this post was submitted on 20 Sep 2023
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AI is such a huge term. Google lens is great, when I'm travelling I can take a picture of text and it will automatically get translated. Both of those are aided by machine learning models.
Generative text and image models have proven to have more adverse affects on society.
I think we're at a point where we should start normalizing using more specific terminology. It's like saying I hate machines, when you mean you hate cars, or refrigerators or air conditioners. It's too broad of a term to be used most of the time.
Yeah, I think LLMs and AI art have overdominated the discourse to the degree that some people think they're the only form of AI that exists, ignoring things like text translation, the autocompletion of your phone keyboard, Photoshop intelligent eraser, etc.
Some forms of AI are debatable of their value (especially in their current form). But there's other types of AI that most people consider highly useful and I think we just forget about it because the controversial types are more memorable.
AI is a tool, its value is dependent on whatever the application is. Transformer architectures can be used for generating text or music, but they were also originally developed for text translation which people have fewer qualms with.
AFAIK two of those are generative AI based or as you said LLMs and AI art
Be the trend setter. What slang would you use(that I'll use)?
Why not the type of AI? In that.case, LLM.
Its not a matter of slang, its referring to too broad of a thing. You don't need to go as deep as the type of model, something like AI image generation, or generative language models is what you would refer to. We'll hopefully start converging on shorthand from there for specific things.
I'd like people to make a distinction between AI and machine learning, machine learning and neural networks (the word deep is redundant nowadays). And then have some sense of different popular types of neural nets: GANs, CNN, Transformer, stable diffusion. Might be nice if people know what is supervised unsupervised and reinforcement learning. Lastly people should have some sense of the difference between AI and AGI and what is not yet possible.
I'm kind of surprised people are more concerned with the output quality for chatGPT, and not where they source their training set from, like for image models.
Language models are still in a stage where they aren't really a product by themselves, they really need to be cajoled into becoming a good product, like looking up context via a traditional search and feeding it to the model, or guiding it towards solving problems. That's more of a traditional software problem that leverages large language models.
Even the amount of engineering to go from text prediction model trained on a bunch of articles to something that infers you should put an answer after a question is a lot of work.