The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlymg structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them.
– from Laird et al., "SOAR: An architecture for general intelligence"
Computers have always been faster than humans at consuming, calculating and computing data, and artificial intelligence is a boon for our global economy. Yet, computers do have their limitations. They are machines, after all, and lack native precision when attempting to recognize and interpret language, objects or images on demand. In those instances -- especially when verification is required to proceed with a transaction -- most programs require a fail-safe checkpoint. Ever notice the requests to verify that you're not a robot when submitting data online?
Artificial Intelligence and related fields have increased in scope and reach in the recent years. As its popularity grows there has been some indecisive understanding of the technical jargons that are under AI. These are often used interchangeably but there is quite a distinction in the approaches and objectives of these terms.One such technology is Cognitive computing which is associated with Artificial Intelligence but is actually very different from the latter. Though both represent the next big wave in supercomputing, the technologies hold separate meaning when brought to practical use. Tech target defines AI as "the simulation of human intelligence processes by machines, especially computer systems.
Cognitive Robotic Process Automation is the next step in the evolution of robotic process automation trends. Many of the leading robotic process automation companies are already eyeing the big shift towards the cognitive automation. Cognitive robotic process automation is basically a combination of robotic process automation and Data Analytics, which together make it easy and effective to manage processes that are information-intensive, in an intelligent and efficient manner. By that definition, it is a marriage between artificial intelligence and cognitive computing methods. By incorporating artificial intelligence, cognitive automation broadens the scope and depth of actions that would typically be associated with RPA.
In collaboration with more than 20 national universities, iFlytek launched its "Brain Science and Education" program on Saturday. The program will focus on the study of cognitive development of children, in a bid to explore new methods for individual learning and teaching. The domestic intelligent voice recognition technology company will invest more than 2 billion yuan ($299.03 million) in the program over the next 10 years. "I believe the program will be of great significance to the development of China's cognitive science and education industry," Liu Qingfeng, iFlytek's chairman, said at the launch event for the program, which was held in Beijing on Saturday. Liu also introduced some major breakthroughs the company achieved in the past year that would be applied to the new program.
Neuroethology has been an active field of study for more than a century now. Out of some of the most interesting species that has been studied so far, weakly electric fish is a fascinating one. It performs communication, echo-location and inter-species detection efficiently with an interesting configuration of sensors, neu-rons and a simple brain. In this paper we propose a cognitive architecture inspired by the way these fishes handle and process information. We believe that it is eas-ier to understand and mimic the neural architectures of a simpler species than that of human. Hence, the proposed architecture is expected to both help research in cognitive robotics and also help understand more complicated brains like that of human beings.
This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science. The first section mentions several useful general references, and the others provide supplementary readings on specific topics. If you would like to suggest some additions to the list, contact Tom Griffiths.
Cognitive computing means giving computers the ability to work out complex problems for themselves. Just like humans, cognitive computers benefit greatly from experience, learning better ways to solve problems with each encounter. When a traditional system of rules finds a task impossible, cognitive computing sees only an opportunity to expand its knowledge. The necessity for cognitive computing in the Internet of Things (IoT) arises from the importance of data in modern business. In the smart IoT venues of the future, everyone from startups to enterprises to homeowners will use data to make decisions using facts rather than instincts.
The study of artificial intelligence has frequently benefitted from close engagement with other branches of cognitive science, and computational theories of cognition have in turn contributed to models of the mind in philosophy, neuroscience, and animal cognition. However, even as we stand at the threshold of a new era of developments in artificial intelligence, disciplinary differences and disparate theoretical vocabularies still linger, and the goal of a unifying theory of human, animal, and artificial minds remains elusive. To that end, the Varieties of Mind conference aims to bring together leading researchers in psychology, animal cognition, artificial intelligence, and philosophy of mind to explore questions including the following. Please note that purchasing full conference tickets includes Public Lecture 1, Public Lecture 2, Debate 1 and Debate 2. There is no need to sign up to the other events. To be added to the waiting list, please contact Gaenor Moore.
Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel. Their computer algorithm starts with abundant data about the proteins and metabolites in a biofuel-producing microbial pathway, but no information about how the pathway actually works. It then uses data from previous experiments to learn how the pathway will behave. The scientists used the technique to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells. The new approach is much faster than the current way to predict the behaviour of pathways, and promises to speed up the development of biomolecules for many applications in addition to commercially viable biofuels, such as drugs that fight antibiotic-resistant infections and crops that withstand drought.
Course Description What is our role in the universe as human agents capable of knowledge? What makes us intelligent cognitive agents seemingly endowed with consciousness? This is the second part of the course'Philosophy and the Sciences', dedicated to Philosophy of the Cognitive Sciences. Scientific research across the cognitive sciences has raised pressing questions for philosophers. The goal of this course is to introduce you to some of the main areas and topics at the key juncture between philosophy and the cognitive sciences.