That was a few more than 10 papers, but the field is moving fast.

Machine learning underpins Sidecar's optimization technology for product listing ads, and we're not shy about making that fact known. But we understand that the term might sound a little bit sci-fi to some. However, since it's so central to what we do and who we are, we thought a simple explanation of machine learning was in order -- no computer science degree required. At its simplest, machine learning means giving computers the power to teach themselves to make decisions using historical examples, rather than explicitly programming them to perform a task. To illustrate this concept, I'm going to borrow an example from The Data Skeptic, a popular -- and highly recommended -- podcast by Kyle Polich.

In a recent scientific study highlighted by The Atlantic, researchers from the University of Vermont and the University of Adelaide determined the core emotional trajectories of stories by taking advantage of advances in computing power and natural language processing to analyze the emotional arcs of 1,737 fictional works in English available in the online library Project Gutenberg. Their research, published online, involved assigning happiness scores to 10,000 frequently used words determined by a crowdsourcing project, then breaking up blocks of text to analyze the happiness of each block and mapping those scores on an emotional trajectory. The researchers recognize that emotional arcs and plot structures may be similar, but are not necessarily the same. They also point out multiple well-known theories that all narratives can be boiled down three plots, seven plots, a different seven plots, 20 plots, or even 36 plots depending on who you read and believe. Furthermore, the researchers realize that multiple, connected emotional arcs appear in longer, more complex works of fiction, so they limited their study sample to works of fiction between 10,000 words and 200,000 words.

Although artificial intelligence has become commonplace--most smartphones contain some version of AI, such as speech recognition--the public still has a poor understanding of the technology. As a result, a diverse cast of critics, driven by fear of technology, opportunism, or ignorance, has jumped into the intellectual vacuum to warn policymakers that, sooner than we think, AI will produce a parade of horrible outcomes. Unfortunately, their voices have grown so loud that we are nearing a tipping point where their narratives may be accepted as truth, which would create a real risk that policymakers will decide to ratchet back the pace of progress. With the White House convening a discussion later this week on the social and economic implications of artificial intelligence technologies, to be followed just days later by the 25th International Joint Conference on Artificial Intelligence, Rob Atkinson rebuts these pervasive and pernicious myths in the Huffington Post.

Chat bots have ballooned in popularity in recent months, and now we're seeing some interesting examples of how that technology, where computers interact and respond to human requests, is being used to solve other problems. Today, Zendesk is taking the wraps off "Automatic Answers", a service for businesses to reply to emails from customers without ever having a human employee get involved. Automatic Answers is not your average, run-of-the mill email autoresponder. The service was built using a machine learning platform that Zendesk's in-house teams of data scientists and engineers, which are based out of Melbourne, Australia, have been developing on for a while now. That machine learning platform was first announced last year and it also powers a service Zendesk announced last October, Satisfaction Prediction, which is able to monitor customer-company interactions to -- as its name implies -- determine whether the customer is getting what she or he needs. The machine learning/AI element means that the responses in Automatic Answers are not only reading and responding specifically to what you the customer is asking, but it is technically getting smarter with each response (and presumably using a bit of Satisfaction Prediction to figure out if it's getting it right).

New York University's Information Law Institute in collaboration with the White House is slated to host an extensive public symposium on Thursday, July 7 in a bid to focus on the near-term influence of AI (artificial intelligence) technologies across the economic and social systems. A brand-new series of workshops and an inter-agency working group that will delve into understanding more about the advantages and hazards of artificial intelligence was announced by the White House on May 3, 2016. There is a lot of excitement surrounding artificial intelligence and how to create computers that are capable of intelligent behavior. Following years of consistent but sluggish progress on making computers smarter at mundane tasks, a slew of developments in the research community and industry have recently sparked momentum and investment in the advancement of this work. The Social and Economic Implications of AI Technologies in the Near-Term will concentrate on problems of the next five to 10 years, particularly focusing on four themes; What impact AI will have on social inequality, ethics, healthcare and labor.

Up until recently, artificial Intelligence (AI) was regularly associated with sci-fi films and dystopian futures -- but a shift has occurred and it already plays a much more commonplace and integral role in our everyday lives. From betting on the Kentucky Derby, to helping us interact with our banks, emails, and families through AI assistants such as Alexa and Siri, AI is becoming ever more important. It is also becoming more integral to businesses. The 2016 Accenture Technology Vision report showed that 70 percent of corporate executives are making more investments in AI-related technologies than they were two years ago, with 55 percent stating that they plan on using machine learning, deep learning, as well as embedded AI solutions like Amelia. Businesses are using this technology to fundamentally change the way they operate and to drive a new, more productive relationship between people and machines.

People across the world are turning amateur photos into elaborate works of art with a new viral app that relies on AI technology to let users instantly transform mundane images into Picasso paintings. Prisma, an app that has attracted 1 million daily users as of Thursday, is reinventing the concept of filtering photos with technology. While the concept of adding filters to photos has been around for years, the Prisma iOS app is unique in the way that it relies on a "combination of neural networks and artificial intelligence" to remake the image. What that means is the Prisma tools aren't the kind of art filters that Instagram uses where the filters overlay the original photo. Instead, Prisma goes through different layers and recreates the photo from scratch, according to the app makers, who are based in Moscow.

The challenge asks computers to make sense out of specific sentences with grammar that humans can understand, but that may be obtuse to machines. For instance, in the sentence "The city councilmen refused the demonstrators a permit because they feared violence," computers aren't able to parse who the word "they" is actually talking about. In contrast, human readers can understand it because of context clues. That's exactly the type of thinking researchers are looking to improve, namely with deep learning. The contest featured a grand prize of 25,000 for entrants who could achive 90 percent accuracy with similar sentences, and the best came from Quan Liu, a researcher from the University of Science and Technology of China as well as Nicos Issak, a researcher from the Open University of Cypress.

The O'Reilly AI Conference, happening September 26-27 in New York, will cover all the most essential--and intriguing--topics in applied AI. You'll learn how to implement AI in your projects, uncover AI's limitations and untapped opportunities, and explore how AI will change the business landscape.