IEEE Future Directions
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Learning technologies have been focussing on human beings and how to improve human learning. Significant advances have been made in the last decades leveraging computer and internet power compounded with the availability of more and more flexible, and ubiquitous, devices. This evolution will continue as more understanding on learning processes in the brain becomes available and more effective technologies for gathering, communicating, rendering and personalising information are becoming affordable. Research is going on looking at the possibility of augmenting brain learning capability by tweaking with the brain, as an example through electrical stimulation of the hippocampus or elevating magnesium levels in the brain. (Research results in 2016 pointed out the fragile nature of memories in our brain and the possibility that electrical stimulation of the hippocampus may actually destroy memories, rather than improving the memory processes. A lot of caution is needed in this area).
At the same time machine learning is progressing rapidly, thanks to more processing power and more storage availability in machines, plus the possibility to leverage on the experiences of thousands of machines in the cloud.
Autonomous systems can greatly benefit from embedded learning capabilities and from learning from each other and as a community. This machine learning tends to merge into the human learning given the overlapping of several aspects, although clear difference exists (today making learning easier for humans but the balance is rapidly shifting to the machines).
Learning has, for eons, implied access to something, somebody, who own the knowledge and that was willing to share it in a way that could be “learned”. One way of sharing, of course, is to write down the knowledge in a book and have others reading the book. This goes for explicit knowledge and we can see this kind of knowledge (easily) passed on to an autonomous system by “uploading it” to its “brain” (extending its data base, its programming capabilities).
There is another kind of knowledge, implicit knowledge, like riding a bike, that cannot be coded into a book. You will never learn to ride a bike by reading a book, no matter how precisely it has been written or how many times you read it. You have to experience, fail and learn from failure.
This kind of learning is possible for autonomous systems that can be programmed to experience and improve (learn). Walking robots can learn to walk better and to walk on rough terrain by experience. Roomba learns about its environment by exploring it as it does its vacuum cleaning chores (watch clip).
