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.

Carl Vondrick is a doctoral candidate and researcher at MIT, where he studies computer vision and machine learning. His research focuses include leveraging large-scale data with minimal annotation and its applications to predictive vision and scene understanding. Recently his work has received a lot of media attention, including features in Forbes, Wired, CNN and PopSci, and other media outlets worldwide. As part of his work with MIT CSAIL, Carl built a deep learning vision system for AI to learn and understand human behaviour and interactions, using popular TV shows like The Office, Desperate Housewives, and YouTube videos. The resulting algorithm analyzes videos, then uses what it learns to predict how humans will behave.

Intelligence was the talk of Dreamforce - Salesforce's annual tech conference in San Francisco this month - with the SaaS giant's latest announcement'Einstein' promising to bring complex data science techniques and predictive algorithms seamlessly into all of their cloud-based CRM products. Here's how Salesforce used a spending spree on artificial intelligence (AI) startups and talent to bring these smart features to customers, all without opening up their precious data. Before he went on an AI acquisition binge, Salesforce CEO Marc Benioff said there was anxiety within the organisation around applying predictive algorithms to customer data they can't see, because customers want to keep their data private and secure. Read next: What is Salesforce's AI powered Einstein product? When can customers try Einstein and how much will it cost?

Shivon Zilis will participate in a panel discussion at Strata Hadoop World New York 2016, "Where's the puck headed?," considering the big trends in big data and explaining what the field will look like down the road. A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive.

You almost certainly should do (a) subject-wise cross-validation rather than (b) record-wise cross-validation. In some sense, an independent observation is a different subject. If you want to predict performance on new subjects, you must test on subjects you didn't train on! In typical settings, repeated observations of the same individual are correlated with each other even after conditioning on features. Hence with record-wise cross-validation, your test set isn't independent of your training set!

Elon Musk famously called it "our greatest existential threat". Physicist Stephen Hawking said that, limited by slow biological evolution, humans wouldn't be able to compete and would be superseded. But the technology that sparked those fears - artificial intelligence - is also being touted as the biggest potential advance in our history. A recent international study found that 50 per cent of experts questioned believe that artificial intelligence - or AI - will be smarter than humans within the next 24 years. And 90 per cent of those surveyed believed that milestone would be reached within 60 years.