"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.
Playing with dolls uses a brain region that helps children develop empathy for other people and social processing skills, a study has discovered. Researchers from Cardiff found that doll play activated the use of the so-called posterior superior temporal sulcus (pSTS) more than other creative activities. In addition, the social benefits of the dolls were observed even when children played alone -- rather than with others -- and were equal among girls and boys. The findings support the pioneering theories of the Swiss'father of developmental psychology' Jean Piaget, who argued in 1945 that pretend play was inherently social. 'We use this area of the brain when we think about other people, especially when we think about another person's thoughts or feelings,' said paper author and developmental researcher Sarah Gerson of Cardiff University.
The Defense Advanced Research Projects Agency (DARPA) is setting its sights on developing an AI system with a detailed self-understanding of the time dimensions of its learned knowledge. DARPA's Time-Aware Machine Intelligence (TAMI) research program and incubator is looking to develop a new class of neural network architectures that incorporate an explicit time dimension as a fundamental building block for network knowledge representation," according to the TAMI program solicitation. The overall goal is to create an AI system that will be able to "think in and about time" when exercising its learned task knowledge in task performance. Current neural networks do not explicitly model the inherent time characteristics of their encoded knowledge. Consequently, state-of-the-art machine learning does not have the expressive capability to reason with encoded knowledge using time.
Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview.
AI and digital platforms challenge how we understand reality and our role in it. Because it mirrors our identity, technology provokes us to revisit outdated creeds while at the same time giving us the reins to personalize our human experience. Is AI going to take over the world? Will AI take my job? Internet users are particularly interested in how the AI-human symbiosis will shape over time.
The next industrial revolution is already happening. Artificial intelligence (AI) is ushering in an era of technologies that are faster, more adaptable, more efficient, and making the world more digitally connected. AI is best described as complementary to human intelligence, delivering the computing power to crunch numbers too big for people and recognize patterns too tedious for the human eye. In a Harvard Business Review study of 1,500 companies, it was found that the most significant performance improvements were made when humans and machines worked together. As AI becomes one of society's greatest assets, it's especially helpful for solving problems that seem larger than life -- like protecting our natural environment.
Element Human CEO Matt Celuszak is working on making robots capable of understanding human emotions. Forbes associate editor Thomas Brewster talks at length with him to know how AI can go beyond facial recognition. Celuszak points out that artificial intelligence is good at spotting patterns. For instance, in a camera, your emotions draw up pixels which the AI tools can detect. His staff then take all the data and categorize them according to emotions like smiling.
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?
Using those primitives, DeepMind generated a dataset known as Procedurally Generated Matrices(PGM) that consists of triplets [progression, shape, color]. The relationship between the attributes in a triplet represent an abstract challenge. For instance, if the first attribute is progression, the values of the other two attributes must along rows or columns in the matrix. In order to show signs of abstract reasoning using PGM, a neural network must be able to explicitly compute relatioships between different matrix images and evaluate the viability of each potential answer in parallel. To address this challenge, the DeepMind team created a new neural network architecture called Wild Relation Network(WReN) in recognition of John Rave's wife Mary Wild who was also a contributor to the original IQ Test. In the WReN architecture, a convolutional neural network(CNN) processes each context panel and an individual answer choice panel independently to produce 9 vector embeddings. This set of embeddings is then passed to an recurrent network, whose output is a single sigmoid unit encoding the "score" for the associated answer choice panel.
Researchers from several American universities are collaborating to develop artificial intelligence based software to help people on the autism spectrum find and hold meaningful employment. The project is a collaboration between experts at Vanderbilt, Yale, Cornell and the Georgia Institute of Technology. It consists of developing multiple pieces of technology, each one aimed at a different aspect of supporting people with Autism Spectrum Disorder (ASD) in the workplace, according to Nilanjan Sarkar, professor of engineering at Vanderbilt University and the leader of the project. "We realized together that there are some support systems for children with autism in this society, but as soon as they become 18 years old and more, there is a support cliff and the social services are not as much," Sarkar said. The project began a year ago with preliminary funding from the National Science Foundation. The NSF initially invested in around 40 projects, but only four -- including this one -- were chosen to be funded for a longer term of two years.
A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them'smart.' The study was published in Electronics. Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance.