Automation will replace some jobs, but also create new ones. Up to a third of job roles in Europe could be made redundant by automation over the next 20 years as companies battle to increase productivity and fill skills gaps created by an ageing population, according to Forrester. The tech analyst's latest Future of Jobs Forecast estimates that as many as 12 million jobs could be lost to automation across Europe by 2040, primarily impacting workers in industries such as retail, food services, and leisure and hospitality. Mid-skill labour jobs that consist of simple, routine tasks are most at risk from automation, the report said. These roles make up 38% of the workforce in Germany, 34% of the workforce in France, and 31% of the workforce in the UK. In total, 49 million jobs in'Europe-5' (France, Germany, Italy, Spain, and the UK) could potentially be automated, according to Forrester.
Automation will replace some jobs, but also create new ones. Up to a third of jobs in Europe could be made redundant by automation over the next 20 years as companies battle to increase productivity and fill skills gaps created by an ageing population, according to Forrester. The tech analyst's latest Future of Jobs Forecast estimates that as many as 12 million jobs could be lost to automation across Europe by 2040, primarily impacting workers in industries such as retail, food services, and leisure and hospitality. Mid-skill labour jobs that consist of simple, routine tasks are most at risk from automation, the report said. These roles make up 38% of the workforce in Germany, 34% of the workforce in France, and 31% of the workforce in the UK. In total, 49 million jobs in'Europe-5' (France, Germany, Italy, Spain, and the UK) face being lost to automation, according to Forrester.
Significant hurdles leaders face this year include managing talent, formulating strategies, operational plans, and organizing employee tasks in ways that ensure everyone accesses growth opportunities. These challenges emphasize the importance of good strategy, and are essential for organizational survival. Vijay Pereira, Professor and head of department of people and organizations, at NEOMA Business School in France, believes artificial intelligence (AI) can help leaders undertake these challenges. For example, his recent work concludes that evolutionary computation and data mining can explore large databases or social media to locate potential talented individuals for recruitment purposes. In addition, machine learning helps reanalyze and recognize patterns from data collected from existing decision support systems to help organizations improve their strategic planning processes.
Analyzing the effect of business cycle on rating transitions has been a subject of great interest these last fifteen years, particularly due to the increasing pressure coming from regulators for stress testing. In this paper, we consider that the dynamics of rating migrations is governed by an unobserved latent factor. Under a point process filtering framework, we explain how the current state of the hidden factor can be efficiently inferred from observations of rating histories. We then adapt the classical Baum-Welsh algorithm to our setting and show how to estimate the latent factor parameters. Once calibrated, we may reveal and detect economic changes affecting the dynamics of rating migration, in real-time. To this end we adapt a filtering formula which can then be used for predicting future transition probabilities according to economic regimes without using any external covariates. We propose two filtering frameworks: a discrete and a continuous version. We demonstrate and compare the efficiency of both approaches on fictive data and on a corporate credit rating database. The methods could also be applied to retail credit loans.
Sign up for the New Economy Daily newsletter, follow us @economics and subscribe to our podcast. The French government will use algorithms and artificial intelligence to identify small firms at risk of collapse in the wake of the Covid pandemic, and provide as much as 3 billion euros ($3.7 billion) to protect them. Finance Minister Bruno Le Maire presented the new measures on Tuesday as part of a plan that also includes an extension of crisis lending facilities to the end of 2021 and simplified procedures for restructuring debts of small firms. The new tools come amid warnings that a jump in insolvencies as governments pare back crisis aid could hamstring the economic recovery. The OECD said Monday that shoring up balance sheets of small firms is a crucial part of targeted fiscal support in the transition out of the crisis.
This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.
It's impossible to say precisely how artificial intelligence will disrupt the job market, so researchers at PwC have taken a birds eye view from the top down, and pointed to the results of sweeping economic changes. Their prediction, in a new report out Tuesday, is that it'll all balance out in the end. But the rise in robots and machine-learning software will make the country more productive over the next two decades, growing at a 2% annual clip, to put nearly the same number of jobs back in the system: 7.2 million, PwC estimates. To be clear those new jobs won't involve building robots or coding AI-powered software, which will only make up around 5% of employment, says John Hawksworth, PwC's chief economist. Instead around 1.5 million, or 22%, of the new jobs will be in health and social work.
As AI and machine learning transform industries by automating much of the work currently done by humans, women's careers will be disproportionately affected. That's according to a McKinsey Global Institute report published earlier this year ("The future of women at work: Transitions in the age of automation"), which found that women predominate in occupations that'll be adversely impacted. About 40% of jobs where men make up the majority in the 10 economies (Canada, France, Germany, Japan, the U.K., the U.S., China, India, Mexico, and South America) contributing over 60% of GDP collectively could be displaced by automation in our 2030, compared with the 52% of women-dominated jobs with high automation potential. Mekala Krishnan, a senior fellow at McKinsey's Boston-based business and economics research arm and a member of the board of the Global Fund for Women, spoke about the research (which she coauthored) at MIT Technology Review's EmTech MIT conference at the MIT Media Lab. Krishnan pointed out that monotonous or repetitive tasks are ripe for automation.
With the unemployment rate at a low 3.7% and the skills shortage severe, corporations need to get creative about finding talented job candidates. IBM is among the technology giants testing new methods involving artificial intelligence to overcome the labor market challenges. AI has been applied to the job application process directly as a method to prevent human bias in hiring decisions. Now more companies are using AI assessment tools to reverse-engineer job roles and find candidates often overlooked by recruiters. IBM introduced its SkillsBuild platform in France in May 2019 with the goal of identifying job skills and employment opportunities for members of disadvantaged communities.
In this study, we present a machine learning approach to infer the worker and student mobility flows on daily basis from static censuses. The rapid urbanization has made the estimation of the human mobility flows a critical task for transportation and urban planners. The primary objective of this paper is to complete individuals' census data with working and studying trips, allowing its merging with other mobility data to better estimate the complete origin-destination matrices. Worker and student mobility flows are among the most weekly regular displacements and consequently generate road congestion problems. Estimating their round-trips eases the decision-making processes for local authorities. Worker and student censuses often contain home location, work places and educational institutions. We thus propose a neural network model that learns the temporal distribution of displacements from other mobility sources and tries to predict them on new censuses data. The inclusion of multi-task learning in our neural network results in a significant error rate control in comparison to single task learning.