The World Economic Forum (WEF) is upon us this week, and the Future of Work is one of its key themes. This is a good opportunity to catch up on the trends unfolding in this domain right now, and to ponder on the insights of the people taking note and shaping this discussion. Automation and AI is part of this discussion as well, with the jury still out as to how exactly this will shape labor, workforce dynamics, and workplace transformation among others. Based on the WEF's latest report on the Future of Jobs, we highlight the major forces at play today. We discuss how these effect the technology behind the job market with Panos Alexopoulos, Head of Ontology at Textkernel, a Careerbuilder company.

It's a strange paradox of our times that we are repeatedly told that AI and automation will destroy jobs, whilst at the same time most of the western world is struggling with incredibly low levels of productivity growth. A recent study led by Stanford University suggests that the failure of AI investments to pull through into higher productivity numbers might be down to the economic model that underpins how companies get their data. It suggests that by not paying us for the data we freely give up to Google, Facebook et al, it tends to result in poor quality data that therefore makes it harder to derive useful insights from. It's an interesting angle, but whilst I can empathize entirely with the desire to re-align society so that the data we provide can be monetized, it's harder to see the financial inducement that can encourage us to share more accurate data on Facebook. An alternative argument is provided by a recent paper from Accenture.

Robot Learning from Demonstration (LfD) research deals with the challenges of enabling humans to teach robots novel skills and tasks (Argall et al. 2009). The practical importance of LfD is due to the fact that it is impossible to pre-program all the necessary skills and task knowledge that a robot might need during its life-cycle. This poses many interesting application areas for LfD ranging from houses to factory floors. An important motivation for our research agenda is that in many of the practical LfD applications, the teacher will be an everyday end-user, not an expert in Machine Learning or robotics. Thus, our research explores the ways in which Machine Learning can exploit human social learning interactions--Socially Guided Machine Learning (SGML).