Investigating expectations and needs regarding the use of large language models at Bavarian university clinics - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9ERmQtRzNGc2p1Qk5SenM5UktteTlmUVVlQzFPN2oxTmJiOWs3YjJpcUpFQnA5Mkpmbk1DZXNMNWNGVlJOZUZFdmhNUFYybVlNVkNyS1lORUJDd3QyUTR3?oc=5" target="_blank">Investigating expectations and needs regarding the use of large language models at Bavarian university clinics</a> <font color="#6f6f6f">Nature</font>
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Building behavioural response models of public figures using Brain scan data (Predict their next move using psychological modelling) [P]
Hey guys, I’m the same creator of Netryx V2, the geolocation tool. I’ve been working on something new called COGNEX. It learns how a person reacts to situations, then uses that pattern to simulate how they would respond to something new. You collect real stimulus and response pairs. A stimulus is an event. A response is what they said or did. The key is linking them properly. Then you convert both into structured signals instead of raw text. This is where TRIBE v2 comes in. It was released by Meta about two weeks ago, trained on fMRI scan data, and it can take text, audio, images, and video and estimate how a human brain would process that input. On its own, it reflects an average brain. It does not know the individual. COGNEX uses TRIBE to first map every stimulus and response into this s
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Tokens, training, context windows, and temperature — the four concepts that explain everything about large language models. You know how your phone suggests the next word when you’re texting? Type “I’m going to the” and it suggests “store” or “park.” Now imagine that autocomplete was trained on every book, every website, every conversation ever written — and instead of suggesting one word, it could write entire essays, solve math problems, and generate working code. That’s fundamentally what a Large Language Model does. And once you understand four concepts — tokens, training, context windows, and temperature — you’ll know more about how AI works than 95% of people who use it daily. No PhD required. Concept 1: Tokens — How AI Reads AI doesn’t read letters or words the way you do. It reads
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trunk/83e9e15421782cf018dae04969a387901ba8ec1b: Fix Python refcounting bugs in profiler_python.cpp (#179285)
Use Py_XNewRef with PyDict_GetItemString to properly convert borrowed refs to strong refs owned by THPObjectPtr (fixes leak on 3.13+ where the Py_INCREF was applied to an already-owned ref from PyMapping_GetItemString, and fixes potential NULL deref on Add Py_NewRef for Py_None passed to PyTuple_SetItem (which steals refs) Wrap PyObject_Call results in THPObjectPtr to avoid leaking return values Use PyObject_CallOneArg instead of PyTuple_Pack + PyObject_Call Clear exception from PySequence_Index when gc callback not found Remove unused thread_state_ member from ThreadLocalResults Authored with Claude. Pull Request resolved: #179285 Approved by: https://github.com/Skylion007
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