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3 Growth Stocks That Could Soar More Than Nvidia -- The Motley Fool

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NVIDIA's (NASDAQ:NVDA) graphic cards have long been favorites among hardcore gamers, but who would've thought the chipmaker's stock would explode the way it has in recent times? The share price has more than tripled in just the past year, turning NVIDIA into a near eight-bagger in just five years. Of course, there's more to its run than just graphics processors. It's more an artificial intelligence computing company today, having made huge headway in two of the hottest technology fields of our times: AI and self-driving cars. For investors looking to find the "next NVIDIA," the trick is to find a company that is sitting on a big growth opportunity, or is already tapping into a soon-to-heat-up trend, but that is still flying under Wall Street's radar.


Windows 10 Creators Update: The 5 biggest changes

PCWorld

Microsoft just announced that the Windows 10 Creators Update will start rolling out on April 11, building upon the foundation laid by vanilla Windows 10 and its subsequent "November" and "Anniversary" updates. While not every feature that Microsoft promised at the Creators Update's reveal last fall actually made the final cut, it's still overflowing with helpful new extras that polish rough edges and just plain make things more fun. You'll need to read PCWorld's comprehensive Windows 10 Creators Update review for our hands-on impressions, or PCWorld's mammoth Creators Update roundup for nitty-grittier feature details, but here are the five biggest changes you'll encounter when it rolls out to your device. The first change you'll notice in the Windows 10 Creators Update is literally the first thing you'll see when you boot up: An improved installation process. Microsoft's taken a lot of heat for Windows 10's deeper hooks into your personal data.


Windows 10 Creators Update review: Gaming, inking, and Edge win, while mixed reality loses

PCWorld

Microsoft's Windows 10 Creators Update offers the most significant upgrade to Windows 10 since its launch, splashing a bright, cheery coat of fun over Windows 10's productivity foundation. Microsoft announced Wednesday morning that this free upgrade will begin rolling out to existing users as soon as April 11. New users will need to pay $120 for Windows 10 Home or $200 for Windows 10 Pro--remember, Windows 10 itself is no longer free. Insiders already have the Creators Update, as the company also confirmed Wednesday, and we used the Insider build to write this review. The Creators Update adds numerous new capabilities that Windows previously lacked.


Microsoft's Windows 10 Creators Update is an OS done right

Mashable

With 400 million machines now running Windows 10, it's proving to be an ascendant operating system, and the disastrous Windows 8 is barely a spec in Microsoft's rear-view mirror. Even though Windows 10 only has 19 percent of the desktop OS market, according to Net Market Share, and Windows 7 still has almost 50 percent, the momentum is clear. The Windows users who held onto Windows 95 for years, and that still cling to Windows 7 like a lifeboat in rough operating system seas, are slowly but surely migrating to what very well may be the best Windows ever. SEE ALSO: Microsoft'Windows 10 Cloud' could challenge Google's Chrome OS Those who feared the Start Button-less Windows 8 are embracing the software-as-service model of Windows 10 (and, of course, breathing a sigh of relief over the return of the Start menu). It doesn't hurt Windows 10 that Microsoft has, in recent years, paired it with some remarkable home-grown hardware: Surface Pro 4, Surface Book and the new Surface Studio.


Evolution Strategies as a Scalable Alternative to Reinforcement Learning

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Our finding continues the modern trend of achieving strong results with decades-old ideas. For example, in 2012, the "AlexNet" paper showed how to design, scale and train convolutional neural networks (CNNs) to achieve extremely strong results on image recognition tasks, at a time when most researchers thought that CNNs were not a promising approach to computer vision. Similarly, in 2013, the Deep Q-Learning paper showed how to combine Q-Learning with CNNs to successfully solve Atari games, reinvigorating RL as a research field with exciting experimental (rather than theoretical) results. Likewise, our work demonstrates that ES achieves strong performance on RL benchmarks, dispelling the common belief that ES methods are impossible to apply to high dimensional problems. ES is easy to implement and scale.


Learning language by playing games

AITopics Original Links

MIT researchers have designed a computer system that learns how to play a text-based computer game with no prior assumptions about how language works. Although the system can't complete the game as a whole, its ability to complete sections of it suggests that, in some sense, it discovers the meanings of words during its training. In 2011, professor of computer science and engineering Regina Barzilay and her students reported a system that learned to play a computer game called "Civilization" by analyzing the game manual. But in the new work, on which Barzilay is again a co-author, the machine-learning system has no direct access to the underlying "state" of the game program -- the data the program is tracking and how it's being modified. "When you play these games, every interaction is through text," says Karthik Narasimhan, an MIT graduate student in computer science and engineering and one of the new paper's two first authors.


University of Florida News

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Emotions tend to run high in hospitals, and patients or patients' loved ones can be rude to medical professionals when they perceive inadequate care. But berating your child's doctor could have harmful -- even deadly -- consequences, according to new research. The findings by University of Florida management professor Amir Erez and doctoral student Trevor Foulk reinforce their prior research that rudeness has "devastating effects on medical performance," Erez said. A Johns Hopkins study estimated that more than 250,000 deaths are attributed to medical errors in the U.S. annually--which would rank as the third-leading cause of death in the U.S., according to statistics from the Centers for Disease Control and Prevention. Some errors could be explained by a doctor's poor judgment due to a chronic lack of sleep.


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.