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.

The following was originally published on the CMS-Connected News Articles. Do you know that some of the content you read wasn't written by human beings? Automated Insights states, its software created one billion stories last year, many with no human intervention. This content you are reading was written by a real person, but just think, what are the chances you haven't consumed that type of content without knowing it? So what does it mean for content marketing?

Downtown Havana resident Margarita Marquez says she received a special Christmas gift this year: web access at home, a rarity in Cuba, a country with one of the lowest internet penetration rates in the world. Ms Marquez, a 67-year-old retired university professor, was among those selected by the government two weeks ago to participate in a pilot project bringing the web into the homes of 2,000 inhabitants of the historic centre of the island's capital. Most of Communist-ruled Cuba's 11.2 million inhabitants only have access to internet at wi-fi hotspots, and only then if they can afford the 80p hourly tariff that represents around five per cent of the average monthly state salary. Only five per cent of Cubans are estimated to enjoy internet at home, which requires government permission. This is usually granted mainly to academics, doctors and intellectuals.

The recent report from the U.S. Bureau of Statistics shows hat there is a 4.9 percent decline in the unemployment rate of the country. Although not much, it is still positive when compared to a recent study conducted at Oxford University saying that 47 percent of the jobs in America will be eliminated within the next 25 years. The study was conducted by Dr. Michael Osborne, a professor at Oxford University's Machine Learning Research Group in the Department of Engineering Science. It looked closely at more than 700 job types in different industries in the United States keeping into consideration the skills required for the job and the tasks that workers perform in the job. They found out that a great number of jobs, especially in logistics, administrative, and transportation, are in high risk of getting eliminated because of automation. Even jobs in the service industry are in grave danger of being given to machines in the future.

A handful of startups are employing artificial intelligence technologies and big data in an attempt to diagnose dementia, particularly Alzheimer's disease. The effort could lead to better interventions and even therapeutic drugs if it becomes possible to detect cognitive decline before it really starts. The benefits to society – not to mention market potential – for the early detection of dementia and Alzheimer's disease are huge. According to the World Health Organization, there were 47.5 million people worldwide with dementia in 2015, with 7.7 million new cases each year. The total number of people with dementia is projected to reach 75.6 million in 2030 and almost triple by 2050 to 135.5 million.

It's been a year that should dispel doubts the car industry is going through a technological revolution. The convergence of traditional motor manufacturing and new digital technologies has been shifting slowly through the gears for a couple of years. But in 2016, things hit top speed. A string of deals, strategic announcements and investments has even left some experts wondering if a bubble is growing. "Manufacturers finally really understood the need for change," says Martin Benecke, Frankfurt-based analyst at IHS Automotive. "They see the competitive threat from Google and Apple.

A new artificial intelligence system can take still images and generate short videos that simulate what happens next similar to how humans can visually imagine how a scene will evolve, according to a new study. Humans intuitively understand how the world works, which makes it easier for people, as opposed to machines, to envision how a scene will play out. But objects in a still image could move and interact in a multitude of different ways, making it very hard for machines to accomplish this feat, the researchers said. But a new, so-called deep-learning system was able to trick humans 20 per cent of the time when compared to real footage. Researchers at the Massachusetts Institute of Technology (MIT) pitted twoneural networks against each other, with one trying to distinguish real videos from machine-generated ones, and the other trying to create videos that were realistic enough to trick the first system.

Booking.com, a major brand within Priceline Group (NASDAQ:PCLN), will start using AI within its mobile apps to provide users instant booking options to a location's main venues and attractions. This option will be provided through QR (quick response) code and would be based on the personal preferences of a user based on their past booking history. This highly personalized Booking Experiences tool has been launched on Booking.com's Further roll-out for Dubai, London and Paris is expected to be done by the end of July. New York City will get the feature by this fall.

A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges. In this paper, we focus on the time-varying interconnections among a set of actors in multiple contexts, called layers. Current literature lacks flexible statistical models for dynamic multilayer networks, which can enhance quality in inference and prediction by efficiently borrowing information within each network, across time, and between layers. Motivated by this gap, we develop a Bayesian nonparametric model leveraging latent space representations. Our formulation characterizes the edge probabilities as a function of shared and layer-specific actors positions in a latent space, with these positions changing in time via Gaussian processes. This representation facilitates dimensionality reduction and incorporates different sources of information in the observed data. In addition, we obtain tractable procedures for posterior computation, inference, and prediction. We provide theoretical results on the flexibility of our model. Our methods are tested on simulations and infection studies monitoring dynamic face-to-face contacts among individuals in multiple days, where we perform better than current methods in inference and prediction.

Clustering is a widely used unsupervised learning method for finding structure in the data. However, the resulting clusters are typically presented without any guarantees on their robustness; slightly changing the used data sample or re-running a clustering algorithm involving some stochastic component may lead to completely different clusters. There is, hence, a need for techniques that can quantify the instability of the generated clusters. In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which correspond to clusters where the co-occurrence probability of each data item within a cluster is at least $1 - \alpha$. We demonstrate how solving the core clustering problem is linked to finding the largest maximal cliques in a graph. We show that the method can be used with both clustering and classification algorithms. The proposed method is tested on both simulated and real datasets. The results show that the obtained clusters indeed meet the guarantees on robustness.