China is investing massively in artificial intelligence (AI), from chips to algorithms. Last summer, China's State Council issued an ambitious policy blueprint calling for the nation to become "the world's primary AI innovation center" by 2030, by which time, it forecast, the country's AI industry could be worth $150 billion. In one of the government's latest moves to catch up with the United States, China plans to build a $2.1 billion AI technology park in Beijing's western suburbs. But China's advantages in AI go beyond government commitment. Because of its sheer size, vibrant online commerce and social networks, and scant privacy protections, the country is awash in data, the lifeblood of AI's deep learning systems.

Mustafa Suleyman, who cofounded Google's deep-learning subsidiary, wants the artificial-intelligence community to focus on ethics in 2018. His argument: Writing in Wired UK, Suleyman explains that machine learning has the potential to improve or worsen inequalities in the world. To make sure it ends up being a net positive, he says, research into AI ethics needs to be prioritized. What's been done: This isn't a new concern for Suleyman. DeepMind established its own ethics and society research team earlier this year to work on these sorts of issues.

Jeffrey Shomaker is Founder of 21 SP, Inc., a start-up working on genetic-based medicine software. Before that, he held business positions (eg, Strategic Planning) at Elan Phamaceuticals (previously Athena Neurosciences) and DrugAbuse Sciences. Also, he held technical computer programming positions at Hewlett-Packard and Texas Instruments. Global Big Data Conference's vendor agnostic Global Artificial Intelligence(AI) Conference is held on January 19th, January 20th, & January 21st 2017 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc..). It will be the largest vendor agnostic conference in AI space.

China is investing massively in artificial intelligence (AI), from chips to algorithms. Last summer, China's State Council issued an ambitious policy blueprint calling for the nation to become "the world's primary AI innovation center" by 2030, by which time, it forecast, the country's AI industry could be worth $150 billion. In one of the government's latest moves to catch up with the United States, China plans to build a $2.1 billion AI technology park in Beijing's western suburbs. But China's advantages in AI go beyond government commitment. Because of its sheer size, vibrant online commerce and social networks, and scant privacy protections, the country is awash in data, the lifeblood of AI's deep learning systems.

Google's DeepMind team last week revealed a speedy new approach to training deep learning networks that combines advanced algorithms and old school video games. DeepMind, the team responsible for AlphaGo, appears to believe machines can learn like humans do. Using its own DMLab-30 training set, which is built on ID Software's Quake III game and an arcade learning environment running 57 Atari games, the team developed a novel training system called Importance Weighted Actor-Learner Architectures (IMPALA). With IMPALA, an AI system plays a whole bunch of video games really fast and sends the training information from a series of "actors" to a series of "learners." Normally, deep learning networks figure things out like a single gamer traversing a gaming engine.

If you haven't yet heard of generative adversarial networks, don't worry, you will. The hottest topic in deep learning, GANs, as they're called, have the potential to create systems that learn more with less help from humans. Just ask Ian Goodfellow, who hatched the idea for GANs in 2014 when he was still a Ph.D. student at the University of Montreal. Now a research scientist at Google, Goodfellow explained the workings and whys of GANs to a rapt crowd at the GPU Technology Conference last week. GANs remove one of the biggest obstacles to advancing AI, and particularly deep learning: the huge amount of human effort required.

Hetzner provides dedicated servers on a monthly basis. Figures here reflect hourly prorated costs. I have commented on my experience with using AWS, Softlayer and GCE in my prior post. Ordering an instance on LeaderGPU and Paperspace is plain sailing without any complicated settings. The provisioning time for Paperspace and LeaderGPU was a tad longer (couple of minutes) when compared to AWS or GCE which were up within a few seconds.

The images above are examples of the three possible classes I needed to predict: no traffic light (left), red traffic light (center) and green traffic light (right). The challenge required the solution to be based on Convolutional Neural Networks, a very popular method used in image recognition with deep neural networks. The submissions were scored based on the model's accuracy along with the model's size (in megabytes). Smaller models got higher scores. In addition, the minimum accuracy required to win was 95%.

In this post I'll share how I've been studying Deep Learning and using it to solve data science problems. It's an informal post but with interesting content (I hope). I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist.

We'll be continuing to build packages and tools that make using TensorFlow from R easy to learn, productive, and capable of addressing the most challenging problems in the field. We'll also be making an ongoing effort to add to our gallery of in-depth examples. To stay up to date on our latest tools and additions to the gallery, you can subscribe to the TensorFlow for R Blog. While TensorFlow and deep learning have done some impressive things in fields like image classification and speech recognition, its use within other domains like biomedical and time series analysis is more experimental and not yet proven to be of broad benefit. We're excited to how the R community will push the frontiers of what's possible, as well as find entirely new applications.