Fully Autonomous AI

Totschnig, Wolfhart

doi:10.1007/s11948-020-00243-z

Cited by 31 publications

(7 citation statements)

References 18 publications

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“…Likewise, there are arguments against the so-called finality argument, i.e. that "the final goal that a rational agent happens to have at the beginning of its existence will be really final" (unlike humans, who can always reconsider their final goals and possibly change them) [30]. The rationale is as follows: an agent's goal is not separate from its understanding of the world, and this understanding can change over time as the agent learns from experience; thus the agent's understanding of the given goal may change as well.…”

Section: Human-driven Ai Vs Autonomous Aimentioning

confidence: 99%

Identity of AI

Devedžić

2022

Discov Artif Intell

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With the explosion of Artificial Intelligence (AI) as an area of study and practice, it has gradually become very difficult to mark its boundaries precisely and specify what exactly it encompasses. Many other areas of study are interwoven with AI, and new research and development topics that require interdisciplinary approach frequently attract attention. In addition, several AI subfields and topics are home to long-time controversies that give rise to seemingly never-ending debates that further obfuscate the entire area of AI and make its boundaries even more indistinct. To tackle such problems in a systematic way, this paper introduces the concept of identity of AI (viewed as an area of study) and discusses its dynamics, controversies, contradictions, and opposing opinions and approaches, coming from different sources and stakeholders. The concept of identity of AI emerges as a set of characteristics that shape up the current outlook on AI from epistemological, philosophical, ethical, technological, and social perspectives.

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Section: Human-driven Ai Vs Autonomous Aimentioning

confidence: 99%

Identity of AI

Devedžić

2022

Discov Artif Intell

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“…ML is a subset of AI that has become the mainstream of AI technology (Shin et al, 2021). ML extracts and analyzes the features of input samples to classify new homogeneous samples (Totschnig, 2020). ML automatically improves and optimizes computer algorithms and programs by analyzing the data rather than relying on explicit program instructions (Nichols et al, 2019;Cho et al, 2021).…”

Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Bao

Sun

Zhan

et al. 2022

Front. Cell Dev. Biol.

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Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

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“…The second cause of utility function instability is that an AI may change its own utility function autonomously (Dennis, 2020;Totschnig, 2020). It may do so to compensate for being boundedly rational.…”

Section: Utility Function Instabilitymentioning

confidence: 99%

The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest

Naudé

2022

SSRN Journal

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Fully Autonomous AI

Cited by 31 publications

References 18 publications

Identity of AI

Identity of AI

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest

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