This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source github.com/deepmind/bsuite, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.

To ramp up difficulty, we are running a series of milestone challenges of increasing difficulty, culminating with the NeurIPS challenge. By everything, we mean image video text time series tabular data. All challenges are with code submission and have prizes and opportunities to publish and/or present at conferences. The first challenge AutoCV has already ended! Its results will be presented at the IJCNN 2019 conference.

CHICAGO & LONDON--(BUSINESS WIRE)--Arturo, the artificial intelligence and deep-learning property analytics start-up, today announces it has entered into a partnership with UK-based global specialty (re)insurer Canopius AG ("Canopius"). Arturo's deep-learning property analysis capability will be integrated into Canopius, providing physical property characteristic and predictive analytics, enabling more informed and differentiated pricing decisions at the point of underwriting Marek Shafer, chief digital officer, Canopius, said: "Arturo's, AI-powered image analytics capability is hugely impressive. Canopius is excited to be harnessing this pioneering technology, which will help to fine-tune our risk selection process and improve point-of-sale underwriting." John-Isaac Clark, CEO at Arturo said: "Arturo was built from the ground up within American Family Insurance and we deeply understand the specific challenges our insurance customers are trying to solve. We are incredibly excited to announce our relationship with Canopius and looking forward to working together to further enhance our product to meet their current and future needs."

Technologists and privacy advocates have been warning about – and campaining against – the prevailing, arguably exponentially growing, tendency of state-aided and private companies to mine, gather, repackage, and monetize user data. Probably the biggest story of the week, a saga that has occupied the headlines of media outlets worldwide – the undercover Cambridge Analytica report caught on camera by Channel 4's investigative reporters – shed light on the power and dangers of social media. Rather, the report has shed light on how easy it is to propagandize and subvert entire populations. Allowing tremendous, unprecedented reach, social networks have, since their inception, been considered a precious tool of the marketing industry. Where is the line between marketing and propaganda, real and fake news drawn?

By analyzing linguistic markers in social media posts, it's possible to create a deep learning model that can give an individual insight into his or her mental health far earlier than traditional approaches. More than 300 million people suffer from depression and only a fraction of them receive adequate treatment. Depression is the leading cause of disability worldwide and nearly 800,000 people every year die due to suicide. Suicide is the second leading cause of death in 15–29-year-olds. Diagnoses (and subsequent treatment) for depression are often delayed, imprecise, and/or missed entirely.

Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in our brain. Using the speed of modern computers and large data sets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience. Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artifical intelligence algorithms -- based on the very slow brain dynamics -- which outperform learning rates achieved to date by state-of-the-art learning algorithms. In an article published in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years. "The current scientific and technological viewpoint is that neurobiology and machine learning are two distinct disciplines that advanced independently," said the study's lead author, Prof. Ido Kanter, of Bar-Ilan University's Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center.

Seems if it's very lame to be discussed on this?.. I'm really not talking about'dream prediction', but because most of the dreams that we have, are something that we happen to see that happen in daily life, or of course of something back few years ago, or that we hear, we see, we speak about. If at all, our biological brain, was analogically, a sophisticated GAN GPT2 sort of model, would it be real to hypothetically assume that such model gets activated while we are slept? On such assumption, how could be the generator & discriminator be in such model system combined with text generation?. And is this sort of research for dream simulation, prediction really worth it?

In recent years, data-driven methods have been utilized to learn dynamical systems and partial differential equations (PDE). However, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data. To overcome these challenges, in this work, a deep-learning based data-driven method, called DL-PDE, is developed to discover the governing PDEs of underlying physical processes. The DL-PDE method combines deep learning via neural networks and data-driven discovery of PDEs via sparse regressions, such as the least absolute shrinkage and selection operator (Lasso) and sequential threshold ridge regression (STRidge). In this method, derivatives are calculated by automatic differentiation from the deep neural network, and equation form and coefficients are obtained with sparse regressions. The DL-PDE is tested with physical processes, governed by groundwater flow equation, contaminant transport equation, Burgers equation and Korteweg-de Vries (KdV) equation, for proof-of-concept and applications in real-world engineering settings. The proposed DL-PDE achieves satisfactory results when data are discrete and noisy.

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.

It is well known that neural networks are susceptible to adversarial perturbations and are also computationally and memory intensive which makes it difficult to deploy them in real-world applications where security and computation are constrained. In this work, we aim to obtain both robust and sparse networks that are applicable to such scenarios, based on the intuition that latent features have a varying degree of susceptibility to adversarial perturbations. Specifically, we define vulnerability at the latent feature space and then propose a Bayesian framework to prioritize features based on their contribution to both the original and adversarial loss, to prune vulnerable features and preserve the robust ones. Through quantitative evaluation and qualitative analysis of the perturbation to latent features, we show that our sparsification method is a defense mechanism against adversarial attacks and the robustness indeed comes from our model's ability to prune vulnerable latent features that are more susceptible to adversarial perturbations.