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AI everywhere

#artificialintelligence

"We invented a computing model called GPU accelerated computing and we introduced it almost slightly over 10 years ago," Huang said, noting that while AI is only recently dominating tech news headlines, the company was working on the foundation long before that. Nvidia's tech now resides in many of the world's most powerful supercomputers, and the applications include fields that were once considered beyond the realm of modern computing capabilities. Now, Nvidia's graphics hardware occupies a more pivotal role, according to Huang – and the company's long list of high-profile partners, including Microsoft, Facebook and others, bears him out. GTC, in other words, has evolved into arguably the biggest developer event focused on artificial intelligence in the world.


Machine Learning and AI Have Roots in Neural Networks

#artificialintelligence

Artificial intelligence (AI) and machine learning are surging in popularity as these technologies become the foundation for making networks smarter, faster, and more intuitive. Today machine learning and AI are being touted as key elements to making the Internet of Things (IoT) and 5G a success. In fact at the recent Mobile World Congress conference in Barcelona, Spain, carriers like SK Telecom and Reliance talked about using machine learning by feeding it with analytics from network monitoring. Plus, big name companies like IBM are incorporating AI into well-known projects like Watson, which is being used for everything from security to IoT to the cloud. But AI and machine learning aren't new technologies.


Deep Learning Institute Workshop hosted by Dedicated Computing, NVIDIA and Milwaukee School of Engineering

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Dedicated Computing is co-hosting a Deep Learning Institute workshop in collaboration with NVIDIA and Milwaukee School of Engineering (MSOE). The workshop will take place at MSOE on April 13, 2017. Deep learning is a new area of machine learning that seeks to use algorithms, big data, and parallel computing to enable real-world applications and deliver results. Machines are now able to learn at the speed, accuracy, and scale required for true artificial intelligence. This technology is used to improve self-driving cars, aid mega-city planners, and help discover new drugs to cure disease.


*Applause* YouTube's caption upgrade shows how machine learning is helping the disabled

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FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.


*Applause* YouTube's caption upgrade shows how machine learning is helping the disabled

#artificialintelligence

FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.


*Applause* YouTube's caption upgrade shows how machine learning is helping the disabled

#artificialintelligence

FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.


*Applause* YouTube's caption upgrade shows how machine learning is helping the disabled

#artificialintelligence

FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.


*Applause* YouTube's caption upgrade shows how machine learning is helping the disabled

#artificialintelligence

FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.


Bringing brains to computers

AITopics Original Links

For decades, scientists have fantasized about creating robots with brain-like intelligence. This year, researchers tempted by that dream made great progress on achieving what has been called the holy grail of computing. Today, a wide variety of efforts are aimed at creating intelligent computers that can progressively learn and make smarter decisions. Millions of dollars this year were poured in efforts to create "silicon brains," or neuromorphic chips that mimic brain-like functionality to make computers smarter. The new chips could give eyes and ears to smart robots, which will be able to drive, identify objects, or even point out rotten fruit.


Universities, IBM join forces to build a brain-like computer

AITopics Original Links

IBM and four universities are planning a research project into cognitive computing, which seeks to build computers that operate in a manner closer to the human mind. The goal is to create systems that extend well beyond Watson, IBM's computer that famously competed on the trivia game show Jeopardy and defeated two former champions, IBM said in a news release. The project will be undertaken with Carnegie Mellon University, the Massachusetts Institute of Technology, New York University and Rensselaer Polytechnic Institute, IBM said. IBM linked the research with "big data," the term for using computers in new ways to process large volumes of structured and unstructured data in order to make it more accessible and useful. Topics to be explored include how applications can boost group decision making, how processing power and algorithms apply to artificial intelligence, how systems should be designed for more natural interaction and how deep learning impacts automated pattern recognition in science.