this post was submitted on 21 Nov 2023
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I have spent most of my professional career trying to get engineers to unionize and fight for better wages/benefits. The vast majority are apathetic and passive. Now we have a brand new technology that won't make the profession obsolete, but will make the need for their labor obsolete. I'm personally in a safe spot. I have the portfolio and network to ensure I will have opportunities to work no matter what. But how many engineers simply do not have that? MBAs will raze this profession to the ground for short-term profits. They'll destroy every pipeline that takes in entry-level engineers and gives them a chance to grow.
As far as I know, you're just another engineer who doesn't see what's coming down the road. Pleasant speech hasn't worked to get people to wake up over the past two years, and I'm running out of patience like you're all running out of time.
And that's the problem. You claim to be in ML research, and you don't even know what your own tools and field are capable of. I haven't just made frameworks that are "copies" of a framework but in another language, or a combination of existing frameworks. GPT-3.5 and GPT-4 are capable of novel outputs when given sufficient guidance. I've gotten it to fill in architecture gaps and implement complex, recursive functions. I don't believe for a second that it's "self aware". But it's far more capable than even you realize.
I'm willing to bet that if I were in your shoes I could get at least some usable output from it for your use case.
You talk a confident, condescending game for someone who cant substatiate any of their claims lol.
EDIT: Dear lord after looking through your comments i regret enabling you. Fuck isreal, free palestine, AI is overhyped...
Okay Tankie.
LOL
Let's play a little game, then. We bothe give each other descriptions of the projects we made, and we try to make the project based on what we can get out of ChatGPT? We send each other the chat log after a week or something. I'll start: the hierarchical multiscale LSTM is a stacked LSTM where the layer below returns a boundary state which will cause the layer above it to update, if it's true. the final layer is another LSTM that takes the hidden state from every layer, and returns a final hidden state as an embedding of the whole input sequence.
I can't do this myself, because that would break OpenAI's terms of service, but if you make a model that won't develop I to anything, that's fine. Now, what does your framework do?
Here's the paper I referenced while implementing it: https://arxiv.org/abs/1807.03595
Cool. I'll get back to you with a DM after the holidays. I'm not going to devote a week to proving a random internet stranger wrong, but I'll at least give you a few hours.