Traditionally, economists have focused on a rational decision maker - the "homo economicus" - to model human behavior. The observation of various deviations of behavior from the benchmark of optimizing rational decision making has motivated an entire field, behavioral economics. Research in this field has identified a plethora of different, partly distinct and partly interacting, behavioral biases, which are related to cognitive limitations, stress, limited memory, preference anomalies, and social interactions, among others. These biases are typically established by comparing actual behavior against a theoretical benchmark, often in simplistic, unrealistic, or abstract settings that are unfamiliar to the decision makers. Field evidence for behavioral biases among professionals is still scarce, mostly because of the difficulty to establish a rational benchmark in complex real-world settings. Consequently, most contributions focus on documenting a behavioral deviation in one particular dimension. This makes it often difficult to compare the behavioral biases documented in the literature. Moreover, deviations from rational behavior are usually seen as being related to suboptimal performance. However, this connotation often rests on a priori reasoning or value judgments because it is typically even harder or impossible to identify the consequences of deviations from the rational benchmark than the deviations themselves.

People around the world enjoy "virtual human" characters, whether in Hollywood films, Japanese anime, or video games. In recent years, AI-powered virtual humans have increasingly insinuated themselves into our daily lives. The virtual pop icon Teresa Teng has performed songs with Taiwanese singer Jay Chou, achieving huge success. The popular Chinese debate show "I CAN I BB" hosted a spirited episode on whether "Falling in love with an AI human can be considered true love or not," where many people argued it is possible for a human to fall in love with an AI. Are there limits to such human-machine relationships?

Our central goal is to quantify the long-term progression of pediatric neurological diseases, such as a typical 10-15 years progression of child dystonia. To this purpose, quantitative models are convincing only if they can provide multi-scale details ranging from neuron spikes to limb biomechanics. The models also need to be evaluated in hyper-time, i.e. significantly faster than real-time, for producing useful predictions. We designed a platform with digital VLSI hardware for multi-scale hyper-time emulations of human motor nervous systems. The platform is constructed on a scalable, distributed array of Field Programmable Gate Array (FPGA) devices.

Predicting the behavior of human participants in strategic settings is an important problem in many domains. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant's cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art, which relies on expert-constructed features. Papers published at the Neural Information Processing Systems Conference.

Is your friendship circle ready for a virtual human? AI is getting better and better by the day, and as technological advancements are made it's becoming increasingly popular. Still, it's early days and we've yet to see the full potential of this exciting technology. However, Samsung recently debuted artificial humans called Neons, who scarily resemble the real thing. Has the age of virtual humans arrived?

Cognitive biases are widely accepted as something that makes us human. Every day, systematic errors in our thought process impact the way we live and work. But in a world where everything we do is changing rapidly--from the way we store information to the way we watch TV--what really classifies as rational thinking? It's a question with no right or wrong answer, but to help us decide for ourselves, today's infographic from TitleMax lists 50 cognitive biases that we may want to become privy to. In the name of self-awareness, here's a closer look at three recently discovered biases that we are most prone to exhibiting in the modern world.

In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: i.e., gaining either $100 or $130 with certainty. But in real world scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty--and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $100 with certainty or $130 only 80% of the time, people tend to make the risk-averse choice--even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.

To achieve optimal human-system integration in the context of user-AI interaction it is important that users develop a valid representation of how AI works. In most of the everyday interaction with technical systems users construct mental models (i.e., an abstraction of the anticipated mechanisms a system uses to perform a given task). If no explicit explanations are provided by a system (e.g. by a self-explaining AI) or other sources (e.g. an instructor), the mental model is typically formed based on experiences, i.e. the observations of the user during the interaction. The congruence of this mental model and the actual systems functioning is vital, as it is used for assumptions, predictions and consequently for decisions regarding system use. A key question for human-centered AI research is therefore how to validly survey users' mental models. The objective of the present research is to identify suitable elicitation methods for mental model analysis. We evaluated whether mental models are suitable as an empirical research method. Additionally, methods of cognitive tutoring are integrated. We propose an exemplary method to evaluate explainable AI approaches in a human-centered way.

Recently, it has been widely accepted by the research community that interactions between humans and cyber-physical infrastructures have played a significant role in determining the performance of the latter. The existing paradigm for designing cyber-physical systems for optimal performance focuses on developing models based on historical data. The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models. As a result, many existing models do not or only partially address those context factors of a new design owing to the lack of capabilities to capture the context factors. This limitation in many existing models often causes performance gaps between predicted and measured results. We envision a new design environment, a cyber-physical human system (CPHS) where decision-making processes for physical infrastructures under design are intelligently connected to distributed resources over cyberinfrastructure such as experiments on design features and empirical evidence from operations of existing instances. The framework combines existing design models with context-aware design-specific data involving human-infrastructure interactions in new designs, using a machine learning approach to create augmented design models with improved predictive powers.

Couger CEO Atsushi Ishii appeared as a technical specialist on the program "What is Human? What is Human? is an educational entertainment program that explores the definition of being human by looking at the latest applications of AI and discussing trending AI-related topics. The program featuring Couger's Atsushi Ishii examined the intersection of AI, work and what it means to be human. One popular claim when it comes to work and AI comes from the University of Oxford's Professor Osborne. He has previously stated that within the next 10 to 20 years, about 47 percent of US jobs risk being replaced by automation.