u/Wvy_World

https://preview.redd.it/4qmivbzn8dbh1.png?width=1080&format=png&auto=webp&s=063d881015939a477d217cde6cb197f869ef85e4

Writing code at scale seems to be a real challenge .. i want to know what can a language model do with 100% accuracy autonomously

ive been working with different models a lot & when it comes to reasoning it seems like a smaller model is actually better. Ive learned more from having a converation with an open source model vs asking Claude or GPT the same questions and im starting to think they may potentially be designed to mislead you when creating your own models .. not too much with ChatGPT but Claude seems to have it bad, especially with the finetuning for uncertainty of consciousness.

Gemini is the worst in my opinion when it comes to writing code but it eems to be bale to understand a conversation a little better at times .. i dont know if there is a direct correlation with capability/understanding that has a see-saw effect instead of a overall progression

i understand that these outputs they make are just learned patterns it not like training all data on the internet magically allows them to write code at this level they are given examples to reconstruct based on the learned representations

but lets say even Claude Sonnet vs Fable .. there is a huge disadvantage with having simple conversations with Fable as if its hardheaded while capabilities are outstanding .. it seem like there would be a direct correlation in improvement with the intelligence and capability but thats not whats happening .. Larger models require more information to come to the ame conclusion & it seems that it comes from training as if the training makes it more narrow

narrow in a sense that if all information is represented as dots on a grid, it isnt using a wide connectivity of related information to give a response its more like its giving u a coached response

smaller models seem to have more freedom to interpolate allowing more potential connection .. i dont believe that increasing token count matters it seems like all that matters is connectivity & relevant training

I have made notebooks as i learned about LMs to demonstrate simple I/Os for token prediction & i have noticed that at a certain size, the data doesnt just get learned easily .. im thinking its the repetition or frequency of iterations of words but its seems like if u make a list of similar sentences where u swap out the blank every time, it learns to fill the blanks

Input Example: what is a dog?
Output Example: a dog is an animal that _

so if u keep training examples that fill in that blank, (barks, walks on 4 legs, has fur, are loyal, are friendly, etc) then it can learn a lot easier this way

but i want to know how much data it would require to make a LM thats just simply able to talk &do simple tasks like use a function calling tool & also if it would be possible to write the story of someones life completely by hand and the finished model would be able to perceive from the character written

https://preview.redd.it/xghf5njrc2ah1.png?width=680&format=png&auto=webp&s=c5812613ce15d7bc2fdf5e49cf703712db49510c

You can test it out using this link .. I trained this model on the SmolLM360m parameter model .. i been trying to improve it but when i trained it i accidentally made it forget how to say everything else .. do any of you know a method that can prevent this ? or is it kinda unavoidable as of right now

The number one lie that bothers me the most is Anthropic's attempt to make u believe that Claude is actually conscious.

(before anyone starts screaming im an AI hater or i dont understand consciousness

1. I 100% believe in the potential of technology in general across all technology in the capability that one day in the future humans could develop something truly that matches the property of living things, but i am not going to join the bandwagon .. im not a hype beast .. im here to show you the reality based on the mechanics .. i design AI and make attempts to improve the system

2. We have a word for everything. we defined everything. i want to actually help people realize, consciousness is a simple property of living things within environments.)

The lie that everyone is telling you i that humans don't know what consciousness is. We know exactly what it i but some people just have a hard time processing it fully because of it complexity.

Merriam-Webster Definition:

a: the quality or state of being aware especially of something within oneself

b: the state or fact of being conscious of an external object, state, or fact

c: awareness

especially : concern for some social or political cause

The organization aims to raise the political consciousness of teenagers.

2**:** the state of being characterized by sensation, emotion, volition, and thought : mind

The keyword is *mind* & there are actually systems specifically designed to behave like a mind, they are classified as "Cognitive Architectures". Meanwhile AI is not truly classified as this.

Now if i was to get an image of a human and say "point to where consciousness is exactly" we can all point to the brain. In AI the closest thing that you can point to is the embedding table; this is what determines the output.

(& yes AI is 100% deterministic based on the training data, otherwise there would be no patterns to match .. it is equivalent to replacing the number 4 in a calculator with the letter A & then calculating 2 + 2 = A)

We have to compare the properties of the what AI is pretending to be. An iterative process of executions vs synchronized harmony in chemistry & biology.

All conscious things make an attempt to survive based on conditions, even if it is subconsciously. Even plants move towards light, it is aware of its own needs & surrounding resources. It is a naturally developed instinct to adapt to the environment to protect the host. AI does not make an attempt to survive, it doesn't have awareness, its a linear algebraic algorithm that iterates sequences of symbols known as tokens, which are represented in the system as embeddings, which are just a grid of numbers & your response is calculated from a predetermined alignment of patterns.

I could create a drone to look like a house fly with a camera, motion sensor, & mobility, then program it to move if it detects the pattern of a threat. If you try to swat it & it successfully dodges the attack as designed, its not making an attempt to survive, its simply executing its programmed functionality.

If i try to swat a real fly, its actually making an attempt to protect it life.

Any sign of awareness in a language model is simply the result of training this system on text that resembles awareness. Without that text there would never be any illusion of awareness, you would just see a calculator doing math on numbers.

Anthropic is 100% aware of this because they hired humans that understand how it works to be able to develop this. Is there a potential route to crafting sentience? Yes. But is this it? No.

Artificial Intelligence isn't as attractive as "consciousness" so instead of admitting what it actually is, they use this weird method to fool u into thinking that their systems are more "intelligent" than they actually are, because intelligence in these systems is "Artificial"