The future of design is uncertain, with technology gradually blurring the lines of ownership and creativity. So how do we look ahead? At the Design Thinkers track at TNW Conference, designers from all fields shared their experiences and visions for the future. We've highlighted the key takeaways: Chris had large ambitions for his talk, which he shared with the crowd: "Thanks for coming out so early in the morning on day two of the conference. Hopefully I can bring some delight to your hangovers."

It's Norman: also known as the first psychopathic artificial intelligence, just unveiled by US researchers. The goal is to explain in layman's terms how algorithms are made, and to make people aware of AI's potential dangers. Norman "represents a case study on the dangers of Artificial Intelligence gone wrong when biased data is used in machine learning algorithms," according to the prestigious Massachusetts Institute of Technology (MIT). Pinar Yanardag, Manuel Cebrian and Iyad Rahwan, part of an MIT team, added: "there is a central idea in machine learning: the data you use to teach a machine learning algorithm can significantly influence its behavior." "So when we talk about AI algorithms being biased or unfair, the culprit is often not the algorithm itself, but the biased data that was fed to it," they said via email.

If you use the application please cite the paper. The input corpus is represented as two files: Cooccur.csv and Word2Id.csv. The first file follows the original libfm format (http://www.libfm.org) The folder data_pos contains the modeling of such example corpus using Part-of-Speech (POS) as additional contextual attribute, while the folder data_dependency contains the input data to train dependency-based embeddings. The Word2Id.csv file contains the symbols vocabulary, and looks like this: The first column contains the word form or the POS tag, the second column an unique identifier, the third column a meta information to distinguish words from POS tags (i.e., 0 for words, 2 for POS tag).

A whole lot was put into Fortnite: Battle Royale with yesterday's v4.4 patch, and a whole lot was datamined out of that patch as well. Perhaps the most important reveals from the datamine via @twoepicbuddies were the dual loading screens for week 7 and 8. The first loading screen shows what exactly has emerged from the pod in the center of the Dusty Divot meteor, a new robotic-looking villain that also just so happens to be the blockbuster skin reward for doing 7 out of 10 weeks worth of challenges. I might not have automatically assumed our new friend, called "The Visitor" was evil until I saw the season 8 loading screen seen above where he marches straight to the Snobby Shores skull villain base and appears….to This is season 4's comet.

When it comes to digital payments, honest players around the globe – genuine consumers, merchants, processors and financial institutions – want two things: speed and security. But since the introduction of EMV, fraudsters have shifted their focus toward card-not-present (CNP) transactions (a February 2018 study by Javelin revealed fraudulent transactions are 81 percent more likely to occur online than at the POS). In a new article in The Green Sheet, Dave Excell, founder and CTO at Featurespace, explains why the application of machine learning models is essential to preserving the integrity of transactions around the globe by delivering a risk- and friction-free experience. Click here to read Dave's article: "Machine learning can safeguard global payments".

This week we saw a huge milestone with China beating Amazon to successfully establish fully commercialized drone delivery, a new type of online to offline buying experience combining China's Spotify with retail, and artificial intelligence (AI) predicting which roads will flood in advance in order to reduce traffic congestion. China is pushing the boundaries of technological advancement faster than any country on Earth and This Week In China Tech is the place to stay on top of the news that you won't find in the Western media. Aerial photo taken on May 10, 2018 shows a drone carried with parcels, taking off from a branch post office in Weicheng Township, Qingzhen City of southwest China's Guizhou Province. You probably remember how Amazon captured the consumer imagination when they announced their concept for delivery drones at the end of 2016, but they never really materialized. Chinese retailers have now delivered fully operational drone delivery systems, confirming 17 authorized routes last week (article in Chinese).

VTech might be synonymous with cordless phones in most people's minds, but the company has also put out a succession of reliable baby monitors, many of which we've reviewed. It's not a big leap from baby cams to security cams, and the VC931 HD Pan and Tilt Home Monitoring Camera shows the company is as adept at helping you keep your house as safe as your other precious assets. The VC931's ball-shaped design takes its cue from vintage webcams, a look home security monitors have moved away from over the last few years. Despite its somewhat dated appearance, this camera is packed with the features security DIYers prize: motion detection, night vision, two-way talk, and even a sleep mode for privacy. You also have your choice of video storage options--you can save video directly to the camera or to the cloud.

If you are already using a pre-curated dataset, such as Labeled Faces in the Wild (LFW), then the hard work is done for you. You'll be able to use next week's blog post to create your facial recognition application. But for most of us, we'll instead want to recognize faces that are not part of any current dataset and recognize faces of ourselves, friends, family members, coworkers and colleagues, etc. To accomplish this, we need to gather examples of faces we want to recognize and then quantify them in some manner. This process is typically referred to as facial recognition enrollment. We call it "enrollment" because we are "enrolling" and "registering" the user as an example person in our dataset and application.

The advancement of artificial intelligence is concerning to many, from those who fear the growing data collection of A.I. assistants to Elon Musk and his fears that "super-intelligence" will bring humanity's end. How can scientists prevent an A.I. from destroying human civilization? To answer this question, scientists might need to study at an A.I. gone bad. Such was the impetus behind Norman, the robot considered to be the "world's first psychopathic A.I." A team of scientists from Scalable Cooperation at the MIT Media Lab, led by Pinar Yanardag, Manuel Cebrian, and Iyad Rahwan, fed the A.I. biased data to see how it might influence its behavior. In April, the team began to expose Norman to potentially damaging biases and bad data to later see how the image-captioning robot "sees" pictures.

It s Norman: also known as the first psychopathic artificial intelligence, just unveiled by US researchers. The goal is to explain in layman's terms how algorithms are made and to make people aware of AI's potential dangers. Norman "represents a case study on the dangers of Artificial Intelligence gone wrong when biased data is used in machine learning algorithms," according to the prestigious Massachusetts Institute of Technology (MIT). Pinar Yanardag, Manuel Cebrian and Iyad Rahwan, part of an MIT team, added: "there is a central idea in machine learning: the data you use to teach a machine learning algorithm can significantly influence its behavior." "So when we talk about AI algorithms being biased on unfair, the culprit is often not the algorithm itself, but the biased data that was fed to it," they said via email.