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Back in 2008, when we first started developing our solutions, the work of Diamond, Mortenson and Pissarides provided the scientific basis for our job and skills matching technology. With their Nobel prize winning labor market theory and the DMP model, they provided a first coherent, complete framework to think about labor market dynamics in a structured way. In their theory, labor markets are viewed as markets with search frictions: workers look for suitable jobs and employers look for suitable workers, » Read more about: “No unemployed candidates will be considered at all” – the crux of unemployment. »
In recent years there have been many posts, articles and reports on how AI and automation will shape the future of work. Depending on the author’s perspective or agenda, these pieces go one of two ways: either the new technology will destroy jobs and have devastating effects on the labor market, or it will create a better, brighter future for everyone by destroying only the boring jobs and generating better, much more interesting ones. As always, » Read more about: AI, automation and the future of work – beyond the usual bubbles »
This is part of a series of articles we conduct to analyze government policies and practices on the strategies to build AI workforce. Previously, we have analyzed how Singapore is helping mid-career PMETs to switch to the tech sector and a collaborative effort between government, tech companies and education providers in Saudi Arabia. Our third stop is China.
As the world’s major economies have announced the development of artificial intelligence as a national strategy, » Read more about: Building the AI-ready workforce: China’s Artificial Intelligence Plan pushed by both central and local governments »
To follow the trend of future work, upskilling and reskilling, and digital transformation, we are posting a series of articles to analyze government policies and practices to learn how countries are taking strategies to build their workforce for these challenges. In the previous article, we examined how Singapore is helping mid-career PMETs to switch to the tech sector. Our second stop is Saudi Arabia.
The Kingdom of Saudi Arabia has shown strong commitment to the implementation and development of AI as it seeks to diversify the economy, » Read more about: Building the AI-ready workforce: A collaborative effort between government, tech companies and education providers »
In one of our recent posts, we explained the difference between an ontology and a taxonomy. Although choosing an ontology over taxonomies is an important step towards smart and accurate matching, it is not the only aspect to consider. Even if you are only interested in a monolingual ontology, but even more so with multilingual ones, localization is another key feature. For high performance and satisfactory matching results, it is simply not enough for the ontology to cover your language of choice, » Read more about: The importance of localizing ontologies, illustrated on the education systems in Peru and Colombia »