The learning curves for given architecture of Artificial Intelligence

Each particular technology that poses ability to learn will produce next learning curves. By looking at this curves we can suggest if particular architecture achieved the human-level abilities.

Let’s look closer at each part.

List of skills — all possible tests or tasks that at least one human can pass. Imagine this pile of skills: the whole variety from distinguishing geometry shapes to speech recognition, all possible games from mastering chess up to mastering Go, from making music up to new scientific discoveries.

The difference between people and machine is that human can acquire any of these tasks by choosing right curriculum and machine cannot. No need to reinvent something. The curriculum helps to acquire well-studied knowledge and abilities using the most effective methods.

Also, humans have the huge list of common abilities that help to acquire more complex and specific skills. For example, the ability to read, write and speak gives the infinite power of learning any other subject: building planes, play piano, quantum physics.

List of skills axis. From here it’s possible to assume that more common skills have to be acquired at first — to help acquire more specific fields of skills. With this principle, we can build kind of sequential list of skills — from most common to the most interesting from the science point of view. These skills were reflected on an image as human-level skills.

Time of learning axis defines time consumption for learning with given computational or other resources. The problem here is that higher level skills require more computational power. Because of it, learning time becomes huge.

There are two curves of possible learning. The first half of curve A(from point O to point N) is similar to B to some extent. With one difference — curve B may have architecture limit and in this sense are same to A or may not have it. The second option leads us to the topic of technological singularity.