Now that machines can learn: Can we combine human and artificial intelligences?


Key idea: Traditionally the impact from automation has affected the manufacturing sector; however the coming wave of innovation based on artificial intelligence might also bounce Higher Education. The growing deployment of learning machines can transform what it means to be an expert (associated with “white collars” professionals). These new forms of intelligence will raise challenging questions not only from a technological viewpoint but also from educational, social, economic and ethical perspectives.

The disruption of digital technologies is transforming a large number of sectors in modern societies (i.e. economy, manufacturing, communications, health, etc, WEF, 2016). But now the changing technological landscape could challenge existing conceptions of what are universities for and what it means “to know”.

During the last decades the increasing use of digital technologies transformed the way people access information (i.e. open access, open data), how communities generate new knowledge (i.e. social media, sharing economy), how knowledge is being distributed (i.e. TED talk, MOOCs), how information is being renovated (online platforms such as Twitter, Snpachat or Periscope are built-in the obsolescence of information). But lately these disruptions are also affecting how knowledge is applied (i.e. artificial intelligence, collaborative computation).

The domestication of artificial intelligence (i.e. Alexia, Siri, Cortana or Google Now) is simply an expression of how these “knowledge machines” (“the ability of computers to think and reason like the human mind”, Jin and Wang, 2013) are acquiring growing relevance in daily life.

Up till now the most valuable resource in any organization was its human resources. In the context of a “knowledge society” it has been repeatedly emphasized the importance of making a strategic use of people’s knowledge. Likewise, from the lifelong learning perspective the ongoing professional learning allows organization to keep up to date with accelerated change in society. During the last decades universities have benefited with the explosive demand for higher education (the interest that MOOCs have gained are a simply an example of this growing demand for up-skilling, WEF, 2014).

However, the recent development of artificial intelligence, enhanced by large volumes of data (Big Data) generated from endless information sources and the growing data processing platforms can disrupt the “knowledge society” landscape. The fast development of “knowledge machines” might threaten the traditional understanding of a knowledge-based workforce. It is not sci-fi anymore, recent studies warn the impact that this “knowledge machine” will have on the workforce (Frey and Osborne, 2013).

Although still in its infancy, these new “knowledge machines” are developing remarkable capacities (such as voice, text, audio and image recognition). It is still early to know if jobs will be replaced, but it is increasingly expected that these new “knowledge machines” could generate shifts in the demand of workforce.,As Kelly (2016) suggests it is expected an exponential growth of these artificial intelligence technologies with dramatic drop in its price, where “knowledge machines” will become a commodity like electricity.

In a not too distant future, higher educations institutions will have to deal with these new challenges not only from a technological point of view but also from educational, social, economic and ethical perspectives.

Further questions: What means to know in a context in which machines are developing learning capabilities? What new forms of “knowledge currencies” will be valued in the future? What roles will play higher education institutions in the era of knowledge machines? How to address the ethical implication of these new scenarios?


You can find more information in the book: “Impending Innovation” (summary in English but the whole book in Spanish).

Reading List:

  • Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. OUP Oxford. Frey, C. B. y Osborne, M. A. (2013). The future of employment: how susceptible are jobs to computerisation (p. 2013). Ox: Oxford Martin School, University of Oxford. http://
  • Jin, S., & Wang, B. (2013). A lexical trunk approach to the teaching of English for science and technology reading. Theory and Practice in Language Studies,3(7), 1221.
  • Kelly, K. (2016). The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future.
  • World Economic Forum. (2014). Education and skills 2.0: new targets and innovative approaches. World Economic Forum, Geneva, Switzerland.
  • World Economic Forum. (2016). Digital Transformation of Industries Demystifying Digital and Securing $100 Trillion for Society and Industry by 2025. World Economic Forum, Geneva, Switzerland.