Agency, said the government should look to companies like Uber and Amazon for models of how to use data to advance their missions. "These companies are successful, and the government will be successful when we break down the organizational silos" of units that may working on their own data analytics and data sets, he said. "The days of separate business units and organizations going off and doing their own thing, I think are long gone. I think the federal government is leaning hard and fast in changing that approach," Myers said.

"Words that will Inspire" is an analysis on 2,500 TED talks using text analytics and machine learning on R to find the factors that make some talks more popular than others. What was the motivation for doing this project? I am part of a meetup group called Data Scientist speakers in London that meets regularly to practice data science talks and receive feedback to improve public speaking. Every year at the club we have a competition to see who can come with the best data science story. I joined this competition and wanted to make participate with something special: I wanted to combine my data science skills to analyse famous speeches or talks and use these insights to build an entirely new one.

Google is now ready to release its Coral developer board globally after completing improvements throughout its six-month beta period. Google unveiled its Coral edge kit in March, offering developers a Raspberry Pi-like board with an attachable Google Edge TPU machine-learning accelerator. The kit is aimed at engineers and researchers who want to run TensorFlow models at the edge of a network, outside the data center. The Coral Dev Board itself costs $149, which includes a detachable Coral system-on-module (SoM) that can now be bought as a standalone product for $114. The SoM includes Google's Edge TPU with the NXP IMX8M SoC, Wi-Fi and Bluetooth, memory, and storage.

"In order to identify the rare few drug candidates that induce desired epigenetic effects, scientists need methods to screen hundreds of thousands of potential compounds," says Alexey Terskikh, Ph.D., associate professor in Sanford Burnham Prebys' Development, Aging and Regeneration Program and senior author of the study. "Our study describes a powerful image-based approach that enables high-throughput epigenetic drug discovery." Epigenetics refers to chemical tags on DNA that allow cellular machinery greater or less access to genes -- thus altering gene expression. Nearly all changes in a cell, including reaction to a drug and environmental stress, are reflected by its epigenetic state. Several medicines that target epigenetic alterations are approved by the U.S. Food and Drug Administration (FDA) for the treatment of cancer, and researchers are working to find additional epigenetic-based therapies.