How can industrial intelligence change the employment structure of the floating population?

As the wave of industrial intelligence (AI) swept, the demographic dividend era in the Chinese labor market continued to decrease. This study aimed to explore how AI reshaped the labor employment structure of the floating population. Additionally, it clarified the internal mechanism of AI on the employment structure of the floating population based on the existing AI model and the theoretical model of AI technology. At the same time, the workforce was divided into high-, medium-, and low-skilled groups according to education level. Empirical analysis was conducted using relevant data from 31 Chinese provinces spanning from 2012 to 2018. The aim was to test the impact of AI technology on the employment of different types of floating populations. The results indicated that: (1) industrial robots impacted heterogeneous skilled floating population labor by bipolar promotion and central substitution. (2) The application of industrial robots had a promotion effect on unfinished school and primary school groups, a substitution effect on middle school, high school/technical secondary school, and college specialties, and a promotion effect on college undergraduate and graduate students. (3) Distinguish employment status, industrial robot application had a significant negative impact on low-skilled employees and significant positive effects on high-skilled employers. Hence, it was recommended to put forward corresponding policy suggestions to address this issue.

Bailando++: 3D Dance GPT With Choreographic Memory.

Our proposed music-to-dance framework, Bailando++, addresses the challenges of driving 3D characters to dance in a way that follows the constraints of choreography norms and maintains temporal coherency with different music genres. Bailando++ consists of two components: a choreographic memory that learns to summarize meaningful dancing units from 3D pose sequences, and an actor-critic Generative Pre-trained Transformer (GPT) that composes these units into a fluent dance coherent to the music. In particular, to synchronize the diverse motion tempos and music beats, we introduce an actor-critic-based reinforcement learning scheme to the GPT with a novel beat-align reward function. Additionally, we consider learning human dance poses in the rotation domain to avoid body distortions incompatible with human morphology, and introduce a musical contextual encoding to allow the motion GPT to grasp longer-term patterns of music. Our experiments on the standard benchmark show that Bailando++ achieves state-of-the-art performance both qualitatively and quantitatively, with the added benefit of the unsupervised discovery of human-interpretable dancing-style poses in the choreographic memory.