We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedforward lateral connections. At each layer, these connections form a lateral recurrent residual block, where the feedforward connection represents a skip connection and the recurrent connection represents the residual. Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder. This way, the top layer is relieved from the pressure of modeling lower-level spatial and temporal details. Furthermore, we extend the basic version of VLN to incorporate ResNet-style residual blocks in the encoder and decoder, which help improving the prediction results. VLN is trained in self-supervised regime on the Moving MNIST dataset, achieving competitive results while having very simple structure and providing fast inference.

Apple has stayed true to its promise and published its first academic paper on artificial intelligence. The world's most valuable company has traditionally kept its AI research private but earlier this month Ruslan Salakhutdinov, director of AI research at Apple, made a pledge to start being more open. The new Apple paper -- published December 22 and titled "Learning from simulated and unsupervised images through adversarial training" -- gives an insight into some of the techniques that Apple is using to develop AI. In the study, which was published through the Cornell University Library, Apple researchers explain a technique that can be used to improve how an algorithm learns to "see" what is in an image. The paper's six authors state that using synthetic images (such as those seen in a video game), as opposed to real-world images, can be more efficient when it comes to training AI models known as neural networks, which are designed to think in the same way as the human brain. Because synthetic image data is already labelled and annotated while real-world images aren't.

Merriam-Webster's word of the year for 2016 was "surreal." According to the online dictionary, surreal is "… used to express a reaction to something shocking or surprising." Surreal is also a good word to describe some of the key events in the technology world that took place this past year. Artificial Intelligence (AI): The extremely rapid advancements that have been made in AI during 2016 were stunning. Research into AI is 50 years old, but only recently have new algorithms and new computing power come together to enable truly innovative applications. From a technical point of view, AI implementations have mostly switched from a structured and rule-oriented world to one that is data-driven and relies on machine learning.

No one knows what happened at Atlit-Yam. The ancient village appeared to be thriving until 7000BC. The locals kept cattle, caught fish and stored grain. They had wells for fresh water, stone houses with paved courtyards. Community life played out around an impressive monument: seven half-tonne stones that stood in a semicircular embrace around a spring where people came to drink.

Neuroscience and artificial intelligence experts from Rice University and Baylor College of Medicine have taken inspiration from the human brain in creating a new "deep learning" method that enables computers to learn about the visual world largely on their own, much as human babies do. In tests, the group's "deep rendering mixture model" largely taught itself how to distinguish handwritten digits using a standard dataset of 10,000 digits written by federal employees and high school students. In results presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona, Spain, the researchers described how they trained their algorithm by giving it just 10 correct examples of each handwritten digit between zero and nine and then presenting it with several thousand more examples that it used to further teach itself. In tests, the algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms that were trained with thousands of correct examples of each digit. "In deep-learning parlance, our system uses a method known as semisupervised learning," said lead researcher Ankit Patel, an assistant professor with joint appointments in neuroscience at Baylor and electrical and computer engineering at Rice. "The most successful efforts in this area have used a different technique called supervised learning, where the machine is trained with thousands of examples: This is a one. "Humans don't learn that way," Patel said. "When babies learn to see during their first year, they get very little input about what things are.

Volkswagen Digital Lab Berlin – IT experts from Volkswagen are working new mobility services as well as networked vehicle services. WOLFSBURG, 29-Dec-2016 -- /EuropaWire/ -- The Volkswagen Group is reinforcing its team with highly qualified IT specialists and strongly expanding its know-how in the fields of artificial intelligence, big data, virtual reality, smart production and connectivity. For these tasks, Volkswagen is increasingly recruiting highly qualified lateral entrants from a variety of high-tech sectors, the gaming industry and top-level research. In the next three years alone, the Group will be securing the services of more than 1,000 IT experts. Dr. Karlheinz Blessing, Member of the Board of Management of the Volkswagen Group responsible for Human Resources, says: "people who want to shape the future of mobility are coming to Volkswagen. We are tackling the major challenges of the future with the best people: digitalization, software development, E-mobility, autonomous driving and mobility services. For these fields, we are reinforcing our team with top-class experts."

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

Working in the field of big data and AI means that I see the leading edge advances that come with it. It also means routinely getting freaked out when you think too closely about the possibilities and implications of those advances and where they might be taking us. Manufacturing are the first places we see robots and automation eliminating human jobs, but it's hard to think of an industry that will be left unaffected as robots and AI become more affordable and widespread. It's estimated that between 35 and 50 percent of jobs that exist today are at risk of being lost to automation. Repetitive, blue collar type jobs might be first, but even professionals -- including paralegals, diagnosticians, and customer service representatives -- will be at risk.

Besides government regulations, bad weather, weight restrictions, and all the other issues that plague Amazon's budding drone delivery service, the company must also face the prospect of thieves shooting down drones to steal their packages. It's a problem that Amazon has been working on since at least 2014, when it filed a patent for "countermeasures" to protect drones against everything from gunshots to hackers breaching its navigation software. The patent was approved last week, GeekWire reported, offering insight into how Amazon intends to safeguard drone-borne packages of the future. The patent describes two main lines of defense for the drones. The first are electronic systems designed to detect signal jammers or other hacking attempts, including a backup communications interface if the primary one is compromised.

If Zoe Dewaghe wants ice cream for breakfast, she gets ice cream for breakfast. There's a different set of rules for her younger brother, Zach: He gets oatmeal instead. That's because five-year-old Zoe Dewaghe has a rare genetic disease called Sanfilippo syndrome. She'll gradually lose the ability to speak, to move, to recognize her surroundings. Most patients don't live into adulthood. "Once we found out what was wrong with her, we were like, 'You can eat whatever the heck you want,'" Zoe's mother, Liz, said ruefully. "Because pretty soon, you won't be able to eat." There's no approved treatment for Sanfilippo syndrome.