Segments has become a guide to a weekly practice of engaging with social mediums while learning & sharing to nourish neuroplasticity. In efforts to keep my socials intentional, I've decided to add more structure and enjoyment to my corner. Four days of the week are dedicated to sharing in the context of the four segments introduced in my Segments: Grow Learn Build Play article. Grow Learn Build Play Build is a framework comprised of metrics to guide multi-applicable 4 week builds. It's a systematic way to integrate and implement while firing those neural networks.

One way to increase the intelligence of a robot might be to train it with a series of missions, analogous to the missions or levels in a video game. In a developmental robot, the training would not be simply learning--its "brain" structure would actually change. Biological development shows some extremes that a robot could go through, like starting with a small seed that constructs itself, or creating too many neural connections and then in a later phase deleting a whole bunch of them. As another example of development vs. learning, a simple artificial neural network is trained when the weights have been changed after a series of training inputs (and error correction if it is supervised). It would be like growing completely new nodes, network layers, or new networks entirely during each training level. Or you can imagine the difference between decorating a skyscraper (learning) and building a skyscraper (development).

One way to increase the intelligence of a robot might be to train it with a series of missions, analogous to the missions or levels in a video game. In a developmental robot, the training would not be simply learning -- its "brain" structure would actually change. Biological development shows some extremes that a robot could go through, like starting with a small seed that constructs itself, or creating too many neural connections and then in a later phase deleting a whole bunch of them. As another example of development vs. learning, a simple artificial neural network is trained when the weights have been changed after a series of training inputs (and error correction if it is supervised). It would be like growing completely new nodes, network layers, or new networks entirely during each training level.