Recent comments in /f/MachineLearning

currentscurrents t1_j8zh4aa wrote

>scraping all kinds of copyrighted materials and then profiting off the models while the people doing all the labor are getting either nothing (for content generation)

Yeah, but these people won't be doing that labor anymore. Now that text-to-image models have learned how to draw, they don't need a constant stream of artists feeding them new art.

Now artists can now work at a higher level, creating ideas that they can render into images using the AI as a tool. They'll be able to create much larger and more complex projects, like a solo indie artist creating an entire anime.

>LLMs... barely have any legitimate use-cases

Well, one big use case: they make image generators possible. Those rely on embeddings from language models, which are a sort of neural representation of the ideas behind the text. It grants the other network the ability to work with plain english.

Right now embeddings are mostly used to guide generation (across many fields, not just images) and semantic search. But they are useful for communicating with a neural network performing any task, and my guess is that the long-term impact of LLMs will be that computers will understand plain english now.

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athos45678 t1_j8zewjb wrote

It’s 29 cents a gig per month over the storage limit, and i rarely go over the storage limit if i am carefully managing files. Definitely the biggest drawback though. You can always just use wkentaro’s gdrive package to pull from google drive as well

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tornado28 t1_j8zcrc2 wrote

People will use them to make money in unethical and disruptive ways. An example of an unethical way to use them is phishing scams. Instead of sending out the same phishing email to thousands of people, scammers may get some data about people and then use the language model to write personalized phishing emails that have a much higher success rate.

Disruptive applications will take jobs. Customer service, content creation, journalism, and software engineering are all fields that may lose jobs as a result of large language models.

The other disruptive possibility is that LLMs will be able to themselves rapidly build more powerful LLMs. I use GitHub copilot every day and it's already very good at writing code. It takes at least 25% off the time it takes me to complete a software implementation task. So it's very possible a LLM could in the near future make improvements to it's own training script and use it to train an even more powerful LLM. This could lead to a singularity where we have extremely rapid technological development. It's not clear to me what the fate of humankind would be in this case.

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baffo32 t1_j8zbmua wrote

dry is a very basic software engineering principle that means to include only one copy of every sequence of code. it looks like machine learning people did not learn this as they weren’t trained as software engineers. DRY stands for “don’t repeat yourself”, and if not respected then it gets harder and slower more and more to maintain, improve, or bugfix software, the larger and older it gets.

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buzzbuzzimafuzz t1_j8zafoo wrote

The mess that has been Bing Chat/Sydney, but instead of just verbally threatening users, it's connected with APIs that let it take arbitrary actions on the internet to carry out them out.

I really don't want to see what happens if you connect a deranged language model like Sydney with a competent version of Adept AI's action transformer to let it use a web browser.

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drinkingsomuchcoffee OP t1_j8zael9 wrote

I am the "bad guy" of the thread, so anything I say will be seen negatively, even if it's correct. This is typical human behavior, unfortunately.

I have a feeling most people here do not understand DRY done well, and are used to confusing inheritance hierarchies and incredibly deep function chains. Essentially they have conflated DRY with bad code, simple as that.

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