Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to understand and process human languages. Let's check out how NLP works and learn how to write programs that can extract information out of raw text using Python! Note: If you don't care how NLP works and just want to cut and paste some code, skip way down to the section called "Coding the NLP Pipeline in Python". As long as computers have been around, programmers have been trying to write programs that understand languages like English. The reason is pretty obvious -- humans have been writing things down for thousands of years and it would be really helpful if a computer could read and understand all that data.

I was interviewed by IBM to share my thoughts on a topic related to data science about which I'm passionate: How do we know what we know and, once we know it, how do we know it's the truth? IBM turned that interview into a comic strip (see below) and I summarize my points in this post. You would think that, because we have access to so much information in this digital age, getting to the truth would be relatively quick and easy. As I've discussed before, people hold beliefs that are not supported by the information available to them. Take, for example, the 27% of Americans who don't believe there is solid scientific evidence of climate change, the rise of the "anti-vaxxers" who think that they know more about science and public health than the overwhelming majority of doctors, immunologists and other health professionals, the "flat-earthers" who ignore the ample evidence that the earth is a sphere, the increase in Google Trends that shows that searches of the term "flat earth" have more than tripled over the past two years and more. I've given this topic a lot of thought and I would like to talk about the problems about the importance of everybody gaining some knowledge on statistics and critical thinking (scientific method, evidence-based decisions).

Why are so many AI researchers so worried about lethal autonomous weapons? What makes autonomous weapons so much worse than any other weapons we have today? And why is it so hard for countries to come to a consensus about autonomous weapons? Not surprisingly, the short answer is: it's complicated. In this month's podcast, Ariel spoke with experts from a variety of perspectives on the current status of LAWS, where we are headed, and the feasibility of banning these weapons. Guests include ex-Pentagon advisor Paul Scharre (3:40), artificial intelligence professor Toby Walsh (40:51), Article 36 founder Richard Moyes (53:30), Campaign to Stop Killer Robots founders Mary Wareham and Bonnie Docherty (1:03:38), and ethicist and co-founder of the International Committee for Robot Arms Control, Peter Asaro (1:32:39). You can listen to the podcast above, and read the full transcript below. You can check out previous podcasts on SoundCloud, iTunes, GooglePlay, and Stitcher. If you work with ...

The latest: Several nonprofits are among the latest members in the Partnership on AI, a group established to address the ethical and other challenges presented by artificial intelligence. The effort began with big companies like Apple, Google, Facebook and Microsoft, but now includes a growing roster of academic institutions and nonprofit groups alongside some of the biggest names in tech. LGBT rights organization GLAAD is among the new members, as is the Joint Center for Political and Economic Studies, which was set up in 1970 as a think tank serving black elected officials. Joint Center president Spencer Overton said his organization is especially concerned with how AI could disproportionately impact employment in communities of color. Overton said that 27% of black workers are concentrated in just 30 jobs at high risk to automation.

Welcome to part 4 of my series on the AI of Total War. A game that completely re-built the campaign AI systems to accommodate for an increasingly more complex series of mechanics, resources and consequences. Rome II's adoption of the Monte Carlo Tree Search (MCTS) algorithm is a critical step in bring the campaign AI up to spec for more contemporary entries in the series. In this entry I'm going to look at how the MCTS systems were improved upon, as well as how the diplomacy systems have been scaled up for the modern era as Rome gave way to 2015's Total War: Attila. Attila is the ninth entry in the Total War franchise and transposes the conflict to the late 4th and early 5th century: an phase of history known as the Migration Period.

"Space is big," wrote Douglas Adams in The Hitchhiker's Guide to the Galaxy. "You just won't believe how vastly, hugely, mind-bogglingly big it is. I mean, you may think it's a long way down the road to the chemist's, but that's just peanuts to space." Adams's assertion comes repeatedly to mind when reading David Sumpter's Outnumbered, which attempts to reckon with the sheer scale of the systems that manage much of our digital lives. It's easy, when faced with the numbers at hand, to succumb to a kind of vertigo: Facebook has two billion users, who make tens of millions of posts every hour.

This is the 3rd article of series "Coding Deep Learning for Beginners". Here, you will be able to find links to all articles, agenda, and general information about an estimated release date of next articles on the bottom of the 1st article. They are also available in my open source portfolio -- MyRoadToAI, along with some mini-projects, presentations, tutorials and links. You can also read the article on my personal website, hosted with Jekyll in order to improve readability (supporting code syntax highlighting, LaTeX equations and more. Some of you may wonder, why the article series about explaining and coding Neural Networks starts with basic Machine Learning algorithm such as Linear Regression.

Territory Manager, Autodesk Africa, Mr. Simon Bromfield, who was in Nigeria for the Autodesk Future Forum, spoke with Emma Okonji on the need for different sectors of the economy to adopt emerging technologies for business growth. What is your view about Nigeria's manufacturing industry? One of the challenges about Nigeria's manufacturing industry is the issue of doing things in silos instead of through collaboration and integration. Everyone wants to control his or her business and they do not want to share ideas with others. Nigerians need to work together to build better projects and that is the secret of global industries because they share ideas and build on existing ideas that are workable.

The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. In a world where 90% of the data ever generated has been created in the last two years, it's becoming increasingly clear we could do with a little help to make sense of it all. That's where artificial intelligence comes in, with its ability to recognise patterns in data and make insightful predictions based upon them. With 2.5 quintillion bytes of data being generated each day it's a technology that's both essential and potentially revolutionary. Volumes of data of that size are hard to comprehend and, if dealing with the data was left to us alone, would be even harder to draw value from.

A swarm of bees or a colony of ants works together to complete tasks that would be impossible for individuals to carry out. Researchers in Belgium have now shown that a group of robots can pull off the same feat--including figuring out the correct sequence in which the tasks must be performed. The researchers deployed a swarm of 20 two-wheeled robots, each 50 millimeters high, in a hexagonal playing field. Their job was to enter three booths in the correct order. Forming chains, the researchers report this month in Science Robotics.