Artificial Stupidity

Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making.
This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.

To hold developers responsible, it is important to establish the concept of AI ownership. In this paper we review different obstacles to ownership claims over advanced intelligent systems, including unexplainability, unpredictability, uncontrollability, self-modification, AI-rights, ease of theft when it comes to AI models and code obfuscation. We conclude that it is difficult if not impossible to establish ownership claims over AI models beyond a reasonable doubt.

Artificial Intelligence (AI) achieved super-human performance in a broad variety of domains. We say that an AI is made Artificially Stupid on a task when some limitations are deliberately introduced to match a human's ability to do the task. An Artificial General Intelligence (AGI) can be made safer by limiting its computing power and memory, or by introducing Artificial Stupidity on certain tasks. We survey human intellectual limits and give recommendations for which limits to implement in order to build a safe AGI.

The complexity of dynamics in AI techniques is already approaching that of complex adaptive systems, thus curtailing the feasibility of formal controllability and reachability analysis in the context of AI safety. It follows that the envisioned instances of Artificial General Intelligence (AGI) will also suffer from challenges of complexity. To tackle such issues, we propose the modeling of deleterious behaviors in AI and AGI as psychological disorders, thereby enabling the employment of psychopathological approaches to analysis and control of misbehaviors. Accordingly, we present a discussion on the feasibility of the psychopathological approaches to AI safety, and propose general directions for research on modeling, diagnosis, and treatment of psychological disorders in AGI.

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two dispa...

"Callum Cant is a PhD student attending University of West London, and a gifted wordsmith. He writes concisely, sardonically, and with admirable clarity about how and why toxic class warfare and on-the-job worker dehumanization by bosses continue to persist into the early decades of the 21st century, enabled by the “new” algorithmic-managed industrial platform economy. His book relates how, in mid-September of 2016, he contrived a plan whereby he could work while a student to earn a living wage and, meanwhile, advance his personal education goals (in particular those pertaining to the successfully pursuit of original substance for his dissertation research).

Callum’s book relates intimately and precisely how he became a bicycle food-delivery courier in the employ of corporate Deliveroo, in Brighton, England. At the time of his hire, Deliveroo was a budding “platform company” aspiring to become a “unicorn” (a fantastically profitable beast-of-a-platform) in a highly competitive and rapidly evolving economic supply niche driven by high consumer demand.

Deliveroo’s proselytizing to entice entry-level bicycle couriers offered to provide its specialized precarious workforce (comprised in large part of poor, struggling, mostly male, college students) freedom and dignity in exchange for their paycheck. The courier work promised newbies that they could be “independent contractors” in this new platform economy. However, technically and legally, they would not be Deliveroo “employees.” And therein lies the rub."

Keywords: gig economy; anarcho-syndicalism; organized labor movements; food-delivery services; 'heroic workerism''; boss-managed platform economy; The  Precriate  ...

This work dealing with philosophy, non-philosophy and artificial intelligence (but also stupidity and various powers and weakness, alienations and servitudes, etc.) started in 2006 has remained in abeyance since and perhaps deserves to be repeated.
I'm sharing it for possible collaboration.

Este artigo apresenta o projeto experimental em arte e tecnologia Burrice Artificial. Com base no cenário pandêmico que se instaura globalmente, em 2020, pela Covid-19, o projeto busca explorar outras formas de entendimento da situação, levando em consideração as grandes mediadoras do período: as tecnologias digitais. Através do som, o trabalho faz uma reflexão sobre os protestos realizados em São Paulo durante o período de abril a junho de 2020, que ficaram conhecidos como janelaços. Os áudios dos protestos foram gravados e transcritos automaticamente em texto por um sistema de Inteligência Artificial, nativo de muitos modelos de smartphones, o que acaba por revelar que tipo de leitura e decodificação esses aparelhos fazem do cenário socialmente conturbado do período.