The possibilities that IoT brings to the table are endless. IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT. IoT will produce a tsunami of big data, with the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to an astronomical level. This data will hold extremely valuable insights into what's working well or what's not. Also, IoT will point out conflicts that arise and provide high-value insight into new business risks and opportunities as correlations and associations are made.

Take our end of the year blog reader survey. Complete and be entered to win 1 of 5 $25 Visa gift cards. What Does Artificial Intelligence Mean in Human Resources and Recruiting? One of the most talked about trends in HR and recruiting in the second half of 2016 has been AI and artificial intelligence. Artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal."

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Artificial intelligence could cause the biggest affliction to the entire human civilization, with the potential to outstrip all the advantages it comes with if left unmanaged. This was the powerful statement made by renowned scientist Stephen Hawking on Wednesday at a new AI research center at Cambridge University in London, as reported by US News & World Report. Companies are now purely focused on developing artificial-based intelligence systems that can imitate human behavior. The potential benefits of AI are huge, and no one can predict what humans might achieve when their minds get amplified by AI, said Hawking. The tools can definitely help to undo some of the damages that have already occurred besides eradicating poverty and disease, he added.

Google's DeepMind has been making use of Blizzard's StarCraft II Game as a testing platform for AI as well as important machine learning research. It's not just Google that can benefit from this platform either; it's open to anyone who wants to make use of it and is available worldwide. The company commented, "We've worked closely with the StarCraft II team to develop an API that supports something similar to previous bots written with a'scripted' interface, allowing programmatic control of individual units and access to the full game state (with some new options as well)." The two companies are still working together now, this time creating "curriculum scenarios." These are tasks that get more difficult as they are completed and are designed to allow AI researchers to get the system up and running, benchmark algorithms, and advance.

In October 2016, President Obama started a public dialogue around the future of artificial intelligence. At the White House Frontiers Conference, hosted in partnership with University of Pittsburgh and Carnegie Mellon University, he shared his vision for AI research. There was also a first-of-its-kind report--Preparing for the Future of Artificial Intelligence--which outlined how the government can be involved in researching, developing, and regulating future technologies. And for Wired magazine, President Obama wrote a guest piece and was interviewed about the future of artificial intelligence. While some experts voiced concern over President Obama's optimism that jobs will not be displaced, and his grasp of the potential dangers of artificial general intelligence, his efforts were largely praised by the AI community.

Another TensorFlow feature you typically want to use is checkpointing – saving the parameters of your model to restore them later on. Checkpoints can be used to continue training at a later point, or to pick the best parameters setting using early stopping. Checkpoints are created using a Saver object.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

In today's summary we dive into the architecture of WaveNet and its successor ByteNet which are autoregressive generative models for generating audio and respectively sentences on character-level. The architectures behind both models are based on dilated causal convolutional layers which recently got much attention also in image generation tasks. Especially modeling sequential data with long term dependencies like audio or text seem to benefit from convolutions with dilations to increase the receptive field. Without further introduction we start right away with the main components behind WaveNet, which will later also appear in the architecture of ByteNet. The key ingredient are so called dilated causal convolutions which have some advantages over standard convolutions.

Adobe held its annual MAX conference for users of its Creative Cloud earlier this month. That's where the company usually announces new and upcoming features to applications like Photoshop or Premiere Pro. This year, however, Adobe also introduced Sensei, its new artificial intelligence- and machine learning-based platform that combines Adobe's knowledge of working with photos, videos, documents and marketing data with a unified AI and machine learning framework. Just like Microsoft and Google are trying to imbue all of their products with "intelligence," Adobe, too, is now on a mission to bring more smarts to its products -- be that in the form of machine learning-based tools and features, or through smarter traditional analytics. Sensei is Adobe's version of this.