There were new announcements from Nvidia's GPU Technology Conference and Google's TensorFlow Dev Summit while France announced its own national AI strategy in a report and vowed €1.5bn in public funding. TensorFlow conference - Google hosted the TensorFlow Dev Summit on Friday in Mountain View, California, and announced a range of updates for its code, which remains the most popular software framework for AI and machine learning. Here's a list of the main highlights; all these tools are now going to be rolled out to researchers: If you're a TF nerd, there are more details on the blog and the Youtube channel. Also, here is the download link to TensorFlow 1.7.0 for you to play around with over the Easter weekend. La AI stratégie de France - France's President Emmanuel Macron acknowledged the importance of AI and the need to catch up to the the United States and China.

Keisuke Sakaguchi Title: "Robust Text Correction for Grammar and Fluency" Abstract: Robustness has always been a desirable property for natural language processing. In many cases, NLP models (e.g., parsing) and downstream applications (e.g., MT) perform poorly when the input contains noise such as spelling errors, grammatical errors, and disfluency. In this talk, I will present three recent results on error correction models: character, word, and sentence level respectively. For character level, I propose semi-character recurrent neural network, which is motivated by a finding in Psycholinguistics, called Cmabrigde Uinervtisy (Cambridge University) effect. For word-level robustness, I propose an error-repair dependency parsing algorithm for ungrammatical texts.

Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.

Independent component analysis (ICA) is a cornerstone of modern data analysis. Its goal is to recover a latent random vector S with independent components from samples of X=AS where A is an unknown mixing matrix. Critically, all existing methods for ICA rely on and exploit strongly the assumption that S is not Gaussian as otherwise A becomes unidentifiable. In this paper, we show that in fact one can handle the case of Gaussian components by imposing structure on the matrix A. Specifically, we assume that A is sparse and generic in the sense that it is generated from a sparse Bernoulli-Gaussian ensemble. Under this condition, we give an efficient algorithm to recover the columns of A given only the covariance matrix of X as input even when S has several Gaussian components.

Another such invention, 3-D printing, is now scaling up. All over the world, an impressive diversity of people and organizations, ranging from startups and hobbyists to construction and engineering firms, are successfully prototyping 3-D-printed buildings. The government of Dubai has set a goal of 3-D printing 25% of every new building by 2030. Prototype single-family dwellings have been 3-D-printed in China, Italy, Russia--and Texas. Global infrastructure firm AECOM ACM 2.59% uses 3-D printing to prefabricate jail cells and hospital rooms.

Chemical synthesis is a scientific procedure that uses simple chemical compounds to construct complex ones. It is the exact process used to create most of the substances we use in our daily life – from drugs to those components inside our electronic devices. However, the steps involved are often time intensive and extremely complicated that it may take years for chemists to master or achieve breakthroughs in certain procedures. Nonetheless, artificial intelligence has come to the rescue. To make it simple for everyone we can say, artificial intelligence is the new lab technician for chemical synthesis.

"Immunotherapy has profoundly changed the management of multiple cancers," said Roger Sun, MD, PhD candidate under Eric Deutsch, MD, PhD, and Charles Ferté, MD, PhD, at the laboratory INSERM U1030 at Gustave Roussy in Villejuif, France. "However, most patients do not respond to this type of treatment. That is why we need to identify biomarkers that allow identification of patients who are most likely to respond to immunotherapy." Studies utilizing biopsy samples of tumor tissues have confirmed the link between immune-cell infiltration into tumors and patients' treatment responses; however, Sun noted, because cancers are heterogeneous, biopsies only reflect the local aspect of the tumor. "Medical computational imaging, also known as radiomics, is a new field of research that aims to translate standard imaging like CT, MRI, or PET into objective data and use them as biomarkers," he explained.

To empower Vera speak naturally and understand candidate's response, engineers have combined speech recognition technologies from Google, Amazon.com, Microsoft, and Russia's Yandex and fed it with 13 billion examples of syntax and speech from TV, Wikipedia, and job listings to expand the software's vocabulary.

Artificial intelligence (AI) can produce digital biomarkers of ageing and frailty by gathering physical activity data from smartphones and other wearables, scientists have found. The finding, published in the journal Scientific Reports, untaps the emerging potential of combining wearable sensors and AI technologies for continuous health risk monitoring with real-time feedback to life and health insurance, healthcare and wellness providers. "Artificial Intelligence is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition, and other fields," said Peter Fedichev from the Moscow Institute of Physics and Technology (MIPT) in Russia. "Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," said Fedichev. The researches analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES).

Although Mr Macron promotes the use of digital algorithms to make public and private services more efficient, he said in an interview with Wired magazine, that the technology should be open to public scrutiny to ensure public trust. He said: "My role is not to block this change, but to be able to train or retrain people for them to get opportunities in this new world." While AI could revolutionise major industries in the fields of medicine, energy, transport, defence and finance, Mr Macron warned that unless the technology can be trusted, "people will eventually reject this innovation". He guaranteed that all the AI algorithms created by his government will be transparent to reduce the threat to democracy which AI poses.