Today was a day for the history books. The UK has voted to leave the European Union and opened a deep crack in the heart of Europe. As a consequence of this result, Prime Minister David Cameron will step down by October urging for a fresh leadership. At this point nobody knows the repercussions of these results. Will the Brexit hurt the economy of the UK and ignite a new recession?

In what is being perceived as another attempt to tighten its control over the internet, China's internet regulator on Saturday announced new rules that ban search engines from showing subversive information and obligate them to clearly identify paid results. The new regulations, which will take effect from Aug. 1, come close on the heels of the death of a 21-year-old college student, who is believed to have undergone an unapproved, experimental cancer treatment he found using the search engine Baidu. "Some search results lack objectivity and fairness, go against corporate morals and standards, misleading and influencing people's judgment," the Cyberspace Administration of China -- the country's internet regulator -- reportedly said. "Internet search providers should earnestly accept corporate responsibility toward society, and strengthen their own management in accordance with the law and rules, to provide objective, fair and authoritative search results to users." In addition, search engines would also be required to censor "rumors, obscenities, pornography, violence, murder, terrorism and other illegal information" -- regulations that the Chinese government claims are needed to safeguard the security of its citizens.

As we all know, Google is constantly looking to provide more relevant results for its users -- hence, the regular algorithm updates that frequently frustrate webmasters and anyone else's SEO efforts. The Pagerank algorithm that founders Sergey Brin and Larry Page introduced in the early days of Google was a step in the right direction, but it certainly wasn't the ultimate solution for improving the quality of search results. In fact, the search giant recently unveiled a new AI (yes, that stands for artificial intelligence) called RankBrain to help the engine better understand the queries users type into the search field. The real intention of this AI wasn't to change visitors' search engine results pages (SERPs) -- rather, it was to predict them. As a machine-learning system, RankBrain actually teaches itself how to do something instead of needing a human to program it.

We've all been there - you are just in the middle of finishing an important email when your laptop crashes and you lose your hard work. But it seems that your laptop alone might not be to blame. A new series of tests conducted by Microsoft suggest that Google Chrome could be the reason your laptop battery is always dying. The tests streamed the same video on four unplugged, identical laptops, each on a different browser. While Google Chrome is the most popular internet browser, a series of tests by Microsoft - who are Google's rival - have shown how much the search engine is draining your laptop battery.

Ryan Jones is a well-known SEO and Manager of Search Strategy & Analytics at SapientNitro. Ryan Jones is a veteran of SEJ Summit Chicago, having spoken there last year. This year, I'm so excited for Ryan's presentation on machine learning and how it affects search, which is a topic I'm sure most of you want to learn more about. Check out Ryan's insight below and feel free to ask questions in the comment section! I hate to spoil the presentation here, but the short answer is: if you've been doing SEO properly RankBrain doesn't change anything.

Machine teaching (MT) studies the task of designing a training set. Specifically, given a learner (e.g., an artificial neural network or a human) and a target model, a teacher aims to create a training set which results in the target model being learned. MT applications include optimal education design for human learners and computer security where adversaries aim to attack learning-based systems. In this work, we formulate pool-based MT as a state space search problem. We discuss the properties and challenges of the resulting problem and highlight opportunities for novel search techniques. In our preliminary study we use a beam search approach, and find that training and evaluating empirical risk of models dominate the run time of the search. Toward the goal of better search techniques for future work, we develop optimizations ranging from implementation details for specific learners to algorithm changes applicable to general blackbox learners. We conclude with a discussion of open problems and research directions.

Probabilistic inference over large data sets is a challenging data management problem since exact inference is generally #P-hard and is most often solved approximately with sampling-based methods today. This paper proposes an alternative approach for approximate evaluation of conjunctive queries with standard relational databases: In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known PTIME self-join-free conjunctive queries: A query is in PTIME if and only if our algorithm returns one single plan. Furthermore, our approach is a generalization of a family of efficient ranking methods from graphs to hypergraphs. We also adapt three relational query optimization techniques to evaluate all necessary plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers. We also note that the techniques developed in this paper apply immediately to lifted inference from statistical relational models since lifted inference corresponds to PTIME plans in probabilistic databases.

China's equivalent of Google is under fire. Search engine Baidu has been criticised following the death of 21-year-old student Wei Zai, who used the search engine to research esoteric treatments for his cancer. After Wei Zai's death, the state-run People's Daily attacked Baidu, claiming it was ranking search results in exchange for money. "There have been hospitals making profits at the cost of killing patients who were directed by false advertisements paid at a higher rank in search results," the article claimed, adding, "profit considerations shall not be placed over social responsibility". The Chinese party newspaper may have its own reasons for wanting to control Baidu; a powerful search engine is a gateway to the outside world and a challenge to any repressive state.

In this March 23, 2010, file photo, the Google logo is seen at the Google headquarters in Brussels. SAN FRANCISCO – Google defended itself against charges of political bias in its search algorithm after a video alleged it distorted display results for Hillary Clinton. Creators of the video on the YouTube channel SourceFed, which had accumulated over 15 million views on Facebook and nearly 300,000 on YouTube by Friday evening, alleged Google's search engine Autocomplete feature suppresses searches pairing the presumptive Democratic nominee with criminal activity. If someone types "Hillary Clinton cri," Google's search suggests phrases like "Hillary Clinton crime reform" and "Hillary Clinton crisis", said Matt Lieberman, host and writer for SourceFed, in the video. Yet after typing the same phrase on Yahoo or Bing search, suggestions on crimes and criminal activity are among the first suggestions, he notes.

This week researchers demonstrated that by analyzing a person's Web searches they could in some cases predict an upcoming diagnosis of pancreatic cancer. Unlike traditional medical professionals, they have the advantage of access to a trove of data that Microsoft collects through its search engine, Bing. The Microsoft researchers identified Web users who had recently searched for queries indicating they have pancreatic cancer, such as "I was told I have pancreatic cancer, what to expect," and then looked back months earlier to examine patterns in the symptoms that the users searched for. This included phrases such as "dark or tarry stool," "abdominal swelling," "dark urine" and "yellowing skin." From this analysis they realized trends in the queries of users who were soon to be diagnosed with pancreatic cancer, identifying 5 to 15 percent of cases with low false-positive rates.