A true artificial intelligent system is one that can learn on its own

Artificial General Intelligence (AGI) or True Artificial Intelligence.


A true artificial-intelligent system is one that can learn on its own. 

We're talking about neural networks from the likes of Google's DeepMind, which can make connections and reach meanings without relying on pre-defined behavioral algorithms. 

True Artificial Intelligence, can improve on past iterations, getting smarter and more aware, allowing it to enhance its capabilities and its knowledge.

Reinforcement learning is expected to help develop True Artificial Intelligence.
Example,  A baby tries to move its hands and feet, at some time it tries  to turn to its side, 
once it is able to turn to the side on its own, it attempts to crawl - these are natural actions and not based on previous learning supervised by parents or  dataset.
Crawling  baby attempts to stand up one day. It might not succeed initially and fall down. But the baby learns from the experience and continues in its attempt to stand on its own. The first baby steps in attempt to stand or walk may be assisted by parents. But from attempt to crawl to walk, it is experience that is reinforcing the learning of the baby.  
Once the baby is able to walk easily, it tries to run, jump and so on. All the while it is conscious about need to maintain balance and wrong steps can make it fall down.
The synergy between cognitive abilities of the baby, its learning and experiences help the baby in this journey.  

The general purpose Artificial platform would need all three deductive, inductive, and abductive reasoning.

Deductive reasoning starts out with a general statement or hypothesis and examines the possibilities to reach a specific, logical conclusion. The scientific method uses deduction to test hypotheses and theories. In deductive inference, we hold a theory and based on it we make a prediction of its consequences. That is, we predict what the observations should be if the theory were correct.  We go from the general — the theory — to the specific — the observations.

Inductive reasoning is the opposite of deductive reasoning. Inductive reasoning makes broad generalizations from specific observations. In inductive inference, we go from the specific to the general. We make many observations, discern a pattern, make a generalization, and infer an explanation or a theory.

Abductive reasoning usually starts with an incomplete set of observations and proceeds to the likeliest possible explanation for the group of observations. It is based on making and testing hypotheses using the best information available. It often entails making an educated guess after observing a phenomenon for which there is no clear explanation.

It is abductive reasoning that helps us jump from one context to another and is the most often missed tool in the AI arsenal.  You might say that the abductive reasoning capability we need would start with some sort of hypothetical scenario generator.  Those hypotheticals would then be winnowed down to the best estimate or guess.

Development of Commercially available Artificial General Intelligence may take several years to be completed. Till then its  advances in Artificial Narrow Intelligence that's available.