Recent advancements in machine learning are reaching a level of sophistication that's exceeding the expectations of industry analysts and executives alike. We're familiar with Google DeepMind's AlphaGo that bested the greatest masters of the ancient Chinese game "Go" 10 years earlier than expected. More recently a new exhibition at the New York Gallery Metro Pictures depicts machine made images to people using algorithms. Retailers are redefining customer experiences with real-time personalization and convenience. Even most stock trades are governed by automated analysis of market outcomes and determination of future trends faster and more accurately than humans alone.

The NVIDIA Deep Learning Accelerator (NVDLA) is a free and open architecture that promotes a standard way to design deep learning inference accelerators. With its modular architecture, NVDLA is scalable, highly configurable, and designed to simplify integration and portability. The hardware supports a wide range of IoT devices. Delivered as an open source project under the NVIDIA Open NVDLA License, all of the software, hardware, and documentation will be available on GitHub.

If you ask the world of technology to slow down a bit, you're instantly branded as a technophobe. But perhaps a bit of reflection is needed in the artificial intelligence (AI) arena, reports Telecoms.com Google's Deepmind is one of those which is starting to think a bit deeper about the big, burgeoning world of computer intelligence. The team has announced the formation of DeepMind Ethics & Society to "complement" the work of its engineers and scientists, and make sure we don't get a little ahead of ourselves. It is usually a conversation which is relegated to comments boards and conspiracy websites, but the industry does need to have a good look at whether the development of the technology is continuing to work for us. This will be the primary objective of the DeepMind Ethics & Society team; making sure the ethical and social impact of the technology is beneficial to society on the whole.

Deep learning, a variation of Machine Learning (ML), represents the major driver toward artificial intelligence(AI), reports Gartner. Gartner's 2017 Hype Cycle for Emerging Technologies notes deep learning is receiving additional attention because it harnesses cognitive domains that were previously the exclusive territory of humans, mainly image and voice recognition and text understanding. Today, most common use cases of ML through deep learning are in image, text and audio processing -- but increasingly also in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion, and providing predictive or even prescriptive maintenance. Gartner's advice for harnessing deep learning and related technologies around Machine Learning include starting with simple business problems for which there is consensus about the expected outcomes, and gradually moving toward complex business scenarios.

"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.

It's an amazing time to get into Machine Learning. There are tools and resources available to help anyone with some coding skills and a problem to solve to do interesting work. I've been following along with Practical Deep Learning For Coders and the Reinforcement Learning Course by David Silver. Machine Learning without a PhD is an exellent intro to some of deep learning techinques. These along with all the papers linked from Hacker News and Two Minute Papers have inspired me to give some ideas a try.

Over our last few blog posts, we've gone through the various ways you can use BigML's new Deepnet resource, via the Dashboard, programmatically, and via download on your local machine. Is there a little wizard pulling an elaborate console with cartoonish-looking levers and dials? Well, as we'll see, Deepnets certainly do have a lot of levers and dials. So many, in fact, that using them can be pretty intimidating. Thankfully, BigML is here to *be your wizard* so you aren't the one looked shamefacedly at Dorothy when she realizes you're not as all-powerful as you might have thought.

Here is the announcement that some developers found in their mailbox two days ago. After almost ten years of development, we have the regret to announce that we will put an end to our Theano development after the 1.0 release, which is due in the next few weeks. We will continue minimal maintenance to keep it working for one year, but we will stop actively implementing new features. Theano will continue to be available afterwards, as per our engagement towards open source software, but MILA does not commit to spend time on maintenance or support after that time frame. The software ecosystem supporting deep learning research has been evolving quickly, and has now reached a healthy state: open-source software is the norm; a variety of frameworks are available, satisfying needs spanning from exploring novel ideas to deploying them into production; and strong industrial players are backing different software stacks in a stimulating competition.

The landscape of technical professions is constantly changing, and the resurgence of work in Artificial Intelligence has opened up new opportunities that differ from traditional Data Engineering and Data Science positions. Data Engineers build data pipelines and infrastructure to ensure a constant availability of transformed data. Data Scientists analyze and build models from these data to develop new product features or drive the bottom line of the business. The goal of newly-formed AI teams is to build intelligent systems, focused on quite specific tasks, that can be integrated into the scalable data transformations of Data Engineering work and the data products and business decisions of Data Science work. The differences between Artificial Intelligence, Data Science, and Data Engineering can vary considerably among companies and teams.

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.