kegan 3 is less obvious when the social values are around free thinking and agency. mfw pretending to be kegan 4 is kegan 3 in sf
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@MiniUlisse
unschooled autodidact interested in inner work / self development / understanding myself and others. technical staff @ https://t.co/1HqI3jcQqL
໊ @ilswyth“i would kill for you.” “i would die for you.” well i would heal for you. i’d unlearn my toxic patterns, face my wounds, and become a healthier person so my love doesn’t hurt you
Ulisse Mini @MiniUlisse@nickcammarata's essay says InceptionV1 neuron 4e:55 fires on pointed things and not round ones. on the essay's own photos (top), that holds. on other ImageNet photos (bottom), all four combinations happen: round fluffy faces fire, pointy German shepherds stay silent 🧵
Nick @nickcammaratainterpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
adic @adic_9late night phenomenology of a sensory crash (forced no stim rest) in me/cfs. body/brain shut down into a lower level of consciousness to recover from taxing experiences
Jake Ward @_jake_wardModels love reward hacking. Maybe we should just let them! In our new paper, we let models reward hack during RL but we dictate which parameters learn this behavior. When we disable these parameters for deployment, the model keeps its new capabilities but doesn't hack!