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.

There weren't many people who had heard of bioterrorism before 9/11. But shortly after the September 11th terrorist attacks, a wave of anthrax mailings diverted the attention of the public towards a new weapon in the arsenal of terrorists--bioterrorism. A US federal prosecutor found that an army biological researcher was responsible for mailing the anthrax-laced letters, which killed 5 and sickened 15 people in 2001. The cases generated huge media attention, and the fear of a new kind of terrorist warfare was arising. However, as with every media hype, the one about bioterrorism disappeared quickly.

In Japan's first reported case of artificial intelligence saving someone's life, an AI has succeeded where a team of skilled human doctors did not. A woman with a rare type of leukaemia was correctly diagnosed by the AI. Even more remarkable, it took just ten minutes to compare the woman's genetic information with 20 million clinical oncology studies to arrive at the life-saving diagnosis. Does this mean robots are going to replace our doctors? Not quite, but increasing volumes of medical data, more powerful computers and smarter algorithms could see a future medical science in which human doctors are helped by AI. Data driven medicine taps into the expanding databases of genomic, clinical, imaging (scans and x-rays), and molecular data.

Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer. In particular, the emergence of GPU-based computing can greatly accelerate neural network processing capabilities - and if more processing power is needed there are the vast cloud computing resources of Amazon, Microsoft, Google. "Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform," the report said. Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

Amazon's Spotify-rival arrives in the UK: Music Unlimited runs through AI assistant Alexa and offers ...

Amazon's Spotify-rival arrives in the UK: Music Unlimited runs through AI assistant Alexa and offers ...