That's right, sophisticated robots can now create works of art comparable to the old masters. The RobotArt gallery has amassed an impressive collection to show what the world's most creative androids and algorithms (and their creators) have come up with. On Wednesday the 11th, the international contest, now in its third year, announced the top ten teams, all of which walk away with cash prizes for their creations. In addition, winning artworks from the past three years will go on display at the Seattle Art Fair on August 2-5.

Search engine giant Baidu has recently unveiled China's first cloud-to-edge artificial intelligence (AI) chip--Kunlun--at Baidu Create 2018. The move repositions the company in not only the Chinese market but also globally, says leading data and analytics company GlobalData. With Kunlun, Baidu joins the ranks of a select few companies that not only offer an AI platform to help enterprises deploy AI-infused solutions but also have their own hardware to maximize AI processing. Built to accommodate the high performance requirements of a wide variety of AI scenarios, Kunlun includes training chip '818-300' and inference chip '818-100'. Rena Bhattacharyya, Technology Analyst at GlobalData, says: "Well-established players such as IBM, Microsoft, Google and Amazon are fine-tuning their AI platforms to make it easier and faster for customers to incorporate a wide range of AI technologies.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, China, Denmark, the EU Commission, Finland, France, India, Italy, Japan, Mexico, the Nordic-Baltic region, Singapore, South Korea, Sweden, Taiwan, the UAE, and the UK have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. This article summarizes the key policies and goals of each strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. It also includes countries that have announced their intention to develop a strategy or have related AI policies in place. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible. I also plan to write an article for each country that provides an in-depth look at AI policy. Once these articles are written, I will include a link to the bottom of each country's summary. June 28: Publication of original article, included Australia, Canada, China, Denmark, EU Commission, Finland, France, Germany, India, Japan, Singapore, South Korea, UAE, US, and UK.

Apple Inc.'s new head of artificial intelligence will also oversee the Siri digital assistant, taking on that responsibility from software executive Craig Federighi, according to the company's website. Apple listed John Giannandrea on the site as its first Chief of Machine Learning and AI Strategy. He's also responsible for Core ML, an Apple tool that helps developers integrate machine learning features into their apps, along with "Siri technologies," Apple said on Giannandrea's new online biography. Apple also updated the biography of Federighi, who runs iPhone and Mac software, to indicate he's no longer in charge of Siri. Apple shifted responsibility of Siri from Eddy Cue, a senior vice president in charge of most services and original content, to Federighi just last year.

We created Facebook AI Research over four years ago to focus on advancing the science and technology of AI, and we've always done this by collaborating with local academic communities. FAIR relies on open partnerships to help drive AI forward, where researchers have the freedom to control their own agenda. Ours frequently collaborate with academics from other institutions, and we often provide financial and hardware resources to specific universities. Today, we're announcing new additions to FAIR who are helping us build new AI-specific labs and strengthen existing offices: This dual affiliation model is common across FAIR, with many of our researchers around the world splitting their time between FAIR and a university. This model allows people within FAIR to continue teaching classes and advising graduate students and postdoctoral researchers, while publishing papers regularly.

Some tasks such as fighting spam, content moderation, etc. by their very nature require an online system. Offline systems, on the other hand, don't need to run in real-time. They can be built to run efficiently on a batch of inputs at once and can take advantage of approaches like Transductive Learning. Some online systems can be reactive, and can even do the learning in an online fashion (aka online learning), but many online systems are built and deployed with a periodic offline model build that is pushed to production. Systems that are built using online learning should especially be sensitive to adversarial environments.

The notion of a secondary network predicting the parameters of a primary network is also well exemplified by HyperNetworks, which predict weights for entire layers (e.g., a recurrent neural network layer). From this perspective, the FiLM generator is a specialized HyperNetwork that predicts the FiLM parameters of the FiLM-ed network. The main distinction between the two resides in the number and specificity of predicted parameters: FiLM requires predicting far fewer parameters than Hypernetworks, but also has less modulation potential. The ideal trade-off between a conditioning mechanism's capacity, regularization, and computational complexity is still an ongoing area of investigation, and many proposed approaches lie on the spectrum between FiLM and HyperNetworks (see Bibliographic Notes).

For each hotel we receive dozens of images and face the challenge of choosing the most "attractive" image for each offer on our offer comparison pages, as photos can be just as important for bookings as reviews. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need an "attractiveness" assessment. We addressed the need to automatically assess image quality by implementing an aesthetic and technical image quality classifier based on Google's research paper "NIMA: Neural Image Assessment". NIMA consists of two Convolutional Neural Networks (CNN) that aim to predict the aesthetic and technical quality of images, respectively. The models are trained via transfer learning, where ImageNet pre-trained CNNs are fine-tuned for each quality classification tasks.

In this post we introduce how to use TensorFlow.js by demonstrating how it was used in the simple project Neural Titanic. In this project, we show how to visualize the evolution of the predictions of a single layer neural network as it is being trained on the tabular Titanic Dataset for the task of binary classification of passenger survival. Here's a look at what we will produce and a link to the live demo: This article assumes that you have a basic understanding of modern frontend JavaScript development and a general awareness of basic machine learning topics. Please let me know in the comments if you have any questions. Neural networks are finally getting their rightfully deserved day in the sun after many years of development by a rather small research community spearheaded by the likes of Geoffrey Hinton, Yoshua Bengio, Andrew Ng, and Yann LeCun. Traditional statistical models do very well on structured data, i.e. tabular data, but have notoriously struggled with unstructured data like images, audio, and natural language.