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Technological progress has been recently associated with a crowding-out of cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as ones related to social intelligence, flexibility and creativity. The nature of soft skills makes them hardly replaceable by machine work and among subsets of soft skills, creativity is one of the hardest to define and codify. Therefore, creativity-intensive occupations have been shielded from automation. Given this framework, our study contributes to a nascent field on interdisciplinary research to predict the impact of artificial intelligence on work activities and future jobs using machine learning. In our work, we focus on creativity, starting from its possible definitions, then we get significant insights on creativity patterns and dynamics in the Italian labour market, using a machine learning approach. We make use of the INAPP-ISTAT Survey on Occupations (ICP), where we identify 25 skills associated with creativity. Then, we apply matrix completion—a machine learning technique which is often used by recommender systems—to predict the average importance levels of various creative skills for each profession, showing its excellent prediction capability for the specific problem. We also find that matrix completion typically underestimates the average importance levels of soft skills associated with creativity, especially in the case of professions belonging to the major group of legislators, senior officials and managers, as well as intellectual professionals. Conversely, overestimates are typically obtained for other professions, which may be associated with a higher risk of being automated.

Occurrence of protected and rare species is regarded as a strong argument for establishing protected areas and monitoring biodiversity, but while protected species are clearly identified, some problems exist to define rare species. It is thus important to know whether common and unprotected native species are reliable indicators for protected and rare species. The aims of this paper were to: (a) analyse the distribution of rarity and commonness of species, by using different criteria and (b) test if groups of species with different conservation value (aliens, unprotected natives and protected natives) differ in terms of their rarity distribution, using the data collected in 604 plots sampled within 21 protected areas of the central Italy. Three different criteria were used to classify species as rare or common. Pearson correlation, least-squares regressions and Chi-square test were used to compare the species richness patterns or rare and common species as well as protected, unprotected native, and alien species. The number of species classified as common and rare widely differ according to the adopted criterion. The number of common and rare species were statistically correlated at both the plot and protected area scales, even if at the plot scale the predictive capacity was rather low. Protected species were significantly rarer than expected, while unprotected species were significantly more common than expected; alien species confirmed to be particularly rare in our study area, with some major alien species being totally absent in the recorded flora. The richness patterns of common and rare species defined according to different criteria have been found to be correlated one to the other, and both are well related to the richness of protected and alien species at both the plot and PA scales. Protected species were better related to common species, while alien species were better related to rare species. Despite rare species were numerically more than common species, and the richness pattern of total species was better predicted by common species than rare species. Common species confirmed to be good indicators of species richness patterns and also of protected species.