This paper aims to show that a simple framework, utilizing basic formalisms from set theory and category theory, can clarify and inform our theories of the relation between mind and matter. I've found that theories of the mind-body relationship tend to cause three core difficulties in my understanding: 1. I'm usually left with a fuzzy picture of the relationship the author is trying to illustrate. When in some cases it does appear to clarify itself, I find myself later questioning whether the image I hold is actually the one intended. This paper aims to make some progress against these issues by introducing a framework for clearly expressing the ideas behind theories of the mind-matter relationship. It consists of three parts. In order to work with these theories on equal conceptual grounds, we require a common philosophical foundation that allows the different theories to be engaged with on the same terms. To address this need, the first part of the paper outlines a conservative philosophical approach that gives us these equal terms without presupposing theoretical categories and relationships that the theories themselves deal with. In the second part, we lay down formal descriptions of several specific theories (e.g. With each system resting on equal footing, we can compare relationships between theories and pick out conceptual issues within them.

A large literature suggests that people are intuitive Dualists--they consider the mind ethereal, distinct from the body. Past research also shows that Dualism emerges, in part, via learning (e.g., Barlev & Shtulman, 2021). But whether learning is sufficient to give rise to Dualism is unknown.The evidence from human learners does address this question because humans are endowed not only with general learning capacities but also with core knowledge capacities. And recent results suggest that core knowledge begets Dualism (Berent, Theodore & Valencia, 2021; Berent, 2023). To evaluate the role of learning, here, we probe for a mind-body divide in Davinci--a large language model (LLM) that is devoid of any innate core knowledge. We show that Davinci still leans towards Dualism, and that this bias increases systematically with the learner's inductive potential. Thus, davinci (a GPT-3 model) exhibits mild Dualist tendencies, whereas its descendent, text-davinci-003 (a GPT-3.5 model), shows a full-blown bias. It selectively considers thoughts (epistemic states) as disembodied--as unlikely to show up in the body (in the brain), but not in its absence (after death). While Davinci's performance is constrained by its syntactic limitations, and it differs from humans, its Dualist bias is robust. These results demonstrate that the mind-body divide is partly learnable from experience.They also show how, as LLM's are exposed to human narratives, they induce not only human knowledge but also human biases.

The "brain in a jar" is a thought experiment of a disembodied human brain living in a jar of sustenance. The thought experiment explores human conceptions of reality, mind, and consciousness. This article will explore a metaphysical argument against artificial intelligence on the grounds that a disembodied artificial intelligence, or a "brain" without a body, is incompatible with the nature of intelligence.[1] The brain in a jar is a different inquiry than traditional questions about artificial intelligence. The brain in a jar asks whether thinking requires a thinker.

The "brain in a jar" is a thought experiment of a disembodied human brain living in a jar of sustenance. The thought experiment explores human conceptions of reality, mind, and consciousness. This article will explore a metaphysical argument against artificial intelligence on the grounds that a disembodied artificial intelligence, or a "brain" without a body, is incompatible with the nature of intelligence.[1] The brain in a jar is a different inquiry than traditional questions about artificial intelligence. The brain in a jar asks whether thinking requires a thinker.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The "brain in a jar" is a thought experiment of a disembodied human brain living in a jar of sustenance. The thought experiment explores human conceptions of reality, mind, and consciousness. This article will explore a metaphysical argument against artificial intelligence on the grounds that a disembodied artificial intelligence, or a "brain" without a body, is incompatible with the nature of intelligence. The brain in a jar is a different inquiry than traditional questions about artificial intelligence.

The "brain in a jar" is a thought experiment of a disembodied human brain living in a jar of sustenance. The thought experiment explores human conceptions of reality, mind, and consciousness. This article will explore a metaphysical argument against artificial intelligence on the grounds that a disembodied artificial intelligence, or a "brain" without a body, is incompatible with the nature of intelligence.[1] The brain in a jar is a different inquiry than traditional questions about artificial intelligence. The brain in a jar asks whether thinking requires a thinker. The possibility of artificial intelligence primarily revolves around what is necessary to make a computer (or a computer program) intelligent.

In last month's column, "Artificial intelligence: I think therefore I am?," I felt I only scratched the surface of what we understand to be artificial intelligence (AI) and, in this month's post, I want to expand my thoughts a little further. So, last month, I suggested that what we understand today as "AI" is nothing more than clever programming and smart technology and, I dare say, that's largely true, despite others suggesting otherwise. We don't have thinking machines, since software engineers have programmed our technology to behave in a pre-determined manner, along with predefined behaviors and outcomes. You may recall, over a bottle of red, I presented the philosophical conjecture provided by René Descartes and the Scottish philosopher George Campbell's work surrounding their rationale regarding the separation of the mind and body.

Since 1980, the temperature of the planet has risen by 0.8 degrees Celsius, resulting in unprecedented melting of the Greenland ice sheet and the acidification of oceans. In 2015, 175 million more people were exposed to heat waves compared with the average for 1986 to 2008, and the number of weather-related disasters from 2007 to 2016 was up by 46 percent compared with the average from 1990 to 1999. This is nothing in comparison to the horrors that await us as temperatures continue to rise. According to recent projections, global temperatures are set to increase by 3.2 degrees by the end of century. This will lock in sea level rises that will mean that the cities, towns, and villages currently occupied by 175 million people--including Hong Kong and Miami--will eventually be underwater. There is overwhelming scientific evidence that warming is largely caused by the actions of human beings.

In last month's column, "Artificial intelligence: I think therefore I am?," I felt I only scratched the surface of what we understand to be artificial intelligence (AI) and, in this month's post, I want to expand my thoughts a little further. So, last month, I suggested that what we understand today as "AI" is nothing more than clever programming and smart technology and, I dare say, that's largely true, despite others suggesting otherwise. We don't have thinking machines, since software engineers have programmed our technology to behave in a pre-determined manner, along with predefined behaviors and outcomes. You may recall, over a bottle of red, I presented the philosophical conjecture provided by René Descartes and the Scottish philosopher George Campbell's work surrounding their rationale regarding the separation of the mind and body.

There is no doubt that AIs are biased. But many declare AI's inequalities exist because we humans are flawed, rather than the machines. "Are machines doomed to inherit human biases?" the headlines read. "Human bias is a huge problem for AI. Here's how we're going to fix it." But these narratives perpetuate a dangerous algorithm-first fallacy that needs to be nixed.