Collaboration Design Sharing economy Social Software

Augmenting human intelligence with artificial intelligence. Part 2: Lifelong learning

I decided to write a couple of draft/work in progress texts under the title “Augmenting human intelligence with artificial intelligence”. I am publishing the texts in several parts. The first part was called metamedia. This piece is about lifelong learning (organizations and other stuff). 

Part 2: Lifelong learning (organisations and other stuff)

Woman worker in the Douglas Aircraft Company plant 1942. Photo by Alfred T. Palmer. Photo restoration by Mbz1 [Public domain], via Wikimedia Commons.

There seems to be a commonly shared understanding that the world of work is ongoing a dramatic change. Although the reported fears of losing jobs because of automatization and move from permanent jobs to freelance and gig economy have not happened so far (in Finland) there are some reliable studies predicting that about 50% of the current jobs will be lost. For instance, if you think the 600 000 employees of Amazon, it is reasonable to assume that many of their current human-made tasks related to logistics will be automated. 

I am not fully convinced about the Phenomenon of Bullshit Jobs, but the point that we should aim to free people to do more things that are “meaningful contribution to the world” should be central when we develop automatization and artificial intelligence (AI). Simply, we should get rid of all painful, hard, harmful and dangerous jobs. We should let machines to do them and let people focus on meaningful things. The future with AI and the automatization of work, however, brings two major challenges.

Firstly, we should understand what kind of work is meaningful and should be primarily done by people. Secondly, related to the first challenge, we should broaden our industrial management mindset, where the aim is to have well-defined processes of production that are manageable with indicators and (simple) quantification.

Meaningful work is work that benefits other people, communities, societies, and the environment. The list of work that should be left primary for people — whose intelligence should be augmented with AI  — is long: from caretaking professionals to highly skilled professionals and from R&D personnel and artists to teachers. All these jobs will also benefit from AI, but are not, or should I say should not be replaced, by AI or automatization. Neither should they be squeezed to models of industrial management.

For this kind of work, we need management and organizations that are very different from those designed for the industrial era. We may call them Soulful Organizations with self-management and independence of small teams facilitated by information technology. The soulful organizations rely on common visions, mission, and values instead of strategy documents. They aim to minimize bureaucracy and focus on outcomes instead of production. They are networks, and networks of networks, that efficiently share information. Crucial in this type of organizations are the digital tools to facilitate, not to manage, the operations. As said already, in these jobs the AI’s role is to assist the professionals to do their job better.

In the new kind of meaningful work learning is not something separated from the work. Continuous deep learning that results as higher quality must be integrated into the work. Rethinking the original idea of lifelong learning can be here very useful. Lifelong learning is not about productivity or employability. Lifelong learning is an attitude and a set of skills needed for leading one’s own personal development. Lifelong learning is to make life meaningful. When your work is meaningful, getting better in it is motivating and rewarding by itself.

During the industrial era, learning was divided into formal-, non-formal and informal learning. When more and more of our activities are mediated by metamedia, this categorization is becoming obsolete. Still 20 years ago, it was common that people got a degree from a school (formal learning) and then got a job where they then once in a while attended some training programs (non-formal learning). Informal learning was a matter of reading newspapers and books, listen to a radio, watching television, participating in civil society activities and having a chat with your family and friends in social events. The socio-cultural and education background strongly defined your informal learning activities. The cycle was simple: (1) a lot of economic, social, cultural and educational capital >>> (2) more informal learning >>> (3) more economic, social and cultural capital.

The above cycle still works. With the AI, automatization of meaningless work and meaningful work done by people we must take care that all will have access to the positive circle to increase their economic, social, cultural and educational capital. The formal education, the school learning, play here an important role.

Schools, in all levels from primary to universities, should aim to increase pupils’ cultural and educational capital and prepare them with skills and attitudes needed to be a great informal learners that will build soulful organizations doing meaningful work. Graduates from formal education should be curious, explorative, critical, self-organizing and self-regulating, understand how researchers work and able to do research themselves. Learning a bit of “systemized common sense” will be useful, too.

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