It's hard enough for non-technical users to deal with ransomware infections: understanding public-key cryptography, connecting to the Tor anonymity network and paying with Bitcoin cryptocurrency. A new malicious program now makes it even more difficult by completely locking victims out of their computers. The new Petya ransomware overwrites the master boot record (MBR) of the affected PCs, leaving their operating systems in an unbootable state, researchers from antivirus firm Trend Micro said in a blog post. The MBR is the code stored in the first sectors of a hard disk drive. It contains information about the disk's partitions and launches the operating system's boot loader.

As a new year kicks off, the words "big data" continue to be spoken in multiple industries all over the world. In much the same way, 2016 look to continue that trend, with more in-roads being made for big data in areas such as finances, health care, manufacturing, and even sports organizations. At the same time, machine learning is quickly catching on. Both big data and machine learning are often mentioned in the same breath, yet the two are not synonymous. Machine learning can be performed without the massive and complex data sets people often think of when referring to big data.

"Why don't these companies feel like they're giving away the farm when they give away their code and their ideas? Because other companies don't have Google's computing power, they don't have Twitter's computing power, and they don't have the data." Why don't these companies feel like they're giving away the farm when they give away their code and their ideas? Because other companies don't have Google's computing power, they don't have Twitter's computing power, and they don't have the data, right?

Ryan Adams knows his timing has been perfect. A professor of computer science at Harvard since 2011 and a cohost of the machine-learning podcast Talking Machines, Adams was leading a group doing research on intelligent algorithms when his 15-month-old machine-learning startup, Whetlab, was purchased by Twitter last summer. Whetlab's technology automates some of the hardest parts of building large-scale machine-learning systems. It was created to take on difficult machine-learning challenges like visual objection recognition and speech processing. Harvard researchers started using the tool in a wide range of projects, from biomedical robots to chemistry problems, and Netflix used an early open-source version to experiment with deep learning as well. Now on leave from Harvard, Adams spoke to Business Reports senior editor Nanette Byrnes at Twitter's offices in Cambridge, Massachusetts, about the exploding interest in machine learning.

Apple's acquired a startup with a keen focus on developing artificial intelligence systems that run locally on your device rather than at an offsite server farm. So it makes sense that the company would be interested in bringing this sort of imaging tech to its handsets and computers especially considering that new camera the iPhone 6s series is sporting. This purchase could point toward a smarter Photos app, or it could be nothing at all. Giant tech companies buy smaller ones all the time and Apple's no stranger to that. But if Tim Cook and Co. can make using the imaging program less frustrating as a whole, we'll really be in business.

The network which recently partnered with Silicon Valley-based startup accelerator Plug and Play, eyes big growth potential in this space and has already made investments in startups like vPhrase and ConfirmTkt. It is set to announce one more investment in this vertical in the next one month, reports ET. Commenting on the investment plans, Apoorv Ranjan Sharma, President, Venture Catalyst, said, "We believe AI is going to hugely impact all verticals of business and will be critical for those like ecommerce since the algorithms developed by such companies have the power to read data in a much faster and efficient way, impacting business decisions and saving both time and costs." Sharma has already invested in couple of AI startups for example ConfirmTkt which has developed an algorithm to predict whether a ticket booked is likely to be confirmed or not. "Currently all online ticket-booking platforms allow ticket bookings but there is no mechanism to predict whether it will be confirmed or not," Sharma adds further.

Elon Musk invested millions in an effort to make sure that artificial intelligence is used for good instead of evil, but for much of the general public AI still seems like science fiction -- something far out in the distant future. However, if you talk to people who work closely with this kind of technology, which has been called deep neural networks, deep learning, smart machines, or machine intelligence, you'll find out that it has advanced significantly in the past few years, and even bigger progress is coming very soon. There are several signposts that indicate this progress, including big enterprises running their own experiments with AI systems, as well as a sudden wave of tech giants taking certain technologies open source. "The vast preponderance [of projects in enterprises] is still experimentation," Gartner Fellow and vice president Tom Austin told InformationWeek in an interview. He estimates that about half of large enterprises are experimenting with "smart computing" projects.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid). Deep learning is often thought of as a set of algorithms that'mimics the brain'. A more accurate description would be an algorithm that'learns in layers'.

According to The Verge, Facebook is using artificial intelligence to produce detailed maps illustrating population density and the access to internet across the globe. This should help Facebook bring internet to parts of the world that are without access. Facebook has analysed 20 countries and 21.6 million square kilometres amounting to 350TB of data. Facebook is also reported to be creating deep learning AI which aims to find out what matters to Facebook users. Facebook is definitely not new to the AI game.

On Wednesday afternoon in the South Korean capital, Seoul, Lee Se-dol, the 33-year-old master of the ancient Asian board game Go, will sit down to defend humanity. On the other side of the table will be his opponent: Alphago, a programme built by Google subsidiary DeepMind which became, in October, the first machine to beat a professional human Go player, the European champion Fan Hui. That match proved that Alphago could hold its own against the best; this one will demonstrate whether "the best" have to relinquish that title entirely. Related: Google throws down the gauntlet. But can anyone beat its computer at Go? Lee, who is regularly ranked among the top three players alive, has been a Go professional for 21 years; Alphago won its first such match less than 21 weeks ago.