Is artificial intelligence capable of understanding? An analysis based on philosophical hermeneutics

2021 ◽  
pp. 209660832110564
Author(s):  
Jing Wang
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Problem Solving ◽  
Human Intelligence ◽  
Philosophical Hermeneutics ◽  
Empty Shell ◽  
Deep Blue ◽  
Public Resource ◽  
Human Understanding ◽  
The Subject

From Deep Blue to AlphaGo, the rapid advance of artificial intelligence (AI) in the areas of problem solving and deep learning has lent credence to the prospect that it may one day develop an ability for understanding similar to that of humans or even surpass human intelligence. However, understanding is not a piece of knowledge, a method or an ability. Knowledge can be possessed as an impersonal and public resource. In a certain sense, it can be objectified by a group's understanding, which is characterized by certainty, whereas understanding seems to be in a state of constant transformation and movement. Moreover, a method cannot be separated from the subject and is always subsumed by understanding and interpretation. For a method to be useful, it must be the product of understanding and interpretation. Understanding is not enabled by a method; rather, it is understanding that possesses the method. Finally, understanding cannot be described and defined simply as ability. As an important manifestation of human intelligence, understanding is not an empty shell of method filled by its objects, but an appreciation and extension of the meaning of the objects. Computers are good at dealing with simple and formalized activities that are not associated with a context, but the human activities of understanding are not formalized. From the perspective of philosophical hermeneutics, understanding is filled with elements of reflection and in itself is a form of self-understanding. Furthermore, AI lacks the fore-structure of human understanding. Therefore, whether understanding can be viewed from the perspective of historicity is an important difference between human intelligence and AI, and the missing historical connection of computational programs of AI may be an important reason why it cannot acquire understanding in a real sense.

Artificial Intelligence

2019 ◽  
pp. 13-22
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

Artificial Intelligence

2021 ◽  
pp. 1-16
Author(s):  
Satvik Tripathi
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Human Mind ◽  
Human Intelligence ◽  
Future Prospects ◽  
Technology Advances

Artificial intelligence refers to the replication of human intelligence in machines that are encoded to think like humans and imitate their actions. The word may also be applied to any machine that displays qualities related to a human mind for example understanding, learning, and problem-solving. As technology advances, previous benchmarks that defined artificial intelligence become out-dated. Artificial intelligence has made its way to almost every sector and has resulted in better efficiency of the traditional processes. In this chapter, the author discusses the current applications, future prospects, and possible threats of artificial intelligence.

Abduction and Hypothesis Withdrawal in Science

1998 ◽  
pp. 180-187 ◽  
Author(s):  
Lorenzo Magnani
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Scientific Reasoning ◽  
Scientific Discovery ◽  
Data Driven ◽  
Negation As Failure ◽  
Weak Hypothesis ◽  
The Subject ◽  
Epistemological Model ◽  
Problem Solving Process

This paper introduces an epistemological model of scientific reasoning which can be described in terms of abduction, deduction and induction. The aim is to emphasize the significance of abduction in order to illustrate the problem-solving process and to propose a unified epistemological model of scientific discovery. The model first describes the different meanings of the word abduction (creative, selective, to the best explanation, visual) in order to clarify their significance for epistemology and artificial intelligence. In different theoretical changes in theoretical systems we witness different kinds of discovery processes operating. Discovery methods are "data-driven," "explanation-driven" (abductive), and "coherence-driven" (formed to overwhelm contradictions). Sometimes there is a mixture of such methods: for example, an hypothesis devoted to overcome a contradiction is found by abduction. Contradiction, far from damaging a system, help to indicate regions in which it can be changed and improved. I will also consider a kind of "weak" hypothesis that is hard to negate and the ways for making it easy. In these cases the subject can "rationally" decide to withdraw his or her hypotheses even in contexts where it is "impossible" to find "explicit" contradictions and anomalies. Here, the use of negation as failure (an interesting technique for negating hypotheses and accessing new ones suggested by artificial intelligence and cognitive scientists) is illuminating

scholarly journals Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation

2020 ◽  
Vol 10 (20) ◽  
pp. 7347
Author(s):  
Jihyo Seo ◽  
Hyejin Park ◽  
Seungyeon Choo
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Problem Solving ◽  
Architectural Design ◽  
Semantic Segmentation ◽  
Data Sets ◽  
Functional Requirements ◽  
Architectural Drawings ◽  
Design Alternatives ◽  
Automation Technology

