In the past year, the field of Natural Language Processing (NLP) has seen the rise of pretrained language models such as as ELMo (Peters et al., 2018), ULMFiT (Howard and Ruder, 2018) and BERT (Devlin et al., 2019). These approaches train a deep - learning language model on large volumes of unlabeled text, which is subsequently fine - tuned for particular NLP tasks. Applying these models to th e General Language Understanding Evaluation (GLUE) benchmark introduced by Wang et al. (2018) has achieved the best performance to date on tasks ranging from sentiment classification to question answering (Devlin et al., 2019). The benefit of these models has also been demonstrated in specialized NLP domains. BioBERT (Lee et al., 2019), a version of BER T trained exclusively on biomedical text, was able to significantly increase performance on biomedical named entity recognition. Further refining this model on clinical text produced an increase in performance in medical natural language inference (Alsentz er et al. 2019). While large pretrained models offer significantly increased performance, they come with their own constraints, as the number of parameters in the classic BERT - base model exceeds 100 million. As such, their computational cost can thus be p rohibitively high at both training and prediction time (Devlin et al., 2019). More recent work has addressed this challenge by'distilling' the models, training smaller versions of BERT which reduce the number of parameters to train by 40% while retaining more than 95% of the full model performance and even outperforming it on two out of eleven GLUE tasks (Sanh et al., 2019).

--Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach--referred to as GeoTrackNet--for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks, then uses a contrario detection to detect abnormal events. The neural network helps us capture complex and heterogeneous patterns in vessels' behaviors, while the a contrario detection takes into account the fact that the learned distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method. Nowadays, about 90% of the world trade is carried by maritime traffic, and it is growing consistently [2]. Maritime surveillance and Maritime Situational A wareness (MSA) are vital demands. In this context, anomaly detection is one of the most important tasks, because anomalies may involve accidents (loss of navigation, damages in engine, etc.) or illegal activities (smuggling, illegal transshipment, etc.). Initially designed for collision avoidance, the Automatic Identification System (AIS) has quickly become the main source of information for maritime surveillance thanks to its information richness. This paper is an extension of the MultitaskAIS presented in [1]. While [1] presents the ability of handling noisy and irregularly sampled data as well as the computational benefit of this architecture for multiple tasks in maritime surveillance, this paper focuses on detailing the most important task: anomaly detection.

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword - referred to as the expectation of the distribution - and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach it-eratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%. 1 Introduction Event detection on microblogging platforms such as Twitter aims to detect events preemptively.

New research that appears in the journal PLOS ONE suggests that machine learning can be a valuable tool for predicting the risk of premature death. The scientists compared the accuracy of artificial intelligence prediction with that of statistical methods that experts are currently using in medical research. New research suggests that healthcare professionals should use deep learning algorithms to predict premature death risk accurately. An increasing amount of recent research is suggesting that computer algorithms and artificial intelligence (AI) learning can prove highly useful in the medical world. For instance, a study that appeared a few months ago found that deep learning algorithms can accurately predict the onset of as early as in advance.

If a project uses Lightning, you can see the core of what's happening by looking in the training step… of any project! That's an incredible step towards helping achieve more reproducible deep learning code.

In the previous post, we learned what Artificial Neural Networks and Deep Learning are. Also, some neural network structures for exploiting sequential data like text or audio were introduced. If you haven't read that post, you should sit back, grab a coffee, and slowly enjoy it. It can be found here. This new post will cover how to use Keras, a very popular library for neural networks to build a Chatbot. The main concepts of this library will be explained, and then we will go through a step-by-step guide on how to use it to create a yes/no answering bot in Python.

The wave of excitement over artificial intelligence is spreading, and like a tsunami is anticipated to touch every possible surface. And yet as this wave crashes down, many consumers and companies struggle to identify the subsets of AI, and what these distinctions mean. Decision-makers are still unclear about the difference between'machine learning,' and'deep learning', and the significance that this distinction has in keeping their system's users and data safe. Machine learning and deep learning are neither synonyms, nor competing technologies, and yet the variation between them makes all the difference for companies attempting to harness the power of data while trying to determine the best path to success. There are lots of wrong terms and usage, and like any great trend, people end-up following them in the wrong way.

Combining the power of Canon Medical's Advanced intelligent Clear IQ Engine (AiCE) with Deep Learning Spectral Reconstruction imaging capabilities, Canon Medical Systems USA, Inc. introduces the Aquilion ONE / PRISM Edition, a spectral CT system designed for deep intelligence. The advanced system integrates artificial intelligence (AI) technology to maximize conventional and spectral CT capabilities and automated workflows while providing intelligent clinical insights to assist physicians in making more informed decisions across the patient's care cycle. The Aquilion ONE / PRISM Edition offers opportunity for innovation within medical imaging with the power to illuminate clinical insights and initiate business opportunities designed to improve patient outcomes. "The intelligent technologies that make up the Aquilion ONE / PRISM Edition give healthcare providers the clinical confidence they need to reach new heights – from both a clinical and business perspective," said Erin Angel, managing director, CT Business Unit, Canon Medical Systems USA, Inc. "Canon Medical's deep learning reconstruction technology is pushing routine diagnostic imaging into the age of AI assisted imaging, revolutionizing patient care by enabling improved diagnostic confidence. We are committed to delivering products that aren't just a glimpse into the future of imaging - they are the future of imaging."

Fujitsu Laboratories Ltd. and Fujitsu Research and Development Center Co., Ltd. have innovated an AI technology for video-based behavioral analysis. Dubbed "Actlyzer", the tech can recognize a variety of subtle and complex human activities without relying on large amounts of training data. Deep learning technologies conventionally demand large amounts of video data for training systems to recognize individual behaviors, and video data must be collected from scratch in order to add each new behavior. This time-consuming process means that it can often take several months to introduce functional AI into the field. Taking advantage of the fact that human behaviors generally consist of a combination of basic movements and actions, (e.g.

The episode has become legendary in computer science history. So guess who turned up at the 2012 contest? Hinton [the "father" of neural networks revival] and that really shook things up. He didn't know anything about the field of computer vision, so he took two young guys to change it all! One of them [Alex Krizhevsky] he locked up in a room, telling him: "You can't come out until it works!"