Psychological research was in a kind of rut in 1955 when George A. Miller, a professor at Harvard, delivered a paper titled "The Magical Number Seven, Plus or Minus Two," which helped set off an explosion of new thinking about thinking and opened a new field of research known as cognitive psychology. The dominant form of psychological study at the time, behaviorism, had rejected Freud's theories of "the mind" as too intangible, untestable and vaguely mystical. Its researchers instead studied behavior in laboratories, observing and recording test subjects' responses to carefully administered stimuli. Dr. Miller, who died on July 22 at his home in Plainsboro, N.J., at the age of 92, revolutionized the world of psychology by showing in his paper that the human mind, though invisible, could also be observed and tested in the lab. "George Miller, more than anyone else, deserves credit for the existence of the modern science of mind," the Harvard psychologist and author Steven Pinker said in an interview.

Google has acquired Boston Dynamics, a company that builds robots that mimic the movements of humans and animals with stunning dexterity and speed. "We are looking forward to this next chapter in robotics and in what we can accomplish as part of the Google team," Boston Dynamics co-founder Marc Raibert said via email. Boston Dynamics is the eighth robotics company that Google has acquired in the past six months, according to The New York Times, which first reported the news on Friday. Earlier this month, the Times reported that Google has named former Android chief Andy Rubin as the company's lead for its robotics projects. On its YouTube channel, Boston Dynamics has videos of its impressive robots, including WildCat, a four-legged robot designed to run fast in all terrains, Cheetah, which tops 28 miles-per-hour, and Petman, a human-like robot that balances himself as he walks, squats and does calisthenics, and simulates human physiology by controlling its temperature, humidity and sweating, according to the company.

When Kenneth Wayne Jennings, noted for holding the record for the longest winning streak of 74 games on the U.S. syndicated game show, bowed to IBM's Watson as the new "Jeopardy!" That was probably one small step for a computer but a giant leap for computing. It's ironic to say that Watson's dominance on the game show didn't come out of the blue. The result was a culmination of over a decade of IBM's research. "It opened up a new chapter in information technology called cognitive computing--based on the idea of a natural interaction between systems and people," says Zachary (Zach) Lemnios, vice president of strategy for IBM Research.

Cognitive modeling is an area of computer science that deals with simulating human problem solving and mental task processes in a computerized model. Such a model can be used to simulate or predict human behavior or performance on tasks similar to the ones modeled. Cognitive modeling is used in numerous artificial intelligence ( AI) applications, such as expert system s, natural language programming, and neural network s, and in robotics and virtual reality applications. Cognitive models are also used to improve products in manufacturing segments such as human factors engineering, and computer game and user interface design. Research into cognitive modeling is currently being conducted by academic and industry groups, including MIT, IBM, and Sandia National Laboratories.

Using humanoid robots to study human behavior.

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.

Machine learning is on the rise due to the technological convergence of the growth of big data, decreasing data storage costs, increasing computing power, improved artificial intelligence algorithms and acceleration of cloud computing. Machine learning is the ability for computers to learn without explicit programming. It's analogous to the human ability to identify an octopus based on the set of data input that goes to the brain, such as eight arms, tentacles, lack of skeleton and other characteristics, without having prior knowledge of every type of cephalopod mollusk in existence. However, human decision-making is subject to numerous cognitive biases that can easily distort judgement. For example, iconoclastic author Tom Peters highlights 159 cognitive biases that impact management decision-making (Peters, Tom.

Many robotics applications and scenarios that involve interaction with humans are safety or performance critical. A natural path to assessing such notions is to include a cognitive model describing typical human behaviors into a larger modeling context. In this work, we set out to investigate a combination of such a model with formal verification. We present a general and flexible framework utilizing methods from probabilistic model checking and discuss current pitfalls. We start from information about typical behavior, obtained from generalizing specific scenarios by the usage of inverse reinforcement learning. We translate this information in order to define a formal model exhibiting stochastic behavior (whenever significant data is present) or nondeterminism (if the model is underspecified or no significant data is present) that can be analyzed. This model for a human can be combined with a robot model by using standard parallel composition. The benefit is manyfold: First, safe or optimal strategies for involved robots regarding a human can be synthesized depending on the given model. In general, verification can determine if such benign strategies are even possible. Furthermore, the cognitive model itself can be analyzed with respect to possible unnatural behaviors; thereby feedback to developers of such models is provided. We evaluate and describe our approaches by means of a well-known model for visiomotor tasks and provide a framework that can readily incorporate other models.

A Bot monitoring tool that will capture user questions to the bot and send them to a slack channel. Any time, the bot does not have an answer, it will ping a human in slack and then the human will provide the answer to the bot. In turn, the bot will respond to the user.

Machine learning is on the rise due to the technological convergence of the growth of big data, decreasing data storage costs, increasing computing power, improved artificial intelligence algorithms and acceleration of cloud computing. Machine learning is the ability for computers to learn without explicit programming. It's analogous to the human ability to identify an octopus based on the set of data input that goes to the brain, such as eight arms, tentacles, lack of skeleton and other characteristics, without having prior knowledge of every type of cephalopod mollusk in existence. However, human decision-making is subject to numerous cognitive biases that can easily distort judgement. For example, iconoclastic author Tom Peters highlights 159 cognitive biases that impact management decision-making (Peters, Tom.