Artificial intelligence presents an optimized alternative by performing problem-solving knowledge and problem-solving processes under specific conditions. This makes it possible to creatively examine various design alternatives under conditions that satisfy the functional requirements of the building. In this study, in order to develop architectural design automation technology using artificial intelligence, the characteristics of an architectural drawings, that is, the architectural elements and the composition of spaces expressed in the drawings, were learned, recognized, and inferred through deep learning. The biggest problem in applying deep learning in the field of architectural design is that the amount of publicly disclosed data is absolutely insufficient and that the publicly disclosed data also haves a wide variety of forms. Using the technology proposed in this study, it is possible to quickly and easily create labeling images of drawings, so it is expected that a large amount of data sets that can be used for deep learning for the automatic recommendation of architectural design or automatic 3D modeling can be obtained. This will be the basis for architectural design technology using artificial intelligence in the future, as it can propose an architectural plan that meets specific circumstances or requirements.

scholarly journals Artificial Intelligence Hits the Barrier of Meaning

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 51 ◽  
Author(s):  
Melanie Mitchell
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Systems ◽  
Human Intelligence ◽  
Interdisciplinary Science

Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.

Artificial Intelligence Surpassing Human Intelligence: Factual or Hoax

2020 ◽  
Author(s):  
Sana Khanam ◽  
Safdar Tanweer ◽  
Syed Khalid
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Research Paper ◽  
Human Intelligence ◽  
Technological Changes ◽  
The Future ◽  
The Subject ◽  
Trending Topics ◽  
Near Future

Abstract Artificial intelligence is one of the most trending topics in the field of Computer Science which aims to make machines and computers ‘smart’. There are multiple diverse technical and specialized research associated with it. Due to the accelerating rate of technological changes, artificial intelligence has taken over a lot of human jobs and is giving excellent results that are more efficient and effective, than humans. However, a lot of time there has been a concern about the following: will artificial intelligence surpass human intelligence in the near future? Are computers’ ever accelerating abilities to outpace human jobs and skills a matter of concern? The different views and myths on the subject have made it even a more than just a topic of discussion. In this research paper, we will study the existing facts and literature to understand the true definitions of artificial intelligence (AI) and human intelligence (HI) by classifying each of its types separately and analyzing the extent of their full capabilities. Later, we will discuss the possibilities if AI eventually can replace human jobs in the market. Finally, we will synthesize and summarize results and findings of why artificial intelligence cannot surpass human intelligence completely in the future.

Digging deeper on “deep” learning: A computational ecology approach

2017 ◽  
Vol 40 ◽  
Author(s):  
Massimo Buscema ◽  
Pier Luigi Sacco
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Processes ◽  
Human Intelligence ◽  
Fundamental Feature ◽  
Alternative Approach ◽  
Multiple Domains ◽  
Computational Ecology ◽  
Cross Fertilization

AbstractWe propose an alternative approach to “deep” learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform “new” artificial intelligence.

scholarly journals Does the Human Brain Resort to AI's Deep Learning in Order to Solve Problems

2020 ◽  
pp. 73-86
Author(s):  
Prof. M S S El Namaki ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Problem Solving ◽  
Human Brain ◽  
Learning Technologies ◽  
Human Brains

Problem solving is a daily occurrence in business and, also, in human brains. Businesses resort to a variety of modes in order to find an answer to these problems.Human brains adopt, also, a variety of measures to solve their own brand of problems. Artificial Intelligence technologies seem to have been extending a helping hand to business in the search for problem solving mechanisms. Machine learning and deep learning are currently recognized as prime modes for business insight and problem solving. Does the human brain possess competencies and instruments that could compare to the deep learning technologies adopted by AI?

scholarly journals The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Raffaele Nuzzi ◽  
Giacomo Boscia ◽  
Paola Marolo ◽  
Federico Ricardi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Computer Science ◽  
New Technology ◽  
Eye Diseases ◽  
Human Intelligence ◽  
Basic Principles ◽  
The Impact ◽  
And Training ◽  
Clinical Overview

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

Artificial Intelligence in Medicine

2021 ◽  
pp. 49-68
Author(s):  
Gia Merlo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
New Technology ◽  
Technological System ◽  
Medical Professionals ◽  
Human Intelligence ◽  
Connected Health ◽  
The Future

Disruptive forces are challenging the future of medicine. One of the key forces bringing change is the development of artificial intelligence (AI). AI is a technological system designed to perform tasks that are commonly associated with human intelligence and ability. Machine learning is a subset of AI, and deep learning is an aspect of machine learning. AI can be categorized as either applied or generalized. Machine learning is key to applied AI; it is dynamic and can become more accurate through processing different results. Other new technologies include blockchain, which allows for the storage of all of patients’ records to create a connected health ecosystem. Medical professionals ought to be willing to accept new technology, while also developing the skills that technology will not be able to replicate.

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