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"
SPLICE Software, which specializes in using big and small data and voice technologies to drive customer engagement, announced that the company was named one of the 10 Most Trustable Insurance Tech Solution Providers to Watch in 2020 by The Technology Headlines, a knowledge platform for enterprise IT community leaders. The publication's January issue includes a feature story on SPLICE and Tara Kelly, the company's founder, president and CEO. In its overview of the company, the article highlights Kelly's inspiration for creating SPLICE: a faulty AI-driven customer interface at a bank that left her thinking there had to be a better way to use customer data in automated interactions. Nearly a decade and a half later, SPLICE is providing that "better way" for clients in the insurance, financial services and retail sectors, giving forward-thinking organizations the ability to deliver personalized, data-driven messages seamlessly across multiple platforms. "Trust is incredibly important in any industry, but especially in the insurance sector, so it's an honor to be included on the 10 Most Trustable list," said Kelly.
Marlyse is a third-year PhD student in the Computer Science and Artificial Intelligence Laboratory at MIT. She received her B.S. in Aeronautics and Astronautics from MIT in 2017. Her current research in the Model-based Embedded and Robotic Systems Group focuses on multi-vehicle online planning, incorporating complex dynamics and constraints. She is also interested in risk-aware planning, fault protection and diagnosis, and adaptive sampling. Outside of the lab, she enjoys playing soccer, dancing, and reading science fiction.
Join the meetings by pointing your web browser to: https://zoom.us/j/7371462221 Join the CSIG LinkedIn Group to get reminders about talks and discuss them. Replays before Dec 2015: Dial 877.471.6587 or 402.970.2667 and enter the call's Replay ID when prompted for a program ID number. The Replay ID is listed in the Recording column of each date. "Solving Large-Scale Machine Learning Problems in a Distributed Way"
Showcase your interdisciplinary side with this greeting card featuring the various disciplines that comprise cognitive science, along with the scientific truism saying "Cognitive Science Is Interdisciplinary". Whether it is linguistics, psychology, philosophy, computer science, anthropology, or artificial intelligence, make others do a double-take at how creative and interdisciplinary cognitive science really is with a dose of educational fun!
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.