Back in the 1980s, Artificial Intelligence (AI) just had to look good in the movies and be able to power fancy talking cars, zappy spaceships and various forms of fantastical cyborgs who would one day roam the planet and possibly destroy the human race. Fast-forward to 2019 and we find ourselves deep in the AI renaissance (or perhaps first'real' birth of AI, rather than any form of rebirth) as we now have the processing power, memory capacity, cloud network breadth and sophisticated algorithmic intelligence to actually apply AI to our lives. But there's a problem -- we (the humans) who build the AI brains need to be able to construct them with a pure enough form of digital DNA such that they stay clean of any form of discriminatory bias. Major cloud networks have already been criticized for employing software that discriminates against women; a well-known search engine has been accused of featuring ethnic bias in results when looking for'unprofessional hairstyles'; an equally well-known social network has been criticized for showing certain job ads only to men; and the list goes on. The question the tech industry must now face is: how to we rid AI of bias in all its forms and ensure fair play for all in the age of computer-driven decision making? One set of answers comes from Women Leading in AI (WLinAI), a network of leaders working in tech, science, politics, business and think tanks – the group is demanding that the UK government takes back control of technology.

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two tasks are associated by cross&compress units, which automatically share latent features and learn high-order interactions between items in recommender systems and entities in the knowledge graph. We prove that cross&compress units have sufficient capability of polynomial approximation, and show that MKR is a generalized framework over several representative methods of recommender systems and multi-task learning. Through extensive experiments on real-world datasets, we demonstrate that MKR achieves substantial gains in movie, book, music, and news recommendation, over state-of-the-art baselines. MKR is also shown to be able to maintain a decent performance even if user-item interactions are sparse.

Music genre recognition based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task. However, most of existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification. Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audios and develop a novel CNN architecture in this paper. The proposed CNN architecture takes the long contextual information into considerations, which transfers more suitable information for the decision-making layer. Various experiments on several benchmark datasets, including GTZAN, Ballroom, and Extended Ballroom, have verified the excellent performances of the proposed neural network. Codes and model will be available at "ttps://github.com/CaifengLiu/music-genre-classification".

Do neutrinos, the elementary particles, have something in common with fake news on social media? The peculiar and positive answer comes from a group of researchers at USI Institute of Computational Science, and it shows how both their behaviour can be represented using the same data structure. Such structure is based on a non-Euclidean geometry and can be studied through a new class of algorithms: the Graph Convolutional Neural Networks (GCNN). Such algorithms are highly complex mathematical models, and the research work carried out by Federico Monti, member of Prof. Michael Bronstein group, earned him the award for the best scientific contribution assigned by ICMLA, the most important international conference in the field. Monti, in collaboration with other colleagues from New York University, Berkeley and Imperial College, had the opportunity to collaborate with the Lawrence Berkley National Laboratory on data acquired by the IceCube Neutrino Observatory at the South Pole.

Artificial Intelligence provides critical information to decision-makers when lives are at stake. But how do we make sure that it doesn't become smarter …

Hierarchical Neural story generation had the most realistic paragraphs of any text generation paper I came across last summer when I was doing research on this topic. I also find it fun that they used a subreddit as their training set (writingprompts). The architecture is a mixture of self attention layers and convolutional layers. Generate a prompt and then generate a full story. I tried to extend their work to prompt, outline, story but the results were meh (they were similar quality to not bothering with the outline step).

Deep learning is a buzzword that has been hyped by the business and technical press for years, often with relatively meager results that failed to live up to expectations. But over the past 18 months, according to a new report from Tractica, the true power of deep learning has been realized, thanks to advances in hardware and algorithms that use pattern recognition applied in a continuous learning loop, enabling them to train themselves to perform tasks without requiring explicit programming code. The sheer power of deep learning, however, is likely to lead to the development of more powerful and disruptive applications of tomorrow, such as driverless cars, personalized education, and preventative healthcare. Tractica forecasts that, with this expanding set of applications, the worldwide deep learning software market will grow from $3 billion in 2017 to $67.2 billion by 2025. "Deep learning has been a key point of focus for many companies, given its potential to transform entire industries," says principal analyst Keith Kirkpatrick.

Today, many companies use AI and location technology to drive more accurate predictions and strengthen decision-making. Hear Joseph Sirosh, Corporate Vice President of the Cloud AI Platform at Microsoft, talk about how AI combined with location intelligence magnifies understanding and improves business. To learn more about the power of location technology for better insights and better decisions, download our free e-books Making Sense of Digital Transformation and Making the Most of the Internet of Things. We talk to business and technology leaders who share analysis, insights, and stories on data science, the Internet of Things, Smart Communities and other forces driving digital transformation and leveraging the power of location intelligence.

Until last year, Brandon Tory, a senior software Artificial Intelligence engineer at Google and rapper, led a secret double life. Raised in Brockton, Massachusetts, a neighborhood known for crime and drugs, he lived with his family in a shelter as a teenager. Tory knew he wanted to be some kind of scientist, but also had a passion for music. He wanted to have a huge impact in both creativity and science. At University of Massachusetts Amherst he studied computer engineering and then worked as a senior Apple engineer in Cupertino.