image description (contains clarifications on background elements)

Lots of different seemingly random images in the background, including some fries, mr. crabs, a girl in overalls hugging a stuffed tiger, a mark zuckerberg “big brother is watching” poser, two images of fluttershy (a pony from my little pony) one of them reading “u only kno my swag, not my lore”, a picture of parkzer parkzer from the streamer “dougdoug” and a slider gameplay element from the rhythm game “osu”. The background is made light so that the text can be easily read. The text reads:

i wanna know if we are on the same page about ai.
if u diagree with any of this or want to add something,
please leave a comment!
smol info:
- LM = Language Model (ChatGPT, Llama, Gemini, Mistral, ...)
- VLM = Vision Language Model (Qwen VL, GPT4o mini, Claude 3.5, ...)
- larger model = more expensivev to train and run
smol info end
- training processes on current AI systems is often
clearly unethical and very bad for the environment :(
- companies are really bad at selling AI to us and
giving them a good purpose for average-joe-usage
- medical ai (e.g. protein folding) is almost only positive
- ai for disabled people is also almost only postive
- the idea of some AI machine taking our jobs is scary
- "AI agents" are scary. large companies are training
them specifically to replace human workers
- LMs > image generation and music generation
- using small LMs for repetitive, boring tasks like
classification feels okay
- using the largest, most environmentally taxing models
for everything is bad. Using a mixture of smaller models
can often be enough
- people with bad intentions using AI systems results
in bad outcome
- ai companies train their models however they see fit.
if an LM "disagrees" with you, that's the trainings fault
- running LMs locally feels more okay, since they need
less energy and you can control their behaviour
I personally think more positively about LMs, but almost
only negatively about image and audio models.
Are we on the same page? Or am I an evil AI tech sis?

IMAGE DESCRIPTION END

i hope this doesn’t cause too much hate. i just wanna know what u people and creatures think <3

  • JayDee@lemmy.sdf.org
    6·
    12 hours ago

    I think we should avoid simplifying it to VLMs, LMs, Medical AI and AI for disabled people.

    For instance, most automatic text capture ais (optical Character Recognition, or OCR) are powered by the same machine learning algorithms. Many of the finer-capability robot systems also utilize machine learning (Boston Dynamics utilizes machine learning for instance). There’s also the ability to ID objects within footage, as well as spot faces and referencing it with a large database in order to find the person with said face.

    All these are Machine Learning AI systems.

    I think it would also be prudent to cease using the term ‘AI’ when what we actually are discussing is machine learning, which is a much finer subset. Simply saying ‘AI’ diminishes the term’s actual broader meaning and removes the deeper nuance the conversation deserves.

    Here are some terms to use instead

    • Machine Learning = AI systems which increase their capability through automated iterative refinement.
    • Evolutionary Learning = a type of machine learning where many instances of randomly changed AI models (called a ‘generation’) are run simultaneously, and the most effective is/are used as a baseline for the next ‘generation’
    • Neural Network = a type of machine learning system which utilizes very simple nodes called ‘neurons’ for processing. These are often used for image processing, LMs, and OCR.
    • Convolution Neural Network (CNN) = a Neural network which has an architecture of neuron ‘fliters’ layered over each other for powerful data processing capabilities.

    This is not exhaustive but hopefully will help in talking about this topic in a more definite and nuanced fashion. Here is also a document related the different types of neural networks