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NVIDIA : Launches New SHIELD TV, The Most Advanced Streamer 4-Traders

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LAS VEGAS, NV--(Marketwired - Jan 4, 2017) - CES -- NVIDIA (NASDAQ: NVDA) today unveiled the new NVIDIA SHIELD TV -- an Android open-platform media streamer built on bleeding-edge visual computing technology that delivers unmatched experiences in streaming, gaming and AI. Sporting a sleek, new design and now shipping with both a remote and a game controller, SHIELD provides the best, most complete entertainment experience in the living room. "NVIDIA's rich heritage in visual computing and deep learning has enabled us to create this revolutionary device," said Jen-Hsun Huang, founder and chief executive officer of NVIDIA, who revealed SHIELD during his opening keynote address at CES. "SHIELD TV is the world's most advanced streamer. Its brilliant 4K HDR quality, hallmark NVIDIA gaming performance and broad access to media content will bring families hours of joy. And with SHIELD's new AI home capability, we can control and interact with content through the magic of artificial intelligence from anywhere in the house," he said.


Engineering Intelligent Systems using Machine Learning

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What is Next in MLTechnology? Use Cases & Demo 1 2 3 4 5 4. Machine Learning "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" – T. Michell (1997) Example: A program for soccer tactics • Task: Win the game • Performance: Goals • Experience: (x) Players' movements (y) Evaluation 6. A few thousand years ago: Manual Plowing Today:Automated Plowing Path of Machine Evolution… 7. Automation Evolution System that Do • Replicate repetitive human actions System that Think • Cognitive capabilities handle judgment-oriented tasks System that Learn/Adapt • Learn to understand context and adapt to users and systemsRobotic Automation CognitiveAutomation IntelligentAutomation Natural Language Processing Big Data Analytics Artificial Intelligence Machine Learning Large Scale Processing Adaptive Alteration Rule Engine Screen Scraping Workflow Unstructured Data Processing (Extraction) Knowledge Modelling (Ontologies) Implementation: • Macro-based applets • Screen Scraping data collection • Workflow Implementation • Process Mapping • Business Process Management Implementation: • Built-in Knowledge repository • Learning capabilities • Ability to work with unstructured data • Pattern recognition • Reading source data manuals Implementation: • Artificial Intelligence Systems • Natural Language Understanding and Generation • Self Optimizing / Self Learning • Predictive Analytics / hypothesis generation • Evidence based learning Capabilities Capabilities Capabilities 8. Evolution of Machine Intelligence • Raw computing power can automate complex tasks!Great Algorithms Fast Computers • Automating automobiles into autonomous automata!More Data Real- Time Processing • Automating question answering and information retrieval!Big Data In- Memory Clusters • Deep Learning Smart Algorithms Master Gamer Deep Learning • New algorithm learns handwriting of unseen symbols from very few training examples (unlike typical Deep Learning) ImproveTraining Efficiency IBM Deep Blue Google Self Driven Cars Watson Jeopardy Deepmind Atari Game One Shot Learning 9. Why Machine Learning? Human Behavior & their Life are not logical like Code, not linear like a Formulas and not consistent like Rules, so it is hard for Machines to understand & respond to humans, that is the challenge for todays Digital world. Unless, Machine starts to Learn this ever changing human behavior, it can neither understand effectively nor respond intelligently & personally with its human counterpart.


Engineering Intelligent Systems using Machine Learning

#artificialintelligence

What is Next in MLTechnology? Use Cases & Demo 1 2 3 4 5 4. Machine Learning "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" – T. Michell (1997) Example: A program for soccer tactics • Task: Win the game • Performance: Goals • Experience: (x) Players' movements (y) Evaluation 6. A few thousand years ago: Manual Plowing Today:Automated Plowing Path of Machine Evolution… 7. Automation Evolution System that Do • Replicate repetitive human actions System that Think • Cognitive capabilities handle judgment-oriented tasks System that Learn/Adapt • Learn to understand context and adapt to users and systemsRobotic Automation CognitiveAutomation IntelligentAutomation Natural Language Processing Big Data Analytics Artificial Intelligence Machine Learning Large Scale Processing Adaptive Alteration Rule Engine Screen Scraping Workflow Unstructured Data Processing (Extraction) Knowledge Modelling (Ontologies) Implementation: • Macro-based applets • Screen Scraping data collection • Workflow Implementation • Process Mapping • Business Process Management Implementation: • Built-in Knowledge repository • Learning capabilities • Ability to work with unstructured data • Pattern recognition • Reading source data manuals Implementation: • Artificial Intelligence Systems • Natural Language Understanding and Generation • Self Optimizing / Self Learning • Predictive Analytics / hypothesis generation • Evidence based learning Capabilities Capabilities Capabilities 8. Evolution of Machine Intelligence • Raw computing power can automate complex tasks!Great Algorithms Fast Computers • Automating automobiles into autonomous automata!More Data Real- Time Processing • Automating question answering and information retrieval!Big Data In- Memory Clusters • Deep Learning Smart Algorithms Master Gamer Deep Learning • New algorithm learns handwriting of unseen symbols from very few training examples (unlike typical Deep Learning) ImproveTraining Efficiency IBM Deep Blue Google Self Driven Cars Watson Jeopardy Deepmind Atari Game One Shot Learning 9. Why Machine Learning? Human Behavior & their Life are not logical like Code, not linear like a Formulas and not consistent like Rules, so it is hard for Machines to understand & respond to humans, that is the challenge for todays Digital world. Unless, Machine starts to Learn this ever changing human behavior, it can neither understand effectively nor respond intelligently & personally with its human counterpart.


