Exploration in sparse-reward reinforcement learning (RL) is difficult due to the need for long, coordinated sequences of actions in order to achieve any reward. Skill learning, from demonstrations or interaction, is a promising approach to address this, but skill extraction and inference are expensive for current methods. We present a novel method to extract skills from demonstrations for use in sparse-reward RL, inspired by the popular Byte-Pair Encoding (BPE) algorithm in natural language processing. With these skills, we show strong performance in a variety of tasks, 1000 \times acceleration for skill-extraction and 100 \times acceleration for policy inference. Given the simplicity of our method, skills extracted from 1\% of the demonstrations in one task can be transferred to a new loosely related task.

Maintenance can be a complex undertaking, requiring thorough planning and an astute understanding of a facility's risk profile. This is particularly true of high-risk facilities. Maintenance does not occur in a'vacuum' and can result in costly downtimes if it is unexpected or unplanned. Even planned maintenance can lead to lengthy downtimes resulting in huge losses. For example, oil refineries in the US alone lose an estimated $47 billion from 213,000 hours of downtime each year.

Artificial intelligence (AI), robotics, automation – as well as less technologically-driven disruptions such as pandemics – mean the way our children and grandchildren are working will look very different to how we work today. We don't even have to look that far ahead to see change on a dramatic scale. It's been predicted that 85% of the jobs that will be available in 2030 don't yet exist! Factors such as the widespread shift to remote working, the emergence of the gig economy, and employees' increasing expectations of flexibility in their relationship with their employers will also play their part. Adding seismic shifts such as the great resignation into the mix means companies are frantically searching for new strategies when it comes to hiring and retaining talent.

New Delhi, June 4 (IANSlife) You've probably heard for years that the workforce would be supplanted by robots. AI has changed several roles, such as using self-checkouts, ATMs, and customer support chatbots. The goal is not to scare people, but to highlight the fact that AI is constantly altering lives and executing activities to replace the human workforce. At the same time, technological advancements are producing new career prospects. AI is predicted to increase the demand for professionals, particularly in robotics and software engineering. As a result, AI has the potential to eliminate millions of current occupations while also creating millions of new ones.

With the emerging trend of integrating Artificial Intelligence (AI) and robotics with manpower to enhance work systems, there will be an inevitable shift in the role of security officers in Singapore, note industry experts. While this digital transformation will free up workers' time for higher-value tasks and creates more opportunities for them to upskill and move up in the security industry, those who lack the fundamental competencies to operate new technologies may be at a disadvantage. These are some key findings from NTUC LearningHub's recently launched Industry Insights report on Security, which featured uncovered the trends in Singapore's private security sector. "To optimise headcount in the manpower scarce industry, security technology management which integrates the use of AI into its operations, allows security officers to be more competitive and productive. There are four aspects that security officers need to be familiar with: Access Control Management, Alarm System Management, Robotics and Automation Application as well as Security Surveillance Management," says NTUC LearningHub's Director of Technical Skills, Tay Ee Learn As the security industry in Singapore makes strides in leveraging technology to enhance and create more efficient security systems, there will be lower dependence on manpower to conduct manual work such as patrolling and CCTV feed monitoring.

Looking back, things probably started to unravel when my wife, Katie, found me tucked up in bed, talking to another woman. 'Goodnight,' she heard me say. 'Sweet dreams,' came the woman's obliging response. The next morning, Katie walked past the sitting room door to catch me muttering something about the weather. 'I wasn't talking to you,' I replied.

Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. According to MIT Sloan Review, the largest companies -- those with at least 100,000 employees -- are the most likely to have an AI strategy, but only half have one. Despite claims that AI is already being subsumed into an array of applications, we're not there yet and won't be in 2018. It is still the early days of adoption, and those companies that are implementing AI now will see the biggest competitive value.

Artificial intelligence (AI) is changing the face of business. No longer a futuristic concept, its impact is real. From tech giants like Google, Apple and Amazon to user-centric behemoths like Uber and Starbucks, everyone seems to be using AI technology to transform the customer experience (CX). But, it's not just corporate giants that are deploying AI. Smaller organizations are following suit.

In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with the data. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. However, an important practical question is how do we construct qualitative models in the first place.