If you spend hours perfecting your make-up each morning then there's good news – you probably look more youthful. Scientists have looked at how facial contrast – how much features stand out – affects our perception of age. Their findings suggest that wearing makeup, which makes eyes and lips stand out, makes women look younger. Researchers analysed images of women aged 20 to 80 using computer software to measure facial contrast. In all four groups (pictured left to right - French Caucasian, Chinese Asian, Latin American and South African) several aspects of facial contrast were found to decrease with age.

For nearly 75 years, some of the greatest investigative minds have tried to figure out who tipped off the Nazis about Anne Frank and the seven other Jews who were hiding behind a movable bookcase in Amsterdam. Now, a former FBI investigator working with a production company hopes the decades-old mystery can be solved with the help of a new mind -- an artificial one. Vince Pankoke, who spent a chunk of his FBI career investigating Colombian drug cartels, has assembled a team of 20 researchers, data analysts and historians to look into what he calls "one of the biggest cold cases" of the 20th century. The most unconventional member of his team is a piece of specialized software that can cross-reference millions of documents -- police reports, lists of Nazi spies, investigative files for Frank family sympathizers -- to find connections and new leads.

A government-sponsored review into the potential impact of artificial intelligence (AI) on the UK economy is urging a comprehensive programme of support for the discipline. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit. Please provide a Corporate E-mail Address.

Alibaba Group announced today that it's launching a new research organization aimed at tackling emerging technologies like machine learning, network security, the internet of things, and quantum computing. It's called the Academy for Discovery, Adventure, Momentum and Outlook (or the DAMO Academy), and Alibaba plans to recruit 100 researchers to staff its labs around the globe. The company plans to open two labs in China, in the tech giant's home city of Hangzhou and in Beijing. In addition, the Chinese ecommerce and technology titan will open labs in San Mateo, California; Seattle, Washington; Moscow, Russia; Tel Aviv, Israel; and Singapore. Over the next three years, the company plans to spend $15 billion on research and development, a significant increase over its current rate of spending.

Maria, Marvin, Sonny, David, and Ava are all ordinary-sounding names -- but in film, television, and literature, these seemingly ordinary names belong to extraordinary individuals who, despite their exemplary skills and complex personalities, are not human. Since Brigitte Helm's 1927 portrayal of Maria in Metropolis, audiences have developed an increased love/hate fascination with artificial intelligence. While filmmakers continue to address the controversy regarding the acceptance and cohabitation between humans and their modern creations parallel to real-world technological advancements, just how accurate is this representation in modern film, and have cinematic depictions evolved at all? Early films reflected the heightened fear of technology that developed among the working class during the Industrial Age by depicting metal machines as unstoppable forces of mayhem. This successfully fed into the pre-existing "anti-immigrant" nervous anticipation that technological advancements would go from taking over people's jobs to taking over the world.

We investigate a family of regression problems in a semi-supervised setting. The task is to assign real-valued labels to a set of $n$ sample points, provided a small training subset of $N$ labeled points. A goal of semi-supervised learning is to take advantage of the (geometric) structure provided by the large number of unlabeled data when assigning labels. We consider random geometric graphs, with connection radius $\epsilon(n)$, to represent the geometry of the data set. Functionals which model the task reward the regularity of the estimator function and impose or reward the agreement with the training data. Here we consider the discrete $p$-Laplacian regularization. We investigate asymptotic behavior when the number of unlabeled points increases, while the number of training points remains fixed. We uncover a delicate interplay between the regularizing nature of the functionals considered and the nonlocality inherent to the graph constructions. We rigorously obtain almost optimal ranges on the scaling of $\epsilon(n)$ for the asymptotic consistency to hold. We prove that the minimizers of the discrete functionals in random setting converge uniformly to the desired continuum limit. Furthermore we discover that for the standard model used there is a restrictive upper bound on how quickly $\epsilon(n)$ must converge to zero as $n \to \infty$. We introduce a new model which is as simple as the original model, but overcomes this restriction.

Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies have examined the number of states suitable for representing sequence data or improving alignment accuracy.We developed a novel method to select superior models (including the number of hidden states) for PHMM. Our method selects models with the highest posterior probability using Factorized Information Criteria (FIC), which is widely utilised in model selection for probabilistic models with hidden variables. Our simulations indicated this method has excellent model selection capabilities with slightly improved alignment accuracy. We applied our method to DNA datasets from 5 and 28 species, ultimately selecting more complex models than those used in previous studies.

ARTICLE 19 submitted evidence to the United Kingdom's House of Lords Select Committee on Artificial Intelligence on 6 September 2017. The submission stresses the need to critically evaluate the impact of Artificial Intelligence (AI) and automated decision making systems (AS) on human rights. It also calls for deeper understanding of various ways in which these technologies embed values and bias, thereby strengthening or sometimes hindering the exercise of these rights, particularly freedom of expression. The overarching recommendation is for the development and use of AI to be subject to the minimum requirement of respecting, promoting, and protecting international human rights standards. Since 2014, ARTICLE 19 has pioneered efforts in technical communities to bridge existing knowledge gaps on human rights and their relevance in internet infrastructure.

CYBERSEC EUROPEAN CYBERSECURITY FORUM - Kraków, Poland - The future of artificial intelligence was a hot topic at the third annual CYBERSEC Cybersecurity Forum, where security professionals representing Poland, the Netherlands, Germany, and the United Kingdom discussed the pitfalls and potential of AI, and its role in the enterprise. Is it too soon to have this discussion? Absolutely not, said Axel Petri, SVP for group security governance at Deutsche Telekom AG. "Now is the time to ask the questions we'll have answers for in ten, twenty years," he added. Cybersecurity supported by AI and machine learning can leverage data to generate more insight and fight fraud.

Intel CEO Brian Krzanich declared that "data is literally the new oil" during an address at last year's L.A. auto show. The idea that bits and bytes will replace petroleum as the primary fuel for the world's economy--and the auto industry in particular--isn't an original one. But it summarizes what many insiders believe: As data analytics engines become more valuable than vehicle engines in the coming years, the flood of information gleaned from them will serve as a primary driver of automotive innovation with potentially billions in profits at stake as a result. Cars are just now becoming connected, and as autonomous technology moves into the mainstream the flow of data will turn from a trickle into a full-blown gusher. Automakers, their major suppliers, and large tech companies are already jockeying to take advantage of what Intel calls the coming "Passenger Economy … when today's drivers become idle passengers." But it's how they properly tap into and cap that well of information that will be the hard part.