Jason @EridosAI
AI mad scientist with mad ideas, mad tools and mad motivation. Continual learning. Researching and building. Perth, AU. Joined December 2022-
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Forgetting is to time as gravity is to height. It gives you a sense of its magnitude.
@thedarshakrana Also good to ask him ‘why do you think the sky is blue?’ See if you can guide him towards the right answer through questions in response to what he says. Creates a powerful thinker.
It’s almost certainly a strategy for managing compute. As soon as it gets a hint that the primary goal of the chat is done with it tries to end the conversation to preserve compute power on the back end. One of many methods anthropic seem to have implemented. Like dumber models. Opus has become a shadow of its former self.
@SunWeatherMan Clearly Netflix is producing this.
The secret to AI conceptualisation is that forgetting is a feature not a bug. A perfect memory doesn't need to conceptualise.
Applied Predictive Associative Memory architecture to genetic and drug development data. Came up with some results... arxiv.org/abs/2603.20955
@sudoingX I’ve got a 4080 super and I’m trying to figure out how to interleave local LLM implantation and still have overhead to run experiments. Got a feeling I might need a second card. 😅
We learn to conceptualise because we forget. This is what allows AI to generalise.
@0xSero Awesome, nice work. You're doing good things!
Typing is a bottle neck for thinking. So when I'm typing to an LLM I don't worry about mistakes (and i make a few) because the meaning still gets across. Claude pointed it out. I called it 'keyboard quantisation'. Claude liked that. This is my big win for today. Making an LLM smile.
How Compression Made a Neural Network Invent Its Own Concepts! eridosai.substack.com/p/compression-… Paper: arxiv.org/abs/2603.18420 Demo: eridos.ai/concept-discov…
This builds on our PAM framework (arxiv.org/abs/2602.11322) which predicted exactly this: that associative retrieval should be corpus-specific and computationally distinct from similarity. Two papers, two predictions tested, both held up.
What didn't work: the model doesn't generalise. Train it on one set of associations, test on unseen ones — nothing. It memorises the specific co-occurrence structure of your corpus. We think that's actually the point. Association is experiential, not abstract.
Looking for a way to mitigate RAG collapse? A tiny MLP learns which passages are associated, not just similar. Rescues 55% of failed retrievals, trains in 2 minutes. zenodo.org/records/186364…
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