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Last Notes

2026-02-08 15:41:42 UTC

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I didn’t take that to mean that you blamed arXiv for this issue. My comment was more so to point out the difficulty in proposing any solution. As such an appeal process indicates that arXiv has made their final decision and will only change as a result of outside feedback.
The gamification of systems makes me wonder if this would lead people to find a simple peer review journal in order to force the appeal. Simple thoughts on a complex issue.

2026-02-08 14:34:09 UTC

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Wow! I wonder how much effect notions like Goodhart’s law is having on this. As individuals focus on publishing to increase the credibility. Unfortunate that AI misinformation in papers has seemed to proven the need for this to some degree.
Still the clause that appeal is only available if the paper has been approved for publication elsewhere. That’s a final decision that offloads moderation to another team.

2026-01-29 21:24:30 UTC

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I should have specified. I’m not surprised students have turn to this given that schools have started checking through these means. I like the view that plagiarism, in terms of completely stealing another’s work, harms the culprit. As it defeats the entire purpose of education. This approaches the question of the end of education. Is it merely to be able to work in industry or to provide the student deeper understanding/a foundation for further study?

2026-01-29 20:46:44 UTC

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this unfortunately has seemed like the logical conclusion. I wonder if the version control on most document writers could help? Like if a student is working in Google Docs you could possibly tell if they are copy/pasting or taking the time to type up the document. Of course, some could still generate a paper, type up a sentence or two, watch a YouTube video, and repeat to make it look human. Of course, than this is work which calls to mind why plagiarize in this way.

2026-01-29 17:43:56 UTC

Who are these eminent philosophers?
Anthropic describes this constitution as being written for Claude. Described as being "optimized for precision over accessibility." However, on a major philosophical claim it is clear that there is a great deal of ambiguity on how to even evaluate this. Eminent philosophers is an appeal to authority. If they are named, then it is possible to evaluate their claims in context. This is neither precise nor accessible.
#AI #LLM #Claude #philosophy #anthropic
https://cdn.masto.host/indiewebsocial/media_attachments/files/115/979/474/448/148/461/original/d4431c11379a9e93.png

2026-01-29 16:52:33 UTC

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What's really interesting about this is seeing how this is our original conversation coming full circle.
The tragedy is watching it develop despite all the challenges and shortcomings.

2026-01-28 03:14:13 UTC

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Woah! This notion of interpolation threshold and double descent was a key insight that I was missing. Looking at the time stamps of papers detailing double descent most of them were published right after I had concluded my Deep Learning course. As I never worked at an industry, I missed this development. Initial perspective is that this seems to explain the generational by generational improvements of these models.
Well, this is going to fill my reading list for the next couple of days.

2026-01-27 01:31:47 UTC

Can someone clarify, in academia and industry are LLM hallucinations the result of overfitting, or simply a false positive?
I'm beginning to think that hallucinations are evidence of overfitting. It seems surprising that there are few attempts to articulate the underlying cause of hallucinations. Also, if the issue is overfitting, then increasing training time and datasets may not be an appropriate solution to the problem of hallucinations.
#AI #ML #llm

2026-01-22 16:40:06 UTC

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I like that analogy.
Unfortunately? I never got into functional programming, so I had to look up hylomorphism as a CS concept. I have found that computational and philosophical terms often have a high degree of similarity, but not so much here.
Aristotelian hylomorphism supposes that substances are composed of matter and form. Wherein form is the principle of intelligibility and provides the end. Intellect extracts the form from material beings. Hence language alone cannot suffice.

2026-01-22 16:13:42 UTC

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I had failed to challenge the base assumptions. All the talk of #GenAI had led me to adopt that very language while trying to critique it. There is the reality that the prompt from the user is necessary, but all of the creativity is explicitly encoded into the dataset. I suppose then the generative aspect more so derives from the trade secrets of dataset creation.
Thank you. This is where I am trying to think out loud and work on refining my own ideas and how to communicate them.

2026-01-22 15:24:32 UTC

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I have not read Hans yet, but I should probably add his work to my list.
I consider it fortunate to find myself indebted to the Aristotelian metaphysics of hylomorphism. Which, while it does not address all questions, it certainly helps to ground contemporary philosophy in a realist tradition. In that regard, one of the shortcomings of LLMs is believing that associations of words without experience constitutes knowledge. Rather than language deriving from experience.

2026-01-22 14:56:14 UTC

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I suppose to a certain extent this is the epistemological question of LLMs. Does data arrive at true understanding, or statistical associations? This insight seems to be growing in popularity, but now has to climb the mountain of sunk cost from investments. As such, many of these conversations are on the fringe of being impossible to even have.
The greatest irony, is believing we have moved beyond these challenges. Whereas the reality is we have merely stopped engaging with them.

2026-01-22 02:47:40 UTC

Open Question:
What is the end of AI systems?
Like the end for a car is transportation. Therefore, we continue to iterate on the design to improve power, efficiency, or style.
Can a similar focus on end be applied to AI? Some seem clear like AlphaGo, self-driving cars, medical imaging, etc. Really the challenge seems to be with LLMs. Is the lack of clear end contributing to misuse and harm caused by LLMs?
#AI #chatbots #LLM #philosophy #openquestion #discussion

2026-01-20 17:33:41 UTC

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5/ Process generative:
These are models like ChatGPT and Dall-E. Their goal is to generate new responses to text or image inputs by the user. As such, they are trying to encode the entirety of language or visual representation. The recent success in this context has enabled the explosion of #AI services. The challenge of encoding all this information should explain all the shortcomings. These models operate based on relations learned from a large dataset. Everything develops from this dataset.

2026-01-20 17:26:45 UTC

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4/ Functional creativity:
This is a more novel way to describe reinforcement learning. It is acknowledges how models like AlphaGo and AlphaFold are able to develop revolutionary advancements through policy adaptation. The important aspects are a well-defined scope and a definitive evaluatory framework. The game Go and protein folding fit these qualifications well. The challenge with these models is the process of creating/defining the environment for policy development. #AI #philosophy

2026-01-20 17:15:32 UTC

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3/ Data clustering:
From a popular perspective, this is the least interesting of the distinctions. These models categorize new data points based on readily available historical data. Taking a large number of arbitrary data points, these models are able to organize them according to a degree of similarity. Then when new data points are added they are similarly categorized. These models find their true use in data science. Hence their lack of popular appeal. #AI #philosophy

2026-01-20 17:06:49 UTC

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It seems to me that when we discuss #AI we are often discussing an application like ChatGPT. However, this is only one type of AI models. I would like to propose a three-fold distinction for discussing AI from a philosophical perspective. Each distinction is a different way of processing data, with time often being the varying factor. The three distinctions can be called data clustering, functional creativity, and process generative. #philosophy 2/

2026-01-20 01:20:48 UTC

I’m curious to know how often the distinctions between the various types of algorithms has been made? There has been a lot of discussion about #ai. Yet this is often used as a very generic term to describe everything from ChatGPT to AlphaFold without much distinction that the underlying algorithms are often very different. This difference seems like something that should be obvious and yet is not a common distinction in articles. Is this well known or distinction worth exploring? #philosophy