It's certainly an exciting time be involved in Natural Language Processing (NLP), not only for those of us who are involved in the development and cutting-edge research that is powering its growth, but also for the multitude of organizations and innovators out there who are finding more and more ways to take advantage of it to gain a competitive edge within their respective industries. With the global NLP market expected to grow to a value of 16 billion by 2021, it's no surprise to see the tech giants of the world investing heavily and competing for a piece of the pie. More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, Yahoo, Intel, Apple and Salesforce. It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago.

For Matt Osborne, finding exploited children typically starts with a walk on the beach, and it ends with hands cuffed behind his back. It's almost always the same--Osborne and a few friends travel somewhere that's known for sex tourism and walk along the beach or hang in area nightclubs, not to look for girls but to be seen themselves. A group of white American men is easy to spot in heavily-touristed resort towns in Asia, Central America, and South America, so it doesn't take long to make a connection. "They approach us," Osborne says. "At first, everything is innocuous. Want to go jet ski or parasailing? Buy a margarita or beer? They offer us drugs, and the conversation always turns to girls. And if you let them talk long enough and say, 'What else do you have? What else do you have?' Then sooner or later, they always offer us young girls."

This paper develops robust confidence intervals in high-dimensional and left-censored regression. Type-I censored regression models are extremely common in practice, where a competing event makes the variable of interest unobservable. However, techniques developed for entirely observed data do not directly apply to the censored observations. In this paper, we develop smoothed estimating equations that augment the de-biasing method, such that the resulting estimator is adaptive to censoring and is more robust to the misspecification of the error distribution. We propose a unified class of robust estimators, including Mallow's, Schweppe's and Hill-Ryan's one-step estimator. In the ultra-high-dimensional setting, where the dimensionality can grow exponentially with the sample size, we show that as long as the preliminary estimator converges faster than $n^{-1/4}$, the one-step estimator inherits asymptotic distribution of fully iterated version. Moreover, we show that the size of the residuals of the Bahadur representation matches those of the simple linear models, $s^{3/4 } (\log (p \vee n))^{3/4} / n^{1/4}$ -- that is, the effects of censoring asymptotically disappear. Simulation studies demonstrate that our method is adaptive to the censoring level and asymmetry in the error distribution, and does not lose efficiency when the errors are from symmetric distributions. Finally, we apply the developed method to a real data set from the MAQC-II repository that is related to the HIV-1 study.

This Wednesday, the tech billionaire investor announced an investment in Luminance, a newly launched startup that uses artificial technology to read contracts in order help law firms with the arduous process of due diligence for mergers and acquisitions (M&A). It's not a "sexy" piece of technology, Lynch argues -- but one that has huge implications for the way we live our lives, and is indicative of a quiet revolution in artificial intelligence. What this is is probably an example of what's going to be changing a lot of things. If you can get machine technology to be reading contracts, it's going to be changing a lot of the world around us ... you're seeing the beginning of a new age." He has since founded venture capital firm Invoke Capital -- the vehicle through which the investment in Luminance was made. This week, Business Insider sat down with the investor to discuss Luminance, Brexit, his augmented reality plans, and why he likes having an "unfair advantage." Mike Lynch is an investor in Luminance -- but was also instrumental in helping create it. "The bit that makes it possible is the machine learning, and that was being done by some research people at Cambridge, and I actually have a connection because my PhD a long, long time ago was in machine learning," Lynch said. "I was introduced to them, and what they were doing looked great, but I said to them'look, you gotta go and meet some real world people.' "So they started getting real data and they met up with [law firm] Slaughter and May, and basically the machine learnt from Slaughter and May how to do these thing and at that point they made a little company. They got a CEO who is a lady who'd actually been involved in a lot of M&A deals over their career and we funded it, and it's been developing the product, and today it comes out into the bright lights of day."

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September 21, 2016 -- NEW YORK – LexisNexis Legal & Professional today announced its intent to acquire privately held Intelligize, Inc., provider of the industry standard for U.S. Securities and Exchange Commission (SEC) intelligence. The acquisition would expand the LexisNexis securities and M&A offering with new content, innovative tools and analytics while complementing the capabilities of the company's existing securities solution, Lexis Securities Mosaic . Closing of the transaction is subject to certain customary conditions. Based in Reston, VA, Intelligize provides a leading analytics solution enabling legal, academic and business professionals to efficiently mine data and insights from SEC filings, M&A contracts, transactional agreements and corporate governance documents. This enables customers to streamline compliance processes, benchmark peer disclosures, clearly identify market standards, draft documents based on SEC filed precedent and clear SEC reviews in an efficient manner.

Travers Smith has become the latest firm to adopt AI technology in a bid to boost efficiency.

SHANGHAI--A Chinese man whose son was killed while driving a Tesla Motors Inc. TSLA -0.82 % vehicle applied to a local Beijing court to investigate whether the car's Autopilot driving system was engaged. In January, 23-year-old Gao Yaning died in a crash in the northeastern province of Hebei while driving a Tesla Model S. Six months later his father, Gao Jubin, filed a lawsuit accusing Tesla of exaggerating Autopilot's capabilities. At a court hearing Tuesday, he asked for an independent investigation of the cause of the crash. "The family insists the investigation should be done by a third party, rather than Tesla," said Cui Qiuna, a lawyer for the Gao family. The court will study the family's request.

Wells Fargo CEO testifies, GoPro announces new drone, fast-food chain antibiotics report, and LAUSD considers starting school later. Wells Fargo CEO testifies, GoPro announces new drone, fast-food chain antibiotics report, and LAUSD considers starting school later. Warning, this video contains graphic content: Tulsa police released several police car and helicopter videos Sunday after Terence Crutcher, an unarmed 40-year-old black man, was fatally shot by a white police officer on Friday. Warning, this video contains graphic content: Tulsa police released several police car and helicopter videos Sunday after Terence Crutcher, an unarmed 40-year-old black man, was fatally shot by a white police officer on Friday.

With natural language processing, we have a pile of documents (that's Supreme Court cases in this project), and we need to get to their true essence. Most words aren't helpful in this process, so we drop them (stopwords). We also know that words like "liking" are really the same as "like" in this context (shh don't tell my literature professors from college I said that), so we lemmatize, which means we replace all those -ings with their roots. After this, we have a few choices. We need to turn the words into "vectors" (fancy term for number, really) and use those vectors to inform our topic groups.