From Google's advertisements to Amazon's product suggestions, recommendation engines are everywhere. As users of smart internet services, we've become so accustomed to seeing things we like. This blog post is an overview of how we built a product recommendation engine for Hubba. I'll start with an explanation of different types of recommenders and how we went about the selection process. Then I'll cover our AWS solution before diving into some implementation details. Content-based recommenders use discrete properties of an item, such as its tags.

This is a Guest Post written by pricing expert, Richard Burcher, Managing Director of Validatum about how to avoid the trap of seeing efficiency combined with hourly billing result in margin erosion, and instead to use AI to increase productivity and boost law firm income. The article was written ahead of a Validatum conference this summer at the offices of CMS Cameron McKenna in London and the survey data below is from that audience. We have been thinking about AI from a pricing perspective for some time, so earlier this week we picked up that theme for the latest bi-monthly Validatum Pricing Forum. We were a little surprised (pleasantly) at the numbers (35) given what we thought was the relatively niche title of the session: 'The perfect pricing storm; artificial intelligence and hourly billing.' We took the opportunity to poll the audience with several live on-screen and anonymous questions, the results for which took us by surprise.

What was once viewed as the content of science fiction movies, artificial intelligence looks to be much more of a reality than previously expected. Artificial intelligence marketing can play such a huge role in the development of brand analysis and consumer interactions. Between sentiment analysis, customer service opportunities, and advertising optimization, artificial intelligence allows marketers to get a better understanding of their consumer base. Sentiment analysis is the ability to understand the overall feeling and mood of any piece of text written or even posted by a consumer. With AI, brands are able to automate their analysis by getting up to date looks at what their consumers are saying about them and whether or not the reaction is positive or negative. Rather than having someone comb through hundreds even thousands of social media mentions, with AI, this can be done in a matter of seconds.

How Many Arlington Asset Investment Corp (NYSE:AI)'s Analysts Are Bearish? Forget ideology, liberal democracy's newest threats come from technology and bioscience Ai Weiwei planned to sculpt a'Redline.' Chinese censors say he crossed one.

How Many Arlington Asset Investment Corp (NYSE:AI)'s Analysts Are Bearish? Forget ideology, liberal democracy's newest threats come from technology and bioscience

I've been thinking, mostly in the context of teaching, about how to specifically teach debugging of machine learning. Personally I find it very helpful to break things down in terms of the usual error terms: Bayes error (how much error is there in the best possible classifier), approximation error (how much do you pay for restricting to some hypothesis class), estimation error (how much do you pay because you only have finite samples), optimization error (how much do you pay because you didn't find a global optimum to your optimization problem). I've generally found that trying to isolate errors to one of these pieces, and then debugging that piece in particular (eg., pick a better optimizer versus pick a better hypothesis class) has been useful. For instance, my general debugging strategy involves steps like the following: First, ensure that your optimizer isn't the problem. You can do this by adding "cheating" features -- a feature that correlates perfectly with the label.

This week's top Machine Learning stories, including why you'll never write emails the same way again! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

When Gary Kasparov lost to chess computer Deep Blue in 1997, IBM marked a milestone in the history of artificial intelligence. On Wednesday, in a research paper released in Nature, Google earned its own position in the history books, with the announcement that its subsidiary DeepMind has built a system capable of beating the best human players in the world at the east Asian board game Go. Go, a game that involves placing black or white tiles on a 19x19 board and trying to remove your opponents', is far more difficult for a computer to master than a game such as chess. DeepMind's software, AlphaGo, successfully beat the three-time European Go champion Fan Hui 5–0 in a series of games at the company's headquarters in King's Cross last October. Dr Tanguy Chouard, a senior editor at Nature who attended the matches as part of the review process, described the victory as "really chilling to watch".

The BBC Reith Lectures in 1967 were given by Edmund Leach, a Cambridge social anthropologist. "Men have become like gods," Leach began. "Isn't it about time that we understood our divinity? Science offers us total mastery over our environment and over our destiny, yet instead of rejoicing we feel deeply afraid." That was nearly half a century ago, and yet Leach's opening lines could easily apply to today.

Tech giants including Google, Twitter, Salesforce, Apple, Intel, Yahoo, IBM, and AOL bought nearly 30 AI startups in the last five years, according to CB Insights. Venture capital (VC) interest in AI boomed in recent years as well. The boom started two year ago as "2014 marked a banner year for VC investment in U.S.-based AI startups, with capital invested and deal count increasing year-on-year by 183 percent and 41 percent, respectively," stated a report by PitchBook, an M&A database. According to an analysis done by his firm, Magister Advisors, the median price paid to AI startups per employee is 2.4 million.