It has been over 10 years since the Netflix Prize finished, and I was not expecting to write a blog post about it at this point. However, just in the past couple of weeks I have found myself talking about it extensively both in the context of a Twitter thread and discussion, as well as the shooting of an upcoming documentary series. Given that there seems to be continued interest as well as misunderstanding around the prize and its outcome, I thought it might be worth to "set the record straight" in a dedicated post. TLDR; While I am often misquoted as having said that the Netflix Prize was not useful for Netflix, that is only true about the grand prize winning entry. Along the way, Netflix got far more than our money's worth for the famous prize.

I wrote about recommender systems last week, but there is so much discussion around their effects right now in the mainstream tech press that they deserve a second issue. As a recap, I said that there were two things that made recommender systems super ineffective, and that YouTube, one of the premier companies tech using recommendations, suffers from both a lot of the first and a lot of the second. Recommender systems today have two huge problems that are leading companies (sometimes at enormous pressure from the public) to rethink how they're being used: technical bias, and business bias. The real problem is YouTube's business model. YouTube is THIRSTY for advertising money, at all times.

Artificial Intelligence (AI) is working its way into almost every industry you can think of – including video games, healthcare, autonomous vehicles, cybersecurity, retail, and banking. With the growth of AI came the introduction of other terms such as "Machine Learning" and "Deep Learning". You may have heard them before but knowing how they relate to AI and how they are different can be confusing. Thanks to astonishing advancements in artificial intelligence (AI) and its sub-segments machine learning and deep learning, companies are achieving new levels of efficiency in data analysis that impact their entire business. The term, "artificial intelligence" was first created in 1956, but has become more popular today.

What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage. Recommendation engines can be gamed because they amplify the effects of thought bubbles. The 2016 US presidential election showed how important it is to understand how recommendation engines work and the limitations and strengths they offer.

In 1714, the British Parliament passed the Longitude Act, which offered serious money to anyone who could devise a practical method to measure longitude at sea. While the determination of longitude might seem a trivial thing in today's world of smartphones and GPS satellites, at the time it was an immense technical challenge. It took many years, but the strategy worked, leading to the development of the marine chronometer, a handheld mechanical marvel that undoubtedly saved the lives of countless sailors. Prizes have, of course, since been used to spur innovation in many other spheres. "These were typically offered by governments," says Josh Lerner of the Harvard Business School, who has studied the effectiveness of such prizes.

This article is an excerpt from the Pearson Addison-Wesley book "Pragmatic AI" by Noah Gift. Reprinted here with permission from Pearson and 2019. What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage.

"In machine learning, is more data always better than better algorithms?" No. There are times when more data helps, there are times when it doesn't. Probably one of the most famous quotes defending the power of data is that of Google's Research Director Peter Norvig claiming that "We don't have better algorithms. We just have more data.". This quote is usually linked to the article on "The Unreasonable Effectiveness of Data", co-authored by Norvig himself (you should probably be able to find the pdf on the web although the original is behind the IEEE paywall).

This is a guest post by author William Hertling. The views expressed here are his own and do not reflect those of his employer, the IEEE, or IEEE Spectrum. At South by Southwest Interactive last month, I debated the future of artificial intelligence with my co-panelists. The roboticist on the panel argued that AI is an intellectually challenging field where the problems are difficult, and therefore can be solved only by highly intelligent people working on obscure mathematics and algorithms. The future, he argued, will look much like the past: a series of incremental, hard-won improvements in very narrow fields.

Today, even though similar approaches are in use, they are usually just one part of complex recommendation approaches that can include large collections of algorithms and data sources. The data set was again provided year that the summer school on Recommender Systems by Moviepilot and was co-organized by TU Berlin. By 2007, the Netflix Prize had The second track focused on recommendation of scientific attracted thousands of participating teams, and the papers. The challenge attracted 30 participating Netflix Prize concluded. At the by Simon Fraser University and Yelp who also 2010 ACM RecSys conference, the seed for what provided the data. CAMRa attracted a moderate the 2014 challenge did not focus on classical recommendation, number of participants, but contributed to establishing but rather on prediction of user engagement, the RecSys Challenge series.

"In machine learning, is more data always better than better algorithms?" No. There are times when more data helps, there are times when it doesn't. Probably one of the most famous quotes defending the power of data is that of Google's Research Director Peter Norvig claiming that "We don't have better algorithms. We just have more data.". This quote is usually linked to the article on "The Unreasonable Effectiveness of Data", co-authored by Norvig himself (you should probably be able to find the pdf on the web although the original is behind the IEEE paywall).