IT PAYS to have false memories. Our brains use them to generalise new information – but lack of sleep gets in the way. False memory was discovered in an experiment that asked volunteers to memorise lists of related words and then recall them. When they learned "bed", "drowsy" and "dream", about half later also remembered the word "sleep". That's because well-rested brains normally use "associative memory" to link related concepts together.

Alibaba has gone from an ecommerce company to its own economy. The Tmall Global Shopping day, Alibaba's Single's Day event, recorded 120.7 billion RMB (17.8 billion USD) in sales in one day. They also revealed some interesting plans for the coming future. Robotics, AI, AR, and VR will be adopted across sectors like ecommerce, logistics, service sector, and finance to make user's daily life smarter. YunOS, a cloud-based data and service oriented operating system for the Internet of Things, will power a wide range of smart devices including smartphones, wearables, Internet cars, robots, and smart household appliances.

Food and agribusiness comprise a $5 trillion industry that accounts for 10 percent of global consumer spending, 40 percent of employment and 30 percent of greenhouse-gas emissions. This massive industry does not change easily, but change – transformative innovation – is precisely what's needed. We collected some examples of artificial intelligence techniques that can help farmers to save thousands of dollars. Three years ago Belarusian startup OneSoil was created. It's an online service to monitor the status of sown areas, which can increase productivity and save resources with the help of artificial intelligence.

This is the second installment in a three-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Part Three will cover commercial machine learning and deep learning software and services. There are thousands of open source projects on the market today, and we cannot cover them all. We've selected the most relevant projects based on usage reported in surveys of data scientists, as well as development activity recorded in OpenHub. In this post, we limit the scope to projects with a non-profit governance structure, and those offered by commercial ventures that do not also provide licensed software. Part Three will include software vendors who offer open source "community" editions together with commercially licensed software.

Automation, AI, and robotics are the real forces behind the disruption to the global job market, and they will accelerate in 2017. As we do each week, the editors of ZDNet will continue to put our heads together in 2017 and give you our weekly Monday Morning Opener--an editorial that zooms in on the most important topic in tech for the week. Now that we're sailing into the new year, we've also put a few brain cycles into zooming out on the big issues that will shape the year ahead. So after giving you the 5 biggest tech trends of 2016, we've now got the four big ones that will dominate 2017. For all the talk about artificial intelligence, robotics, and machine learning in 2016, the shadow that they cast--the real thing people are afraid of--is automation.

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems. In this tutorial, you will discover how to implement an autoregressive model for time series forecasting with Python. Autoregression Models for Time Series Forecasting With Python Photo by Umberto Salvagnin, some rights reserved. A regression model, such as linear regression, models an output value based on a linear combination of input values.

On the west coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can photograph the water from above. The photos are a way of locating dugongs, or sea cows, in the bay near Perth--part of an effort to prevent the extinction of these endangered marine mammals. The trouble is that Hodgson and her team don't have the time needed to examine all those aerial photos. There are too many of them--about 45,000--and spotting the dugongs is far too difficult for the untrained eye. Deep learning is remaking Google, Facebook, Microsoft, and Amazon.

A Queensland startup has celebrated a new deal with a major European customer after it perfected new deep learning technology that recognises images beyond simple shape and texture matching. TrademarkVision's deal with the European Union Intellectual Property Office this month comes after a successful beta test that saw around 1,000 trademark image searches conducted each day. The EUIPO becomes the first governmental agency to take up the technology, although the terms of the contract were not disclosed. In the past year, the company has been working on deep learning technology to take its software to the next level of intelligence, and the move has borne fruit in a spectacular way with the EU deal. "We've focused on machine learning techniques so the system can recognise objects in trademarks and logos much like humans do. Despite the wide variety of ways humans pictorially depict objects in logos, deep learning has helped to solve this semantic challenge in a quick and robust way," said TrademarkVision founder and chief executive Sandra Mau.

Each of these trucks is the size of a small two-story house. None has a driver or anyone else on board. Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. At this one, known as West Angelas, the vehicles work alongside robotic rock drilling rigs. The company is also upgrading the locomotives that haul ore hundreds of miles to port--the upgrades will allow the trains to drive themselves, and be loaded and unloaded automatically.

Artificial intelligence is changing and improving the ways we manage our money. Research suggests Australians are ready to embrace fintech banking solutions, and the launch of three new London-based chatbot startups may be a sign the rest of the world is gearing up for a revolution too. Artificial intelligence (AI) has been rapidly progressing over the past two decades, with machines reaching and exceeding human performance on an increasing number of tasks. Just this week, the White House released a report entitled Preparing for the future of Artificial Intelligence, which describes the ways in which AI has and continues to yield new opportunities for progress in critical areas such as health, education, energy, and the environment. Another important area of business, ripe for disruption, is finance and banking.