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To reach human-level intelligence, AI systems must truly understand language


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TROY, N.Y. The original goal of human-like artificial intelligence was abandoned decades ago in favor of less ambitious approaches, two cognitive scientists argue in a new book. If that initial vision is to be realized, they say, AI systems will require a full understanding of language and meaning, the development of which remains a daunting but doable challenge.
In Linguistics for the Age of AI, published by MIT Press, co-authors Marjorie McShane and Sergei Nirenburg, both faculty in the Department of Cognitive Science at Rensselaer Polytechnic Institute and co-directors of the Language-Endowed Intelligent Agents Lab, present a novel approach to language processing for AI systems. ....

Peter Baldwin , Lisbet Rausing , Sergei Nirenburg , Marjorie Mcshane , Intelligent Agents Lab , Cognitive Science , Rensselaer Polytechnic Institute , Language Endowed Intelligent Agents Lab , Practical Approach , Ontological Semantics , Knowledge Based Machine , Graduate Postgraduate Education , Language Linguistics Speech , Technology Engineering Computer Science , Computer Science , Multimedia Networking Interface Design , Research Development , Robotry Artificial Intelligence , பீட்டர் பால்ட்வின் , மார்ஜோரி மக்ஶேந் , அறிவாற்றல் அறிவியல் , ரென்சீலர் பாலிடெக்நிக் நிறுவனம் , ப்ர்யாக்டிகல் அணுகுமுறை , அறிவு அடிப்படையிலானது இயந்திரம் , பட்டதாரி போஸ்ட்‌க்ர்யாஜுயேட் கல்வி , மொழி மொழியியல் பேச்சு ,

Algorithm helps artificial intelligence systems dodge


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In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward.
Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action steer right, steer left, or continue straight to avoid hitting a pedestrian that its cameras see in the road.
But what if there s a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called adversarial inputs, it might take unnecessary and potentially dangerous action. ....

Michael Everett , Ford Motor Company , Department Of Aeronautics , Transactions On Neural Networks , Certified Adversarial Robustness , Deep Reinforcement , Neural Networks , Astro Professor Jonathan , Mathematics Statistics , Algorithms Models , Technology Engineering Computer Science , Computer Science , Multimedia Networking Interface Design , Software Engineering , Theory Design , Research Development , Robotry Artificial Intelligence , மைக்கேல் எவரெட் , ஃபோர்ட் மோட்டார் நிறுவனம் , துறை ஆஃப் வான்செலவுத்துறை , பரிவர்த்தனைகள் ஆன் நரம்பியல் நெட்வொர்க்குகள் , ஆழமான வலுவூட்டல் , நரம்பியல் நெட்வொர்க்குகள் , ஆஸ்ட்ரோ ப்ரொஃபெஸர் ஜொனாதன் , கணிதம் புள்ளிவிவரங்கள் , தொழில்நுட்பம் பொறியியல் கணினி அறிவியல் ,

Rise of marine predators reshaped ocean life as dramatically as sudden mass extinctions


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IMAGE: A seminal 1981 study organized the a history of ocean life into three hierarchies, with certain animals reigning the seas during each time periods. Two mass extinctions cleared the way.
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Credit: Jeff Gage/Florida Museum of Natural History
GAINESVILLE, Fla. - Evolutionary arms races between marine animals overhauled ocean ecosystems on scales similar to the mass extinctions triggered by global disasters, a new study shows.
Scientists at Umeå University in Sweden and the Florida Museum of Natural History used paleontological databases to build a multilayered computer model of the history of marine life over the last 500 million years. Their analysis of the fossil record closely echoed a seminal 1981 study by paleontologist J. John Sepkoski - with one key difference. ....

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Researchers introduce a new generation of tiny, agile drones


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IMAGE: Insects remarkable acrobatic traits help them navigate the aerial world, with all of its wind gusts, obstacles, and general uncertainty.
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Credit: Courtesy of Kevin Yufeng Chen
If you ve ever swatted a mosquito away from your face, only to have it return again (and again and again), you know that insects can be remarkably acrobatic and resilient in flight. Those traits help them navigate the aerial world, with all of its wind gusts, obstacles, and general uncertainty. Such traits are also hard to build into flying robots, but MIT Assistant Professor Kevin Yufeng Chen has built a system that approaches insects agility. ....

Zhijian Ren , Pakpong Chirarattananon , Kevin Yufeng Chen , Siyi Xu , Daniel Ackerman , City University Of Hong Kong , Research Laboratory Of Electronics , Harvard University Ph , Department Of Electrical Engineering , News Office , Professor Kevin Yufeng Chen , Electrical Engineering , Computer Science , Research Laboratory , Harvard University Phd , City University , Hong Kong , Technology Engineering Computer Science , Multimedia Networking Interface Design , Software Engineering , Theory Design , Robotry Artificial Intelligence , டேனியல் அக்கர்மன் , நகரம் பல்கலைக்கழகம் ஆஃப் ஹாங் காங் , ஆராய்ச்சி ஆய்வகம் ஆஃப் மின்னணுவியல் , ஹார்வர்ட் பல்கலைக்கழகம் பீயெச் ,