It accelerates many laborious and error-prone clinical tasks, including data abstraction, patient history summarization, and clinical trial matching.
(I make some brief remarks on this work in an interview at Meta: about.fb.com/news/2024/12...)
This last one is big, and has immediate real-world impact: we built an AI framework for processing cancer clinical text, centered on a custom embedding model trained from 8B tokens of real patient notes.
[3] "HopeLLM: A Cancer Specializing Framework for Multi-Data Abstraction from Patients' Entire Clinical Note Histories" by Lu, Mehdinia, et al. at the ML4H symposium.
ahli.cc/ml4h/schedule/
[2] "Empathic Coupling of Homeostatic States for Intrinsic Prosociality" by Yoshida and Man, at the IMOL workshop
neurips.cc/virtual/2024...
We implement a tiny version of empathy in multi-agent RL by coupling the internal drive states of homeostatic agents - and get prosocial behavior!
[1] "Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift" by Man, Damasio, and Neven, at the NeuroAI workshop
neurips.cc/virtual/2024...
We show that a homeostatic-like mechanism in neural networks helps them to adapt to concept shift.
Previous thread: x.com/therealkings...
I'm so excited to be attending my first NeurIPS – after many years of gawking from the outside (FOMOing) at all the wonders taking place in this golden age of AI.
Come say hello! We will be presenting three workshop papers:
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Academic writing — get utilized to it.
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thanks for the mention, @gmusser.bsky.social
update: the discussed work is now accepted at the NeuroAI Workshop at NeurIPS 2024 arxiv.org/abs/2205.08645
