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.

What were you doing when you were 14 years old? Sean Le Van, from Aneheim in California, will put you to shame. The teenager has created a contextually aware, artifically intelligent bot that you can converse with on the web. The bot – which Le Van calls Acuman, an acronym for Artificial Chatting Utility Matching Algorithmic Nodes – acts as a personal assistant that responds to every-day language and learns information about a user's life and personality. Le Van used his own scripting language and natural language processing algorithms to create Acuman, which uses the data gathered from conversations to analyse and create categorised visual representations in the form of infographics.

The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting 2000 participants. This year it was held in NYC and I attended on behalf of Init.ai. Three of the tutorial sessions I attended were quite impactful. Anyone working on conversational apps, chatbots, and deep learning would be interested in these topics. I've written before about Residual Neural Network research, but listening to Kaiming was informative.

The first attempts by startups using artificial intelligence to design websites haven't amounted to much. A much-anticipated company called The Grid has failed to deliver on expectations. There is still no AI art hanging in MOMA yet. Instead of building more clever bots, startups like Wix are mining troves of user data to train algorithms that offer sound design advice for business websites. Website building platforms are experimenting with AI as part of the future of design.

The World Economic Forum has put together a list of the top 10 emerging technologies that will change our lives. The list includes nanosensors that will circulate through the human body, a battery that will be able to power an entire town and socially aware artificial intelligence that will track our finances and health. These are not far-flung visions, according to the forum. They are technologies that are on the cusp of having a meaningful impact. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Bernard Meyerson, chairman of the World Economic Forum council that compiled the list of the top 10 emerging technologies in 2016.

The Budapest BI Forum is the leading vendor-independent business intelligence and analytics conference in Hungary. One of the main feature of the event is the multi-subject approach: we have Pydata, Rstats, Data visualization, machine learning and BI talk in the 2 day s of the conference. This way speaking at the Budapest BI Forum in any of the tracks also provides a special chance to learn about other interesting fields of BI and analytics. This year we will have the following track, with a separate CFP for each. All speakers are welcome to submit more than one talks to the same or to different tracks.

A key challenge when working on what we could term "large-scale" PPC accounts is efficiency. There will always be more that you could do if given an unlimited amount of time to build out and optimize an AdWords campaign; therefore, the trick is managing priorities and being efficient with your time. In this post, I will talk about how concepts from machine learning could potentially be applied to help with the efficiency part. To paraphrase Steve Jobs, a computer is like "a bicycle for the mind." The generally understood meaning of this statement is that, in the same way a bike can increase the efficiency of human-powered locomotion, computers can increase human mental productivity and output.

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. Thanks to technologies that generate, store and analyze huge sets of data, companies are able to perform tasks that previously were impossible. But the added benefit does come with its own setbacks, specifically from a security standpoint. With reams of data being generated and transferred over networks, cybersecurity experts will have a hard time monitoring everything that gets exchanged -- potential threats can easily go unnoticed.