Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, the Machine Reading Comprehension task has made significant progress. However, most of the results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is French Native Reading Comprehension dataset that consists of 25,000+ questions on a set of Wikipedia articles. A baseline model is trained which achieves an F1 score of 88.0% and an exact match ratio of 77.9% on the test set. The dataset is made freely available at https://fquad.illuin.tech.

Last Tuesday, 14th January 2020, was a big day for the Norwegian IT sector as the government's national strategy for artificial intelligence was presented at a breakfast meeting at MESH, central Oslo. Over 160 people from business, academia and the public sector participated in the launch, as well as many who followed the event online. The presented strategy claims to serve as a framework for both public and private sectors that aim to develop and use artificial intelligence, especially in areas where Norway already is greatly positioned and has strong foundations, such as in health, oil and gas, energy, and marine industry. Regarding the current digital development, we see many countries have high ambitions where one worth mentioning is the UK. Their AI strategy was initiated in 2017, which has by 2019 opened 16 New Centres for Doctoral Training in AI at universities across the country, industry funding for new Masters positions and numerous governmental funded scholar-ships.

RPA skills are most in demand in 2019, according to ISG, a technology research and advisory firm. Select from free eLearning courses and customized learning trails to take you from RPA basics to the top RPA skills and expertise to advance your career.

The concept of AI has been around for many decades. British mathematician Alan Turing proposed in 1950 that it might be possible for machines to use information to reason, solve problems, and make decisions. His framework is the basis of the Turing Test, which says an AI system learns until indistinguishable from a human being in its ability to hold a conversation. In 1956, a team presented proof of concept on AI at the Dartmouth Summer Research Project on Artificial Intelligence. Also in the 1950s, a group of researchers at Massachusetts Institute of Technology (MIT) began work that would become the MIT Computer Science and Artificial Intelligence Laboratory.

Pluralsight proudly creates the creators of tomorrow: the people who develop the technology that lifts the human condition. We do this through the tech industry's leading learning platform for serious Developer, IT, and Creative professionals. Our team of 700 innovators and disruptors are serving over 1M users in 150 countries to conquer the tech skills gap. Iris is Pluralsight's learning intelligence platform, an innovative and unique user experience, whose aim is to use data to create a smarter, personalized learning journey. It is cutting edge and a key component of Pluralsight's strategy.

Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain. Meanwhile Medline, a database of biomedical abstracts, introduces nearly a million new documents per-year. Applications that could benefit from understanding this wealth of publicly available information, such as scientific writing assistants, chat-bots, or descriptive hypothesis generation systems, require new domain-centered approaches. A conditional language model, one that learns the probability of words given some a priori criteria, is a fundamental building block in many such applications. We propose a transformer-based conditional language model with a shallow encoder "condition" stack, and a deep "language model" stack of multi-headed attention blocks. The condition stack encodes metadata used to alter the output probability distribution of the language model stack. We sample this distribution in order to generate biomedical abstracts given only a proposed title, an intended publication year, and a set of keywords. Using typical natural language generation metrics, we demonstrate that this proposed approach is more capable of producing non-trivial relevant entities within the abstract body than the 1.5B parameter GPT-2 language model.

While learning models are typically studied for inputs in the form of a fixed dimensional feature vector, real world data is rarely found in this form. In order to meet the basic requirement of traditional learning models, structural data generally have to be converted into fix-length vectors in a handcrafted manner, which is tedious and may even incur information loss. A common form of structured data is what we term "semantic tree-structures", corresponding to data where rich semantic information is encoded in a compositional manner, such as those expressed in JavaScript Object Notation (JSON) and eXtensible Markup Language (XML). For tree-structured data, several learning models have been studied to allow for working directly on raw tree-structure data, However such learning models are limited to either a specific tree-topology or a specific tree-structured data format, e.g., synthetic parse trees. In this paper, we propose a novel framework for end-to-end learning on generic semantic tree-structured data of arbitrary topology and heterogeneous data types, such as data expressed in JSON, XML and so on. Motivated by the works in recursive and recurrent neural networks, we develop exemplar neural implementations of our framework for the JSON format. We evaluate our approach on several UCI benchmark datasets, including ablation and data-efficiency studies, and on a toy reinforcement learning task. Experimental results suggest that our framework yields comparable performance to use of standard models with dedicated feature-vectors in general, and even exceeds baseline performance in cases where compositional nature of the data is particularly important. The source code for a JSON-based implementation of our framework along with experiments can be downloaded at https://github.com/EndingCredits/json2vec.

XCS constitutes the most deeply investigated classifier system today. It bears strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and regression tasks, XCS also proved very effective in certain multi-step environments from the domain of reinforcement learning. Especially in the latter domain, recent advances have been mainly driven by algorithms which model their policies based on deep neural networks -- among which the Deep-Q-Network (DQN) is a prominent representative. Experience Replay (ER) constitutes one of the crucial factors for the DQN's successes, since it facilitates stabilized training of the neural network-based Q-function approximators. Surprisingly, XCS barely takes advantage of similar mechanisms that leverage stored raw experiences encountered so far. To bridge this gap, this paper investigates the benefits of extending XCS with ER. On the one hand, we demonstrate that for single-step tasks ER bears massive potential for improvements in terms of sample efficiency. On the shady side, however, we reveal that the use of ER might further aggravate well-studied issues not yet solved for XCS when applied to sequential decision problems demanding for long-action-chains.

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.

EVENT: The UCI Forum for the Academy and the Public will host a two-day conference on "The Future of the Future: The Ethics and Implications of AI." Keynote speaker Bruce Sterling, an award-winning science fiction author, and an interdisciplinary and international panel of writers, academics and communicators will tackle an advance in technology that touches our everyday lives: artificial intelligence. INFORMATION: All events are free and open to the public, but please RSVP here. Visitor parking is available in the Student Center Parking Structure (grid D5 on campus map) and in the Mesa Parking Structure (grid D3 on campus map) for $13 per day or $2 per hour. Media planning to attend should contact Pat Harriman at 949-824-9055 or pharrima@uci.edu. BACKGROUND: The UCI Forum for the Academy and the Public is a collaborative project of the literary journalism program, the School of Humanities and the School of Law that bridges the university and the public via conferences and pop-ups that take on the most pressing issues of our time.