Datafication is a buzzword of the last several years, that is used actively along Big Data industry. Honestly, if you would search the term'datafication' on the internet you probably won't find that much relative information about it, yet it is a word we are hearing a lot these days. However, after analyzing the topic itself, I could say that many of us understand the meaning of the term, but probably named it another way. Datafication, according to MayerSchoenberger and Cukier is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis. Simply said, it is about taking previously invisible process/activity and turning it into data, that can be monitored, tracked, analysed and optimised.

After outcries from users and efforts by other social media platforms to ban the content, Reddit has finally taken action against a community in which users posted fake porn generated by artificial intelligence-powered tools. The community, called deepfakes, contained spliced together content to create photo-realistic images and videos of celebrities and other people appearing in pornographic scenes. The community had amassed nearly 100,000 subscribers before it was shut down. Reddit banned the fake porn community Deepfakes. "Reddit strives to be a welcoming, open platform for all by trusting our users to maintain an environment that cultivates genuine conversation. As of February 7, 2018, we have made two updates to our site-wide policy regarding involuntary pornography and sexual or suggestive content involving minors," a spokesperson for Reddit told International Business times.

Nexus (NXS), announced a partnership with SingularityNET (AGI), to explore the combination of both technologies. SingularityNET is looking to expand horizons creating a decentralized AI network based on blockchain. This new partnership could result in the most secure, scalable and censorship-resistant blockchain AI infrastructure in existence. Nexus and SingularityNET share an aligned mission of creating the safest and most advanced technology. In discussing the future of secure, decentralized AI, SingularityNET Chairman David Hanson commented, "It will become the single most valuable technology in all of history. This is the chance for us to change the world together."

I'm wondering if there has been any work done on CNNs with each filter pooler trying to match a (scalar) value fed back from the most immediate downstream neuron, where this feedback value is ultimately anchored to some cost function on the top layer. I'm imagining a simple architecture where the goal is to derive the type of "inverse graphics" transformation that Hinton speaks of. The top layer feedback values could even be some affine transformation of the input data. The trickling down of activation values would be performed in iterations and in a diffusion process induce a kind of dynamic routing with neighboring filters forced to agree upon a constellation of transformations. After some number iterations, fixed or based on some local maxima convergence criteria, the diffusion would be halted and the weights updated in standard backprop fashion.

Thu 8 Feb 2018 05.25 EST Last modified on Thu 8 Feb 2018 05.26 EST Social news site Reddit has banned its nearly 100,000-strong "deepfakes" community, the original source of face-swapped celebrity pornography. Reddit is where the deepfakes wave began, with one user manually creating the first AI-created video clips. When a second Redditor built a desktop app to do the same thing, the community began to grow rapidly, approaching 90,000 members at the time of its deactivation. In a statement, the company said: "Reddit strives to be a welcoming, open platform for all by trusting our users to maintain an environment that cultivates genuine conversation. "As of February 7 2018, we have made two updates to our site-wide policy regarding involuntary pornography and sexual or suggestive content involving minors.

In the lead up to Pausefest, we sat down with AI expert Dr. John R. Smith from IBM to find out if robots can ever become truly creative. Why is artificial intelligence an important avenue to explore within the realms of technology? Researchers have actually been exploring AI and machine learning technologies for decades, even though it's really only been the past few years that it's gained traction outside of the lab. AI represents a new kind of computing - one that can learn through experience, much like humans do, and interact with humans in a more natural way than was previously possible. At IBM we believe that it will transform the world in dramatic ways - from things simple as how we interact with our devices and computers to access information (think voice recognition), to more profound ways like healthcare, cancer and climate change.

I'll examine each one – it'll be interesting to see what you think. Artificial intelligence, as a concept, has been around for a long time. From Hephaestus building the "fighting machines of the gods" to Mary Shelley's Frankenstein, humans have thought about and created stories around our desire to replace the mighty gods with ourselves for several thousand years. We see it today with modern franchises like "The Terminator" and "The Matrix", and less recently with "2001: A Space Odyssey" and "Do Androids Dream of Electric Sheep/Blade Runner". AI is so popular and accessible as a concept in mainstream media that if you ask people what they think it is, they think they'll be able to answer.

At Netflix, the Content Platform Engineering and Global Product Creative teams know that imagery plays an incredibly important role in how viewers find new shows and movies to watch. We take pride in surfacing the unique elements of a story that connect our audiences to diverse characters and story lines. As our Original content slate continues to expand, our technical experts are tasked with finding new ways to scale our resources and alleviate our creatives from the tedious and ever-increasing demands of digital merchandising. One of the ways in which we do this is by harvesting static image frames directly from our source videos to provide a more flexible source of raw artwork. Merchandising stills are static video frames taken directly from the source video content used to broaden the reach of a title on the Netflix service.

On Articial Intelligence Day on 13 December 2017, 600 artificial intelligence experts and enthusiasts gathered in Dipoli, Aalto University's newly-renovated main building. The organiser, the new Finnish Center for Artificial Intelligence FCAI established by Aalto University and the University of Helsinki, wished to promote matchmaking, information sharing and cross-border collaboration with the event. Representatives of over 180 companies were offered matchmaking opportunities during pitching, demo and poster sessions. One of the large Finnish companies present was Elisa, who's vice president in business development Kimmo Pentikäinen met up with Samuel Kaski, Professor at Aalto University and Head of FCAI, in the AI Day networking area. They discussed the needs of Elisa as an eager partner for research institutions.

"Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal." RapidMiner Auto Model accelerates the entire data science lifecycle using automated machine learning.