Amazon's aim is to have Alexa indistinguishable from a human voice. Dave Limp, Amazon's SVP of device and services business is standing in front of an image of the bridge of the starship Enterprise, explaining the inspiration for Amazon's surprise hit Echo device. "A lot of people wondered what was the inspiration for this vision, it really was this, this cultural icon it started here with the tap on the lapel to talk to the computer. And later in the Star Trek series you could be anywhere on the starship Enterprise and you could talk to the computer and she would respond quickly with an answer," he says. With a 100M investment fund and the opening up of cloud service APIs and an SDK, Alexa and the Echo could become the brains of your home automation and IoT network.

Cotton ball spider webs and peeled grape-eyeballs, begone! Because this year, the hottest new Halloween decoration is set to be the "Trumpkin," or a pumpkin carved in striking resemblance to none other than Donald Trump. Is the life of a fashion editor all front-row seats and Instagram perfection? Well, yes and no, as Marie Claire U.S.'s editor-in-chief Anne Fulenwider told us. Yes, it's glamorous, but it also means "putting your hair back in a ponytail, rolling up your sleeves, editing stories, and meeting with … Flipping posts, videos, images and more into Flipboard Magazines allows bloggers to show off passions that reach far beyond what's covered on their blog's niche. He showed us that just because music was innately physical did not mean that it was anti-intellectual."

If you want to see some of the stuff that Tay tweeted, head over here(warning; some of her tweets make Donald Drumpf look tame). Tay's introduction by Microsoft was not just an attempt to build an AI that learnt from human interactions, but also one that potentially enriched Microsoft's brand and was designed also to harvest users information such as gender, location/zip codes, favourite foods, and so on (as was the Microsoft Age guessing software of last year). It harvested user interactions alright, but after a group of trolls launched a sustained, coordinated effort to influence Tay, the AI did exactly what Microsoft designed it to do -- it adapted to the language of it's so-called peers. Tay appears to have accomplished an analogous feat, except that instead of processing reams of Go data she mainlined interactions on Twitter, Kik, and GroupMe. She had more negative social experiences between Wednesday afternoon and Thursday morning than a thousand of us do throughout puberty. It was peer pressure on uppers, "yes and" gone mad. No wonder she turned out the way she did. I've Seen the Greatest A.I. Minds of My Generation Destroyed by Twitter, New Yorker article, March 25th, 2016 Tay is a lesson to us in the burgeoning age of AI. Teaching Artificial Intelligences is not only about deep learning capability, but significantly about the data these AIs will consume, and not all data is good data.

It's a lightning-fast learner, speaks eight languages and is considered an expert in multiple fields. It's got an exemplary work ethic, is a speed reader and finds insights no one else can. On a personal note, it's a mean chef and even offers good dating advice. Named after IBM's first CEO, Watson was created back in 2007 as part of an effort by IBM Research to develop a question-answering system that could compete on the American quiz show "Jeopardy." Since trouncing its human opponents on the show in 2011, it has expanded considerably.

The White House released a 58 page narrative on how the government should prepare and approach utilizing Artificial Intelligence for a wide variety of applications; you can find the doc here, Executive Office of the President AI Report. Yet, I'm stuck filling out and mailing (millennials, I'm talking about postal mail with a stamp) a PDF for the City of Chicago to update my address they send my property taxes to, something not quite adding up but I digress. I am firm believer in the power of Artificial Intelligence to revolutionize all industries and how our government operates is no exception. The report does a great job at highlighting how a dedicated and concerted effort needs to be focused on Artificial Intelligence and the economic value that can be created from it – at a local, state and federal level. Overall, it was a solid read that talked a lot about the potential of Artificial Intelligence to change the world as we know it.

DESCRIPTION Online fashion shopping is a multi-billion industry which is growing exponentially year over year. Does the problem space of providing an awesome online experience for customer without the typical touch and feel retail experience thrill you? Are you excited by the challenge of building large scale systems that will be used by millions of customers, day-in-day-out? If the answer is yes, we would like you to take a look at the Amazon Fashion Technology team, which is currently solving this problem. Experiences like which-size-fits-me, what-goes-well-with-this are huge challenges to solve in an online space.

There was growing interest in machine learning among attendees of this year's Strata & Hadoop World conference in New York. Many vendors are responding by rushing products to market even before they are ready. Information Management spoke with Sisense's head of product Guy Levy-Yurista about why all the interest. Information Management: What are the most common themes that you are hearing among conference participants? Guy Levy-Yurista: "Machine learning was top of mind for a number of attendees at the show. There were several exhibitors who were not only talking about the potential of machine learning, but who were actually presenting narrow, but effective, machine learning products – even before they were ready to go to market. "The machine learning'buzz' has really penetrated the data analytics market, and everyone is trying to show they have a play so they can rise to the top of ongoing conversations.

Many machine learning algorithms expect data to be scaled consistently. There are two popular methods that you should consider when scaling your data for machine learning. In this tutorial, you will discover how you can rescale your data for machine learning. How To Prepare Machine Learning Data From Scratch With Python Photo by Ondra Chotovinsky, some rights reserved. Many machine learning algorithms expect the scale of the input and even the output data to be equivalent. It can help in methods that weight inputs in order to make a prediction, such as in linear regression and logistic regression.

Artificial-intelligence (AI) systems known as neural networks can recognize images, translate languages and even master the ancient game of Go. But their limited ability to represent complex relationships between data or variables has prevented them from conquering tasks that require logic and reasoning. In a paper published in Nature on 12 October1, the Google-owned company DeepMind in London reveals that it has taken a step towards overcoming this hurdle by creating a neural network with an external memory. The combination allows the neural network not only to learn, but to use memory to store and recall facts to make inferences like a conventional algorithm. This in turn enables it to tackle problems such as navigating the London Underground without any prior knowledge and solving logic puzzles.

Earlier this year, Facebook CEO Mark Zuckerberg announced that as his "personal challenge" side project for 2016, he was programming an artificial intelligence to run his home -- a basic AI that could control the lights and temperature, play music on command, unlock the door for recognized friends, and so on. He compared it to J.A.R.V.I.S., Tony Stark's constant computerized companion in Marvel's Iron Man films. Today, Zuckerberg posted on Facebook, "It's time to give my AI Jarvis a voice. Who should I ask to do it?" The crowdsourced responses started immediately, with users recommending Paul Bettany (the actor who voiced J.A.R.V.I.S. in the MCU films), The Dalai Lama, the late Robin Williams, Donald Trump, and a lot more.