There is a lot of confusion out there about what cognitive bias really is and how it relates to artificial intelligence. One of the most important things to keep in mind is that human and machine cognitive biases are quite different things. Humans and machines can both have biases, but those biases are not the same. While Applied-AI is still in its early days, it is already changing tons of business processes around us and will continue to do so. At a time when the value of data is never higher, many companies are investing in "artificial intelligence," in one or the other form, to help them transform business processes and make decisions faster and more accurate.

State-of-the-art driver-assist systems have failed to effectively mitigate driver inattention and had minimal impacts on the ever-growing number of road mishaps (e.g. life loss, physical injuries due to accidents caused by various factors that lead to driver inattention). This is because traditional human-machine interaction settings are modeled in classical and behavioral game-theoretic domains which are technically appropriate to characterize strategic interaction between either two utility maximizing agents, or human decision makers. Therefore, in an attempt to improve the persuasive effectiveness of driver-assist systems, we develop a novel strategic and personalized driver-assist system which adapts to the driver's mental state and choice behavior. First, we propose a novel equilibrium notion in human-system interaction games, where the system maximizes its expected utility and human decisions can be characterized using any general decision model. Then we use this novel equilibrium notion to investigate the strategic driver-vehicle interaction game where the car presents a persuasive recommendation to steer the driver towards safer driving decisions. We assume that the driver employs an open-quantum system cognition model, which captures complex aspects of human decision making such as violations to classical law of total probability and incompatibility of certain mental representations of information. We present closed-form expressions for players' final responses to each other's strategies so that we can numerically compute both pure and mixed equilibria. Numerical results are presented to illustrate both kinds of equilibria.

With AGI we really don't know how long the journey will be. So any step, although appears to make progress, we really don't know how many more steps will be necessary to reach the goal of AGI. As revealed by Moravec's paradox. A consequence is that we underestimate the complexity of the cognitive process required for seemingly simple behavior. We are largely unconscious of our own thought processes.

A startup that made a name for itself designing AI avatar clones of celebrities is turning its attention towards brands. Patrick Kulp is an emerging tech reporter at Adweek.

Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management. When faced with a challenge, human beings are generally quick to first try to develop creative solutions. We tend to pick the most logical explanation we can find, ignoring all contradictory or unprovable hypotheses in the process. However, this irrational pattern of thinking could eventually sabotage our efforts to create an actual intelligent machine. A cognitive bias known as rationalization is one such phenomenon that is tricky or even dangerous for AI.

One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency, even in complex environments, and its flexibility, even in changing environments. Efficiency is especially impressive because directly computing an optimal plan is intractable, even for modestly complex tasks, and yet people successfully solve myriad everyday problems despite limited cognitive resources. Standard accounts in psychology, economics, and artificial intelligence have suggested this is because people have a mental representation of a task and then use heuristics to plan in that representation. However, this approach generally assumes that mental representations are fixed. Here, we propose that mental representations can be controlled and that this provides opportunities to adaptively simplify problems so they can be more easily reasoned about -- a process we refer to as construal. We construct a formal model of this process and, in a series of large, pre-registered behavioral experiments, show both that construal is subject to online cognitive control and that people form value-guided construals that optimally balance the complexity of a representation and its utility for planning and acting. These results demonstrate how strategically perceiving and conceiving problems facilitates the effective use of limited cognitive resources.

Have you used the customer service app with your bank the past year, or received an unexpected email with an offer you were actually interested in? Maybe it was a well-timed mortgage re-fi or even some savings at a favorite store. There's an enduring and unfortunate misperception that AI serves only to replace human workers and irritate human clients. But what if we don't have to be locked in a zero-sum game with the growing legion of digital intelligence? An increasing number of banks and insurers are finding that it's almost impossible to meet the rising customer expectations and needs that digital services and apps have unleashed.

It is virtually impossible to tease apart human capabilities from human cultural and other background knowledge, so this is necessary to provide an objective point of comparison against humans. Furthermore, a comprehensive understanding of human background knowledge, sufficient to not only recall but apply that knowledge, tests the cognitive capabilities essential to the human kind of understanding. I have recommended that human respondents be drawn from broad populations to ensure that this cultural knowledge is least-common-denominator rather than esoteric. The graders might be able to tell that they are scoring a non-human subject system. Difficulties with the Turing Test have demonstrated that this is probably not an issue. It is a relatively easy task to fool humans into thinking they are interacting with a human, even without human-level cognitive capabilities. Mimicking human interaction styles, though again not necessarily a goal of the subject system, should not be difficult for a system with cognition that is comparable to that of humans. Nevertheless, the reason the protocol attempts to disguise which respondents are human or non-human is not because this contributes to the evaluation, but merely to avoid implicit bias in scoring. All the test questions are raster images - does this mean the system has to do handwriting recognition?

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals.

Would you like to be more rational? Who doesn't want to behave and think more reasonably? Good news: New research, from Harvard psychologist Ellen Langer, suggests mindfulness, or at least an aspect of it, can help. By "mindfulness"--a feature of Buddhism for thousands of years, and a subject of scientific investigation for a few decades--most people mean a mental state you can be in. If you find yourself bringing past or possible future events into your imagination, let those drift off, and attend again to your present sensations, thoughts, and feelings. Being mindful for a few seconds is easy.