Building and training neural networks are no longer just for seasoned computer scientist and grad students. That change began with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and the Google’s TensorFlow. One that stands out is TensorFlow for its powerful, yet handy, functionality, conjoined with the impressive gain of its user base. With the release of TensorFlow 1.0, Google has driven the frontiers of machine learning further in a number of directions.
In the production to making TensorFlow a more-general machine learning structure, Google has added both built-in Estimator functionality, and support for a number of more conventional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest. While there are surely other groundworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems. TensorFlow 1.0 also offers amazing performance improvements and scaling. In one measure, a training session running on a 64-processor machine ran nearly 60 times as fast as one running on a single processor.
One of the many impressive new potentials of Google TensorFlow is that its models can be run on many smartphones. TF1.0 even takes the convenience of the Hexagon DSP that is built into Qualcomm’s Snapdragon 820 CPU. Google is already using this to function applications like Translate and Word Lens even when your phone is completely offline. Before now, Sophisticated methods like those required for translation or speech perception required real-time access to the cloud and its compute servers.
As powerful as TensorFlow is, composing a complicated model directly in its API takes quite a bit of knowledge, and some precise programming. The Keras programming interface provides a more user-friendly layer on top of TensorFlow (and Theano) that make engineering high-end networks cleverly simple. Keras also includes a number of pre-trained models for easy instantiation. Given the intensive nature of assembling the large datasets needed to train models, and the processor-intensive nature of training, it helps creates a major benefit for developers.
Google TensorFlow 1.0 is now ready to download. Currently, Keras is a separate package that is easy to install using pip or your preferred package manager, but Google aims to have it built-in to the 1.2 release of TensorFlow. There are many adjustments but Google is even providing a helpful script that will try to update any existing codes, if needed. Which is typical of machine learning tools, you will get a better performance running on a support GPU, but now there are even options to spin your models up in the cloud. For example, Y combinator-backed startup
Apple Inc. has acquired the rights to Faceshift, a start-up motion capture company that gained notoriety from their work in the Star Wars films.
The Zurich-based company was part of the character design process in the new film Star Wars: The Force Awakens. Their specialty, as their name suggests, is creating realistic facial expressions for animated characters that can convey a wide array of emotions.
As is typical with Apple, they were very ambiguous with the reasons for their purchase.
“Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans,” a spokesman said in a statement.
For now, any suggestions of what they will use the company for is pure speculation. Most guesses involve some sort of facial recognition purpose, for security or otherwise. What is known is that Faceshift employees are already working under the Apple name from their office in Switzerland.
Faceshift joins the ranks of augmented reality firm Mataio, facial recognition firm Polar Rose, and 3-D scanning house PrimeSense as small businesses that have recently been swept up by Apple. All have capabilities in augmented reality, and each could complement the others.
Faceshift was founded by Thibaut Weise, Brian Amberg, and Sofien Bouaziz as a spinoff from the Computer Graphics and Geometry Laboratory at the École Polytechnique Fédérale de Lausanne in Switzerland. The school originally owned two of Faceshift’s facial animation patents but they were transferred to Faceshift at the end of August of this year, just ahead of the transition to Apple.
Faceshift later added offices on the West Coast and London, headed by film and gaming industry vet Doug Griffin and visual effects specialist Nico Scapel, respectively. Prior to Apple’s acquisition, Faceshift had a website that offered a demonstration of how it worked, along with some support documentation for developers using it.
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