Mark Zuckerberg will face a public grilling at the European Parliament on Tuesday, after a rebellion by MEPs over plans to hold the meeting in private. The Facebook founder is coming to Brussels to answer questions about his company's policies on personal data, privacy, and the social network's impact on elections – facing an interrogation by leaders of the assembly's political groups. The tech boss has so far refused similar requests to appear at the UK Parliament– prompting British MPs to warn that they might issue a formal summons for him to appear. In April he spent two days testifying before the US Congress. Antonio Tajani, the president of the European Parliament, had announced last week that the meeting would be held behind closed doors.

We begin this journey through our favorite articles of the month by discovering and reading one of the most influential paper of natural language processing (NLP). Papers can be intimidating to read. So I liked the author's idea of combining screenshots from the original paper, explanations in plain English and code snippets with an actual implementation of the paper in Python. The article can be read at different levels: from the presentation of encoders, decoders and the attention function to real-world examples including the use of regularization and GPU training. One of the world-famous universities of California -- Berkleley -- launches its new data science program.

I mean that both the ways people interpret Shakespeare's meaning when he has Antonio utter the phrase in The Tempest. In one interpretation it is that the past has predetermined the sequence which is about to unfold–and so I believe that how we have gotten to where we are in Artificial Intelligence will determine the directions we take next–so it is worth studying that past. Another interpretation is that really the past was not much and the majority of necessary work lies ahead–that too, I believe. We have hardly even gotten started on Artificial Intelligence and there is lots of hard work ahead. It is generally agreed that John McCarthy coined the phrase "artificial intelligence" in the written proposal2 for a 1956 Dartmouth workshop, dated August 31st, 1955. It is authored by, in listed order, John McCarthy of Dartmouth, Marvin Minsky of Harvard, Nathaniel Rochester of IBM and Claude Shannon of Bell Laboratories. Later all but Rochester would serve on the faculty at MIT, although by early in the sixties McCarthy had left to join Stanford University. The nineteen page proposal has a title page and an introductory six pages (1 through 5a), followed by individually authored sections on proposed research by the four authors.

The local authority, Council of Agriculture (COA), guided seeds manufacturers to improve F1 hybrid seed collecting techniques that resulted in superior harvest performance of cruciferous vegetables, watermelons and cherry tomatoes. The featured exhibitors invited to showcase at the pavilion of Taiwan Seed Varieties this year include Besgrow Seed, Ching Long Seed, Agronew Trading, Taiwan Banana Research Institute, Known-You Seed and Sing-Flow Seed Trading. One of the exhibitors - CH Biotech, developed'Weather Mend,' a product to regulate plant gene expression system with non-genetically modified active components, which is seen as a crucial solutions for crops in facing extreme climate change. This product aims to increase plant survival rate and helps steady food supply. Not only will it initiate plants' defence mechanism in advance, but also reduce losses caused by severe weather.

The UK is about to make a big bet on AI's ability to spot cancer. The Guardian has learned that Prime Minister Theresa May will commit "millions of pounds" in funding for research toward AI that can diagnose cancer and chronic diseases at an early stage. The technology could reduce "avoidable deaths," according to May's prepared speech, and is estimated to save as many as 22,000 lives per year by 2033. It would extend healthy living by another five years as of 2035. There is, however, an important catch.

In 2005, Danish brothers David and Christopher Mikkelsen met a young Afghan refugee called Mansour. Four months after fleeing Kabul and the Taliban with his parents and five siblings, Mansour became separated from them, ending up in Denmark with no idea what had happened to his family or where they were.

It is a heady time in the world of human empowerment. We are seeing technologies that can alter our genetic code, augment our brainpower, enhance our physique and extend our lives. As physicians, our goals are no longer limited to healing injuries and curing disease. The question, "Can you make me better than I have ever been?" has moved from fantasy to near reality. Here are a few of the highlights from recent TED, Near Future and other gatherings.

For California-based Infinera, 2017 was not an easy year. The manufacturer of telecom equipment saw revenue drop from $870 million in 2016 to $740 million in 2017. Gross margin went down from 45 percent to 33 percent. In the end, the company, which employs about 2,000 people across the U.S., Canada, China, India, and Sweden, reported a net loss of $195 million for the year, compared to a net loss of $24 million in 2016. To turn things around, one of the things the company is focused on is technological improvements, CEO Thomas Fallon told investors earlier this year.

Recent developments in Artificial Intelligence (AI) are showing just how far we have come with the technology. Take for example the recent showcase from Google, where a telephone call is made by Google Assistant to book a haircut. The technology is now able to understand the nuances of conversation and is quite astonishing at first watch. With this ability to save time and cut admin, it is no wonder that adoption rates of AI in businesses have grown 60 percent in the past year alone. However, AI needs to be programmed by a human and in today's working environment, this can be problematic.

We consider the problem of predicting power outages in an electrical power grid due to hazards produced by convective storms. These storms produce extreme weather phenomena such as intense wind, tornadoes and lightning over a small area. In this paper, we discuss the application of state-of-the-art machine learning techniques, such as random forest classifiers and deep neural networks, to predict the amount of damage caused by storms. We cast this application as a classification problem where the goal is to classify storm cells into a finite number of classes, each corresponding to a certain amount of expected damage. The classification method use as input features estimates for storm cell location and movement which has to be extracted from the raw data. A main challenge of this application is that the training data is heavily imbalanced as the occurrence of extreme weather events is rare. In order to address this issue, we applied SMOTE technique.