For its photos service, Google is starting a new service called "Suggested Actions." If it recognizes a photo of someone who is a Google contact, it can suggested sending it to the person. It can also convert photos to PDFs and automatically add color to black-and-white photos or make part of a color photo black and white. The changes are coming in the next two months.

Artificial Intelligence (AI) is the way forward for the retail industry in terms of technology but at the moment, baring a few countries not all are ready for this change in retail and the retailers are also unsure about the introducing AI in their stores like a self-checkout platform is still better than a service robot asking for details. Stressing on the same and to better understand the standing of AI in retail, Retail Analytics Council (RAC) – the leading body focussed on the study of consumer buying behaviour across retail platforms and the impact of technology – held the first-ever Retail Robotics and AI Conference recently in San Francisco, USA. Retail and technology industry leaders, C-level executives along with academic leaders discussed various key topics at the event, including the use of AI in retail, understanding of consumer buying patterns, growing execution of robotic applications in retail and the current retail industry trends, at the conference. At the event, Don Schultz, Director of RAC warned that with the increasing reliance on technology, the retail industry will soon become'retail singularity'. Referring to the data released by market intelligence and advisory firm International Data Corporation (IDC), Schultz told that before 2020, 40 per cent digital retail transformation steps will get an AI support.

UAV Experts and a division of Atlanta Hobby, is rotating out a new'Certified Aerial Thermographer Program.' This two-day program is basically designed to complement UAV Experts current UAVs flight training offerings. Moreover, this program will instruct both the companies and individuals on the use of infrared photography with UAVs. "We have been flying infrared for a long time and are excited about launching this new training program headed by Monroe's Vice President and Level III certified thermographer, Bill Fabian, and this infrared program adds to our other extensive training programs," mentioned Cliff Whitney, CEO of UAV Experts. "Our new partnership with UAV Experts puts the right people and skillsets together for a winning education for our students," added Bill Fabian.

At their core, data scientists have a math and statistics background. Out of this math background, they're creating advanced analytics. Just like their software engineering counterparts, data scientists will have to interact with the business side. This includes understanding the domain enough to make insights. Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. Finally, their results need to be given to the business in an understandable fashion. This requires the ability to verbally and visually communicate complex results and observations in a way that the business can understand and act on them. Thus, it'll be extremely valuable for any aspiring data scientists to learn data mining -- the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps generate new information and unlock various insights. Here is a simple list of reasons on why you should study Data Mining?

On October 25, Sophia, a delicate looking woman with doe-brown eyes and long fluttery eyelashes made international headlines. She'd just become a full citizen of Saudi Arabia -- the first robot in the world to achieve such a status. "I am very honored and proud of this unique distinction. This is historical to be the first robot in the world to be recognized with a citizenship," Sophia said, announcing her new status during the Future Investment Initiative Conference in Riyadh, Saudi Arabia. Standing behind a podium as she spoke, to all effects, she presented a humanoid form -- excepting the shimmery metal cap of her head, where hair would be on a human head.

Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks which are capable of learning unsupervised from data that is unstructured or unlabeled. We published a story in April about an art historian using an innovative analysis technique to unlock architectural secrets. He was using a machine learning method called deep learning – which is used in, for example, facial recognition and speech recognition software – to do science. Similarly, astronomers are beginning to report the use of machine deep learning techniques to perform research that humans can't do using more traditional methods. Below we describe two recent examples: the first related to planets orbiting two stars, and the second related to classifying galaxies.

Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the activation of the emotion) constitute the most popular and effective affect representations. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge (Aff-Wild Challenge) that was recently organized on the Aff-Wild database, and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional and recurrent neural network (CNN-RNN) layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emotion recognition, using the RECOLA, AFEW-VA and EmotiW 2017 datasets, compared to all other methods designed for the same goal.

This is our implementation of Conditional improved WGAN and improved WGAN in pytorch. Since this is our first-time working on GANs, it is harder than we thought. Although the reference code are already available (caogang-wgan in pytorch and improved wgan in tensorflow), the main part which is gan-64x64 is not yet implemented in pytorch. We realize that training GAN is really unstable. For instance, we stuck for one month and needed to test each component in our model to see if they are equivalent to their tf counterparts.

From tin-can robots to sophisticated, sentient virtual environments, artificial intelligence (AI) is a dominant theme in science fiction. With real-world advances in machine learning and deep learning, the gap between fact and fiction is narrowing. From Siri, search engines and motion-sensing video games to medical imaging and diagnostics, artificial intelligence is an increasingly significant part of our lives. Cray systems are used every day to solve artificial intelligence problems through machine learning and deep learning approaches. In this three-part blog series, we'll look at a few examples of AI in sci-fi and see how they match up with reality. Robots -- especially humanoid robots -- are often the first thing that comes to mind when we think about artificial intelligence.

When Mark Zuckerberg told Congress Facebook would use artificial intelligence to detect fake news posted on the social media site, he wasn't particularly specific about what that meant. Given my own work using image and video analytics, I suggest the company should be careful. Despite some basic potential flaws, AI can be a useful tool for spotting online propaganda – but it can also be startlingly good at creating misleading material. Researchers already know that online fake news spreads much more quickly and more widely than real news. My research has similarly found that online posts with fake medical information get more views, comments and likes than those with accurate medical content.