Schwitzgebel, Strasser, and Crosby fine-tuned GPT-3 on Dennett's corpus and asked whether readers could pick Dennett's real answers to ten philosophical questions from four machine-generated alternatives, with no cherry-picking beyond mechanical length filters. Even Dennett experts averaged only 5.1 out of 10 (well below the 80% the authors predicted), blog readers got 4.8, and lay participants barely beat chance — though experts did rate Dennett's answers as more Dennett-like overall. Schwitzgebel stresses this isn't a Turing test (one-shot text is far easier to fake than extended interaction), but argues it foreshadows a future where machine outputs are humanlike enough that their moral status becomes genuinely uncertain, motivating his "Design Policy of the Excluded Middle": build machines that clearly lack moral status or clearly have it, not ambiguous ones in between.
My own take is : don't focus on the symbols on paper. focus on the facts about the world it is talking about. Isn't objectivity all about the facts? In future AI will have all the memory about what I have already read and it will just furnish the delta new information in the blog/writing so that I don't spend time on refreshing what I already know.
>Schwitzgebel, Strasser, and Crosby fine-tuned GPT-3 on Dennett's corpus
This sounds like a completely different scenario. How many users who post LLM written blog posts are tuning the weights of their LLMs on a large corpus of their own original writing? I wouldn't doubt that this produces far more convincing and pleasant output than the disgusting slop from out of the box Claude.
That 40T$s will never be repaid. So what kind of monetary reset are we looking at? nonlinear as Luke says or gradual as Lyn says in the following video
"We're Past The Point Of No Return" | Luke Gromen and Lyn Alden
Has the UK really experienced any issues related to not ever paying back its debt? (IMHO, most of the UK's problems can probably be sourced back to Thatcherism and more recently Brexit.)
The only way for a country to repay its debt, versus refinancing it, is to run budget surpluses, which can work if done in a certain way, but most countries avoid for often good reasons:
A person can be partially wrong; partially right. Focus on what kind of arguments they put forth and decide for yourself. If you watch the video, they give plenty of historical precedents for the things they describe.
In support, we chuck in the logs and get detailed analysis of possible problems and causes. AI based support request handling has been literally Godsend.
Hyperscalers later will move to their own chips. Nvidia would follow Apple strategy of selling the hardware. Nvidia would like to have near frontier open-weights model and sell the hardware, otherwise that market will be ceded to Apple hardware of M6 Ultra and future versions.
Depends on your product strategy. If you only care about how your model will be used in the context of a harness (perhaps, specifically the harness that you designed), then the incentive is plainly there to optimize the weights within the context of the harness.
What is being absorbed into weights is tool usage. It is incredibly counterproductive to train models on a specific harness, when instead it can be trained to reason about the tools that it has available to itself and how to best use those tools to accomplish it's goal.
Would you rather hire an engineer that can adopt to your org's prefered tooling, or hire an engineer that can only perform well with their own favorite tools? It's the same thing.
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