The ‘U’ in UX is not what it used to be. At least that’s what Mischa Weiss-Lijn, Head of Experience Design at RMA Consulting, argues. With the proliferation of AI, users are in control of the input, but are relying (often blindly) on the output of their devices. In other words, they are relying on AI to make important decisions.
He argues that this necessitates a shift in how designers go about designing. He said, “We will need to think deeply about how the experiences we ship will impact our users and design them to create proud, empowered centaurs, rather than disempowered de-skilled, and beaten down worker-drones.”
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Samsung is considering creating a fresh US$1 billion fund to purchase tech firms in the field of AI, Chosun Biz (a South Korean newspaper) reported on February 21, Citing an unnamed Samsung official in the US.
“Despite several recent deals, the management pointed out the company still needed more fundamental investments into AI,” an industry source was quoted as saying in the report. Over the past year, Samsung has purchased 10 tech firms, including Harman International, the US car infotainment giant. The US$8 billion Harman deal is the company’s largest-ever purchase
Catherine Lu, a product manager at fraud detection company DataVisor, says AI could detect the semantic meaning behind a web article. If an article was printed, for example, which spoke of a three-headed alien
wandering around Central Park, an NLP engine could look at the headline, the subject of the story, the geo-location, and the main body text. An AI could determine if other sites are reporting the same facts. And the AI could weigh the facts against established media sources.
“The New York Times is probably a more reputable of a source than an unknown, poorly designed website,” Lu told Fox News. “A machine learning model can be trained to predict the reputation of a web site, taking into account features such as the Alexa web rank and the domain name (for example, a .com domain is less suspicious than a .web domain).”
Differencing is a popular and widely used data transform for time series. In this tutorial, you will discover how to apply the difference operation to your time series data with Python.
After completing this tutorial, you will know about the differencing operation, including the configuration of the lag difference and the difference order, know how to develop a manual implementation of the differencing operation, and how to use the built-in Pandas differencing function.
Scientists at the Helmholtz Zentrum München and their partners at ETH Zurich and the Technical University of Munich (TUM) have now used deep learning to determine the development of hematopoietic stem cells in advance. In Nature Methods they describe how their software predicts the future cell type based on microscopy images.
Dr. Carsten Marr said, “Our algorithm classifies light microscopic images and videos of individual cells by comparing these data with past experience from the development of such cells. In this way, the algorithm ‘learns’ how certain cells behave.”
But what is the benefit of this look into the future? As study leader Marr explains, “Since we now know which cells will develop in which way, we can isolate them earlier than before and examine how they differ at a molecular level. We want to use this information to understand how the choices are made for particular developmental traits.”
Equifax and SAS are making strides toward incorporating more AI in their businesses. The former is developing deep learning tools to improve credit scoring and the latter is adding new deep learning functionality to its data mining tools and offering a deep learning API.
“We noticed a couple of years ago,” says Peter Maynard, Senior Vice President of Global Analytics at Equifax, “that we were not getting enough statistical lift from our traditional credit scoring methodology.”
“My team decided to challenge [the notion that deep neural networks are a ‘black box’] and find a way to make neural nets interpretable,” says Maynard. He explains: “We developed a mathematical proof that shows that we could generate a neural net solution that can be completely interpretable for regulatory purposes.”
VR enables remarkably immersive experiences, offering new ways to view the world and the ability to explore novel environments, both real and imaginary. However, compared to physical reality, sharing these experiences with others can be difficult, as VR headsets make it challenging to create a complete picture of the people participating in the experience.
Google Machine Perception researchers, in collaboration with Daydream Labs and YouTube Spaces, have been working on solutions to address this problem wherein they reveal the user’s face by virtually “removing” the headset and create a realistic see-through effect. Their approach uses a combination of 3D vision, machine learning and graphics techniques, and is best explained in the context of enhancing Mixed Reality video (also discussed in the Google-VR blog). It consists of three main components: 1) dynamic face model capture, 2) calibration and alignment, and 3) compositing and rendering.
Sending money internationally can now be done while chatting online with friends and family as money exchange service TransferWise launches its new Facebook Messenger bot today.
Money can be sent between people in the UK, US, Canada, Australia, and Europe directly through the messaging app. As TransferWise gains feedback, it will eventually expand the service to more than 50 countries and 600 routes.
While Scott Miller, head of partnerships at TransferWise, says the bot will take the same amount of time as using the company’s website to complete a transfer, the convenience of being able to stay within the app rather than navigating to another website was a big motivator for developing the system.
For the first time ever, self-driving race cars zoomed through a course in public, with impressive (well, for one of them, anyway) results. Roborace, the self-driving racing series Formula E announced in 2015, made history with its first public trial race at the Buenos Aires ePrix last weekend.
While the Devbot 1 handled the unexpected challenge of dodging a moving dog, driving through the course at full speed was too much to handle for Devbot 2. The car took a corner too sharply, overcorrected, and smashed up against the wall, ending the first ever totally autonomous public race in a crash.