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.

We'd be pleased to get feedback about how this book works out as a textbook, what is missing, or covered in too much detail, or what is simply wrong.

"About 6,000 languages are currently spoken in the world today," says Elizabeth Salesky of MIT Lincoln Laboratory's Human Language Technology (HLT) Group. "Within the law enforcement community, there are not enough multilingual analysts who possess the necessary level of proficiency to understand and analyze content across these languages," she continues. This problem of too many languages and too few specialized analysts is one Salesky and her colleagues are now working to solve for law enforcement agencies, but their work has potential application for the Department of Defense and Intelligence Community. The research team is taking advantage of major advances in language recognition, speaker recognition, speech recognition, machine translation, and information retrieval to automate language processing tasks so that the limited number of linguists available for analyzing text and spoken foreign languages can be used more efficiently. "With HLT, an equivalent of 20 times more foreign language analysts are at your disposal," says Salesky.

In Forrester's US B2B eCommerce Forecast: 2015 to 2020 they quote that "74% of B2B buyers research, at least one-half of their work purchases online. With that percentage nearly doubling to 56% by 2017, B2B sellers will see a significant volume of offline business move online in the next few years." Taking those facts further, at the Forrester Sales Enablement Forum, a study by Andy Hoar, Principal Analyst at Forrester, revealed that he expected 22% of B2B Sales jobs go by 2020. With Enterprise purchases taking place more and more online This means the traditional B2B sales person is being replaced by Search Engines, YouTube, websites etc. The Diagram above is Forester's view on what will replace the B2B Salesperson.

This is a brief overview of my paper "Information Retrieval Performance Measurement Using Extrapolated Pr...," which I'll be presenting on June 8th at the DESI VI workshop at ICAIL 2015. The paper provides a novel method for extrapolating a precision-recall point to a different level of recall, and advocates making performance comparisons by extrapolating results for all systems to the same level of recall if the systems cannot be evaluated at exactly the same recall. Recall, R, is the proportion of the relevant documents retrieved by the information retrieval (IR) system, and precision, P, is the proportion of retrieved documents that are relevant. It is sometimes desirable to have high recall while also having high precision in order to find most of the relevant documents without having a lot of non-relevant documents mixed in, but higher recall is usually accompanied by lower precision. Some IR systems generate a relevance score for each document, allowing the documents to be sorted so that the ones that are deemed most likely to be relevant appear at the top of the list.

Today, Facebook announced Deep Text, an AI engine it's building to understand the meaning and sentiment behind all of the text posted by users to Facebook. In a blog post, Facebook said that it was building the system to help it surface content that people may be interested in, and weed out spam. This might sound like a minor improvement, but it actually has the potential--in theory--to transform the social network most of us use every day into something else we use daily: a powerful search engine. "We want Deep Text to be used in categorizing content within Facebook to facilitate searching for it and also surfacing the right content to users," Hussein Mehanna, an engineering director at Facebook's machine learning team, told Quartz. The universe of that search may not be the whole worldwide web that Google crawls, but it's still massive.

Today, Facebook announced Deep Text, an AI engine it's building to understand the meaning and sentiment behind all of the text posted by users to Facebook.