A critique of contemporary artificial intelligence art: Who is Edmond de Belamy?

Edmond de Belamy is a 2018 painting made by french collective Obvious, created using a type of Artificial Intelligence algorithms called Generative Adversarial Networks, which was sold at Christie's auction house in New York for $432,500. This historic event -the so-called auction of the "first artwork made by an AI" raises 3 interesting questions about authorship, originality, and the arts as a space for scientific inquiry. While some think that the current deployment of Machine Learning algorithms and Artificial Intelligence techniques that we are seeing in the art world today may be seen as the ultimate "Gesamtkunstwerk" or total artwork, other points of view express that not only we need this type of cultural artifacts as a critique of industrialized use of Artificial Intelligence, but also a strict criteria has to be delimited in order to review contemporary art made with Machine Learning techniques

A novel approach for identifying a human-like self-conscious behavior

In this paper a possible extension of Turing test [1] will be presented, which is intended to overcome the limits highlighted by several researchers and scientists in the last seventy years. The main problem related to the execution in Turing test is substantially dealing with the trouble in identification of a human-like intelligence based on a pure evaluation of external behavior of a machine. In this work first of all a description of classical Turing test will be done. After that, some of the main exceptions or oppositions to the Turing test ability to detect “intelligent machines” will be presented. The Lovelace test will be presented as well, as possible alternative to Turing Test, and some considerations on its scope and effectiveness will be made. Furthermore, some references to Penrose and Hofstadter ideas will be recalled, highlighting the strongest troubles in defining and detecting a human-like intelligence, intended as “self-consciousness”. Finally, the new approach will be explained, introducing the new test intended to overcome the troubles highlighted on Turing test execution, based on a model of the self-consciousness obtained by means of the hypersets theory. An example will be presented as well, in order to clarify the proposed approach and its goal

Creativity and Machine Learning: a Survey

There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, machine learning techniques, including generative deep learning, and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field.Comment: 25 pages, 3 figures, 2 table

The Turing Deception

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges -- summarization, and question answering -- prompt ChatGPT to produce original content (98-99%) from a single text entry and also sequential questions originally posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. While Turing's original prose scores at least 14% below the machine-generated output, the question of whether an algorithm displays hints of Turing's truly original thoughts (the "Lovelace 2.0" test) remains unanswered and potentially unanswerable for now

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research