Might deep learning roles continue to evolve in future workplaces, so as to include individuals and not just machines? To what degree will we retain the ability to choose areas of deep learning which also hold economic and social rewards? Answers to questions such as these, are important for people from all walks of life.
One reason it helps to distinguish potential rewards factors, is that so many individuals (wherever possible) choose deep learning for purely personal rewards, even when monetary or social rewards aren't part of the equation. While individuals have pursued deep learning through books for centuries, the digital realm now offers far more extensive possibilities.
Nevertheless, there's a paradox in this recent digital bounty. How do we fully appreciate these added learning possibilities, given the fact there are somewhat limited means to make deep learning count for society as a whole? Given these circumstance, digital learning possibilities don't seem as advantageous as would otherwise be the case. While the internet enables personal learning challenges; by the same token, it's no simple matter to share in personal knowledge quests or applications with others, without the requisite degrees. Also, some who are naturally inclined to pursue informal learning for personal rewards, may struggle to continue these quests during life periods when one's basic ability to survive is being tested.
Consequently, individuals may not be able to sustain either informal or formal deep learning, especially when they choose intellectual paths where economic rewards are not likely. Yet no longer is this merely a problem for "impractical" deep learning, as more pragmatic and hands on forms of deep learning will increasingly face the inroads of workplace automation, in the decades ahead. We have been caught in too many debates about the relative lack of economic value for many disciplines, when in reality, automation continues to erode the aggregate economic value of our supposedly most pragmatic fields of study.
Hence one of the main challenges of our time, is to create new knowledge use platforms which can better integrate both deep learning and learning specific formats into our social networks - especially since automation will take advantage of both. Since automation will do some of the heavy lifting for us, it will become easier to set aside time in a day for interaction which is low skill but holds other personal meaning. Even though full monetary rewards aren't possible for these forms of knowledge networks, we can still do a better job of securing survival means, so that individuals might continue pursuing deep learning, who do so for the emotional and intellectual rewards of the challenge.
What about deep learning in knowledge use systems? Deep learning would be compensated in small increments, as time arbitrage smoothes the human capital investment costs of deep learning through peer to peer assistance. Plus, deep learning is not always a necessity to participate in time based service interaction, since complicated but learning specific material can be divided into components which simultaneously generate more consumption access - much as earlier divisions of factory labour meant greater access to an expanded tradable sector marketplace.
Even though technology will likely negate some of the monetary compensation for deep learning, we can respond by exploring how we most want deep learning to contribute to our interactions with others. When we replace the concept of labour with the concept of time value, we have a more rational response to the roles we might assume alongside technology. Time value as a commodity unit, serves as a vessel in which experiential and pragmatic knowledge use can take place.
Best, by placing the economic value on the reality of our mutual time preferences, we give ourselves a break and remove the necessity of determining the level of skill that is supposedly "necessary" for each reciprocal interaction. These expectations for specific skills levels have proven problematic, for they have assumed the provider has all the pertinent knowledge input (both in education and healthcare) while the recipient has none. The reality has often been quite different.
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