This study was conducted by IDC France with 206 private sector companies with at least 1,000 employees.

Agricultural and fishing industries in Asia are being transformed by technology as the cheap and abundant labor that they long relied on erodes due to demographic pressures. Rising wages across other industries have created a labor shortage in the traditional staples of economies, especially in Southeast Asia. To make up for a shrinking population of farmers, companies are adopting artificial intelligence and drones to help grow food more cheaply and efficiently. In Vietnam, Minh Phu Seafood is building new vinyl shrimp tanks that are shaped like deep bowls. Water is swirled around the tank so that waste collects at the bottom where it can easily be drained.

A company that makes robots to improve the economics of recycling just raised $16 million. AMP Robotics plans to use the money to develop more powerful AI to aid in its green mission. "Over the last few years, the industry has had their margins squeezed by labor shortages and low commodity prices," explains Shaun Maguire, partner at Sequoia, which led the recent fundraising round. "The end result is an industry proactively searching for cost-saving alternatives and added opportunities to increase revenue by capturing more high-value recyclables, and AMP is emerging as the leading solution." The problem is especially urgent with recent news that China will no longer be accepting waste from the U.S. As I've written, even when China was taking our recyclables the process was extremely inefficient, the bales often poorly sorted.

Last Thursday (November 21), the Cambridge Union Society hosted what turned out to be the most popular debate of term: This house believes artificial intelligence will bring more harm than good. The chamber was filled to its maximum capacity, with people having to be turned away at the door. A wealth of news outlets, including the BBC, CNN and the New Scientist attended to cover the event. This fervour of attention is hardly surprising: IBM Research's Project Debater, the first artificial intelligence platform that can debate humans on complex topics, was the leading'speaker' on both the proposition and opposition. The night opened with a brief introduction from the principal investigator of Project Debater, Noam Slonim.

Abstract--In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation tech nique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined thre shold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to a ctively learn the network dynamics, estimate the vehicles' future A oI, and proactively allocate resources. Simulation results sh ow a significant improvement in terms of AoI violation probabili ty, compared to several baselines, with a reduction of at least 50%.

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemma-tised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English-French subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-model- based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.

EXTENDED ABSTRACT Model-free reinforcement learning (or simply reinforcement learning, RL, in what follows) is increasingly used in applications to solve a wide variety of control problems (Kober et al., 2013; Garcıa and Fern andez, 2015; Cheng et al., 2019). The lack of requiring a formal model of the plant renders it appealing for a heuristic, low-cost control design approach that can be easily implemented and adapted to different situations. As a tradeoff, learning processes often require a long training phase where the controller agent learns by trial-and-error how the plant responds to different control actions, and what actions to take to steer its behavior in a desired manner. This problem is particularly relevant when using tabular methods, such as Q-learning, in those situations where reinforcement learning is applied to control dynamical systems defined in continuous spaces (Lillicrap et al., 2019). It is therefore desirable to enhance the learning process by encoding some qualitative knowledge of the system dynamics via appropriate models.

Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field. However, VAEs often lack the ability to produce sharp images and learn high-level features. We propose to alleviate these issues by adding a new branch to conditional hierarchical VAEs. This enforces a division between higher-level and lower-level features. Despite the additional computational overhead compared to a normal VAE it results in sharper and better reconstructions and can capture the data distribution similarly well (indicated by a similar or slightly better OoD detection performance).

Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common DML approach is with triplet loss, despite significant advances in the area of DML. Triplet loss suffers from several issues such as collapse of the embeddings, high sensitivity to sampling schemes and more importantly a lack of performance when compared to more modern methods. W e attribute this adoption to a lack of fair comparisons between various methods and the difficulty in adopting them for novel problem statements. In this paper, we perform an unbiased comparison of the most popular DML baseline methods under same conditions and more importantly, not obfuscating any hyper parameter tuning or adjustment needed to favor a particular method. W e find, that under equal conditions several older methods perform significantly better than previously believed. In fact, our unified implementation of 12 recently introduced DML algorithms achieve state-of-the art performance on CUB200, CAR196, and Stanford Online products datasets which establishes a new set of baselines for future DML research. The codebase and all tuned hyperparame-ters will be open-sourced for reproducibility and to serve as a source of benchmark.

There have been many studies on automated test generation for mobile Graphical User Interface (GUI) applications. These studies successfully demonstrate how to detect fatal exceptions and achieve high code and activity coverage with fully automated test generation engines. However, it is unclear how many GUI functions these engines manage to test. Furthermore, these engines implement only implicit test oracles. We propose Fully Automated Reinforcement LEArning-Driven Specification-Based Test Generator for Android (FARLEAD-Android). FARLEAD-Android accepts a GUI-level formal specification as a Linear-time Temporal Logic (LTL) formula. By dynamically executing the Application Under Test (AUT), it learns how to generate a test that satisfies the LTL formula using Reinforcement Learning (RL). The LTL formula does not just guide the test generation but also acts as a specified test oracle, enabling the developer to define automated test oracles for a wide variety of GUI functions by changing the formula. Our evaluation shows that FARLEAD-Android is more effective and achieves higher performance in generating tests for specified GUI functions than three known approaches, Random, Monkey, and QBEa. To the best of our knowledge, FARLEAD-Android is the first fully automated mobile GUI testing engine that uses formal specifications.