Google's TensorFlow machine learning engine is now available on your iPhone and iPad. The search giant has added support for Apple's iOS to its TensorFlow 0.9 released candidate, on GitHub. Support for iOS follows earlier backing for the framework on Google's own Android smart phone operating system. Other changes for TensorFlow 0.9 include support for Python 3.5 binaries and added support for processing on GPUs on MacOS. TensorFlow is Google's machine-learning library for numerical computing using data flow graphs, released late last year by Google under an Apache 2.0 license.

This year 200 million viewers cheered to to the dance of three robots to Michael Jackson s Thriller and Beyonce s Single Ladies during the Eurovision Song Contest in Stockholm. In the past few years we have seen them entering new areas of life, such as logistics or agriculture. While some of that has been greeted with excitement, there is a lot of skepticism and fear associated with the advent of Advanced Robotics (I will go into a definition in a second). This post is an attempt to provide an accurate representation of the current state of Advanced Robotics and how the space is likely to develop. As a venture capitalist I usually look at tech trends through the prism of young, fast-growing startups.

The legal media has lately had a mania for tech headlines. Many commentators claim that tech, especially artificial intelligence (AI), will do something to Big Law. Tech more likely will do something in it: incremental change. I start with the case against disruption, then look at four headline-grabbing technologies: AI, Bots, Big Data, and Blockchain. By the late 1980s, a few law firms had most of their lawyers using PCs.

Artificial intelligence and machine learning are going thoroughly open source, with some of the biggest tech companies contributing projects to the community. Recently, I covered Google's decistion to open source a program called TensorFlow. It's based on the same internal toolset that Google has spent years developing to support its AI software and other predictive and analytics programs. Now, in a follow-on move, Google is open sourcing SyntaxNet, which is natural-language understanding software that can automatically parse sentences. SyntaxNet is part of its TensorFlow open source machine learning library, and is hardened and tested by Google.

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound.

Two essential tasks in managing description logic knowledge bases are eliminating problematic axioms and incorporating newly formed ones. Such elimination and incorporation are formalised as the operations of contraction and revision in belief change. In this paper, we deal with contraction and revision for the DL-Lite family through a model-theoretic approach. Standard description logic semantics yields an infinite number of models for DL-Lite knowledge bases, thus it is difficult to develop algorithms for contraction and revision that involve DL models. The key to our approach is the introduction of an alternative semantics called type semantics which can replace the standard semantics in characterising the standard inference tasks of DL-Lite.

Accenture identified five forces that would likely have an impact on the healthcare industry now. These forces, which it said will converge in three to five years, include: intelligent automation; the liquid workforce; the platform economy; predictable disruption; and digital trust. Kaveh Safavi, M.D., J.D., senior managing director of Accenture's health practice, said with these five forces, the health industry will increasingly tap digital technologies to augment human labor, personalize care and free up time to focus on where they are needed most. "The outcome of a people-first, digital health strategy is that it liberates the healthcare workforce to focus on more meaningful work that requires judgment and personal interaction," he said. According to the industry report, "Accenture Digital Health Technology Vision 2016, the health industry will increasingly embrace intelligent automation--powered by artificial intelligence (AI), robotics and augmented reality – to streamline basic tasks, such as collecting patient intake data, enabling clinicians to focus where their training and experience have the greatest value. An Accenture's survey found that roughly seven in 10 health executives are investing more in machine learning and AI-related technologies than they were two years ago. Nearly half of health executives also reported extensive use of automation for IT tasks (48 percent) and customer interactions (47 percent). The future workforce will also be empowered by technology to scale clinical expertise to many patients, from wherever that doctor is working. Accenture said this increasingly liquid workforce will allow health organizations to adjust and adapt to meet today's dynamic demands. As patients expect the on-demand services they enjoy in financial services, entertainment and retail to permeate their health experiences, new options will emerge to tailor interactions and augment care services. The report estimates that by the end of 2019, roughly four in 10 people (42 percent) in the healthcare workforce will be contractors, freelancers or internal temporary positions. The platform economy will likewise use digitally enabled business models to capture new growth opportunities and link patient experiences across the health ecosystem. Accenture estimates that demand for health application programming interfaces (APIs) will grow 10-fold by 2021. Nearly 4 in 10 health executives surveyed (39 percent) believe that these online-based services – such as self-scheduling appointments, accessing records and tracking a patient's activity from hospital to home – are very critical to their business' success. And just as platforms are disrupting traditional care models, Accenture believes predictable disruption will force health executives to expect the unexpected. "To that end, 86 percent of healthcare executives feel pressure to reinvent their businesses before they are overtaken by competitors, or disrupted out of their markets.

It's a conundrum: You've got deep learning software, which benefits greatly from GPU acceleration, wrapped up in a Docker container and ready to go across thousands of nodes. But wait -- apps in Docker containers can't access the GPU because they're, well, containerized. Nvidia, developer of the CUDA standard for GPU-accelerated programming, is releasing a plugin for the Docker ecosystem that makes GPU-accelerated computing possible in containers. With the plugin, applications running in a Docker container get controlled access to the GPU on the underlying hardware via Docker's own plug-in system. As Nvidia notes in a blog post, one of the early ways developers tried to work around the problem was to install Nvidia's GPU drivers inside the container and map them to the drivers on the outside.

I work quite a bit with Neural Networks, do you have any information, or possibly a paper link on what you're using for gesture recognition? How are you converting the 3d positions to a feature suitable for doing recognition on? Unless it's just a fixed number of samples per Gesture so you just feed in a 3N vector where N is the number of sample and 3 numbers for position? Edit: So I looked at the documentation and from what I can glean you hold a button, perform the action and then release. It takes this path and samples 11 points within the gesture all measured relative to HMD position, those 11 points from the inputs of the NN.

Roboticists covered a sweeping range of topics at Xconomy's annual Robo Madness West conference last week, from the ethics of artificial intelligence to the powerful impact of robots that have faces. Two themes ran through all the panel discussions, whether they focused on robot design, logistics and manufacturing, drones, or artificial intelligence. Hardware--Ways to make a killing by making cheaper, better versions of certain components. People--In various roles, they make up some of the thorniest challenges to the growth of the robotics/AI sector. Let's talk about hardware first. While robot developers are benefiting from the availability of some cheap, commodity components to build their prototypes, there seems to be plenty of room for innovation, according to the Robo Madness panelists.