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- [Concept]While virtual and mixed reality experiences are trending right now (we’ve seen a lot of cool examples in movies), I feel that there’s a lack in convergence of practical interaction patterns.
- We haven’t seen the iPhone of mixed reality yet, so I decided to explore the user experience and interface aesthetics of mixed reality and share my ideas with the community.
- My goal is to encourage other designers to think and publish ideas on MR interfaces.As technology becomes invisible at all such levels, from a perceptual and cognitive point of view, interaction becomes completely natural and spontaneous.
- And one of the attributes of good interaction design is allowing Natural User Interfaces: those which are invisible to the user, and remain invisible as we learn them.
- Some examples of these interfaces are speech recognition, direct manipulation, and gestures.Apps as ObjectsI started by looking into an interaction that felt very natural: browsing records.I found this interaction interesting because of the following:Direct manipulation of the catalogPerception of progress while browsingFull visual of selected itemMinimal footprint of scrolled itemsI was thinking of a way to apply these principles to an interaction for browsing and launching apps in a mixed reality environment.In this case, the app cards are arranged in a stack and placed below the user’s point of view, at a comfortable reach distance.
While virtual and mixed reality experiences are trending right now (we’ve seen a lot of cool examples in movies), I feel that there’s a lack in convergence of practical interaction patterns. We haven…
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- emergence of big data that has made it possible to create and train a data structure.
- Big data now exists, and it’s increasingly a huge opportunity.”
- Will Ramey, senior product manager for Accelerated Computing at NVIDIA, credits a major factor for this “Big Bang of AI.”
- F rom self-driving cars to hi-tech medical imaging modalities, artificial intelligence (AI) is transforming the landscape of a variety of industries.
- “With big data, came the new challenge of understanding and extracting insights from that data,” said Ramey.
Scientific Computing provides focused coverage of software and related hardware technologies for the scientific and engineering communities, highlighting the latest applications, best practices and integration strategies that can aid in advancing research and in delivering quality results better, faster and cheaper. Key coverage areas include informatics, data analysis, integration and big data. Full-featured Scientific Computing and HPC Source e-magazines offer immediate interaction through audio, video and rich-media pages. Free subscriptions for these digital magazines, as well as the NewsWire daily e-newsletter, are available at www.ScientificComputing.com, where you can also find new product announcements, in-depth expert commentary and educational webcasts.
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“The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable,” says digital visionary Kevin Kelly — and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. “The most popular AI product 20 years from now that everyone uses has not been invented yet,” Kelly says. “That means that you’re not late.”
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- Faster R-CNN Pedestrian and Car Detection
https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0 Code https://github.com/bigsnarfdude/Faster-RCNN_TF Faster RCNN for Pedestrian Detection https://arxiv.org/pdf/1607.07032v2.pdf Tutorial http://kaiminghe.com/icml16tutorial/index.html http://techtalks.tv/talks/deep-residual-networks-deep-learning-gets-way-deeper/62358/ Faster RCNN original https://arxiv.org/pdf/1506.01497v3.pdf Slides ILSCVRC 2015 http://image-net.org/challenges/talks/ilsvrc2015_deep_residual_learning_kaiminghe.pdf Fast RCNN original https://arxiv.org/pdf/1504.08083v2.pdf RCNN original https://people.eecs.berkeley.edu/~rbg/papers/pami/rcnn_pami.pdf SPP-Net paper https://arxiv.org/pdf/1406.4729v4.pdf https://www.youtube.com/watch?v=1PGLj-uKT1w https://www.youtube.com/watch?v=WZmSMkK9VuA
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- Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.
- We note that almost everyone uses supervised learning algorithms .
- I stand corrected and renamed this post to “Top 10 algorithms and methods ” .
- KDnuggets Home > News > 2016 > Sep > News, Features > Top Algorithms and Methods Used by Data Scientists ( 16:n33 )
- We analyzed the usage of top 10 algorithms + Deep Learning by employment type.
Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.
Continue reading “Top Algorithms and Methods Used by Data Scientists”