Artificial Intelligence has been the talk of the town ever since Facebook's Mark Zuckerberg and Tesla's Elon Musk exchanged a war of words over its potential. Other than that several world leaders have expressed their concern over the capabilities of an AI system in the near future. Meanwhile, on September 4, Musk tweeted that countries which grow their superiority in Artificial Intelligence will rule the world and the competition might lead to World War III. He said that Russia, China and more countries are the biggest contenders. This statement came after Russian President Vladimir Putin, speaking to school children, said, "Whoever becomes the leader in this area will rule the world." As a response, Musk tweeted, "It Begins…" Not only then, there have been other famous personalities who have expressed their opinions and concerns on the subject.

Artificial intelligence can figure out a person's sexual orientation by analysing a picture of their face, a controversial new study claims. According to its authors, who say they were "really disturbed" by their findings, the accuracy of an AI system can reach 91 per cent for homosexual men and 83 per cent for homosexual women. The study also concludes that homosexual men and women tend to have "gender-atypical facial morphology, expression, and grooming styles". The paper, titled Deep neural networks are more accurate than humans at detecting sexual orientation from facial images, was co-authored by Stanford University's Yilun Wang and Michal Kosinski, and first spotted by the Economist. In it, they claim to "show that faces contain much more information about sexual orientation than can be perceived and interpreted by the human brain".

With mounting pressure from both public and private sectors to avoid patient harm and lower costs, reducing length of stay had become a top priority for hospitals and health systems across the nation. Yet effecting change can be challenging due to the complex and multi-layered nature of the problem, and the many disciplines that need to be involved in the solution. Hospital leaders are bombarded with options for solving LOS and are left wondering which ones will really move the needle – today, not months from now. Join Diane Karagory, Managing Director of Huron Consulting Group and Dr. Rahul Agarwal of Qventus, to learn about the new approaches that leading organizations are taking to improve discharges and tackle LOS issues.

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment. The result is a dire need to optimize performance and optimize the cost of production per barrel.

In the growing market of Data Science, there are quite some details that people miss out on. These are tools or techniques that can make you a better performer in the field and also ease your efforts and help you focus on the analytics rather than the trivialities. Here, I will introduce you to another GUI based tool – Orange. This tool is great for beginners who wish to visualize patterns and understand their data without really knowing how to code. In my previous article, I presented you another GUI based tool KNIME, follow this link to learn about it further.

For a few years now, artificial intelligence has become a subject of debate far beyond the field of digital technology alone, doubtless because there is a sense of the whole scope of its economic and social consequences. Indeed, the laboratory technology has undergone a qualitative leap – with machine learning in particular – that has allowed it to be incorporated in several processes that until recently still required human intervention. Translation, voice recognition, and classifying images and videos are some of the first areas of experimentation and progress. Others will follow, although the technology is fallible and progress remains uncertain. For now, these new feats have given rise to contradictory reactions.

Last spring, Katie Rivera's daughter came home from the St. Francis Parish School in Bakersfield with some unusual paperwork. The school was pushing parents to sign their children up for a "unique pilot program" taught entirely online and run by a public school district in Los Angeles County. Each student who enrolled in the Lennox Virtual Academy would get a free Chromebook computer to use at school, with access to online classes. All parents had to do was fill out the forms, authorizing St. Francis to share information about their finances and their children's health with the Lennox School District a hundred miles away. "This partnership is expected to bring many benefits for St. Francis students," Principal Kelli Gruszka wrote to parents.

Machine learning is starting to show its potential in multiple fields. According to Gareth Baxendale, head of technology for the NIHR Clinical Research Network, clinical trials are no exception. Could machine learning be used to enhance or improve the success of a clinical trial? There is much excitement around machine learning and the opportunities it can provide, and it's already beginning to permeate every area of our digital life. In fact, you're likely to make use of machine learning almost every day without even knowing it.

Artificial intelligence can accurately guess whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and vice versa.

Chatbots are about to change the world in more ways than we can imagine. Already, bots around the globe can complete a diverse set of varying tasks. From ordering pizza online to mashing faces together in Project Murphy, chatbots are about to become a normal element in everyday life. As the scope of chatbots becomes broader every day, there are new applications popping up constantly. Education has traditionally been known as a sector where innovation moves slowly.