According to a report in BuzzFeed News, Google is training a new Al to understand human conversation and respond in a suitably human fashion by feeding it a diet of trashy romance. BuzzFeed News quotes Google software engineer Andrew Dai as saying: "In the Google app, the responses are very factual. Hopefully with this work, and future work, it can be more conversational, or can have a more varied tone, or style, or register." Apparently romantic novels are good for this because they're, well, formulaic. Although they employ an adult lexicon, unlike children's books, they follow a simple repetitive structure, making learning easier for the AI.

Algorithms help us to choose which films to watch, which music to stream and which literature to read. But what if algorithms went beyond their jobs as mediators of human culture and started to create culture themselves? In 1950 English mathematician and computer scientist Alan Turing published a paper, "Computing Machinery and Intelligence," which starts off by proposing a thought experiment that he called the "Imitation Game." In one room is a human "interrogator" and in another room a man and a woman. The goal of the game is for the interrogator to figure out which of the unknown hidden interlocutors is the man and which is the woman.

Convolutional Neural Networks are great: they recognize things, places and people in your personal photos, signs, people and lights in self-driving cars, crops, forests and traffic in aerial imagery, various anomalies in medical images and all kinds of other useful things. But once in a while these powerful visual recognition models can also be warped for distraction, fun and amusement. In this fun experiment we're going to do just that: We'll take a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network, feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones. Yeah, I'll do real work. But first, let me tag a #selfie.

At this time, all participants are in a listen-only mode. A question-and-answer session will follow the formal presentation. I would now like to turn the conference over to your host, Jandy Tomy, Vice President of Finance and Investor Relations. With me today on the call are Robert Apatoff, President and Chief Executive Officer; and Becky Sheehan, Executive Vice President and Chief Financial Officer. Before we begin, please remember that, during the course of this call, management may make forward-looking statements within the meaning of the Federal Securities Laws that address the Company's expected future business, financial performance, and financial condition. These forward-looking statements involve risks and uncertainties that could cause actual results to be materially different than those expressed in our forward-looking statements. In addition to the Company's reports filed with the Securities and Exchange Commission, please refer to the text in the Company's press release issued today for a discussion of the risks and uncertainties associated with such forward-looking statements. Also, please note that, on today's call, management will refer to certain non-GAAP financial measures, including adjusted EBITDA, adjusted net income, and free cash flow. The Company believes these non-GAAP financial measures provide useful information for investors. Please refer to today's press release for definitions and calculations of these non-GAAP performance measures, as well as reconciliations of the non-GAAP performance measures to the Company's GAAP financial results. Now, I'd like to turn the call over to Robert Apatoff, President and Chief Executive Officer. Good afternoon, everyone, and thank you for joining us today. I will provide a brief overview of our business highlights, integration efforts, and strategic and operating initiatives. Following my comments, our CFO Becky Sheehan will review our financial results and outlook for 2016 in more detail. Finally, I will provide a few closing remarks, and then we'll open up the call to take your questions.

GWYN is based on the Fluid Expert Personal Shopper (XPS) software platform, powered by IBM Watson. She is a gift concierge that intuitively guides customers through their shopping experience to help them select the perfect gift from 1-800-FLOWERS.COM, Inc., including 1-800-Flowers.com,

In an effort to make its apps more conversational, Google fed its AI engine a whopping 2,865 romance novels so it can improve its understanding of language. The idea is to improve the way Google products respond to users. Software engineer Andrew Dai, who led the project, told BuzzFeed News that this sort of work could help make the responses from the company's search app, as well as the'Smart Reply' feature in Inbox, more natural and varied. Our biggest ever edition of TNW Conference is fast approaching! Dai added that romance novels are great for training AI because they mostly follow the same plot – allowing the AI to focus on picking up nuances of language.

Two of the minds behind Siri are set to unveil a new AI-powered digital assistant on Monday, May 9. Specifically, The Washington Post reports that Siri co-founders Dag Kittlaus and Adam Cheyer will take the wraps off of Viv, an AI assistant said to be capable of handling much more complex tasks than its predecessor. Essentially, Viv will set itself apart from Siri by working with natural language to complete a complex series of tasks rather than taking one at a time. For example, you could ask Viv to make a restaurant reservation and buy movie tickets in one command, then Viv would carry out those tasks without sending you to an outside app. Viv could also react on the fly and make recommendations if, say, the movie is sold out. Comparatively, performing the same tasks with an AI assistant like Siri would require you to move between different apps with multiple commands. Of course, all of this requires some heavy integration with third-party services.

In the ever-forward-looking world of the Silicon Valley, lately there's been a lot of hype surrounding the use of AI and machine learning processes in order to build the next generation of software products and features -- with Google's self-driving cars taking the spotlight as the representative for this line of thought. Though largely an unproven concept at this point, given that a working, reliable model could yield untold benefits, it's something that a lot of companies are pushing as the "next big thing" in the world of tech. Not to say that the possibility of making it work isn't there, but there's a lot of challenges that go into building "AI" systems that often go undiscussed, which, in most cases, leads to the product's lack of adoption in the long run. I've put "AI" in quotes here, because what gets categorized as "artificial intelligence" in the media these days isn't actually something that's driven by "intelligence", per se -- the majority of AI or machine learning projects tend to be driven by data, rather than the code itself. If you look under the hood of how Google's self-driving algorithms work, you'll see that a lot of its functionality is heavily reliant on the accuracy of Google Maps, which gives the software enough of an understanding of its environment in order for the car to navigate through its terrain.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I'm a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab's collaborative filtering toolkit, a library originally written and maintained by Bickson.