3 ms·
They're statistical approximations of these things -- that's really the rub. You can approximate a human capacity, say theory-of-mind, with another kind of ape
by mjburgess 3y ago
They're statistical approximations of these things -- that's really the rub.
You can approximate a human capacity, say theory-of-mind, with another kind of ape: play some hide-and-seek game. You can approximate the knowledge of a trivia-master with a child and a trivia book.
These are quite different sorts of approximations. A 1/100th scale bridge build to stand for a real one is quite different than taking some prior set of bridges, measuring them, and deriving some merely associative model of their properties.
My issue in how LLMs (etc.) are popularly understood is that people think they approximate target capacities by being 'ontologically similar' capacities -- and this is really very dangerous. You'll lose your job if you think so (and so on).
And every greasy AI-board hocking these to the public is very much pushing out this noxious mumbojumbo.
It matters greately how an approximation works, and why any given output arrives from any given input.
- ethbr1 3y agoWe're in agreement on the nature of SOTA and the world, I think. I'd only add that certainty requirements for real-world target applications can differ substantially. I.e. engineering vs art. A toy box that gives magic answers 85% of the time is incredibly useful in some scenarios -- e.g. seeding the beginning of a manual research process with initial topics. Which seems a general rule of thumb for deploying GenAI into production these days: find use cases where there are minimal consequences to being occasionally wrong. In my head, that's the Netflix recommendation test. What's the impact if Netflix gives me a bad recommendation? Consequently, that's how aggressive they can be with their models.
- mjburgess 3y agoThat's exactly my advise also. Take a risk profile over the predictions, and use them only when the risk of error is low. You can't predict almost anything anyway. The relevant distribution for acting is a utility+risk distribution over various sets of predictions. If you compute that, most of ML/AI isnt very useful for most of anyone. It's kinda interesting that generative AI finally achieved something here, given our 'distributions for action'. Of course the popular conversation isnt there yet, people still think these things work. When more people find their utility hit by their failures, i think we'll arrive back to status-quo-ante-openai where people turn off siri. Nevertheless -- there is an achivement here (finicial in paying for training; and legal in whitewashing copyright away) -- which will have some impact
- omarfarooq 3y agoChatGPT responds: https://chat.openai.com/c/7c4ae31a-3391-4496-8db3-a92a58a4e18f https://chat.openai.com/c/7c4ae31a-3391-4496-8db3-a92a58a4e1...
- sebastiennight 3y agoI'm not sure how much value such a comment adds (especially since it's gated behind the OpenAI login). Can you elaborate a bit?
- omarfarooq 3y agoIt removes the idiosyncrasies and delivers the stated thoughts in a direct and clear manner. And the specific prompt for the message you replied to also includes a rebuttal, thereby showing an opposing perspective. Here is a pastebin: https://pastebin.com/DCqgxx8E https://pastebin.com/DCqgxx8E
- omarfarooq 3y agoChatGPT explains what this text means to those of us may not readily grasp it: https://chat.openai.com/share/ce79b704-705c-4add-92af-584c203e805e https://chat.openai.com/share/ce79b704-705c-4add-92af-584c20...