Jrcb4 The Impact of Artificial Intelligence on Learning final

 
 
 
 
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RBC (2018). 
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European Commission (EC 2018a), Redecker (2017). 
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The lower division of the seven liberal arts and comprises grammar, logic, and rhetoric, see: 
https://en.wikipedia.org/wiki/Trivium 
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Consisted of arithmetic, geometry, music, and astronomy, see: https://en.wikipedia.org/wiki/Quadrivium 

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Figure 5: Skills of the languages key competence and some associated AI capabilities 
Source: Author’s elaboration. Council recommendation on Key Competences for Lifelong Learning 
2.4.7
 
Direct AI impact on advanced digital skills demand 
The development of new AI and machine learning models requires very high levels of 
competences in several areas. This is one of the reasons why AI experts are now being 
paid extreme salaries. The number of neural AI experts is perhaps doubling annually, but 
the basic knowledge needed for state-of-the-art work in this area requires advanced 
levels of scientific, mathematical and technical skills that are demanding to acquire. 
Development of new AI methods requires good understanding of statistics, linear 
algebra, differential equations, as well as computer architectures and emerging chip 
technologies
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, programming approaches and tools. The required skill set is rather 
scarce, and recent estimates put the number of people with this set at some tens of 
thousands.
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There are some 5,000 persons who have written academic articles or 
presented at AI conferences in recent years.
It may be expected that the high visibility of AI and the current demand will relatively 
rapidly direct talent to this area. As an example, since its launch in May 2018, about 90 
000 students from over 80 countries have enrolled to the six-week Elements of AI –
course organised as part of the AI Education programme of the Finnish Center of AI.
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This introductory course has been popular among policymakers and in private and public 
sector organisations who struggle to make sense of developments in AI. High-level skills 
in AI, however, cannot be acquired quickly, and the scarcity of AI-related skills may have 
serious indirect implications for teaching and learning. In 2017, AI related business 
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One key bottleneck for neural AI is its energy consumption. As a result, many chip designers are now 
trying to develop semiconductor chips that can be used for specific AI applications, see e.g. (Salvo 2018). 
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Element AI has recently calculated the number of people with the required skill set at 22,000, see (Kahn 
2018). 
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https://www.elementsofai.com