Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown, networks have become deeper and more intricate. While these networks are increasingly applied to nontrivial forecasting tasks, there is a need for comprehensive performance analysis of deep reservoirs. In this work, we study the influence of partitioning neurons given a budget and the effect of parallel reservoir pathways across different datasets exhibiting multi-scale and nonlinear dynamics.

Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).

Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets . However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In this work, we introduce an end-to-end system, trained in a multi-task setting, to filter and re-rank answers in the medical domain. We use task-specific pre-trained models as deep feature extractors. Our model achieves the highest Spearman's Rho and Mean Reciprocal Rank of 0.338 and 0.9622 respectively, on the ACL-BioNLP workshop MediQA Question Answering shared-task.

We examine a reductions approach to fair optimization and learning where a black-box optimizer is used to learn a fair model for classification or regression [Alabi et al., 2018, Agarwal et al., 2018] and explore the creation of such fair models that adhere to data privacy guarantees (specifically differential privacy). For this approach, we consider two suites of use cases: the first is for optimizing convex performance measures of the confusion matrix (such as $G$-mean and $H$-mean); the second is for satisfying statistical definitions of algorithmic fairness (such as equalized odds, demographic parity, and the gini index of inequality). The reductions approach to fair optimization can be abstracted as the constrained group-objective optimization problem where we aim to optimize an objective that is a function of losses of individual groups, subject to some constraints. We present two differentially private algorithms: an $(\epsilon, 0)$ exponential sampling algorithm and an $(\epsilon, \delta)$ algorithm that uses a linear optimizer to incrementally move toward the best decision. We analyze the privacy and utility guarantees of these empirical risk minimization algorithms. Compared to a previous method for ensuring differential privacy subject to a relaxed form of the equalized odds fairness constraint, the $(\epsilon, \delta)$ differentially private algorithm we present provides asymptotically better sample complexity guarantees. The technique of using an approximate linear optimizer oracle to achieve privacy might be applicable to other problems not considered in this paper. Finally, we show an algorithm-agnostic lower bound on the accuracy of any solution to the problem of $(\epsilon, 0)$ or $(\epsilon, \delta)$ private constrained group-objective optimization.

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided; what we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We discuss what these essential doctrines might be and why their establishment is required to form connections, possibly growing, between a knowledge store that has been built up and new pieces of information that curiosity will bring back. As more findings are acquired and more bonds are fermented, we need a way to, periodically, reduce the amount of data; in the sense, it is important to capture the critical characteristics of what has been accumulated or produce a summary of what has been gathered. We start with the intuition for this line of reasoning and formalize it with a series of models (and iterative improvements) that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life.

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on density-based clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

Imagine a network of mine sites operated remotely--drilling, analysing core samples, collecting and interpreting data wirelessly from machine to machine, and transmitting real-time information into the cloud, absolutely without physical, human touch. In fact, it is fast becoming the reality in an industry that's increasingly powered by artificial intelligence a.k.a When we think of AI, we think of robots and machines capable of independent thought or autonomous movement. These are possibilities, and even realities, in today's world where practically anything can be automated. AI, however, goes beyond hardware, and its applications are farther-reaching than we can perhaps imagine.

Every year, the OECD Forum brings together experts, academics and thought leaders from the private and public sector to discuss key economic and social challenges on the international agenda. The theme of this year's Forum was "World in EMotion" – a theme that reflects the profound changes brought about by globalisation, shifting politics and digitalisation, and the challenges and opportunities that they present. Nowhere are these changes more rapid – and perhaps far-reaching – than in the field of artificial intelligence (AI), and its implications for values and ethics. I attended a very interesting panel on this subject, alongside Peter Gluckman, Chair of the International Network for Government Science Advice in New Zealand; Geoff Mulgan, Chief Executive of NESTA in the UK; Eric Salobir head of Optic; Pallaw Sharma, Senior Vice President at Johnson & Johnson; and Jess Whittlestone, Research Associate at the Centre for the Future of Intelligence at Cambridge University. As Pallaw explained, technology and AI are not magic powers; they are just extraordinary amplifiers and accelerators that add speed and accuracy.

LOS ANGELES, June 26, 2019 /PRNewswire-PRWeb/ -- An Ovation Fertility abstract about the use of artificial intelligence in IVF, presented at the 35th European Society of Human Reproduction and Embryology (ESHRE) Annual Meeting on June 24, 2019, was pre-selected for consideration for two prestigious awards at the event: The Basic Science Award for Oral Presentation, awarded to the presenting author of the best oral presentation on a basic science topic, and the Fertility Society of Australia Exchange Award, which provides an educational travel grant for the presenting author to present the data of his/her oral presentation at the annual meeting of the Fertility Society of Australia (FSA). Only five presentations at ESHRE 2019 were selected to receive awards and be rewarded with a prize of EURO 2,000, and one additional presentation was selected for the FSA Exchange Award. Special award committees, composed of senior scientists and clinicians, made the selections for each award. Ovation's oral presentation about the use of artificial intelligence in IVF, "Artificial intelligence (AI) technology can predict human embryo viability across multiple laboratories with varying demographics with high accuracy and reproducibility," detailed a study that sought to find out if artificial intelligence (AI) and computer vision can improve embryo viability prediction using static 2D images of Day 5 embryos from multiple laboratories. In most IVF labs, embryologists select which embryos to transfer based on morphological assessment, using high-powered microscopes to examine each embryo and assign it a subjective "grade."

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i.e., patches) and the task is to predict a single class label to the WSI. However, in many reallife applications such as computational pathology, discovering the key instances that trigger the bag label is of great interest because it provides reasons for the decision made by the system. In this paper, we propose a deep convolutional neural network (CNN) model that addresses the primary task of a bag classification on a histopathology image and also learns to identify the response of each instance to provide interpretable results to the final prediction. We incorporate the attention mechanism into the proposed model to operate the transformation of instances and learn attention weights to allow us to find key patches. To perform a balanced training, we introduce adaptive weighing in each training bag to explicitly adjust the weight distribution in order to concentrate more on the contribution of hard samples. Based on the learned attention weights, we further develop a solution to boost the classification performance by generating the bags with hard negative instances. We conduct extensive experiments on colon and breast cancer histopathology data and show that our framework achieves state-of-the-art performance.