Tsukuba, Japan - Depression is a worldwide problem, with serious consequences for individual health and the economy, and rapid and effective screening tools are thus urgently needed to counteract its increasing prevalence. Now, researchers from Japan have found that artificial intelligence (AI) can be used to detect signs of depression. In a study published this month in BMJ Open, researchers from University of Tsukuba have revealed that an AI system using machine learning could predict psychological distress among workers, which is a risk factor for depression. Although many questionnaires exist that screen for mental health conditions, individuals may be hesitant to answer truthfully questions about subjective mood due to social stigma regarding mental health. However, a machine learning system could be used to screen depression/psychological distress without such data, something the researchers at University of Tsukuba aimed to address.

Alexa wants to help your child learn how to read. With Amazon's new Reading Sidekick, kids can say "Alexa, let's read," to an Amazon Kids-enabled Echo device or the Amazon Kids app on a tablet and the artificial intelligence-powered assistant will take turns reading with them. An Amazon Kids subscription ($2.99 monthly) is required. Kids can choose from hundreds of physical and digital books that are supported, with more being added monthly. After asking Alexa to read with them, the AI assistant will ask how much do they want to read: a little, a lot, or taking turns.

In 2016, Columbus, Ohio, beat out 77 other small and midsize US cities for a pot of $50 million that was meant to reshape its future. The Department of Transportation's Smart City Challenge was the first competition of its kind, conceived as a down payment to jump-start one city's adaptation to the new technologies that were suddenly everywhere. Ride-hail companies like Uber and Lyft were ascendant, car-sharing companies like Car2Go were raising their national profile, and autonomous vehicles seemed to be right around the corner. "Our proposed approach is revolutionary," the city wrote in its winning grant proposal, which pledged to focus on projects to help the city's most underserved neighborhoods. It laid out plans to experiment with Wi-Fi-enabled kiosks to help residents plan trips, apps to pay bus and ride-hail fares and find parking spots, autonomous shuttles, and sensor-connected trucks.

Voice assistants equipped with speech-to-text technology have seen a major boost in performance and usage, thanks to the new powerful machine learning methods based on deep neural networks. These methods follow a supervised learning approach, requiring large amounts of paired speech-text data to train the best performing speech-to-text transcription models. After collecting large amounts of relevant and diverse spoken utterances, the complex and intensive task of annotating and labelling of the collected speech data awaits. To get a feel for a typical scenario, let's look at some estimates. On average a typical user query, for example "Do you have the Christmas edition with Santa?", would last for about 3 seconds.

Editor's note: This year What If?, our annual collection of scenarios, considers the future of health. Each of these stories is fiction, but grounded in historical fact, current speculation and real science. IT WAS A scene that the Nobel committee had dearly hoped to avoid. As the recipients of this year's prizes filed into the Stockholm Concert Hall to take their seats, dozens of protesters, including several former laureates, clashed with police in the streets outside. They had gathered to express their opposition to the unprecedented decision to award the Nobel prize in physiology or medicine to an artificial intelligence.

At many firms, the marketing function is rapidly embracing artificial intelligence. But in order to fully realize the technology's enormous potential, chief marketing officers must understand the various types of applications--and how they might evolve. Classifying AI by its intelligence level (whether it is simple task automation or uses advanced machine learning) and structure (whether it is a stand-alone application or is integrated into larger platforms) can help firms plan which technologies to pursue and when. Companies should take a stepped approach, starting with rule-based, stand-alone applications that help employees make better decisions, and over time deploying more-sophisticated and integrated AI systems in customer-facing situations. Of all a company's functions, marketing has perhaps the most to gain from artificial intelligence.

Our homes and offices are only as solid as the ground beneath them. When that solid ground turns to liquid--as sometimes happens during earthquakes--it can topple buildings and bridges. This phenomenon is known as liquefaction, and it was a major feature of the 2011 earthquake in Christchurch, New Zealand, a magnitude 6.3 quake that killed 185 people and destroyed thousands of homes. An upside of the Christchurch quake was that it was one of the most well-documented in history. Because New Zealand is seismically active, the city was instrumented with numerous sensors for monitoring earthquakes.

Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study published in Radiology: Artificial Intelligence. The model has the potential to aid in early detection of cognitive impairment at an individual level. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, Ph.D., from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, China, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years.

Now, Google is preparing to launch an app that uses image recognition algorithms to provide more expert and personalized help. A brief demo at the company's developer conference last month showed the service suggesting several possible skin conditions based on uploaded photos. Machines have matched or outperformed expert dermatologists in studies in which algorithms and doctors scrutinize images from past patients. But there's little evidence from clinical trials deploying such technology, and no AI image analysis tools are approved for dermatologists to use in the US, says Roxana Daneshjou, a Stanford dermatologist and researcher in machine learning and health. "Many don't pan out in the real world setting," she says.

Human interactions with the physical environment are often mediated through information services, and sometimes depend on them. These human interactions with their environment relate to a range of scales,28 in the scenario here from the "west of the city" to the "back of the store," or beyond the scenario to "the cat is under the sofa." These interactions go far beyond references to places that are recorded in geographic gazetteers,37 both in scale (the place where the cat is) and conceptualization (the place that forms the west of the city29), or that fit to the classical coordinate-based representations of digital maps. And yet, these kinds of services have to use such digital representations of environments, such as digital maps, building information models, knowledge bases, or just text/documents. Also, their abilities to interact are limited to either fusing with the environment,44 or using media such as maps, photos, augmented reality, or voice. These interactions also happen in a vast range of real-world contexts, or in situ, in which conversation partners typically adapt their conversational strategies to their interlocutor, based on mutual information, activities, and the shared situation.2 Verbal information sharing and conversations about places may also be more suitable when visual communication through maps or imagery is inaccessible, distracting, or irrelevant, such as when navigating in a familiar shopping mall.