- You can now use Apache MXNet v0.10 and TensorFlow v1.1 with the AWS Deep Learning AMIs for Amazon Linux and Ubuntu.
- Apache MXNet announced version 0.10, available at http://mxnet.io, with significant improvements to documentation and tutorials including updated installation guides for running MXNet on various operating systems and environments, such as NVIDIA’s Jetson TX2.
- Python PIP install packages are now available for the v0.10 release, making it easy to install MXNet on Mac OSX or Linux CPU or GPU environments.
- Visit the AWS Marketplace to get started with the AWS Deep Learning AMI v1.4_Jun2017 for Ubuntu and the AWS Deep Learning AMI v2.2_Jun2017 for Amazon Linux.
- The AWS Deep Learning AMIs are available in the following public AWS regions: US East (N. Virginia), US West (Oregon), and EU (Ireland).
You can now use Apache MXNet v0.10 and TensorFlow v1.1 with the AWS Deep Learning AMIs for Amazon Linux and Ubuntu. Apache MXNet announced version 0.10, available at http://mxnet.io, with significant improvements to documentation and tutorials including updated installation guides for running MXNet on various operating systems and environments, such as NVIDIA’s Jetson TX2. In addition, current tutorials have been augmented with definitions for basic concepts around foundational development components. API documentation is now more comprehensive, with accompanying samples. Python PIP install packages are now available for the v0.10 release, making it easy to install MXNet on Mac OSX or Linux CPU or GPU environments. These packages also include Intel’s Math Kernel Library (MKL) support for acceleration of math routines on Intel CPUs.
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- A team of researchers at UC Berkeley have revealed an ‘Unpaired Image-to-Image Translation’ technique that can do something really interesting: it can turn a painting by Monet into a ‘photograph’… also, it can transform horses into zebras and summer into winter.
- The is yet another addition to the photo style transfer research that it seems like everybody is working on—from the basic tech behind apps like Prisma that transform photos into paintings, to the incredible style-transfer tech from Adobe and Cornell we showed you last week.
- In these examples, it transforms Monet’s impressionist paintings into something resembling real-world photographs:
But the tool goes further than that.
- It can also turn horses into zebras, or winter into summer, by transposing style elements and textures similar to the Adobe algorithm:
It can even do more practical effects, like applying depth of field:
The groundbreaking aspect of this research is that the UC Berkeley team isn’t using “paired examples.”
- “We can imagine all this despite never having seen a side by side example of a Monet painting next to a photo of the scene he painted,” explains the paper.
Photography and Camera News, Reviews, and Inspiration
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- Facebook’s AI search lets you find images by content
- The system will then show you all the images containing a black shirt and save you a lot of time you would have otherwise wasted trying to find the right image yourself.
- For example, if you’re looking for a photo that isn’t tagged but you remember you were wearing a black shirt when it was taken, simply search for “black shirt photo”.
- The new AI system uses the Lumos platform that the company built specifically for image and video understanding.
- The best way to stay connected to the Android pulse.
Facebook has built an AI image search system that is capable of seeing and understanding the content of a photo.
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- According to Accenture, AI will define future customer experience .
- “The power of AI is the power to make better decisions.
- Reese shared a historical example of AI and whether AI should be used to make decision.
- If AI has the power to make better decisions, then any business that has to make decisions, will be able to make more informed and fast decisions.
- Reese notes that AI in business is really heating up.
Artificial intelligence (AI) is the new UI, according to Accenture’s Technology Vision 2017 report, identifying trends that are essential to business suc…
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- NASA JPL Researches Artificial Intelligence for Submersible Drones
- The drones also sensed how the ocean actively changed around them.
- “Autonomous drones are important for ocean research, but today’s drones don’t make decisions on the fly,” said Steve Chien, one of the research team’s members.
- NASA has announced that a team of researchers from its Jet Propulsion Lab (JPL) and other institutions recently visited Monterey Bay, California as part of ongoing research into developing artificial intelligence for submersible drones.
- A fleet of six coordinated drones was used to study Monterey Bay.
NASA has announced that a team of researchers from its JPL and other institutions recently visited Monterey Bay, California.
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- Google supercharges Play Music with machine learning for smarter recommendations
- Google Play Music is getting a much-needed overhaul starting this week, both inside and out.
- The company noted the update will roll out starting this week, so you should be able to try the revamped interface soon.
- The company’s music service will now use those smarts to bring you suitable playlists for every activity it can reliably detect.
- Its Android , iOS and Web apps are getting a new interface that’s powered by machine learning to recommend music based on what you’re doing and where you are.
Google Play Music’s Android, iOS and Web apps are getting a new interface that recommends music based on what you’re doing and where you are.
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- As the program interpreter is end-to-end differentiable, we can optimize this behaviour directly through gradient descent techniques on user specified objectives, and also integrate the program into any larger neural computation graph.
- Through a neural implementation of the dual stack machine that underlies Forth, programmers can write program sketches with slots that can be filled with behaviour trained from program input-output data.
- We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex transduction tasks such as sequence sorting or addition with substantially less data and better generalisation over problem sizes.
- We consider the case of prior procedural knowledge, such as knowing the overall recursive structure of a sequence transduction program or the fact that a program will likely use arithmetic operations on real numbers to solve a task.
- To the end we present a differentiable interpreter for the programming language Forth.
Continue reading “[1605.06640] Programming with a Differentiable Forth Interpreter”