Leisure & Entertainment


AMD chases the AI trend with its Radeon Instinct GPUs for machine learning

PCWorld

With the Radeon Instinct line, AMD joins Nvidia and Intel in the race to put its chips into AI applications--specifically, machine learning for everything from self-driving cars to art. The company plans to launch three products under the new brand in 2017, which include chips from all three of its GPU families. The passively cooled Radeon Instinct MI6 will be based on the company's Polaris architecture. It will offer 5.7 teraflops of performance and 224GBps of memory bandwidth, and will consume up to 150 watts of power. The small-form-factor, Fiji-based Radeon Instinct MI8 will provide 8.2 teraflops of performance and 512GBps of memory bandwidth, and will consume up to 175 watts of power.


'Kubo' tackles deep issues - death, loss, healing - within its dream-like tale

Los Angeles Times

The gorgeously handcrafted stop-motion film seems to embark on that familiar hero's journey, only to find its own way home. "As we structured the thing, we were definitely well aware of the ground we were treading on, the formulas, the templates, the classics of the genre," says "Kubo" director and Laika Entertainment chief Travis Knight. "But while'Kubo' is in that tradition, it takes a different path when it gets to the end." The film looks different as well, with its character models and environments inspired by Japanese folklore. It's not a sequel, it's not based on specific myths or books; it just feels like it is rooted deeply somewhere.


Two Ways to Bring Shakespeare Into the Twenty-First Century

The New Yorker

For the four-hundredth anniversary of Shakespeare's death, Gregory Doran, the artistic director of the Royal Shakespeare Company, wanted to dazzle. He turned to "The Tempest," the late romance that includes flying spirits, a shipwreck, a vanishing banquet, and a masque-like pageant that the magician Prospero stages to celebrate his daughter's marriage. "The Tempest" was performed at the court of King James I, and it may have been intended in part to showcase the multimedia marvels of Jacobean court masques. "Shakespeare was touching on that new form of theatre," Doran told me recently, over the phone. "So we wanted to think about what the cutting-edge technology is today that Shakespeare, if he were alive now, would be saying, 'Let's use some of that.' " The politics behind Shakespeare and stage illusion are more fraught than usual these days.


What makes Bach sound like Bach? New dataset teaches algorithms classical music

#artificialintelligence

What makes Bach sound like Bach? MusicNet is a new publicly available dataset from UW researchers that labels each note of 330 classical compositions in ways that can teach machine learning algorithms about the basic structure of music.Yngve Bakken Nilsen, flickr The composer Johann Sebastian Bach left behind an incomplete fugue upon his death, either as an unfinished work or perhaps as a puzzle for future composers to solve. A classical music dataset released Wednesday by University of Washington researchers -- which enables machine learning algorithms to learn the features of classical music from scratch -- raises the likelihood that a computer could expertly finish the job. MusicNet is the first publicly available large-scale classical music dataset with curated fine-level annotations. It's designed to allow machine learning researchers and algorithms to tackle a wide range of open challenges -- from note prediction to automated music transcription to offering listening recommendations based on the structure of a song a person likes, instead of relying on generic tags or what other customers have purchased. "At a high level, we're interested in what makes music appealing to the ears, how we can better understand composition, or the essence of what makes Bach sound like Bach.


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.


Cutting Through the Machine Learning Hype – RRE Ventures Perspectives

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The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. Pitch decks and headlines today are lousy with references to "artificial intelligence" and "machine learning". But what do those terms really mean?


Cutting Through The Machine Learning Hype

#artificialintelligence

Domo And CEO Josh James Take Aim At Tableau, Bring Flo Rida And Snoop Dogg To Tableau's Conference Let's punch through the noise around machine learning. The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. "It's clear that 9 of 10 investors have very little idea what AI is so if you're a founder raising money, you should sprinkle some AI into your pitch deck.


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.


Artificial intelligence makes PIQ sports wearables smarter, you more efficient

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By bringing artificial intelligence to its wearable tech, PIQ is looking to improve on its design. Until now, sports wearables have largely boiled down to high-tech sensors recording basic data. With the addition of GAIA Intelligence, the company will be able to make the PIQ Robot that much better at improving your performance. Both the PIQ Robot and GAIA Intelligence give coaches and athletes the ability to analyze every movement during a game or match. This data can then be compared with any previous performances as well as with a community's performance overall.


Music and artificial intelligence

#artificialintelligence

Research in artificial intelligence (AI) is known to have impacted medical diagnosis, stock trading, robot control, and several other fields. Perhaps less popular is the contribution of AI in the field of music. Nevertheless, artificial intelligence and music (AIM) has, for a long time, been a common subject in several conferences and workshops, including the International Computer Music Conference, the Computing Society Conference and the International Joint Conference on Artificial Intelligence. In fact, the first International Computer Music Conference was the ICMC 1974, Michigan State University, East Lansing, USA Current research includes the application of AI in music composition, performance, theory and digital sound processing. Several music software applications have been developed that use AI to produce music.