In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and symbolic text input onto a reduced latent semantic space. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.

Variational approaches to approximate Bayesian inference provide very efficient means of performing parameter estimation and model selection. Among these, so-called variational-Laplace or VL schemes rely on Gaussian approximations to posterior densities on model parameters. In this note, we review the main variants of VL approaches, that follow from considering nonlinear models of continuous and/or categorical data. En passant, we also derive a few novel theoretical results that complete the portfolio of existing analyses of variational Bayesian approaches, including investigations of their asymptotic convergence. We also suggest practical ways of extending existing VL approaches to hierarchical generative models that include (e.g., precision) hyperparameters.

Multi-task learning is motivated by the observation that humans bring to bear what they know about related problems when solving new ones. Similarly, deep neural networks can profit from related tasks by sharing parameters with other networks. However, humans do not consciously decide to transfer knowledge between tasks. In Natural Language Processing (NLP), it is hard to predict if sharing will lead to improvements, particularly if tasks are only loosely related. To overcome this, we introduce Sluice Networks, a general framework for multi-task learning where trainable parameters control the amount of sharing. Our framework generalizes previous proposals in enabling sharing of all combinations of subspaces, layers, and skip connections. We perform experiments on three task pairs, and across seven different domains, using data from OntoNotes 5.0, and achieve up to 15% average error reductions over common approaches to multi-task learning. We show that a) label entropy is predictive of gains in sluice networks, confirming findings for hard parameter sharing and b) while sluice networks easily fit noise, they are robust across domains in practice.

At HelloFresh, we want to change the way people eat. Over the past 5 years we've seen this mission spread beyond our wildest dreams. So, how did we do it? Our weekly recipe boxes full of exciting recipes and lovingly sourced, fresh ingredients have blossomed into a community of inspired, energised home cooks that expands across the globe. Our story started in Berlin.

In 2014, three young men from far-flung parts of the world teamed up in Finland with an audacious plan that could soon help doctors save more lives, not to mention money, and chart a new course for healthcare. Oguzhan Gencoglu, who hails from Turkey, is an AI and machine-learning whiz currently working on his Ph.D. in computer science, Hung Ta is a Vietnamese math prodigy with a doctorate in biotechnology, and Timo Heikkinen is a Finnish entrepreneur with a software industry background. Together, they launched Top Data Science, an AI startup based in Helsinki that's developing software that can make sense of millions of data points, alert doctors to unseen medical patterns, help them diagnose disease and track patients during treatment. Their "intelligent" code is already analyzing thousands of MRI images and could one day help radiologists at the Helsinki University Central Hospital diagnose prostate cancer. Another set of algorithms is crunching data from the hospital's intensive care unit and using it to identify high-risk cases that may soon need urgent medical care, as well as flag patients who are progressing well and who could be released to standard hospital care.

Ford will significantly increase its planned investments in electric vehicles to $11 billion by 2022 and have 40 hybrid and fully electric vehicles in its model lineup, Chairman Bill Ford said at the Detroit auto show. The investment figure is sharply higher than a previously announced target of $4.5 billion by 2020, Ford executives said, and includes the costs of developing dedicated electric vehicle architectures. Ford's engineering, research and development expenses for 2016, the last full year available, were $7.3 billion, up from $6.7 billion in 2015. Of the 40 electrified vehicles Ford plans for its global lineup by 2022, 16 will be fully electric and the rest will be plug-in hybrids, executives said. SUVs figured Ford's electric future The automaker's president of global markets, Jim Farley, said on Sunday that Ford would bring a high-performance electric utility vehicle to market by 2020.

An incredible mind-reading device could help sufferers of serious strokes regain the use of their hands. Stroke is a leading cause of long-term disability in both the US and the UK, with about half of all survivors left with severely restricted movement in one hand. A new machine sends signals into a patient's head while moving their paralysed hand with a robotic exoskeleton to strengthen lost connections between brain cells. An incredible mind-reading device could help sufferers of serious strokes regain the use of paralysed hands. The machine (pictured) sends signals into a patient's head while moving the affected hand to strengthen lost connections between brain cells A stroke is a brain attack similar to a heart attack, and is mostly caused by a blockage of a blood vessel to part of the brain.

In my previous article, I wrote about the impact of Robotic Process Automation which drives Enterprise AI. In that article, I said: The first group of workers to feel the impact of RPA and AI as described above will be offshore workers (including offshore developers). Here is an extended prediction to that theme ...In 2018 AI will start to bring jobs back from offshore destinations to the West Before we proceed, the views expressed in this article are personal. They do not reflect the views of any organization, institution or company I have worked with. Also, I have not been involved in the IT Offshore services business (either as a customer or as a provider).

This article was written by Dorian Pyle and Cristina San Jose on McKinsey&Company. Dorian Pyle is a data expert in McKinsey's Miami office, and Cristina San Jose is a principal in the Madrid office. It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.

Global IT spending growth began to turnaround in 2017 with annual increases expected through 2021. However, uncertainty looms as organizations consider potential impacts of Brexit, currency fluctuations, and a possible global recession. Despite uncertainty, businesses will continue to invest in IT as they anticipate revenue growth, but their spending patterns will shift. Projects in digital business, blockchain, IoT, and progress from algorithms to machine learning through artificial intelligence (AI) will continue unabated. In this webinar, Gartner will walk through four different headlines that will emerge in 2018 as massive changes to the status quo.