Mobile maps route us through traffic, algorithms can now pilot automobiles, virtual assistants help us smoothly toggle between work and life, and smart code is adept at surfacing our next our new favorite song. But AI could prove dangerous, too. Tesla CEO Elon Musk once warned that biased, unmonitored and unregulated AI could be the "greatest risk we face as a civilization." Instead, AI experts are concerned that automated systems are likely to absorb bias from human programmers. And when bias is coded into the algorithms that power AI it will be nearly impossible to remove.
Google has unveiled updates for its artificially intelligent voice assistant and new privacy tools to give people more control over how they're being tracked on the go or at home. The company also unveiled a new Pixel phone and smart home display. Google just made ordering pizza, pad thai and fried chicken from your favorite restaurants even easier. The search giant announced on Thursday that it updated apps like Google Maps, Google Search and the Google Assistant to make ordering food online more convenient, so you don't have to download as many third-party apps. "When I was pregnant with my son last year, my cravings were completely overpowering," said Google's senior product manager of food ordering, Anantica Singh, in a blog post.
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Authors: Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, Victor Lempitsky Abstract: Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators.
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Facebook has lifted the curtain on its robotics research. On Monday, the company published a blog post detailing three of its robotics projects. Each focuses on finding ways for robots to teach themselves from experience rather than data prepared for training purposes -- a skill Facebook believes could have implications beyond the field of robotics. "This work will lead to more capable robots," Facebook wrote in the post, "but more important, it will lead to AI that can learn more efficiently and better generalize to new applications." According to the blog post, one project centers on enabling a six-legged robot to teach itself to walk.
"If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don't know how to make the cake." By 2016, Yann LeCun began to hedge with his use of the term "unsupervised learning". In NIPS 2016, he started to call it in even more nebulous terms "predictive learning": I have always had trouble with the use of the term "Unsupervised Learning". In 2017, I had predicted that Unsupervised Learning will not progress much and said "there seems to be a massive conceptual disconnect as to how exactly it should work" and that it was the "dark matter" of machine learning.
Welcome to EURACTIV's Digital Brief, your weekly update on all things digital in the EU. You can subscribe to the newsletter here. With the Brits and the Dutch heading to the polls today, the big news of the week is the story that Facebook has removed around 80 pages spreading fake news or using tactics aimed at unfairly influencing the polls. The takedowns came following a discovery by the human rights group Avaaz, in which it uncovered far-right disinformation networks in France, UK, Germany, Spain, Italy and Poland, posting content that was viewed an estimated 533 million times over the past three months. EURACTIV Digital went to investigate further and paid Avaaz a visit at their recently opened'Citizens' War Room' in Brussels (pictured below).
When Google launched Duplex with a demo at I/O last year, the audience was left wondering how much of the call was staged. The AI-based reservation booking service seemed almost too impressive to be a machine. Now that it's been used for real-world reservations, Google has revealed that it frequently isn't. The company recently told The New York Times that Duplex calls are often still made by human operators at call centers. Roughly a quarter of calls start with a live human voice.
In the last 10 years, we've seen some significant breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be a reality TV show winner. In 2014, Google acquired an AI company called DeepMind, and one of its project, AlphaGo, beat the European Go champion in 2015. In 2016, Google made its TensorFlow library open source, which made machine learning accessible to the masses. Last year, people were left dumbfounded when Google Duplex made a haircut appointment over the phone.