's most controversial moment was years in the making. It took many more for the fallout to come into full view. One morning in 2010, Alex Trebek walked onto the IBM campus not far outside New York City and prepared to inspect what would become the most unusual player in's history. The trip, clear across the country from the show's Culver City set, had been carefully planned. David Ferrucci, a computer scientist at IBM, had spent years leading a team to develop what would become the first and, so far, last nonhuman ever to compete on Longtime host Trebek would watch three practice games played with "Watson," as the system was named, and two human contestants. Then the team would be taken to lunch nearby, and Trebek would ultimately take the stage and host two more Watson practice games himself. By then the preparations for a future televised contest with IBM's creation were well underway, but this was the first time Trebek would encounter the technology in person, and his approval was crucial. Ferrucci was eager to show off one element in particular: the display, which had been rigged to show Watson's top three guesses whenever it answered, along with the numerical confidence rate it had in each one. For Ferrucci, this feature was central to demonstrating the computer's language-processing capabilities, because it showed that Watson wasn't just spitting out answers--it was reasoning. If Watson were ever going to be deployed to industries like health care, its human users wouldn't just want to know its best guess. It would be infinitely more valuable to know if Watson was 95 percent confident or just 30 percent, and whether those confidence levels were in line with its actual accuracy rate. It also made for better viewing. Ferrucci had brought his young daughter to the lab earlier in the process and showed her Watson as it played against human opponents. When Watson declined to ring in, Ferrucci's daughter turned to him and asked if the computer had crashed. He struggled to explain that it hadn't--it just wasn't confident enough to hazard a guess.

Although robots are becoming more advanced with human-like anthropomorphic features and decision-making abilities to improve collaboration, the active integration of humans into this process remains under-explored. This article presents the first experimental study exploring decision-making interactions between humans and robots with visual cues from robotic eyes, which can dynamically influence human opinion formation. The cues generated by robotic eyes gradually guide human decisions towards alignment with the robot's choices. Both human and robot decision-making processes are modeled as non-linear opinion dynamics with evolving biases. To examine these opinion dynamics under varying biases, we conduct numerical parametric and equilibrium continuation analyses using tuned parameters designed explicitly for the presented human-robot interaction experiment. Furthermore, to facilitate the transition from disagreement to agreement, we introduced a human opinion observation algorithm integrated with the formation of the robot's opinion, where the robot's behavior is controlled based on its formed opinion. The algorithms developed aim to enhance human involvement in consensus building, fostering effective collaboration between humans and robots. Experiments with 51 participants (N = 51) show that human-robot teamwork can be improved by guiding human decisions using robotic cues. Finally, we provide detailed insights on the effects of trust, cognitive load, and participant demographics on decision-making based on user feedback and post-experiment interviews.

Toyota has launched Advanced Drive, a new driver assistance technology, with the latest Toyota Mirai and Lexus LS vehicles. Advanced Drive is capable of Level 2 autonomy and can free the driver from operating the accelerator, brakes and even the steering wheel -- under certain traffic conditions and with the driver's supervision, that is. It was designed for highway driving only, and like other available assistance technologies today, it doesn't have full self-driving capabilities yet. Advanced Drive uses data from the vehicle's telescopic camera and LiDAR, as well as information from high-precision maps to detect other vehicles in the same lane. So long as a driver sets the destination in the navigation system, the technology will be able to assess situations and make decisions when it comes to changing lanes, maintaining distance from other vehicles, navigating lane splits and overtaking other vehicles.

DIY kits make excellent presents around the holidays. Not only do they allow children to learn valuable skills through hands-on projects, but also they provide children with a tangible or intangible reward that makes overcoming the challenges inherent in doing something themselves worthwhile. So, if you still need to pick up a holiday present for a child between the ages of five and 13, we've got you covered. Here's a roundup of four fantastic DIY kits that are currently on sale. Help your child develop STEM skills with this enjoyable way to learn computer programming.

The AI Now Institute, People Power Media, and the Anti-Eviction Mapping Project today launched Landlord Tech Watch, a crowdsourced map examining where surveillance and AI technologies are being used by landlords to potentially disempower tenants and community members. The site invites tenants to self-report the types of tech that are being installed in their residences and neighborhoods, and it aims to serve as a resource to help educate about the widespread use and harms of these technologies. Owners and landlords typically purchase and install tech products and platforms without notifying or discussing potential harms with their tenants, and sometimes without even letting them know. For instance, in New York City, rent-stabilized tenants at the Atlantic Plaza Towers in Brownsville were subjected to a facial recognition security system from a third-party vendor. Elsewhere in the city, an elderly tenant in Hell's Kitchen charged that a keyless system installed by his landlord was too complicated, and feared that his movements would be tracked through the technology.

Artificial Intelligence (AI) is more linked to dopamine-reinforced learning than you may think. That's a mouthful, so for now just think of Pavlov's dog study. DeepMind AI published a blog post on their discovery that the human brain and AI learning methods are closely linked when it comes to learning through reward. Their findings were also published in the journal Nature on Wednesday. It's been a well-known fact for a while now that we humans, and many animals, learn through reward. We are motivated by external and internal factors to learn more.

A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics. Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study.

The study conducted by researchers at the University of Vermont in the USA suggests a machine learning algorithm might provide a fast and easy way of diagnosing anxiety and depression – conditions that are difficult to spot and often overlooked in young people. "We need quick, objective tests to catch kids when they are suffering," said study lead author Ellen McGinnis, who is a clinical psychologist at the university's Medical Centre's Vermont Centre for Children, Youth and Families. "The majority of kids under eight are undiagnosed," she added. Early diagnosis of these conditions is critical as children respond well to treatment while their brains are still developing, according to the researchers, but if they are left untreated they are at greater risk of substance abuse and suicide later in life. Standard diagnosis involves a 60-90-minute semi-structured interview with a trained clinician and their primary caregiver. McGinnis, along with University of Vermont biomedical engineer and study senior author Ryan McGinnis, have been looking at ways to overcome this, by using artificial intelligence and machine learning to make diagnosis faster and more reliable.

Every day countless headlines emerge from myriad sources across the globe, both warning of dire consequences and promising utopian futures – all thanks to artificial intelligence. AI "is transforming the workplace," writes the Wall Street Journal, while Fortune magazine tells us that we are facing an "AI revolution" that will "change our lives." But we don't really understand what interacting with AI will be like – or what it should be like. It turns out, though, that we already have a concept we can use when we think about AI: it's how we think about animals. As a former animal trainer (albeit briefly) who now studies how people use AI, I know that animals and animal training can teach us quite a lot about how we ought to think about, approach and interact with artificial intelligence, both now and in the future.

Every day countless headlines emerge from myriad sources across the globe, both warning of dire consequences and promising utopian futures – all thanks to artificial intelligence. AI "is transforming the workplace," writes the Wall Street Journal, while Fortune magazine tells us that we are facing an "AI revolution" that will "change our lives." But we don't really understand what interacting with AI will be like – or what it should be like. It turns out, though, that we already have a concept we can use when we think about AI: it's how we think about animals. As a former animal trainer (albeit briefly) who now studies how people use AI, I know that animals and animal training can teach us quite a lot about how we ought to think about, approach and interact with artificial intelligence, both now and in the future.