Some of my former research teammates at GSU just published an exciting paper! "How advocacy groups on Twitter and media coverage can drive US firearm acquisition: a causal study"
Check it out: https://academic.oup.com/pnasnexus/article/4/6/pgaf195/8160866
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RT: https://twitter.com/datepsych/status/1611690519729696768?t=EKxAMXMKEX778L6D6hjR1g&s=19
Kinda concerned that therapy is so often mentioned as a good use case for #LLMs. Personally, I think AI could be transformative for mental health, but reducing #therapy to a bunch of nice, templated words strung together feels very hollow 1/2
I'd like to share my favorite talks of 2022, spanning disciplines like Cognitive Science, Molecular Biology, AI, Philosophy, and Neuroscience
TALK 1: Lars Chittka showcases the remarkable bee mind through clever experiments. It is striking that a pinhead-sized brain can recognize complex stimuli, use simple tools, learn from 🐝 friends, and possibly even process basic emotions. Coolest of all is an experiment that shows bees represent shape multimodally (word is this study caused a lot of buzz). https://youtu.be/Iut33k3MHyI
TALK 2: Hessam Akhlaghpour lays out an RNA-based theory of universal computation. He first observes that actually, universal computation in Turing's sense is not all that elusive. In systems that use simple rules and in which memory usage can grow, it shows up pretty often. Moreover, universal computation is powerful and can solve a lot of problems, so it would seem odd that life would have evolved without it. His guess is that RNA may implement it, and he goes into great detail on how it could. 🧬https://youtu.be/mOTeek3eC1g
TALK 3: Sanjukta Krishnagopal shows how you can build intelligent neural networks without backpropagation. For context, in AI you can pursue engineering or science, and these are quite different projects. If you want to understand how the brain learns, then it is wise to constrain your modelling work to biologically plausible learning mechanisms. There is no evidence that the brain uses backpropagation -- the standard learning algorithm -- so exploring alternatives is a honey pot scientifically even if performance drops. Krishnagopal shows how a dendrite-inspired gating mechanism can get you learning. https://youtu.be/2Xr0XcyhMCc
TALK 4: Lauren Ross discusses causation in the life and mind sciences. Causation is front and center in science, so it's a good idea to be clear about what the concept means. Ross details a few philosophical views, and also observes that there are many causal structures in nature; we can talk about mechanisms, feedback loops, cascades, just to name a few. There was also a Q&A point that stuck with me: as we can glean from the Lyme's disease example, necessity and sufficiency do not always entail causation. I always thought those were a litmus test 🤔. https://youtu.be/w9cwZjd249c
And here a few more recommendations from the past year... 🍯
- Manjari Narayan https://youtu.be/7-TTS134DnA
- Luiz Pessoa https://youtu.be/m346unJ1yro
- Yejin Choi https://youtu.be/JGiLz_Jx9uI?t=2944
- Jeffrey Bowers https://youtu.be/7C_0vBnWDYo
- Rosa Cao https://youtu.be/gEFtH_z3_xk
Aren't we stumbling in the dark trying to create AGI without first formally defining consciousness, or at least intelligence (assuming there is a distinction)? I feel like we'd need that foundation before an algorithm.
Also, formally defining characteristics of consciousness may be important to alignment efforts. Using #ChatGPT as an example, hardcoding or training models for maximum probable aligned behavior seems far from reliable.
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