Most businesses intend to support a hybrid workplace in the ever-changing work environment of the twenty-first century. Maintaining a hybrid workplace is the most effective way to stay competitive in the labor market and attract and retain the most talented professionals. This has been a disruptive change for companies and has reshaped the way enterprises have traditionally operated. It involves never-before-seen challenges, such as a shift in organizational culture, adopting new digital technologies, and developing new work strategies to ensure that business objectives are met on time. Frost & Sullivan's latest white paper, "The Hybrid Workplace is Here to Stay: Are You Ready?", discusses the key factors a company must consider to successfully adapt to a hybrid work environment.

Machine Learning in Communication marketplace has dominated many areas of the planet in past couple of decades.

Orbis Research Present's'Global Machine Learning Market' enlarge the decision making potentiality and helps to create an efficient counter strategies to gain competitive advantage. Machine Learning market is segmented by Type, and by Application. Players, stakeholders, and other participants in the global Machine Learning market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application in terms of revenue and forecast for the period 2020-2026. The study report offers a comprehensive analysis of Machine Learning market size across the globe as regional and country level market size analysis, CAGR estimation of market growth during the forecast period, revenue, key drivers, competitive background and sales analysis of the payers.

In a previous story, I wrote about how a Game Theoretic approach was influencing developments in the Deep Learning field. In this story, I now write about DeepMind's latest foray into this exciting area. Yesterday, February 19th 2017), DeepMind presents their latest research on this subject titled "Understanding Agent Cooperation". The gist of the research is that, they employed Deep Reinforcement Learning networks in two game environments to study their behavior. The motivation is to study multi-agent systems to better understand and control these kinds of systems. In a previous story (see: "Five Capability Levels of Deep Learning", I laid out a road map as to how Deep Learning will evolve in even greater capabilities.