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 computing and machine learning are going to transform knowledge management. Chatbots, cognitive search, natural language processing (NLP), and semantic technologies accelerate the ability of humans to find what they need to do their jobs. But to foster an intelligence-driven organization that can handle a broad range of topics, the underlying search technology must be extremely robust. KMWorld recently held a webinar featuring Paul Nelson, innovation lead, Accenture Analytics; and Scott Parker, senior product marketing manager, Sinequa, who discussed how cognitive computing is changing knowledge management and what to do about it. Natural language processing is everywhere, Nelson explained, and the five main technologies utilizing this are chatbots, question answer, semantic search, fact extraction, and classification.
The IJCAI-09 Workshop on Learning Structural Knowledge from Observations (STRUCK-09) took place as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) on July 12 in Pasadena, California. The workshop program included paper presentations, discussion sessions about those papers, group discussions about two selected topics, and a joint discussion. As a result, many cognitive architectures use structural models to represent relations between knowledge of different complexity. Structural modeling has led to a number of representation and reasoning formalisms including frames, schemas, abstractions, hierarchical task networks (HTNs), and goal graphs among others. These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations.
However, a number of issues are repeated across chapters, and it is not clear that the authors of each chapter had a chance to read the other chapters while they wrote theirs. The different parts of the book could have been better (more explicitly) named; for example, domains on its own means little to me! The book has an advantage in that it provides a collection of chapters on the foundations of cognitive science written by different people; hence, we see differing points of view from experts in given areas, which could not be achieved by a single author. However, a criticism of the book is that nearly all the chapters are by authors with a U.S. affiliation, with a few from England, and I find it difficult to believe that leading cognitive scientists in other countries could not have written something. Thus, we get an American-Anglo view of cognitive science rather than an international one, such as that given in Ó'Nualláin (1995).
However, I believe that the distinction of "neats" and "scruffies" raised at Cog Sci in '81 didn't define scruffies as people who built expert systems [they didn't really exist as a "real" part of MAD. Instead, I believe AI These are the researchers who read Hawkings and say "gee, if his model of the lo-23 second big bang is right, then the distribution of intergalactic gases should be relatively even. Let's go see if that's true. However, to run our experiments we'll need a more sensitive space-based sensing device, so let's work with the engineers to design one." I think one could make the case (although not from the data collected in Cohen's survey) that the two methodologies are not informed and influenced by each other to the extent they should or could be.
Review of The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology If you are interested in writing a review, contact chandra@ cis.ohio-state.edu. AT question: Which one of the following doesn't belong with the rest? It is the only discipline in the list that is not under attack for being conceptually or methodologically confused. Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. However, all of them come down to one simple observation: Humans seem a lot smarter than computers--not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. All this is because of another truth: The computational paradigm is the best thing to come down the pike since the wheel. The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology, Jerry Fodor, Cambridge, Massachusetts, The MIT Press, 2000, 126 pages, $22.95. Jerry Fodor believes this latter claim. He says: [The computational theory of mind] is, in my view, by far the best theory of cognition that we've got; indeed, the only one we've got that's worth the bother of a serious discussion.… There is, in short, every reason to suppose that Computational Theory is part of the truth about cognition. It is a fascinating read. This dispute about quantity of truth is where the book gets its title. In 1997, Steven Pinker published an important book describing the current state of the art in cognitive science (see also Plotkin ). Pinker's book is entitled How the Mind Works. In it, he describes how computationalism, psychological nativism (the idea that many of our concepts are innate), massive modularity (the idea that most mental processes occur within a domain-specific, encapsulated specialpurpose processor), and Darwinian adaptationism combine to form a robust (but nascent) theory of mind. Fodor, however, thinks that the mind doesn't work that way or, anyhow, not very much of the mind works that way. Fodor dubs the synthesis of computationalism, nativism, massive modularity, and adaptationism the new synthesis (p.
Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees--and, in general, he succeeds. This book begins, "Cognitive science is the interdisciplinary study of mind and intelligence..." (p.
Knowing the difference between a platform powered by AI and one powered by cognitive computing is the key to deciding which is the best for your business. IBM's Watson cognitive computing platform might be going through a defining time right now, and part of that seems to do with a small-but-complex question: What is the difference between artificial intelligence (AI) and cognitive computing? It's an important question for any company and any system that's working within this sector, as our assumptions about these two terms define how we respond to the emerging and existing products that claim to do one or the other. If you don't know the difference between a platform powered by AI and one powered by cognitive computing, and what the implications of those differences, how can you decide which is the best for your business or your application? Artificial intelligence agents decide which actions are the most appropriate to take, and when they should be taken.
Cognitive scientists with varied backgrounds gathered in Berlin to report on and discuss expanding lines of research, spanning multiple fields but striving in one direction: to understand cognition with all its properties and peculiarities. A rich program featuring keynotes, symposia, workshops, and tutorials, along with regular oral and poster sessions, offered the attendees a vivid and exciting overview of where the discipline is going while serving as a fertile forum of interdisciplinary discussion and exchange. This report attempts to point out why this should matter to artificial intelligence as a whole. Although the conference has been the major international venue for cognitive science research for a long time, appealing to all seven discipline pillars -- anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, and psychology -- this year's edition topped every past meeting in terms of number of participants. An impressive figure of more than 1000 accepted contributions, divided among oral presentations (274), posters (685), symposia, workshops, and tutorials, could be accommodated in the program only by increasing the number of parallel sessions to 11 and enlarging the three poster sessions.
David has been a leader in bringing technology and insights to the philanthropic community for a generation. He created the first asset-based wealth screening service as well as the first software to manage screening data. In 1997, he founded Prospect Information Network (P!N), which became the largest wealth screening company before being purchased in 2004. P!N received the InfoCommerce Model of Excellence Award and introduced the first Software-as-a-Service application to support fundraising analytics. David is CEO and co-founder of NewSci, LLC.
I am sure we have all heard about Sophia the robot, as most of us have been fixated on her journey for quite some time now. Like Sophia, who has been constructed using Artificial Intelligence (AI) technology, which has become one of the industry's most followed technology of the season, is being studied by many scientists and researchers to connect the distinctions between machines and humans. How do these machines run on AI technology allowing them to operate independently, learning from their environment to interact how humans do. Isn't it marvelous and something to be in awe of? As Artificial Intelligence (AI) is still developing and advancing to claim the human-level intelligence, let us acknowledge the principles and methods it is deploying to improve the abilities of these machines to think like a human.