"Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures."
– Paul Thagard. Cognitive Science , in The Stanford Encyclopedia of Philosophy.
Technical skills and data literacy are obviously important in this age of AI, big data, and automation. But that doesn't mean we should ignore the human side of work – skills in areas that robots can't do so well. I believe these softer skills will become even more critical for success as the nature of work evolves, and as machines take on more of the easily automated aspects of work. In other words, the work of humans is going to become altogether more, well, human. With this in mind, what skills should employees be looking to cultivate going forward?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks the typically require human intelligence. At its core, AI is the branch of computer science that aims to answer Turing's question in the affirmative. It is an endeavour to replicate or simulate human intelligence in machines. The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.
Also explains sequence and string functions, slicing, concatenating, iterating, sorting, etc. with code examples. Also explains sequence and string functions, slicing, concatenating, iterating, sorting, etc. with code examples. This course combines conceptual lectures to explain how a data structure works, and code lectures that walk through how to implement a data structure in Python code. All the code lectures are based on Python 3 code in a Jupyter notebook. Data structures covered in this course include native Python data structures String, List, Tuple, Set, and Dictionary, as well as Stacks, Queues, Heaps, Linked Lists, Binary Search Trees, and Graphs.
This competition aims at developing the state-of-the-art models for emotion recognition from emotion-labelled facial videos with facial, speech, and text data. Participants are expected to put their efforts for obtaining the highest accuracy of recognizing one out of 7-classes, i.e., the neural and 6 emotions. The rank of participating teams will be determined by the order of their final scores.
Chimpanzees get more selective over who they associate themselves with as they age, new research reveals. In a study spanning two decades in a Ugandan national park, US experts observed social interactions among 21 wild male chimps, ranging in age from 15 to 58 years. Both chimps and humans prefer to be around the company of old friends and spend less time among new faces, the experts conclude. Ageing male chimps have more mutual and positive friendships than younger chimps, who have more one-sided, antagonistic relationships. Chimps also showed a shift from negative interactions to more positive ones as they reached their twilight years, 'like humans looking for some peace and quiet'.
Daniel Pinker believes that we are about to move from the information age to the concept age. In the conceptual age, humans' pursuit of emotion and experience is constantly escalating. People with more creativity and empathy will take the lead, and these people will be people with right brain thinking. The left brain is responsible for logic and analysis, and is a common linear thinking. The right brain is responsible for emotions and feelings and is a comprehensive thinking.
A professor of neuroscience at the University of Surrey claims to have solved the long-standing mystery of what creates human consciousness. According to Dr Johnjoe McFadden, the electromagnetic field produced by the brain's neurons is what produces this uniquely human trait. Vast amounts of research has gone into deciphering why we have the ability to know we think, whereas other animals do not. Previous attempts to understand this have included the spiritual and supernatural, including suggesting it comes from a soul. But Professor McFadden is basing his theory, published in the journal Neuroscience of Consciousness, on well-known scientific fact.
A new feature to be found in modern CAD software releases is KBE (Knowledge Based Engineering) to support diagnosis, selection, and monitoring of tasks. KBE relies on capturing and storing experiential knowledge which includes proprietary design and manufacturing practices exercised during a product development cycle. KBE helps engineering companies to retain and preserve in-house knowledge and intellectual information. A related technology which could significantly augment problem solving capabilities in CAD software is AI (Artificial Intelligence), which was introduced in the mid-1980s. The purpose of AI is to learn and replicate human problem solving capabilities.
MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision. The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain. Led by Kohitij Kar, a postdoc at the McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, the study looked at an area called the ventrolateral prefrontal cortex (vlPFC), which sends feedback signals to the inferior temporal (IT) cortex via a network of neurons. The main goal of this study was to test how the back-and-forth information processing of this circuitry -- that is, this recurrent neural network -- is essential to rapid object identification in primates. The current study, published in Neuron and available via open access, is a followup to prior work published by Kar and James DiCarlo, the Peter de Florez Professor of Neuroscience, the head of MIT's Department of Brain and Cognitive Sciences, and an investigator in the McGovern Institute and the Center for Brains, Minds, and Machines.
A University of Michigan-led research team has uncovered a neural network that enables Drosophila melanogaster fruit flies to convert external stimuli of varying intensities into a "yes or no" decision about when to act. The research, described in Current Biology, helps to decode the biological mechanism that the fruit fly nervous system uses to convert a gradient of sensory information into a binary behavioral response. The findings offer up new insights that may be relevant to how such decisions work in other species, and could possibly even be applied to help artificial intelligence machines learn to categorize information. Senior study author Bing Ye, PhD, a faculty member at the University of Michigan Life Science Institute (LSI), believes the mechanism uncovered could have far-reaching applications. "There is a dominant idea in our field that these decisions are made by the accumulation of evidence, which takes time," Ye said.