Natural and Artificial Intelligence

Nowadays, great amount of data is being created by several sources from academic , scientific, business and industrial activities. Such data intrinsically contains meaningful information allowing for developing techniques, and have scientific validity to explore the information thereof. In this connection, the aim of artificial intelligence (AI) is getting new knowledge to make decisions properly. AI has taken an important place in scientific and technology development communities, and recently develops computer-based processing devices for modern machines. Under the premise, the premise that the feedback provided by human reasoning-which is holistic, flexible and parallel-may enhance the data analysis, the need for the integration of natural and artificial intelligence has emerged. Such an integration makes the process of knowledge discovery more effective, providing the ability to easily find hidden trends and patterns belonging to the database predictive model. As well, allowing for new observations and considerations from beforehand known data by using both data analysis methods and knowledge and skills from human reasoning. In this work, we review main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this emergent field. As well, key aspects to conceptually compare them are provided.

Within the context of this book, entitled “Sustaining Resources for Tomorrow”, it can quickly come to mind how and why we got here now: why after millennia of human history it is so necessary to talk about “Green Energy and Technology”, within which this writing is framed. Actually, to raise these issues today would seem rhetorical, because at this point of the twenty-first century everyone knows how and why we have reached this point. Even in the most disadvantaged or remote places one can, in one way or another, get access to a television, a mobile phone, the Internet. The fact that we continue to feel the need and urgency to address these issues makes us realize how little resolved are still at the global level. Few are those who act 100% in consequence of such a situation. And we can find so many contradictions in our daily life…

The present paper follows the neo-Kantian approach defined by Ferreira (2007, 2011) that assumes semiosis as the dynamo of cognition and views intelligence, in its multiple forms, as an attribute of autonomous entities, i.e., of all living beings. Here the author claims that this perspective is indispensable to design artificial intelligent systems that are context sensitive, that respect the values of the environment/society they are embedded in and that can produce the outcomes they were conceived for.

Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been wellinvestigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.

It is predicted that the number of IoT devices will reach 125 billion by 2030. The collisions among IoT devices will increase as the number of devices grows, which will reduce the communication performance of the IoT systems. To avoid collisions among high-density IoT devices, some researchers studied the solutions to adapt communication parameters of devices including channels to the environment. However, most of them are difficult to be applied to IoT devices with limited memory and computational ability. To solve the disadvantages of the existing work, we propose a lightweight decentralized reinforcement learning-based channel selection approach for high-density IoT systems in this paper. In our proposed approach, IoT devices can select appropriate channels only based on Acknowledge (ACK) information to avoid collisions among IoT devices with low computational complexity and memory requirement. Performance in terms of frame success rate (FSR) is evaluated by experiments using LoRa devices in the real world. Experimental results show that our proposed approach can efficiently avoid collisions in high-density LoRaWAN both with and without varying available channels compared to other lightweight reinforcement learning-based channel selection approaches.

A lot of sources are available to every side of human environment. These sources are genuinely provided by
nature to us. The utilization of these available sources again and again results in “re-source” for us. The
interaction among multiple resources increases the level of availability for maximum possible duration to
the environment. The involvement of both (natural & artificial) intelligence create interaction environment
which works every time and everywhere. The quality work done bounded with time for impactful effect is
known as “smart work”. The theme and framework are required for creating an environment for smart
work. In this article we propose a theorem for theme declaration which is based on suggested lemma. The
theme declaration is measured in probabilistic terms. To perform work in smart fashion we require the
appropriate proportion of both intelligence (natural and human) and their interaction. We have also cited
three varied examples discussing the relevance of intelligent interaction for smart work.