A common challenge in Natural Language Processing (NLP) is Named Entity Recognition (NER) - this is the process of extracting specific pieces of data from a body of text, commonly people, places and organizations (for example trying to extract the name of all people mentioned in a wikipedia article). NER is a problem that has been tackled many times over the evolution of NLP, from dictionary-based, to rule-based, to statistical models and more recently using Neural Nets to solve the problem. Whilst there have been recent attempts to crack the problem without it, the crux of the issue is really that for approach to learn it needs a large corpus of marked up training data (there are some marked up corpora available, but the problem is still quite domain specific, so training on the WSJ data might not perform particularly well against your domain specific data) and finding a set of 100,000 marked up sentences is no easy feat. There are some approaches that can be used to tackle this by generating training data - but it can be hard to generate truly representative data and so this approach always risks over-fitting to the generated data. Having previously looked at Stanford's NLP library for some sentiment analysis, this time I am looking at using the OpenNLP library Further to this, the Stanford library is licensed under GPL which makes it harder to use in any kind of commercial/startup setting.

Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

Editors at the MIT Technology Review recently weighed in with their annual review of the world's 50 Smartest Companies. This list celebrates the most effective pairing of innovation and business across the globe. For the first time, more than 20% of MIT's picks rely on artificial intelligence to support their business at a fundamental level, somewhat redefining what it means to be a truly "smart" company today. How many of these 13 artificial intelligence leaders are you already using? It's working on speech recognition intelligence called Deep Speech 2. This reduces the chance of accidents on autopilot by 50% relative to the safety record of human drivers, according to CEO Elon Musk.

See this talk by Jeremy Howard: At Kaggle, It's a Disadvantage To Know Too Much and "Getting In Shape For The Sport Of Data Science" Start by practicing on toy datasets in MATLAB and walking through simple examples in Statistics and Neural Network Toolbox Below two books are standard introduction texts. They are complementary, the first one is written from a statistician perspective with lots of data analysis examples and the second one is focusing on algorithms. Both are graduate level texts requiring knowledge of algebra, statistics and calculus. It would also help to take a class on optimization but not strictly necessary. See this talk by Jeremy Howard: At Kaggle, It's a Disadvantage To Know Too Much and "Getting In Shape For The Sport Of Data Science" Below two books are standard introduction texts. They are complementary, the first one is written from a statistician perspective with lots of data analysis examples and the second one is focusing on algorithms.

Machine learning isn't confined to science fiction movie plots anymore. It has fueled the proliferation of technologies that touch our everyday lives, including voice recognition with Siri or Alexa, Facebook auto-tagging photos, and recommendations from Amazon and Spotify. And many enterprises are eager to leverage machine learning algorithms to increase the efficiency of their network. In fact, some are already using it to enhance their threat detection and optimize wide area networks. As with any technology, machine learning could wreak havoc on a network if improperly implemented.

CBR looks at five UK start-ups which have been noticeably creating innovations and acquisitions in the AI market. BenevolentAI, which is based in London, is responsible for applying the AI technology in the human health and bioscience sectors. In 2014, the company collected a total of £40 million in funding from both its existing investors and Woodford Investment Management LLP, which was then an incoming investor. The company is currently focused on diseases, such as neurodegeneration, orphan diseases and rare cancers, but have said it remains flexible in case any other opportunities are to arise, due to its artificial intelligence and machine learning capabilities. BenevolentAI uses complex AI to look for patterns in scientific literature, already having managed to identify two potential drug targets for Alzheimer's with the use of AI.

The day before the election, as millions of Americans were feeling confident that the vast majority of the country shared their opinions, a pair of researchers at the University of Southern California Information Sciences Institute published a paper that looked closely at something many of us ignored: the provenance of political tweets. Where do they come from? How many are, in reality, made by humans? And if not, who is designing these crude straw-bots? Analyzing Twitter during three televised debates, they discovered that 20% of all political tweets were made by bots.

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us? The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won't see machines "exhibit broadly-applicable intelligence comparable to or exceeding that of humans," though it does go on to say that in the coming years, "machines will reach and exceed human performance on more and more tasks."

Silicon Valley has a long tradition of making too much of a good thing. Despite my enthusiasm for artificial intelligence--I declared my affection here a month ago--it's plain to see we are in a major AI hype cycle. As evidence, I cite an immutable law of hype, the Dinner Topic Theorem. Last week the Aspen Institute hosted a fascinating discussion about the ethics of artificial intelligence. This week Benchmark Capital, the venture firm, has convened a dinner to discuss "the reality and hype of AI." On the very same night, I'll be in Los Angeles where Fortune and our sister site TheDrive will host a panel on the ethics of autonomous vehicles, which are based on AI.

Google's DeepMind AI Research News & Update: Company Works With Blizzard To Use'Starcraft ... Artificial Intelligence Could Not Replace CEOs...Yet, Study Says Back to the Future 2.1 – Will Amazon Echo & Google Home play a big part in lives in 2017? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.