The so-called digital revolution began half a century ago with the technology of semiconductors and the development of computers, first the mainframe and then the personal computers. Later on, the creation of the internet, the World Wide Web and the development of smartphones contributed to the ubiquity of the digital world in which we live.
The digital revolution is redefining the way in which we work, due to its impact on the vast majority of businesses. The automation of many activities and processes not only partly replaces human activity; it also transforms it, sometimes in a way neither intended, nor was foreseen by the developers. That is the so-called degeneration effect, by which automation tends to transform us from actors into observers. Technologies mature at a pace faster than what most of us can absorb. That creates a need to continuously re-learn, in which obsolete knowledge is to be discarded and new knowledge is to be acquired, to avoid the predicaments derived from some kind of digital illiteracy. Today’s professional not only has to constantly keep on learning, in order not to fall in the digital gap, but has to know how to permanently reinvent himself or herself, making use of technology so as to not become a casualty of its inevitable development.
Facing this digital revolution, universities’ challenge is, more than ever before, to teach how to learn. Any technique or method that may be necessary to learn today will come with an expiration date due to the speed with which new technologies are developed. More important than learning those techniques or methods is to learn how to think, because the capability of bringing true added value is what is unlikely to be automated. The current artificial intelligence generation is called machine learning for a reason: machines learn from the data they are fed with. Algorithms are created that can learn from the data they are furnished with. Therein lies the paramount importance of big data; vast amounts of information are required to train the machines. The so-called training data is required, which enable the generation of the algorithms that will make the predictions; input data, which is what generates the predictions; and feedback data, which is needed to improve the performance, based on gathered experience.
In order to learn, and especially to learn how to think, nothing is better than doing things oneself. Seek knowledge through experimentation, as prescribed by the famous motto Nullius in verba, “in the words of no one.” The key in education is project-based learning, in not taking anything for granted. Question everything!
