Since the Enlightenment, we've been making our own decisions. T his summer, I found myself battling through traffic in the sweltering streets of Marseille. At a crossing, my friend in the passenger seat told me to turn right toward a spot known for its fish soup. But the navigation app Waze instructed us to go straight. Tired, and with the Renault feeling like a sauna on wheels, I followed Waze's advice.

Kant's life was famously dull, but he was less of a hermit than is often supposed.

Data-driven approaches to philosophy have emerged as a valuable tool for studying the history of the discipline. However, most studies in this area have focused on a limited number of journals from specific regions and subfields. We expand the scope of this research by applying dynamic topic modelling techniques to explore the history of philosophy in Colombia and Latin America. Our study examines the Colombian philosophy journal Ideas y Valores, founded in 1951 and currently one of the most influential academic philosophy journals in the region. By analyzing the evolution of topics across the journal's history, we identify various trends and specific dynamics in philosophical discourse within the Colombian and Latin American context. Our findings reveal that the most prominent topics are value theory (including ethics, political philosophy, and aesthetics), epistemology, and the philosophy of science. We also trace the evolution of articles focusing on the historical and interpretive aspects of philosophical texts, and we note a notable emphasis on German philosophers such as Kant, Husserl, and Hegel on various topics throughout the journal's lifetime. Additionally, we investigate whether articles with a historical focus have decreased over time due to editorial pressures. Our analysis suggests no significant decline in such articles. Finally, we propose ideas for extending this research to other Latin American journals and suggest improvements for natural language processing workflows in non-English languages.

Aesthetics is, according to Alexander Gottlieb Baumgarten (1714 - 1762), the science of beauty (Baumgarten, 1750). Putting it more scientifically, aesthetics is the science of critical judgement of the beautiful (Nake, 1974). Therefore, aesthetics is also the science of critical judgement of the ugly as the opposite of the beautiful (Stangneth, 2019). In these latter senses, aesthetics is part of aesthesiology as the science of perception in general, and of the reception of art in particular (Consoli, 2020; Tedesco, 2024), thus overlapping with psychology and the human cognitive neurosciences (Fechner, 1876; Frascaroli et al, 2024; Pearce et al, 2016). For the judgment of an object or event as being beautiful, abstract features, such as regularity, symmetry or orderliness are often invoked (Weyl, 1980).

Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principle of human judgment. The technology of artificial intelligence (AI), based on functionalism, claims to simulate or replicate human judgment. To assess this claim, it is necessary to study whether AI judgment possesses the characteristics of human judgment. This paper argues that AI judgments exhibit a form that cannot be understood in terms of the characteristics of human judgments according to Kant. Because the characteristics of judgment overlap, we can call this AI's uncertainty. Then, I show that concepts without physical intuitions are not easy to explain when their functions are shown through vision. Finally, I illustrate that even if AI makes sentences through subject and predicate in natural language, which are components of judgment, it is difficult to determine whether AI understands the concepts to the level humans can accept. This shows that it is questionable whether the explanation through natural language is reliable.

Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite these efforts, a unified epistemological foundation is still conspicuously absent. Drawing inspiration from Kant's a priori philosophy, we propose the UPAR prompting framework, designed to emulate the structure of human cognition within LLMs. The UPAR framework is delineated into four phases: "Understand", "Plan", "Act", and "Reflect", enabling the extraction of structured information from complex contexts, prior planning of solutions, execution according to plan, and self-reflection. This structure significantly augments the explainability and accuracy of LLM inference, producing a human-understandable and inspectable inferential trajectory. Furthermore, our work offers an epistemological foundation for existing prompting techniques, allowing for a possible systematic integration of these methods. With GPT-4, our approach elevates the accuracy from COT baseline of 22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in the causal judgment task. Without using few-shot examples or external tools, UPAR significantly outperforms existing prompting methods on SCIBENCH, a challenging dataset containing collegiate-level mathematics, chemistry, and physics scientific problems.

Deontological ethics, specifically understood through Immanuel Kant, provides a moral framework that emphasizes the importance of duties and principles, rather than the consequences of action. Understanding that despite the prominence of deontology, it is currently an overlooked approach in fairness metrics, this paper explores the compatibility of a Kantian deontological framework in fairness metrics, part of the AI alignment field. We revisit Kant's critique of utilitarianism, which is the primary approach in AI fairness metrics and argue that fairness principles should align with the Kantian deontological framework. By integrating Kantian ethics into AI alignment, we not only bring in a widely-accepted prominent moral theory but also strive for a more morally grounded AI landscape that better balances outcomes and procedures in pursuit of fairness and justice.

The philosophical world is busy making plans to mark the 300th birthday next year of the German philosopher Immanuel Kant. Non-philosophers might be forgiven for wondering why they should care about the opinions of a man who lived before the onset of cars, computers, and climate change. But arguably the most important thinker of European modernity had insights that can still illuminate some of our most vexing problems. Take the wide-spread concerns about AI that have emerged full force with the development of generative language models like ChatGPT-4. Kant's understanding of the nature of human intelligence can help us work out what, if anything, we have to fear in the face of machines that write, reason, and create exponentially faster than we can. Specifically, Kant's philosophy tells us that our anxiety about machines making decisions for themselves rather than following the instructions of their creators is misplaced.

Last Saturday night, a young woman out on the town in Brisbane saw a dog-shaped robot trotting towards her and did what many of us might have felt an urge to do: she gave it a solid kick in the head. After all, who hasn't thought about lashing out at "intelligent" technologies that frustrate us as often as they serve us? Even if one disapproves of the young woman's action (or sympathises with Stampy the "bionic quadruped", a model also reportedly used by the Russian military), her impulse was quintessentially human. As artificial intelligence and robotics are increasingly deployed to spy on and police us, it may even be a sign of healthy democracy that we're suspicious of and occasionally hostile towards robots in our shared spaces. Nevertheless, many people have the intuition that "violence" towards robots is wrong. However, as my research has shown, the ethics of kicking a robot dog are more complicated than might be expected.

I've been playing around with ChatGPT for a few days. It's the new artificial intelligence product, released 10 days ago by OpenAI, that answers questions and has taken the tech world by storm (you can find it here and it's free to use, at least for now). My interest was piqued by this tweet from a senior research engineer at Microsoft, Shital Shah: "ChatGPT was dropped on us a bit over 24 hours. It's like you wake up to the news of first nuclear explosion and you don't know what to think about it but you know the world will never be the same again." Someone tweeted "Google is dead #ChatGPT", and someone else wrote: "ChatGPT writes and thinks much better than the average college student IMO -- it def undermines the purpose of the assignment."