Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an $f$-divergence ($f$-GANs) or an integral probability metric (Wasserstein GANs) between the model and the data distribution using a discriminator. Wasserstein GANs enjoy superior empirical performance, but in $f$-GANs the discriminator can be interpreted as a density ratio estimator which is necessary in some GAN applications. In this paper, we bridge the gap between $f$-GANs and Wasserstein GANs (WGANs). First, we list two constraints over variational $f$-divergence estimation objectives that preserves the optimal solution. Next, we minimize over a Lagrangian relaxation of the constrained objective, and show that it generalizes critic objectives of both $f$-GAN and WGAN. Based on this generalization, we propose a novel practical objective, named KL-Wasserstein GAN (KL-WGAN). We demonstrate empirical success of KL-WGAN on synthetic datasets and real-world image generation benchmarks, and achieve state-of-the-art FID scores on CIFAR10 image generation.

In the current level of evolution of Soccer 3D, motion control is a key factor in team's performance. Recent works takes advantages of model-free approaches based on Machine Learning to exploit robot dynamics in order to obtain faster locomotion skills, achieving running policies and, therefore, opening a new research direction in the Soccer 3D environment. In this work, we present a methodology based on Deep Reinforcement Learning that learns running skills without any prior knowledge, using a neural network whose inputs are related to robot's dynamics. Our results outperformed the previous state-of-the-art sprint velocity reported in Soccer 3D literature by a significant margin. It also demonstrated improvement in sample efficiency, being able to learn how to run in just few hours. We reported our results analyzing the training procedure and also evaluating the policies in terms of speed, reliability and human similarity. Finally, we presented key factors that lead us to improve previous results and shared some ideas for future work.

Despite of the recent progress in agents that learn through interaction, there are several challenges in terms of sample efficiency and generalization across unseen behaviors during training. To mitigate these problems, we propose and apply a first-order Meta-Learning algorithm called Bottom-Up Meta-Policy Search (BUMPS), which works with two-phase optimization procedure: firstly, in a meta-training phase, it distills few expert policies to create a meta-policy capable of generalizing knowledge to unseen tasks during training; secondly, it applies a fast adaptation strategy named Policy Filtering, which evaluates few policies sampled from the meta-policy distribution and selects which best solves the task. We conducted all experiments in the RoboCup 3D Soccer Simulation domain, in the context of kick motion learning. We show that, given our experimental setup, BUMPS works in scenarios where simple multi-task Reinforcement Learning does not. Finally, we performed experiments in a way to evaluate each component of the algorithm.

The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. This study aimed to explore psychiatrists' opinions about the potential impact of innovations in artificial intelligence and machine learning on psychiatric practice. In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written response to three open-ended questions in the survey. Comments were classified into four major categories in relation to the impact of future technology on patient-psychiatric interactions, the quality of patient medical care, the profession of psychiatry, and health systems. Overwhelmingly, psychiatrists were skeptical that technology could fully replace human empathy. Many predicted that 'man and machine' would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. This study presents timely information of psychiatrists' view about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

The rapid rate of change enabled by software make this industry more vulnerable than most to the falling behind on the innovation curve. This problem has only accelerated in recent years as the number of disruptive technologies have grown at an exponential rate fueled by the growing size of the market and the number of software engineers. The open source community has been a driving source of disruptive technologies such as big data Hadoop and Spark, JavaScript frameworks like Angular and React, and machine learning frameworks like TensorFlow. Software based companies who do not embrace these disruptive technologies face the ever-increasing risk of being pushed aside by those that do. To make this even more challenging, the skills required to enhance the current product and the skills required to innovate using new disruptive technologies are different.

IBM this afternoon issued several updates to its public cloud and Watson AI portfolios, including news that Aegean Airlines, BNP Paribas, ExxonMobil, Elaw Tecnologia SA (a Brazil-based legal management company) and Home Trust "are selecting IBM public cloud as their preferred destination for mission critical workloads," Big Blue said. On the AI front, IBM announced updates to its "Watson Anywhere" strategy designed to scale AI across any cloud and to ease AI implementations, according to the company. Drift Detection – Intended to address the concerns about data privacy and algorithm accountability, IBM announced the "Drift Detection" capability within Watson OpenScale, an AI platform launched last year to detect bias and to enable understanding of how AI arrived at its results. IBM said Drift Detection Drift Detection indicates how far a model has "drifted" by comparing production and training data and the resulting predictions it creates. Alerts are issued when a user-defined drift threshold is exceeded.

Artificial Intelligence is changing banking, health, business, and the military. But so far, it has been slow to go big in K-12 education, said Scott Garrigan, a professor at Lehigh University at a session at the International Society for Technology in Education's annual conference here. But that is likely to change in the coming years, he said. No sector will be untouched by AI. It will produce changes as big as the automobile," Garrigan said. "We have no idea what's going to happen as AI rolls out massively.

Satellites could help locate stranded whales more efficiently and in real-time. Scientists have begun harnessing the power of the technology's high-resolution imagery to detect and monitor whales stranded on the shore from space. The team noted that the use of satellites will help find stranded whales in remote locations, as well as spot potentially deteriorating ocean conditions. Satellites could help locate stranded whales more efficiently and in real-time. Scientists have begun harnessing the power of the technology's high-resolution imagery to detect and monitor whales stranded on the shore from space Chile witnessed one of the largest mass mortality of baleen whales in 2015 on the remote beaches of Patagonia – at least 343 died.

Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2020. Analysts presented their findings during Gartner IT Symposium/Xpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility reaching tipping points over the next five years. "People-centric smart spaces are the structure used to organize and evaluate the primary impact of the Gartner top strategic technology trends for 2020," said David Cearley, vice president and Gartner Fellow. "Putting people at the center of your technology strategy highlights one of the most important aspects of technology -- how it impacts customers, employees, business partners, society or other key constituencies. Arguably all actions of the organization can be attributed to how it impacts these individuals and groups either directly or indirectly. This is a people-centric approach."

Artificial Intelligence (AI) is having a broad and deep impact on the way services are exported globally. Be it for good or bad, there is no getting away from the reality that AI is an agent of disruption. One of the perennial front-runners of Nearshore outsourcing, Costa Rica, appears to be adapting to the AI opportunity faster than most countries in the region. Local companies are intensifying their AI development operations and a number of AI technologies are gaining traction there – all of which will influence Costa Rica's positioning in the next-generation of services delivery. The Latin American nation of nearly five million has long been seen as a tech epicenter of Central America ever since Intel chose it to open the biggest microchip factory in the region in 1997, with an initial investment of US$800 million.