To carry out this idea, he proposed to make a child's brain instead of trying to make an adult brain, and thus educate it to obtain the brain of an adult. In this way, he divides the problem into two parts: the 'child' program and the education process. He explains that we should not expect the first 'child' machine to come out on the first attempt and that we should teach it to see how its learning evolves. Thus, after several attempts, they would be getting better machines (or even worse), something that Turing compared with the process of evolution and that several researchers would later develop under the name of genetic algorithms [3].

-Smart Objects and the Internet of Things are two ideas which describe the future, walk together, and complement each other. Thus, the interconnection among objects can make them more intelligent or expand their intelligence to unsuspected limits. This could be achieved with a new network that interconnects each object around the world. However, to achieve this goal, the objects need a network that supports heterogeneous and ubiquitous objects, a network where exists more traffic among objects than among humans, but supporting for both types. For these reasons, both concepts are very close. Cities, houses, cars, machines, or any another object that can sense, respond, work, or make easier the lives of their owner. This is a part of the future, an immediate future. Notwithstanding, first of all, there are to resolve a series of problems. The most important problem is the heterogeneity of objects. This article is going to show a theoretical frame and the related work about Smart Object. The article will explain what are Smart Objects, doing emphasis in their difference with NotSmart Objects. After, we will present one of the different object classification system, in our opinion, the most complete.

-Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.

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