Tabby's Star or KIC 8462852 was discovered by citizen scientists in 2015 The light from the star dims irregularly, and astronomers do not know why Some theories said it was due to passing comets or alien'megastructures' Now a new paper suggest the material inside the star is undergoing a phase transition Tabby's Star or KIC 8462852 was discovered by citizen scientists in 2015 Some theories said it was due to passing comets or alien'megastructures' Tabby's Star, known officially as KIC 8462852, (pictured in infrared, left and UV, right) has baffled experts since it was discovered in 2015, by scientists scanning the skies for exoplanets How to spot'Santa' in the night sky as the... Wind farms can be DEADLY for birds of prey: Migrating... Watch Mark Zuckerberg show off his artificial intelligence... Can YOU solve this Christmas conundrum? How to spot'Santa' in the night sky as the... Wind farms can be DEADLY for birds of prey: Migrating... Watch Mark Zuckerberg show off his artificial intelligence... Can YOU solve this Christmas conundrum? Tabby's star is a standard F-class star, located in the constellation Cygnus, approximately 1,276 light years from Earth. A paper published earlier this month suggested the star gives off jets that could be a source of energy for an alien civilisation. WHAT IS A DYSON SPHERE?

Last week, we learned about Random Forest Algorithm. Now we know it helps us reduce a model's variance by building models on resampled data and thereby increases its generalization capability.

Unlike most other lists of top experts, this one is a hand-picked selection, not based on influence or Klout scores, or the number of Twitter followers and re-tweets, or other similar metrics. Each of these experts has his/her own Wikipedia page. Some might not even have a Twitter account. All of them have had a very strong academic and research career in the most prestigious places. Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo).

The structure-function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating a disease. The predominant means of collecting structure/function data in biomedicine is reductionist and has thus led to a proliferation of complex data (for example, gene expression arrays, digital images) that captures only a fraction of the structure/function relationship. Gene sequence and expression data illustrates the structure and activities of individual genes but does not explain how these genes collaborate to control cellular and tissue-scale functions. As a result, despite the abundance of molecular details known about wound healing, for example, it is virtually impossible to accurately predict the final functional state of a healing wound.36 This illustrates a need to build models that represent the structural organization at the organ, tissue, cellular, and molecular levels. Furthermore, such models must capture relationships between these scales and relate them to the underlying functional state. Data-driven network/graph analysis is primed to decipher cellular interactions in the intricate relationship between protein-protein interactions, genetic changes, metabolic pathways, and chemical secretions, which comprise cellular events. When extended to the organ level, the key challenge would be to link the local and global structural properties of tissues to the overall morphology and function of a tissue. Only a systems-level understanding of the various cellular processes encompassing multiple biological levels will take into account the multidimensional complexity of these processes. If the principles governing biological organization on a morphological, spectral, local, and global scale can be deduced, the correlation between structural and molecular signaling within the tissue can be understood and applied to inform and accelerate studies of organ development and tissue regeneration. The cell-graph technique11,12,20 aims to learn structure-function relationship by modeling structural organization of a tissue/organ sample using graph theory. Its main hypothesis is that cells in a tissue/organ organize to perform a specific function.

For many years I have been part of discussions about how to diversify computing, particularly about how we recruit and retain a more diverse cohort of computer science (CS) students. I wholeheartedly support this goal, and spend a considerable amount of my effort as chair of ACM-W helping to drive programs that focus on one aspect of this diversification, namely encouraging women students to stay in computing. Of late I have become very concerned about how some elements of the diversity argument are being expressed and then implemented in teaching practices. Problem 1. Women are motivated by social relevance, so when we teach them we have to discuss ways in which computing can contribute to the social good. Problem 2. Students from underrepresented minorities (URM) respond to culturally relevant examples, so when we teach them we have to incorporate these examples into course content.

Techno-optimism is defined as the belief that technology can improve the lives of people. It was famously satired in the U.S. television comedy series "Silicon Valley," with a startup-company's founders pledging to "make the world a better place through Paxos algorithms for consensus protocols." But some people take techno-optimism very seriously. Ray Kurzweil, an accomplished tech innovator, described his techno-optimistic vision in his books: The Age of Spiritual Machines, How to Create a Mind: The Secret of Human Thought Revealed, and The Singularity Is Near. In a keynote address (see https://goo.gl/RwkwK1) at the 2016 meeting of the Computing Research Association, Kentaro Toyama argued that "In spite of the do-gooder rhetoric of Silicon Valley, it is no secret that computing technology in and of itself cannot solve systemic social problems."

One of the most important things that the U.S. can do to improve economic growth is to invest in artificial intelligence, or A.I., said the White House, in a new report. A.I.-driven, intelligent systems have the potential to displace millions, such as truck drivers, from their jobs. But potential negative impacts can be offset by investments in education as well as by ensuring there is a safety net to help affected people, the White House argued, in what will likely be the Obama administration's final report on technology policy. Some of the report's recommendations, which include expanded unemployment help and access to healthcare, may be anathema to a Republican-controlled Congress with a focus on tax reductions and spending cuts. But this report -- "Artificial Intelligence, Automation, and the Economy" (PDF) -- which was in the works well before election day, also describes broader, technological-driven changes that will impact jobs and may pose issues for President-elect Donald Trump.

"Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others--both negatively and positively," the White House report said. "Some jobs may be automated away, while for others, AI-driven automation will make many workers more productive and increase demand for certain skills. Finally, new jobs are likely to be directly created in areas such as the development and supervision of AI as well as indirectly created in a range of areas throughout the economy as higher incomes lead to expanded demand." Researchers across the world have given varying estimates about the size of job losses.

What will be the main focus of your presentation at Efma's upcoming Insurance Summit? My presentation will focus on applying artificial intelligence (AI) to insurance for greater customer centricity. I'll also speak about embracing the shift from a product-focus to client solutions in insurance, how machine learning will enable a more seamless customer experience for insurance customers and ask whether machines will replace humans or if they will complement the work we are doing. What are the key innovations that your company is working on at the moment? Customer experience is one of the key topics we are discussing right now.

The growing popularity of artificial intelligence technology will likely lead to millions of lost jobs, especially among less-educated workers, and could exacerbate the economic divide between socioeconomic classes in the United States, according to a newly released White House report. But that same technology is also essential to improving the country's productivity growth, a key measure of how efficiently the economy produces goods. That could ultimately lead to higher average wages and fewer work hours. For that reason, the report concludes, our economy actually needs more artificial intelligence, not less. To reconcile the benefits of the technology with its expected toll, the report states that the federal government should expand access to education in technical fields and increase the scope of unemployment benefits.