People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person's reaction to an invitation to smoke versus another person's response to a query about their sleep quality. The identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigorous user evaluation with an additional 20 participants. The outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification.

Your report (Scientists try to teach robot to laugh at the right time, 15 September) reminded me of Sir Alan Ayckbourn's 1998 play Comic Potential. Except that in the play, the robot did not need to be taught to laugh. Set in the not too distant future, Comic Potential foresees TV soaps acted by AI robots. As the play opens, just such a TV programme – a hospital soap – is in progress. But in the studio where it is being recorded, the robots are malfunctioning and the action spirals into chaos. The human overseer, desperately trying to restore order, is startled (as were we) to hear one of the AI nurses break into a fit of the giggles, because the action has become so very funny.

The machines are not becoming human. Among those technologists at the forefront of artificial intelligence (AI) -- those developing its deep learning algorithms, probabilistic graphical models and layered neural networks -- this is common, if not humbling, knowledge. What the machines are doing is imitation, without agency. At their most sophisticated when fed massive data sets, AI frameworks, representations and programming imitate a slice of the human brain process. While exciting brain research initiatives are underway, almost every aspect of the brain remains underinvestigated and partially understood at best.

Right now, a robot that has to navigate around a home, like a robotic vacuum, knows what a refrigerator is when it sees one, but unlike a human, it doesn't necessarily know that that means it's in the kitchen. Therefore, if a piece of furniture gets moved, it can disorient the robot unless one takes the time to manually program it with the object's new location. A team of researchers from Facebook's AI program and Carnegie Mellon University are teaming up to change that. Using a system dubbed Goal-Oriented Semantic Exploration, the team is using machine learning to teach robots a little bit of common sense when it comes to the placement of household furniture. Looking for the latest gov tech news as it happens?

Our interest focuses on developing statistical methods for analysis of brain structural connectomes. Nodes in the brain connectome graph correspond to different regions of interest (ROIs) while edges correspond to white matter fiber connections between these ROIs. Due to the high-dimensionality and non-Euclidean nature of the data, it becomes challenging to conduct analyses of the population distribution of brain connectomes and relate connectomes to other factors, such as cognition. Current approaches focus on summarizing the graph using either pre-specified topological features or principal components analysis (PCA). In this article, we instead develop a nonlinear latent factor model for summarizing the brain graph in both unsupervised and supervised settings. The proposed approach builds on methods for hierarchical modeling of replicated graph data, as well as variational auto-encoders that use neural networks for dimensionality reduction. We refer to our method as Graph AuTo-Encoding (GATE). We compare GATE with tensor PCA and other competitors through simulations and applications to data from the Human Connectome Project (HCP).

By the time his play "R.U.R." (which stands for "Rossum's Universal Robots") premiered in Prague in 1921, Karel Čapek was a well-known Czech intellectual. Like many of his peers, he was appalled by the carnage wrought by the mechanical and chemical weapons that marked World War I as a departure from previous combat. He was also deeply skeptical of the utopian notions of science and technology. "The product of the human brain has escaped the control of human hands," Čapek told the London Saturday Review following the play's premiere. "This is the comedy of science." In that same interview, Čapek reflected on the origin of one of the play's characters: The old inventor, Mr. Rossum (whose name translated into English signifies "Mr.

Of the many hot topics at the moment, anything related to artificial intelligence is obviously right up there. Like the holy grail, AI has always been just beyond our reach. But technology is rapidly approaching the threshold beyond which it becomes easy to confuse artificial logic with human intelligence. This leads to ethical discussions about human rights for digital beings, but comes no closer to actually defining these digital beings. The Saudi Arabian government's statement about giving a bot named Sophia human rights, is perhaps one of the most significant statements on this issue.

I'm not what you would call a fan of Westworld. As an AI reporter, telling this to people often triggers an audible gasp, followed by a look of disbelief and disappointment. Despite my lack of enthusiasm for the show, I plan to watch every episode of season two, which starts Sunday on HBO, and developers should too. Earlier this week, I attended a showing of episode one in San Francisco and had an opportunity to chat with members of the cast. I took the interviews because I wanted to know how people playing parts in a show that shapes the world's perception of AI felt about the impact of AI.

Jane Chappell is the vice president of Raytheon's Global Intelligence Solution. You check your mailbox and there's a free subscription to a parenting magazine, a sample of baby formula and coupons for store-brand diapers, but you and your spouse just renewed your AARP membership. Obviously, something has gone wrong, and the algorithm used to process trends and habits to build a unique profile revealed its limitations. Perhaps you shopped for baby clothes for a new grandchild and the resulting consumer data told the retailer you were likely a parent-to-be rather than a retiree. Mailing samples of baby formula rather than coupons for wine-of-the-month club is expensive to both a store's reputation and bottom line.