Google DeepMind Gives Computer 'Dreams' to Improve Learning

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But the newest artificial intelligence system from Google's DeepMind division does indeed dream, metaphorically at least, about finding apples in a maze. Researchers at DeepMind wrote in a paper published online Thursday that they had achieved a leap in the speed and performance of a machine learning system. It was accomplished by, among other things, imbuing technology with attributes that function in a way similar to how animals are thought to dream. The paper explains how DeepMind's new system -- named Unsupervised Reinforcement and Auxiliary Learning agent, or Unreal -- learned to master a three-dimensional maze game called Labyrinth 10 times faster than the existing best AI software. It can now play the game at 87 percent the performance of expert human players, the DeepMind researchers said.


Mark Hamill talks Squadron 42: Oh how far we've come since Wing Commander

PCWorld

Hamill is part of an all-star cast that spent time with game designer Chris Roberts (creator of Wing Commander) to bring full performance-capture acting to the characters in Squadron 42. The upcoming PC action game is part of the massive crowdfunded Star Citizen universe. Performance-capture acting involves three cameras on the face, 50 cameras capturing body movements, and a whole lot of sensors. If it wasn't fun to work with a guy who's playing the player, it could make this job kind of a chore rather than a delight, and luckily for us it was just great fun to do.


NVIDIA Corporation (NASDAQ:NVDA) - NVIDIA Q1'16 Earnings Conference Call: Full Transcript

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With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. We also extended our VR platform by adding special kits to our VR work software development kit that helps to provide an even greater sense of presence with NVR. The P100 utilizes a combination of technologies including NVLink, our high speed interconnect to learning application performance to scale on multiple GPUs, primarily bandwidth and multiple hardcore features design to natively accelerate AI applications. Universities hyperscale vendors and large enterprises developing AI based applications are showing strong interest in the system.


Gamasutra: Kain Shin's Blog - Optimizing AI for The Magic Circle

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HAZARD AVOIDANCE Creatures avoid zap walls unless they have lightning rod. So having another human manually place hint volumes around hazards and set its data properly was not an option we wanted to explore. The cost of this startup evaluation was negligible compared to the noticeable performance gain in areas away from the lava river in Overworld. OPTIMIZATIONS NOT DONE Some potential optimizations were considered, but ultimately not done... Cliff Edge Detection In addition to avoiding hazards, creatures avoid cliff edges, which also involve multiple raycasts.


Edge.org

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Perhaps the most important news of our day is that datasets--not algorithms--might be the key limiting factor to development of human-level artificial intelligence. In 2005, Google software achieved breakthrough performance at Arabic- and Chinese-to-English translation based on a variant of a statistical machine translation algorithm published seventeen years earlier, but used a dataset with more than 1.8 trillion tokens from Google Web and News pages gathered the same year. Finally, in 2015, Google DeepMind announced its software had achieved human parity in playing twenty-nine Atari games by learning general control from video using a variant of the Q-learning algorithm published twenty-three years earlier, but the variant was trained on the Arcade Learning Environment dataset of over fifty Atari games made available only two years earlier. Examining these advances collectively, the average elapsed time between key algorithm proposals and corresponding advances was about eighteen years, whereas the average elapsed time between key dataset availabilities and corresponding advances was less than three years, or about six times faster, suggesting that datasets might have been limiting factors in the advances.


Deep Q-Learning (Space Invaders)

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Ever since I learned about neural networks playing Atari games I wanted to reimplemnted it and learn how it works. Below you can see an AI playing Space Invaders. Average game reward (600 games) after N games played. Blue line is random strategy baseline, red line is best-action strategy baseline.


How artificial intelligence could radically transform education

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Artificial intelligence should be used to provide children with one-to-one tutoring to improve their learning and monitor their well-being, academics have argued. However, in a paper, academics from University College London's Knowledge Lab argue that AI systems could simulate human one-to-one tutoring by delivering learning activities tailored to a student's needs and providing targeted and timely feedback, all without an individual teacher present. It adds: "The increasing use of AIEd systems will enable the collection of mass data about which teaching and learning practices work best. "AIEd systems can provide tailored support to parents in the same way that they can for teachers and students, improving education and outcomes for both parents and their children.