Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach, and decomposition approach, and then discuss the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, batch MTL models are difficult to handle this situation and online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing are reviewed to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works. Finally, we present theoretical analyses and discuss several future directions for MTL.

Reprinted with permission from Quanta Magazine's Abstractions blog. Occasionally the machine may rattle reality enough to have a few of those collisions generate something that's never been seen before. But because these events are by their nature a surprise, physicists don't know exactly what to look for. They worry that in the process of winnowing their data from those billions of collisions to a more manageable number, they may be inadvertently deleting evidence for new physics. "We're always afraid we're throwing the baby away with the bathwater," said Kyle Cranmer, a particle physicist at New York University who works with the ATLAS experiment at CERN.

After two years of development insurtech startup Pineapple is finally set to launch their insurance product -- with the startup claiming today in a statement, to have made insurance as simple "as snapping a picture". The startup's co-founder Ndabenhle Junior Ngulube told Ventureburn in an email today that the startup will on Friday hold an activation for the brand in Johannesburg, Pretoria and Cape Town -- saying more details would be revealed closer to the time. He was able to tell Ventureburn how premiums would be priced. "Once a user has snapped an image of an item they want to insure, we have artificial intelligence (AI) that recognises what the image is and proceeds to place that image in an appropriate category for pricing purposes -- if the AI fails to categorise the item, we allow the user to manually select the appropriate category for the item). We then require the user to enter the value of that item. "Based on this value and it's associated category -- as well as a few ...

Following the launch of the Law Society's Public Policy Technology and Law Commission to examine the use of algorithms in the justice system, and its call for evidence, it is worth considering the interface between lawtech which uses algorithms and the tightening global regulatory framework for data protection and privacy. The quote from Google's research director Peter Norvig, 'We don't have better algorithms. We just have more data,' invites interrogation of the value chain linking AI software and data assets: does the algorithm or the data generate more value for a professional services organisation, or is the added value created by determining the right blend for each context? And can software achieve that? At the commission launch, data scientist Professor Sofia Olhede focused on data quality rather than quantity – returning to the'garbage in, garbage out' principle that is too often forgotten or ignored.

The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man Bookers Prize winning fiction. We consider 275 books shortlisted for Man Bookers Prize between 1969 and 2017. The gender bias is analyzed by semantic modeling of book descriptions on Goodreads. This reveals the pervasiveness of gender bias and stereotype in the books on different features like occupation, introductions and actions associated to the characters in the book.

AI is no longer the stuff of science fiction – it's impacting patients today It's difficult to open a newspaper nowadays without seeing an article about artificial intelligence. But one thing you cannot escape is that AI is here now and it's only going to become more pervasive. While fear of an unknown technology is understandable, in many ways it does a disservice to the incredible impact that AI is already having on the world around us. In the healthcare space alone, it is offering ways to fundamentally rethink clinical practice, speeding up diagnosis, driving patient support programs and aiding drug discovery. In only five years, more than 200 venture capital and private equity deals to fund research into the use of AI in healthcare have been signed.

Rabat – At a July 20 McKinsey & Company conference in Morocco, a presentation on artificial intelligence algorithms highlighted the powerful potential of digitizing sectors of Morocco's economy. The global management consulting firm, which specializes in digital transformation, hosted the conference "Potential of Digital and Artificial Intelligence (AI)" this past Friday. Casablanca Sector General Manager Jalil Bensouda and his associate Yassine Sekkat emphasized in their presentation the possibilities of dynamic digital incorporation in Morocco. "The 20th century Grail was oil. The 21st is the data," Bensouda declared.

My name is Kris Coratti. Thank you for joining us on this very rainy morning. I'm glad you all made it out. We are going to have a fascinating series of discussion this morning on artificial intelligence. This is the latest in our ongoing event series that we call "Transformers." And our speakers this morning are going to explore the regulatory questions around this technology. They going to look at how AI is reshaping the way we live and work. And they're going to discuss how to make sure this technology is used responsibly in the future. Before we begin, I just want to quickly thank our presenting sponsor for this even, Software.org, And so now I'd like to go ahead and welcome to the stage The Washington Post's Tony Romm and Senators Maria Cantwell and Todd Young. And for those who don't know, Senator Cantwell is a Democrat from Washington State. Both are members of a Senate commerce committee which touches on artificial intelligence and many tech issues that we'll talk about today.

The pace of acceleration in the past several years in AI seems to be starting to climb the exponential part of the "S" curve. From playing Go, to translating one human language into another, to predicting and attempting to compose hit tunes, to diagnosing disease from radiographic images, AI is starting to approach or exceed human abilities. These gains are being reported in specific or narrow AI, as opposed to general AI or Artificial General Intelligence (AGI), which some might deem real AI. Without debating the definition or attainability of real AI, I want to muse on whether emerging AI could augment (or even democratize?) This is important because just narrow AI will disrupt a significant portion of the work force within this generation, and empowering entrepreneurs may be a worthy goal for mitigating this inevitability, as well as a productive strategy for regional and national economic growth.

Artifical Intelligence (AI), in the form of IBM Watson, is being used to aid decision making in public health supply chains in developing countries and improve patients' access to life-saving medicines. Photo: Bill and Melinda Gates Foundation"Today's AI technology offers the solution, allowing us to leverage cognitive capabilities to create a transparent, intelligent and predictive supply chain. We asked ourselves what could be achieved if we could get IBM Watson to place chatbots on the platforms available to health workers in African supply chains, so that people can improve their learning." An AI powered chatbot can deliver personalised learning on mobile devices to enhance the supply chain skills of the health workers that staff most African healthcare supply chains," says Deborah Dull, representing the Bill and Melinda Gates Foundation. "What if, through the AI, health workers could know where specific products are in the country?