Human thinking is beyond imagination. Can a computer develop such ability to think and reason without human intervention? This is something programming experts at IBM Watson are trying to achieve. Their goal is to simulate human thought process in a computerized model. The result is cognitive computing – a combination of cognitive science and computer science. Cognitive computing models provide a realistic roadmap to achieve artificial intelligence.

How humans make repeated choices among options with imperfectly known reward outcomes is an important problem in psychology and neuroscience. This is often studied using multi-armed bandits, which is also frequently studied in machine learning. We present data from a human stationary bandit experiment, in which we vary the average abundance and variability of reward availability (mean and variance of the reward rate distribution). Surprisingly, we find subjects significantly underestimate prior mean of reward rates - based on their self-report on their reward expectation of non-chosen arms at the end of a game. Previously, human learning in the bandit task was found to be well captured by a Bayesian ideal learning model, the Dynamic Belief Model (DBM), albeit under an incorrect generative assumption of the temporal structure - humans assume reward rates can change over time even though they are truly fixed. We find that the "pessimism bias" in the bandit task is well captured by the prior mean of DBM when fitted to human choices; but it is poorly captured by the prior mean of the Fixed Belief Model (FBM), an alternative Bayesian model that (correctly) assumes reward rates to be constants. This pessimism bias is also incompletely captured by a simple reinforcement learning model (RL) commonly used in neuroscience and psychology, in terms of fitted initial Q-values. While it seems sub-optimal, and thus mysterious, that humans have an underestimated prior reward expectation, our simulations show that an underestimated prior mean helps to maximize long-term gain, if the observer assumes volatility when reward rates are stable, and utilizes a softmax decision policy instead of the optimal one (obtainable by dynamic programming). This raises the intriguing possibility that the brain underestimates reward rates to compensate for the incorrect non-stationarity assumption in the generative model and a simplified decision policy.

Thank you all for your helpful comments on our Comp Neuro paper. If the results of Figure 1 are indicative, this could further improve the results. The supervised training phase is depicted in the somewhat busy Fig. S2. While we disagree with Reviewer #2's opinion that the connection between neural regression and GPs is completely

Our program induction model takes as input a corpus of black-and-white raster training images, and seeks to synthesize a graphics program that generates each of them. The model estimates a prior over programs for training images, to be deployed on held-out test images. Following [2] we now derive this algorithm starting from a Bayesian viewpoint. With this probabilistic formalism in hand we will then briefly outline the 3-step algorithm which performs inference in this model, but interested readers should consult [2, 1] for a complete algorithmic exposition.

How should we decide among competing explanations of a cognitive process given limited observations? The problem of model selection is at the heart of progress in cognitive science. In this paper, Minimum Description Length (MDL) is introduced as a method for selecting among computational models of cognition. We also show that differential geometry provides an intuitive understanding of what drives model selection in MDL. Finally, adequacy of MDL is demonstrated in two areas of cognitive modeling.

The California Cognitive Science Conference at UC Berkeley is an annual all-day symposium bringing together hundreds of students, researchers, & members of the general public from around the world who are passionate about the interdisciplinary field of Cognitive Science for a day of talks & research presentations. We feature talks given by prominent scientists & thinkers from a wide variety of disciplines, & our acclaimed poster session provides undergraduates with the opportunity to present their original research alongside graduate students & professional researchers. The theme for this year's annual conference is "Forging Connections: Bonding & the Brain." Through this event, our speakers will explore the important roles of social connection in today's rapidly changing world: from the neurobiological & psychological implications of bonding to the consequences of technology, & much more. The conference provides attendees with a glimpse into the latest research in all the fields that comprise Cognitive Science, including but not limited to Psychology, Neuroscience, Computer Science/Artificial Intelligence, Linguistics, Philosophy, & the Social Sciences.

Machine learning, artificial intelligence, natural language processing, deep learning, robotics, and several other technologies have enabled businesses to leverage human intelligence and evaluate inputs for maximum accuracy and precision. For example, you now have image recognition software that acts as a scanner and finds the best search options on Google after interpreting what the image is. So, the application is based on ML, natural language processing, and artificial intelligence. It imitates a human who uses the item or object through the eyes and interprets the results in mind. Although all these disruptive technologies are individually the best in their field, combining them, it's a challenge.

Mindtree, a global technology services and digital transformation company, announced that it has extended its relationship with premium audio and video solutions brand, EPOS as a digital engineering partner to help augment and accelerate the brand's development of audio technologies and solutions. As part of the multiyear engagement, Mindtree will work as an integrated part of EPOS' development organisation, and take part in strengthening its product innovation, time-to-market, and customer satisfaction, especially in EPOS' high-growth enterprise audio and video segment. Mindtree will provide a broad range of competencies and knowledge within development, maintenance, and quality assurance services to support and innovate all product categories of EPOS within the segments of Enterprise Solutions and Gaming. "This collaboration is important for EPOS to ensure and further develop our portfolio of best-in-class solutions and technologies," said Jeppe Dalberg-Larsen, President at EPOS. "I am confident that Mindtree's extensive product engineering and testing capabilities, coupled with its flexible, transparent, and collaborative approach, will strengthen and support our ability to deliver differentiated audio and video technology, and sound experiences." "We are pleased to partner with an acclaimed audio solutions leader such as EPOS in advancing state-of-the-art digital technologies," said Venu Lambu, Executive Director and President of Global Markets at Mindtree.

The human brain is nothing short of a marvel. We are reminded of this fact each time we read about a technology helping it out of boredom or drawing inspiration from it. How often have you heard complaints about the fact that creativity is being sucked out of human minds when they must do monotonous tasks? Well, Artificial intelligence (AI) and cognitive computing are two stellar technologies crafted in order to reduce human intervention and improve business processes across industries. You needn't be confused about the interchangeable usage, because certain features set AI and cognitive computing apart from each other.

Cognitive computing has been on a lot of minds lately. Looking into the capabilities of Artificial Intelligence to imitate human perception to some extent, the technology innovators have discovered that cognitive computing is a better fit for that. We suddenly realized that a lot more can be done in that regard -- instead of imitating only the perception, we can have technology make decisions like humans. Sharing the idea among the team members of AIHunters, we have tasked ourselves with an ambition of cognitive business automation in the media and entertainment industry. Let us take you on a tour of how we did that -- deliver the solution that puts innovation towards optimizing the video processing and post-production, while pushing beyond the limitations of regular AI analysis.