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Abstract
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Introduction
Discussion (The Need for Hybrid Approach)
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Artificial Intelligence: What it Was, and What it Should Be?

Profile image of Hala HameedHala Hameed

International Journal of Advanced Computer Science and Applications

https://doi.org/10.14569/IJACSA.2020.0110609
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7 pages

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Abstract

Artificial Intelligence was embraced as an idea of simulating unique abilities of humans, such as thinking, selfimprovement, and expressing their feelings using different languages. The idea of "Programs with Common Sense" was the main and central goal of Classical AI; it was, mainly built around an internal, updatable cognitive model of the world. But, now almost all the proposed models and approaches lacked reasoning and cognitive models and have been transferred to be more data driven. In this paper, different approaches and techniques of AI are reviewed, specifying how these approaches strayed from the main goal of Classical AI, and emphasizing how to return to its main objective. Additionally, most of the terms and concepts used in this field such as Machine Learning, Neural Networks and Deep Learning are highlighted. Moreover, the relations among these terms are determined, trying to remove mysterious and ambiguities around them. The transition from the Classical AI to Neuro-Symbolic AI and the need for new Cognitive-based models are also explained and discussed.

Figures (5)
Fig. 2. Algorithms used by Machine Learning and Rule-Based AI. Fig. |. The Big Picture of the Current AI. Machine learning is a set of AI techniques that study how machines can learn from a dataset and perform new predictions based on that prior learning. Deep Learning, Artificial Neural Networks (ANN), or sometimes called Connectionist AI (duo to its structure as connections), includes algorithms that
Fig. 2. Algorithms used by Machine Learning and Rule-Based AI. Fig. |. The Big Picture of the Current AI. Machine learning is a set of AI techniques that study how machines can learn from a dataset and perform new predictions based on that prior learning. Deep Learning, Artificial Neural Networks (ANN), or sometimes called Connectionist AI (duo to its structure as connections), includes algorithms that
simulate the mental functions to detect patterns, and classify information. Current DL techniques include Supervised, Unsupervised, Semi-supervised, and Active Learning with different algorithms. On the other hand, Rule-Based AI is a synonym for Symbolic-AI which is a traditional way of representing the problem by applying specified rules to an input, and accordingly, the output is governed by those provided rules. In “Fig. 2”, different algorithms for these categories are specified and listed.
simulate the mental functions to detect patterns, and classify information. Current DL techniques include Supervised, Unsupervised, Semi-supervised, and Active Learning with different algorithms. On the other hand, Rule-Based AI is a synonym for Symbolic-AI which is a traditional way of representing the problem by applying specified rules to an input, and accordingly, the output is governed by those provided rules. In “Fig. 2”, different algorithms for these categories are specified and listed.
Wilson and Keil in 1999 [9] presented another definition known as The Cognitive Modeling definition, which is based on the dimensions of measuring how the computer can think and act humanly, in contrast with think and act rationally, their classification is shown in “Fig. 3”. Fig. 3. Cognitive Modeling Definition of AI.
Wilson and Keil in 1999 [9] presented another definition known as The Cognitive Modeling definition, which is based on the dimensions of measuring how the computer can think and act humanly, in contrast with think and act rationally, their classification is shown in “Fig. 3”. Fig. 3. Cognitive Modeling Definition of AI.
Fig. 4. Illustration of Bongard Problem. objects, any AI model will be able to learn it, however, there is no way to give each conceivable labeled example of the problem to the model [27]. This leads to needs for using cognitive and consciousness exploration-based models.
Fig. 4. Illustration of Bongard Problem. objects, any AI model will be able to learn it, however, there is no way to give each conceivable labeled example of the problem to the model [27]. This leads to needs for using cognitive and consciousness exploration-based models.
Central work of Neuro-Symbolic models is shown in [37 which analyzed the mappings between symbolic frameworks and neural systems, and indicated significant cutoff points on the sorts of information that could be represented in ANN, and showed the incentive in developing hybrid systems. Battaglia has produced a number of interesting papers on physical reasoning with systems that integrate symbolic graphs and deep learning [38]. A lot of similar work, such as [39] [40], 41] have been done to use ANN to give the answers from the messiness of the real world by learning. Then the symbolic part, forming internal symbolic representations, and create explainable rules to formalize the way that captures everyday knowledge, as shown in Fig. 5 [for clear resolution of the figure, refer to the last page]. Fig. 5. The Transitions Affected the Evolution of AI. In the history of AI, one of the largest efforts to create common-sense knowledge in a machine-interpretable form launched in 1984 by Doug Lenat, known as the CYC Project [42]. The main idea of the project was, to build a massive knowledge base containing static facts and heuristics, besides the cognitive and reasoning models needed to create what could be called common sense reasoning. According to Lenat; to simulate human thinking, CYC's team expected to code millions of facts crossing all different areas of human experience including science, society and culture, atmosphere and climate, cash and money, medicinal services, history, and other governmental issues. It was estimated that the CYC project requires a huge number (maybe thousands) of individuals to catch facts about brain science, governmental issues, financial aspects, science, and many, numerous different areas, all in logical structures. Simple declarative semantics models are used in knowledge representation, incorporating conjunctions, disjunctions, quantifiers, equality, and inequality operators. The CYC project has been depicted as "one of the most criticized projects of Artificial Intelligence". Machine learning researcher Pedro Domingos described the project as a "catastrophic failure" for several reasons, including the ceaseless amount of data required to produce any viable outcomes and the inability of evolving its own.
Central work of Neuro-Symbolic models is shown in [37 which analyzed the mappings between symbolic frameworks and neural systems, and indicated significant cutoff points on the sorts of information that could be represented in ANN, and showed the incentive in developing hybrid systems. Battaglia has produced a number of interesting papers on physical reasoning with systems that integrate symbolic graphs and deep learning [38]. A lot of similar work, such as [39] [40], 41] have been done to use ANN to give the answers from the messiness of the real world by learning. Then the symbolic part, forming internal symbolic representations, and create explainable rules to formalize the way that captures everyday knowledge, as shown in Fig. 5 [for clear resolution of the figure, refer to the last page]. Fig. 5. The Transitions Affected the Evolution of AI. In the history of AI, one of the largest efforts to create common-sense knowledge in a machine-interpretable form launched in 1984 by Doug Lenat, known as the CYC Project [42]. The main idea of the project was, to build a massive knowledge base containing static facts and heuristics, besides the cognitive and reasoning models needed to create what could be called common sense reasoning. According to Lenat; to simulate human thinking, CYC's team expected to code millions of facts crossing all different areas of human experience including science, society and culture, atmosphere and climate, cash and money, medicinal services, history, and other governmental issues. It was estimated that the CYC project requires a huge number (maybe thousands) of individuals to catch facts about brain science, governmental issues, financial aspects, science, and many, numerous different areas, all in logical structures. Simple declarative semantics models are used in knowledge representation, incorporating conjunctions, disjunctions, quantifiers, equality, and inequality operators. The CYC project has been depicted as "one of the most criticized projects of Artificial Intelligence". Machine learning researcher Pedro Domingos described the project as a "catastrophic failure" for several reasons, including the ceaseless amount of data required to produce any viable outcomes and the inability of evolving its own.

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