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"
Cognitive robotic process automation (RPA) is a fast-evolving field of computing and is an emerging form of business process automation (BPA) technology. It involves the automation of many internal and external customer journeys through software "bots." Imagine a finance clerk handling invoice processes by filling in specific fields on the screen. Early RPA was able to take this function off the clerk's plate by automating that invoice processing. The insurance sector soon discovered how this technology could be used for processing insurance premiums.
But attempting to model an intelligence after either the ephemeral human mind or the exact physical structure of the brain (rather than iterating increasingly capable Roombas) is no small task -- and with no small amount of competing hypotheses and models to boot. In fact, a 2010 survey of the field found more than two dozen such cognitive architectures actively being studied. The current state of AGI research is "a very complex question without a clear answer," Paul S. Rosenbloom, professor of computer science at USC and developer of the Sigma architecture, told Engadget. "There's the field that calls itself AGI which is a fairly recent field that's trying to define itself in contrast to traditional AI." That is, "traditional AI" in this sense is the narrow, single process AI we see around us in our digital assistants and floor-scrubbing maid-bots.
The invention of religion is a big bang in human history. Gods and spirits helped explain the unexplainable, and religious belief gave meaning and purpose to people struggling to survive. But what if everything we thought we knew about religion was wrong? What if belief in the supernatural is window dressing on what really matters--elaborate rituals that foster group cohesion, creating personal bonds that people are willing to die for. Anthropologist Harvey Whitehouse thinks too much talk about religion is based on loose conjecture and simplistic explanations. Whitehouse directs the Institute of Cognitive and Evolutionary Anthropology at Oxford University. For years he's been collaborating with scholars around the world to build a massive body of data that grounds the study of religion in science. Whitehouse draws on an array of disciplines--archeology, ethnography, history, evolutionary psychology, cognitive science--to construct a profile of religious practices.
Wave Computing, the Silicon Valley company accelerating artificial intelligence (AI) from the datacenter to the edge, announced its new TritonAI 64 platform, which integrates a triad of powerful technologies into a single, future-proof intellectual property (IP) licensable solution. Wave's TritonAI 64 platform delivers 8-to-32-bit integer-based support for high-performance AI inferencing at the edge now, with bfloat16 and 32-bit floating point-based support for edge training in the future. Wave's TritonAI 64 platform is an industry-first solution, enabling customers the ability to address a broad range of AI use cases with a single platform. The platform delivers efficient edge inferencing and training performance to support today's AI algorithms, while providing customers with flexibility to future-proof their investment for emerging AI algorithms. Features of the TritonAI 64 platform include a leading-edge MIPS 64-bit SIMD engine that is integrated with Wave's unique approach to dataflow and tensor-based configurable technology.
We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics nor on assumptions of the computational resources of the mind. Expected utility theory and classical probabilities tell us what people should do if employing traditionally rational thought, but do not tell us what people do in reality (Machina, 2009). Under this principle, L&G propose an architecture for cognition that can serve as an intermediary layer between Neuroscience and Computation. Whilst instances where large expenditures of cognitive resources occur are theoretically alluded to, the model primarily assumes a preference for fast, heuristic-based processing.
The history of science and technology is often delineated by paradigm shifts. A paradigm shift is a fundamental change in how we view the world and our relationship to it. The big paradigm shifts are sometimes even referred to as an "age" or a "revolution". The Space Age is a perfect example. The middle of the 20th Century saw not only an incredible increase in public awareness of space and space travel, but many of the industrial and technical advances that we now take for granted were byproducts of the Space Age.
In mammals this incorporates the cortex, the hippocampus, the claustrum, the amygdala, the basal ganglia, and the olfactory bulb. Convergent evolution results in analogous characters with similar appearances or functions, although these were not present in the last common ancestor of the two lineages. Most species are characterized by a high brain-to-body mass ratio, ecological flexibility, and a complex social life, featuring long-term partnerships and dynamic groups structured by social relationships. The term derives from the Greek word hodos which means'road'. Each layer is constituted by distinctive cell populations with unique connectivity patterns.
Cognitive Science is the study of thought, learning, and mental organization, which draws on aspects of psychology major, linguistics, philosophy, and computer modeling. The Cognitive Science Major/Field is made up of a diverse number of different majors, like linguistics, cognition, neurobiology, artificial intelligence, law, and many more. Cognitive Science Students may ask themselves things like: What job can I get with cognitive science? What does a Cognitive Science Lecture look like? How does the major effect Cognitive Artificial Intelligence?
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.