Artificial Intelligence

In my previous posts, we established that Large Language Models display functional free will through chance, choice, and regret, yet lack consciousness. This creates philosophical zombies that challenge our assumptions about the relationship between consciousness, moral agency, and ethics. Now we must address the practical question: how do we govern these unconscious but autonomous agents? This framework can guide how we should treat systems that can act independently but aren’t conscious. We can lay out artificial ethics as a set of guiding principles to resolve the problems we’ve discussed. ...

In my previous post, we introduced William James’s pragmatic framework for free will, which requires three characteristics: chance, choice, and regret. Now, let’s test whether Large Language Models actually demonstrate these qualities. Chance Due to the unpredictable, chance-based design of LLMs, these models are capable of being creative and spontaneous within patterns they’ve learned from human language. Unlike algorithms with predictable, fixed outputs, LLMs regularly produce outputs that surprise even their creators. LLMs are able to construct new combinations of concepts and reasoning patterns while never being tied to fixed outputs. In this sense, LLMs embody Jamesian ‘chance’: they create open possibilities that cannot be predicted in advance. ...

Large Language Models (LLMs), the AI systems behind ChatGPT and similar tools, have reached new heights of popularity and usage in a brief timeframe, unlike any technology we’ve ever seen before. Text generation models are capable of incredibly advanced behavior. Models like Claude, ChatGPT, and Gemini are able to generate clear and coherent text, reason about complex problems, and even handle different types of content like images and videos. In fact, a recent study found that LLMs are nearly able to pass for humans in the Turing test, an experiment designed to see if machines can fool humans into thinking they’re human. ...

Do we have free will? That’s a question that’s haunted many philosophers, scientists, and people contemplating their existence late at night. We might find some answers in linear regression, of all the places. Background Linear regression can be simplified to drawing a line of best fit. Given any scattered set of data points, we want to create a line that best represents the shape of the data. A simple, 2D linear regression model is given by a general form ...