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Artificial Brains - The quest to build sentient machines

Artificial Brains - The quest to build sentient machines
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Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence | September 2015 Review articles By Ernest Davis, Gary Marcus Communications of the ACM, Vol. 58 No. 9, Pages 92-103 10.1145/2701413 Comments Who is taller, Prince William or his baby son Prince George? Can you make a salad out of a polyester shirt? Back to Top Key Insights To take another example, consider what happens when we watch a movie, putting together information about the motivations of fictional characters we have met only moments before. In this article, we argue that commonsense reasoning is important in many AI tasks, from text understanding to computer vision, planning and reasoning, and discuss four specific problems where substantial progress has been made. Commonsense in Intelligent Tasks The importance of real-world knowledge for natural language processing, and in particular for disambiguation of all kinds, was discussed as early as 1960, by Bar-Hillel,3 in the context of machine translation. Computer vision. The viewer infers the existence of objects that are not in the image at all.

The SyNAPSE Project | Outreach And Impacts | CELEST | NSF Science of Learning Center The ambitious mission of the DARPA sponsored SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) project, launched in early 2009, is to “investigate innovative approaches that enable revolutionary advances in neuromorphic electronic devices that are scalable to biological levels.” DARPA has awarded funds to three prime contractors: HP , HRL , and IBM . Members of CELEST within the Department of Cognitive and Neural Systems at Boston University are on subcontracts with both HP and HRL. HP's principal investigator is Greg Snider , of the Information and Quantum Systems Lab . HRL's principal investigator is Naryan Srinivasan of the Information and Systems Science Office. SyNAPSE is a complex, multi-faceted project, but traces its roots to two fundamental problems. CELEST’s Impact SyNAPSE’s Focus Looking at biological algorithms as a field, very little in the way of consensus has emerged. See the Neurdon blog for more.

NeuroWeb UCSD Neuroradiology Teaching File Database "The JHess Collection" The appearance and size of the images will vary depending on the computer, display resolution, and web browser you are using. TF-Set1 TF-Set10 TF-Set19 TF-Set28 TF-Set2 TF-Set11 TF-Set20 TF-Set29 TF-Set3 TF-Set12 TF-Set21 TF-Set30 TF-Set4 TF-Set13 TF-Set22 TF-Set5 TF-Set14 TF-Set23 TF-Set6 TF-Set15 TF-Set24 TF-Set7 TF-Set16 TF-Set25 TF-Set8 TF-Set17 TF-Set26 TF-Set9 TF-Set18 TF-Set27 "The Visiting Professor Series" A collection of unusual, difficult, and sometimes impossible cases to help make the professor's visit a memorable one! "Normal Anatomy Series" "Text Syllabus" Brain Orbit and ENT Spine {Search Database} This website contains post-graduate educational materials for physicians, fellows, and residents in training. For physician referral to UCSD Medical Center, call 619-543-8273. "Links" Prepared by John R. Pathology provided by Nancy Karpinski, M.D. Computer Consultant: Eman Ghobrial <eghobrial@ucsd.edu>

The AI Revolution: Road to Superintelligence PDF: We made a fancy PDF of this post for printing and offline viewing. Buy it here. (Or see a preview.) Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what’s happening in the world of AI is not just an important topic, but by far THE most important topic for our future. We are on the edge of change comparable to the rise of human life on Earth. — Vernor Vinge What does it feel like to stand here? It seems like a pretty intense place to be standing—but then you have to remember something about what it’s like to stand on a time graph: you can’t see what’s to your right. Which probably feels pretty normal… The Far Future—Coming Soon Imagine taking a time machine back to 1750—a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. 1. Speed.

DARPA, IBM Neurosynaptic Chip and Programming Language Mimic the Brain DARPA, IBM Neurosynaptic Chip and Programming Language Mimic the Brain Engineering is often inspired by nature—the hooks in velcro or dermal denticles in sharkskin swimsuits. Then there’s Darpa's SyNAPSE project. Not content with current computer architecture, SyNAPSE is building a new kind of computer based on the brain. Last year, scientists working on SyNAPSE announced they’d simulated 100 trillion synapses from a monkey brain on Sequoia, one of the world’s most powerful supercomputers. IBM’s Dharmendra S. The way computers currently manipulate information, shuttling it back and forth between memory and processor, is named after the early computer scientist John von Neumann. But the classical approach isn't well suited for creative, adaptive intelligence. True North is built on a network of “neurosynaptic cores” that place memory, processing, and communication close to one another so they can operate in parallel, much as they do in the brain. Image Credit: IBM/Darpa

The Human Memory - what it is, how it works and how it can go wrong Guy Hoffman IBM Scientists Show Blueprints for Brainlike Computing To create a computer as powerful as the human brain, perhaps we first need to build one that works more like a brain. Today, at the International Joint Conference on Neural Networks in Dallas, IBM researchers will unveil a radically new computer architecture designed to bring that goal within reach. Using simulations of enormous complexity, they show that the architecture, named TrueNorth, could lead to a new generation of machines that function more like biological brains. The announcement builds on IBM’s ongoing projects in cognitive computing. “It doesn’t make sense to take a programming language from the previous era and try to adapt it to a new architecture. In a series of three papers released today, Modha’s team details the TrueNorth system and its possible applications. Each core of the simulated neurosynaptic computer contains its own network of 256 “neurons,” which operate using a new mathematical model. “Programs” are written using special blueprints called corelets.

Neuroscience The Open Source Handbook of Neuroscience Neuroscience is a field that is devoted to the scientific study of the nervous system. Such studies may include the structure, function, development, genetics, biochemistry, physiology, pharmacology, and pathology of the nervous system. Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence Hillary Clinton at the Iowa State Fair on August 15, 2015 in Des Moines, Iowa. (Photo by Win McNamee/Getty Images) The meme that now seems to be dominating much of the media coverage of the Democratic Primary is that pundits and experts are underestimating Bernie Sanders’s chances of winning the Democratic nomination for president. Currently, Mr. Sanders is receiving so much press for being underrated that he has become overrated. Mr. Recency bias is essentially the tendency to predict upcoming events based too heavily on recent history, rather than a broader sample. Selection bias is a more general tendency to pick cases to support an argument rather than looking at the broader universe of cases or to make a random sample. Is Al Gore Hillary circa 2000? Competitive primaries make great drama and exciting story lines. This time lapse is only of the reasons primaries, despite the media they receive still remain, misunderstood. While Ms.

Abductive reasoning Abductive reasoning (also called abduction,[1] abductive inference[2] or retroduction[3]) is a form of logical inference that goes from an observation to a hypothesis that accounts for the observation, ideally seeking to find the simplest and most likely explanation. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. One can understand abductive reasoning as "inference to the best explanation".[4] The fields of law,[5] computer science, and artificial intelligence research[6] renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction. History[edit] The American philosopher Charles Sanders Peirce (1839–1914) first introduced the term as "guessing".[7] Peirce said that to abduce a hypothetical explanation from an observed circumstance is to surmise that may be true because then would be a matter of course.[8] Thus, to abduce from involves determining that is sufficient, but not necessary, for allows deriving

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