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.

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.

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.

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.

Early diagnosis is key to gaining the upper hand against a wide range of diseases. Now Microsoft researchers are suggesting that records of the topics that people search for on the Internet could one day prove as useful as an X-ray or MRI in detecting some illnesses before it's too late. The potential of using engagement with search engines to predict an eventual diagnosis – and possibly buy critical time for a medical response -- is demonstrated in a new study by Microsoft researchers Eric Horvitz and Ryen White, along with former Microsoft intern and Columbia University doctoral candidate John Paparrizos. In a paper published Tuesday in the Journal of Oncology Practice, the trio detailed how they used anonymized Bing search logs to identify people whose queries provided strong evidence that they had recently been diagnosed with pancreatic cancer – a particularly deadly and fast-spreading cancer that is frequently caught too late to cure. Then they retroactively analyzed searches for symptoms of the disease over many months prior to identify patterns of queries most likely to signal an eventual diagnosis.

For the second time in months, Syrian President Bashar al-Assad has said "we will fight on to liberate every inch of our land". The last time Assad made a similar statement, he was scolded by the Russian ambassador to the UN who said this was not in line with the Kremlin's policies. At the time, it wasn't - Russia was pushing for a political settlement and was involved in efforts with the United States to bring about a cessation of hostilities to create a conducive atmosphere for peace talks. This time around, however, Assad has so far not been told off. Instead, Russia sent its defence minister to Iran's capital Tehran to take part in talks with his Syrian and Iranian counterparts.

Lately there's been a rumble pretty much everywhere about artificial intelligence, digital personal assistants, the Internet of Things, wearables and apps for everything. I've even written myself about what the rise of digital assistants mean to search. There are some who claim that these new technologies will render search obsolete, passed over for the convenience and joy of an always-available digital world. I think they are wrong. Instead of looking at a search engine as an advertising platform, we need to remember what it actually does for people.

Google goes deeper into machine learning. Google is now using artificial intelligence (AI) to better understand search queries, so what implications will machine learning have for marketing? Last week, Google told Bloomberg News that a "very large fraction" of queries is now being interpreted by an AI system called RankBrain. The revelation about the system, which helps Google navigate around 15 percent of daily queries that are unrecognizable, could provide insight into how brands can best leverage the search-engine giant. With machine learning, RankBrain will likely better understand what users are searching for.