Intelligent offsprings ?

We reflect ourselves on the anthromorphized intelligent systems or how different will it be, the scenarios, when they try to reciprocate/embody our cognitive, psychological norms. I also strongly believe no matter how much we try to create an interface between them and us ,keeping the higher weight on human agency. There is

A probability of intelligence explosion. I often think should it be a human centered intelligence/design or machine centered intelligence/design? I will stress on these thoughts later.

Machine centred design.

Machine centred Intelligence.

emulated offsprings

Lately I have been thinking,how human babies are born very dependent on their parents. They undergo huge brain development, growth and neuron pruning in the first two years of life. The brain development of infants (as well as their social, emotional and cognitive development) depends on a loving bond or attachment relationship with a primary caregiver, usually a parent. The evidence on the powerful role of loving nurture in the emotional, social and cognitive development of children is powerful. Parenting is therefore more important than we could ever have imagined. The emotional and sentimental arousal of owning ones offspring, is it going to be different from owning an emulated belonging ? How does and how will it make a difference ?

The ability to decide, make decisions for someone, specially their own offspring is a unique feeling. With time as the offspring grows and begins developing tits own cognition towards an autonomous decision making sentient. By that time the parents also adapt to their offsprings improvisations. In humans, the ability to adapt to the improvisations of their offsprings are naturally present , or not ? Are the machines ? Or emulations going to have similar adaption when their children improvise ? Or the attachment or giving up

On such decision making privileges will bring dissent among them ?

references :

“In our approach..., a controller neural net can propose a ‘child’ model architecture, which can then be trained and evaluated for quality on a particular task,”

“That feedback is then used to inform the controller how to improve its proposals for the next round. We repeat this process thousands of times — generating new architectures, testing them, and giving that feedback to the controller to learn from.”

In November, the AutoML plans were used to create NASNet, a “child” AI designed for object detection, which outperformed state-of-the-art machine-learning architectures built for academic competitions by humans.