Advanced artificial intelligences reckon there is a good chance of England beating Sweden to progress to the FIFA World Cup semi-finals this weekend. Retail wonk Blue Yonder turned the awesome power of its AI platform away from predicting how many pies Morrisons should shovel onto the shelves and instead focused its unfeeling gaze onto the beautiful game. The AI, which the boffins modestly claim was developed from techniques honed at CERN, gives England a 55.9 per cent chance of winning tomorrow night. IKEA managers will be able to rest easy, knowing that a UK-wide flatpack and meatball boycott is therefore unlikely to be on the cards. Unfortunately, England only have a 9.8 per cent chance of actually lifting the trophy.

At the beginning of 2017, Chinese tech company Baidu, the largest provider of Chinese language internet search as well as other digital products and services, committed to emerging business sectors such as artificial intelligence (AI) and machine learning. Since China has 731 million internet users, almost twice the U.S. population, Baidu's data set is capable of fueling AI algorithms to make them even better. With this focus on artificial intelligence, Baidu is exploring some very intriguing applications for artificial intelligence and machine learning including in their offices where facial recognition technology makes standard ID cards unnecessary and allows you to order tea from a vending machine. They have also recruited top AI talent including one of the world's most notable AI pioneers Lu Qi, who was previously a Microsoft executive before he became Baidu's COO in January 2017. Qi will step down in July 2018 for personal reasons.

With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and labor intensive. This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology. Information Extraction systems takes natural language text as input and produces structured information specified by certain criteria, that is relevant to a particular application. Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural Language Processing (NLP) tasks such as Machine Translation, Question-Answering System, Natural Language Understanding, Text Summarization and Digital Assistants like Siri, Cortana and Google Now. This paper introduces Information Extraction technology, its various sub-tasks, highlights state-of-the-art research in various IE subtasks, current challenges and future research directions.

If AI continues to develop exponentially it will offer significant productivity gains for all sectors of the economy. Crypto offers AI significant advantages versus traditional sources of data for reasons I explain below; and as such, one of the most impactful ways AI will affect our economy will be when it is deployed on top of cryptographic'rails' that define everything from capital raising through to underlying economic activity at the transactional level. Crypto will effectively become the protocol for AI to talk to other AI. Capital will follow where data is abundant and trusted. This will have profound effects on the functioning of capital markets and the theory of the firm over the course of this century.

We asked young scientists this question: How do broad interests benefit your science? Scientists with a variety of hobbies responded that their extracurricular activities have enhanced a wide range of skills, from creativity to communication to resilience. Many also mentioned the value of clearing their minds and relaxing. Follow NextGen and share your own hobbies on Twitter with #NextGenSci. As a rock climber, you have to risk falling in order to become better; the same principle applies in science.

Tinder is finally allowing users to animate their profile prictures. The dating app today confirmed its'loops' feature is available globally, after it was initially tested in Canada and Sweden. It allows two second video loops to be uploaded. The dating app today confirmed its'loops' feature is available globally, after it was initially tested in Canada and Sweden. 'It all started with the swipe--that fun, simple movement that changed the way people meet,' Tinder said in a blog post announcing the new feature.

Groupe PSA and Inria today announced the creation of an OpenLab dedicated to artificial intelligence. The studied areas will include autonomous and intelligent vehicles, mobility services, manufacturing, design development tools, the design itslelf and digital marketing as well as quality and finance. "Artificial intelligence will quickly become an efficiency factor for the group. The OpenLab will work on artificial intelligence algorithms enabling autonomous vehicles to drive in complex environments for example. It will also work on predictive maintenance, powertrain design optimisation and the modelling of complex systems such as cities, to offer mobility services adapted to people's needs" said Carla Gohin, Groupe PSA's Vice President for Research and Advanced Engineering. Inria's project teams will participate in this OpenLab bringing their high-level algorithmic expertise as part of a fruitful dialogue with Groupe PSA's experts on all the identified topics.

Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.

Early detection of breast cancer can increase treatment efficiency. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. To train a Convolutional Neural Network (CNN), which is a deep neural architecture, is necessary a huge amount of data. To overcome this problem, this paper proposes a data augmentation approach applied to clinical image dataset to properly train a CNN. Results using receiver operating characteristic analysis showed that with a very limited dataset we could train a CNN to detect AD in digital mammography with area under the curve (AUC = 0.74).

Also in 2016, in December, we traveled to São Paulo, Brazil, to run the first Brazilian Summer School in Machine Learning, where 202 attendees from 6 Brazilian states came together. We completed another Machine Learning School in Brazil the year after, the BSSML17, this time in Curitiba, Paraná. We came back to Valencia in September 2017 and ran the third edition of our Valencian Summer School in Machine Learning (VSSML17) that brought together 204 attendees from 14 countries and 183 of them from Europe, mostly from Spain. Among the attendees, there were 45 from 28 universities, and 159 representing a record of 92 organizations.