The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.

We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.

The implementation of artificial neural networks in hardware substrates is a major interdisciplinary enterprise. Well suited candidates for physical implementations must combine nonlinear neurons with dedicated and efficient hardware solutions for both connectivity and training. Reservoir computing addresses the problems related with the network connectivity and training in an elegant and efficient way. However, important questions regarding impact of reservoir size and learning routines on the convergence-speed during learning remain unaddressed. Here, we study in detail the learning process of a recently demonstrated photonic neural network based on a reservoir. We use a greedy algorithm to train our neural network for the task of chaotic signals prediction and analyze the learning-error landscape. Our results unveil fundamental properties of the system's optimization hyperspace. Particularly, we determine the convergence speed of learning as a function of reservoir size and find exceptional, close to linear scaling. This linear dependence, together with our parallel diffractive coupling, represent optimal scaling conditions for our photonic neural network scheme.

As fears of artificial intelligence replacing human workers grows, some companies are focusing on the people who make the brands work. Heather Brunner of WP Engine and Barbara Humpton of Siemens USA emphasized the need for changing education, investment in current employees, and rethinking prerequisites that were once considered sacrosanct. Brunner, for instance, noted that WP Engine has removed its college degree requirement and is partnering with more community colleges, workforce development agencies, and coding programs in universities to train its talent. Both women noted that AI isn't going away, but that doesn't necessarily mean humans won't be a critical factor anymore. "The key question people keep asking us is, 'Are we transforming humans out of the equation?' And the answer is'no, we're elevating the role of the human. We're finding out what is truly humanly possible'," Humpton said Tuesday at Fortune's Most Powerful Women International Summit in Montreal.

Because of the complex nature of galaxy morphology and the plethora of available approaches, a large number of further studies exist: Kelly & McKay [168] (Figure 1) demonstrate improvement over a simple split in u-r using mixture models, within a schema that incorporates morphology. Serra-Ricart et al. [169] use an encoder ANN to reduce the dimensionality of various datasets and perform several applications, including morphology. Adams & Woolley [170] use a committee of ANNs in a waterfall' arrangement, in which the output from one ANN formed the input to another which produces more detailed classes, improving their results. Molinari & Smareglia [171] use an SOM to identify E/S0 galaxies in clusters and measure their luminosity function. Genetic algorithms have been employed [173, 174] for attribute selection and to evolve ANNs to classify bent-double' galaxies in the FIRST [175] radio survey data. Radio morphology combines the compact nucleus of the radio galaxy and extremely long jets.

A fully autonomous self-driving car doesn't really need a steering wheel, or a rearview mirror, or even windows to get where it's going. But the first models are still likely to have them. In the coming years and decades, as the public decides how to feel about autonomous cars, the way that self-driving vehicles appear will be arguably as important as how they function. And people, Americans in particular, have clearly defined expectations about what cars ought to look like. "When we're looking at new devices, you could make them anything, right? Any shape, any form," said Robert Brunner, the industrial designer who worked for many years at Apple and now runs his own design studio.