Artificial Intelligence A Byproduct of Natural Intelligence and Their Salient Features

This paper mainly focuses on the creation of Artificial Intelligence (AI) using natural intelligence but the question to be considered whether the natural intelligence can be created using artificial intelligence or not. The Artificial intelligence is the outcome of functionality and capabilities of human brain called neural Network. In this paper, it is presumed that the artificial intelligence is a byproduct of natural intelligence and then we discuss some relationship between both of these, especially the working of natural intelligence. Some other important questions are raised to understand a deep linkage between natural and artificial intelligence. There exists lot of non-material phenomenon created by dint of natural intelligence (not created by human) causing to produce systems run by artificial intelligence theorems and algorithms working at backend. The software based on Knowledge Based Systems (KBS) derives its power from human wisdom and natural intelligence. There are several limitations on artificial intelligence. In creation of natural intelligence there is a great role of spirituality.Humans are creator of artificial intelligence with limited abilities. Actually AI started with invention of machines. The applications of creation of natural  intelligence are vastly and abundantly known to humans of 21st Century, which are incorporated in the areas of Space Science, Anatomy, and motion ofPlants, spin of electron, Electronics, plant intelligence and Neural Science etc. The working of machines depending upon the artificial intelligence doesn't provide creativity or self-motivated innovations, within the meaning of natural intelligence

Natural Intelligence and Artificial Intelligence

peer reviewedCet article présente une approche systémique du concept d’intelligence naturelle en ayant pour objectif de créer une intelligence artificielle. Ainsi, l’intelligence naturelle, humaine et animale non-humaine, est une fonction composée de facultés permettant de connaître et de comprendre. De plus, l'intelligence naturelle reste indissociable de la structure, à savoir les organes du cerveau et du corps. La tentation est grande de doter les systèmes informatiques d’une intelligence artificielle qui devrait avoir une fonction composée des mêmes facultés que celles englobées dans l’intelligence humaine. L'intelligence artificielle doit aussi rester indissociable de la structure, à savoir les organes d'un cerveau artificiel et d'un corps artificiel. En conséquence de quoi, le robot artificiel est la structure idéale pour voir l'émergence d'une intelligence artificielle forte. Notez qu'une intelligence artificielle faible se développe sur des systèmes informatiques par un programme de simulation de l'intelligence. Dans cette communication, des mémoires neuronales programmables seront présentées et simulées.This article presents a systemic approach to the concept of natural intelligence with the aim of creating an artificial intelligence. Thus, the natural intelligence, human and nonhuman animals, is a function composed of faculties to know and understand. In addition, natural intelligence is inseparable from the structure, namely the organs of the brain and body. The temptation is to equip computer systems with artificial intelligence that should have a function composed of the same faculties as those encompassed in human intelligence. The artificial intelligence must also remain inseparable from the structure, namely the organs of an artificial brain and an artificial body. As a result, the artificial robot is the ideal structure to see the emergence of a strong artificial intelligence. Note that a weak artificial intelligence is developed on computer systems by a simulation program of intelligence. In this paper, neural programmable memories will be presented and simulate

Toward evolutionary and developmental intelligence

Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence

Take another little piece of my heart: a note on bridging cognition and emotions

Science urges philosophy to be more empirical and philosophy urges science to be more reflective. This markedly occurred along the “discovery of the artificial” (CORDESCHI 2002): in the early days of Cybernetics and Artificial Intelligence (AI) researchers aimed at making machines more cognizant while setting up a framework to better understand human intelligence. By and large, those genuine goals still hold today, whereas AI has become more concerned with specific aspects of intelligence, such as (machine) learning, reasoning, vision, and action. As a matter of fact, the field suffers from a chasm between two formerly integrated aspects. One is the engineering endeavour involving the development of tools, e.g., autonomous systems for driving cars as well as software for semantic information retrieval. The other is the philosophical debate that tries to answer questions concerning the nature of intelligence. Bridging these two levels can indeed be crucial in developing a deeper understanding of minds. An opportunity might be offered by the cogent theme of emotions. Traditionally, computer science, psychological and philosophical research have been compelled to investigate mental processes that do not involve mood, emotions and feelings, in spite of Simon’s early caveat (SIMON 1967) that a general theory of cognition must incorporate the influences of emotion. Given recent neurobiological findings and technological advances, the time is ripe to seriously weigh this promising, albeit controversial, opportunity

The current and future role of visual question answering in eXplainable artificial intelligence.

Over the last few years, we have seen how the interest of the computer science research community on eXplainable Artificial Intelligence has grown in leaps and bounds. The reason behind this rise is the use of Artificial Intelligence in many daily life tasks, and the consequent necessity of people to understand the intelligent systems' behaviour. Computer vision-related tasks are not an exception, for example, Visual Question Answering tasks. The Artificial Intelligence models that carry out this specific task make an effort to answer questions about what we can watch in a particular image. In this work, we review the existing work about eXplainable Artificial Intelligence on Visual Question Answering which is a problem on which there is still much work to be done. Moreover, we open the discussion about the challenges to overcome regarding this topic, like the future role of Visual Question Answering to address eXplainable Artificial Intelligence issues or difficulties

Quo Vadis, Artificial Intelligence?

Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles