As a result of transformation processes, the German labor market is highly dependent on vocational training, retraining and continuing education. To match training seekers and offers, we present a novel approach towards the automated detection of access to education and training in German training offers and advertisements. We will in particular focus on (a) general school and education degrees and schoolleaving certificates, (b) professional experience, (c) a previous apprenticeship and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide a mapping of synonyms in education combining different qualifications and adding deprecated terms. Second, we provide a rule-based matching to identify the need for professional experience or apprenticeship. However, not all access requirements can be matched due to incompatible data schemata or non-standardizes requirements, e.g initial tests or interviews. While we can identify several shortcomings, the presented approach offers promising results for two data sets: training and re-training advertisements.

Everyone is chattering about ChatGPT. Can it pass the bar exam? No, though it performs well on some sections. Which should force a serious reevaluation of the test's ultimate value to the profession, but instead will convince bar examiners to introduce cavity searches. And, as The Onion points out, ChatGPT was as depressed to take the test as the rest of us.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Undergraduate computing classes typically deliver content through passive lectures and require students to write code from scratch. However, students do not always pay attention in lecture and writing code from scratch can be overwhelming for novice students. Students report feeling frustrated when they cannot figure out what is wrong with their code or wait hours to get help from instructional staff. Students from groups that have been historically marginalized are more at risk of failure in introductory courses since they tend to have less prior programming experience. How can we help students succeed in programming courses, especially those without prior programming experience?

Will robots take over our jobs? This Cornell University and King's College London collaboration examines how artificial intelligence (AI) has influenced major knowledge-intensive services sectors, such as telecommunications and health care -- and how governments, employers and workers have responded to the challenges that smart technologies pose for the world of work. Taking the United States and the United Kingdom as our case studies, we will explore a wide range of emerging issues and countervailing forces (e.g., public policies, professional associations, vocational training systems, licensing bodies and laws, unions and labor market regulation). The study aims to be the first to systematically map these issues in the United States and the United Kingdom, with the goal of launching a mixed-methods project that covers a broader set of country cases. In so doing, the collaboration leverages the interdisciplinary expertise of our institutions to inform policy debates at the intersection of AI and work.

Data science courses are among the most popular globally, with a high likelihood of career prospects, according to the volume of internet searches for skill development or job-oriented courses. Data scientists are needed everywhere. The most fundamental prerequisite for developing any technology in this era of smart technology (which includes smartphones, televisions, watches, etc.) is data, and these data scientists serve as the foundation for machine learning and artificial intelligence specialists. A data scientist will also assist organizations in managing serious crises and assisting them in their resolution through the use of data-driven judgments. Data science is the study of analyzing and obtaining organized, unstructured, and noisy data from various sources. This analysis aids businesses in forecasting outcomes and making data-driven decisions. Data that adheres to a data model, has a clearly defined structure, follows a persistent order, and is simple for both humans and programmes to retrieve is said to be structured data. Unstructured data is not structured in a way that has been predefined, notwithstanding the possibility that it has a native, internal structure. The data is kept in its original format; there is no data model. Media, text, internet activity, monitoring photos, and more are typical instances of large datasets. Data Science – The MUST KNOW to become a successful Data Scientist! How can software engineers and data scientists work together? Corrupted data, a type of unstructured data, is another name for noisy data. It also includes any information that a user's system is unable to effectively analyze and interpret. If handled improperly, noisy data can have a negative impact on the outcomes of any data analysis and skew conclusions. Sometimes, statistical analysis is employed to remove noise from noisy data.

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules.

Earlier this year, President Trump signed an executive order for the "American AI Initiative," to guide AI developments and investments in the following areas: research and development, ethical standards, automation, and international outreach. This initiative is indicative of the changing times, and how, as a country, the U.S. is learning to navigate the implications of AI. Leaders in the business world, specifically, are faced with the responsibility of equipping our employees with the skills necessary for paving long-lasting career paths, and the workforce must discover what will be expected as technology continues to disrupt the norm, and work as we know it. As a global business leader, an AI optimist, and a father, I find myself asking: What will make a career sustainable in 2020 and beyond? Will the future of education rise to meet the demands of the future of work?

Earlier this year, President Trump signed an executive order for the "American AI Initiative," to guide AI developments and investments in the following areas: research and development, ethical standards, automation, and international outreach. This initiative is indicative of the changing times, and how, as a country, the U.S. is learning to navigate the implications of AI. Leaders in the business world, specifically, are faced with the responsibility of equipping our employees with the skills necessary for paving long-lasting career paths, and the workforce must discover what will be expected as technology continues to disrupt the norm, and work as we know it. As a global business leader, an AI optimist, and a father, I find myself asking: What will make a career sustainable in 2020 and beyond? Will the future of education rise to meet the demands of the future of work?

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.