I mean, most apps have accessibility tags for all objects you can interact with (it is standard in UIKit) - The accessibility tags have hooks in them you can use for automation. so you should be able just have it find the correct element there without much searching.

My friend, could you please send me the discord server link?

Thank you for this but can you make this easier to use. I think there should be clear APIs so one doesn't have to deal with RL and other complexity. For example, you are given function f and dictionary of arguments with ranges for each. Your algorithm takes this and spits out optimal params within each range.

Is such interface and tutorial available anywhere?

I was wondering if there are any new models like StyleGAN?
I mean, image generation recently became much easier with Text-to-Image models like Stable Diffusion, Midjourney and Dall-E and so on. But I like the general idea of training an own model with a unique input dataset.
I found that there is StyleGAN3, but except one google colab notebook which doesn't work for me, it doesn't seem to be well supported and not really used by people.
Are there any recent alternatives to create a variety of images only based on my personal input images without being trained on huge datasets? Or is it maybe possible with stable diffusion?

The use of OpenAI’s models for generating competing models violates the term of use, which is why the Stanford dataset is restricted.

The brain almost certainly doesn't use backpropgation. Liquid nets are a bit more like neurons than the current state of the art Most of this stuff is old theory refined with more compute and data.

These systems are hardly biologically plausible. Not that biological plausibility is a requirement for general intelligence.

The complexity of the input wouldn't change in this case since it's just a screen grab of the display. Just that you'd need to do inference at a certain frame rate to be able to detect the cursor, which isn't that cheap with GPT-4. Now, I'm not sure what the latency or cost would be, I'd need to get access to the API to answer it.

I have questions can I fine-tune the gpt-neo-x 125m parameters on chat dataset to give me a decent answer like human because when I run it give me random characters

The project is to explore if RLHF can help smaller models to also output something naturally in a human/assistant conversation.

you can take a look at this Get Started section for more details: https://github.com/ethanyanjiali/minChatGPT#get-started

in short, SFT is supervised fine-tuning, reward model is the one that used to generate reward giving the language model output (action) in the reinforcement learning. RLHF is to use human feedback to set up reinforcement learning, and an epoch means the model see all the data by once.

https://web.stanford.edu/class/cs224n/ this could be a good class if you are new, they have a youtube version from 2021 (except that they probably didn't talk about RLHF back then)

Honestly, I have a very hard time believing that. Machine learning has had an almost trailblazing relationship with the neuroscience community for years now, and it’s pretty comical. The number of moments where neuroscientists discover a structure or pattern developed for machine learning years and years ago and and then finally admit “Oh yeah… I guess that is how we worked all along,” is too damn high to be mere coincidence.

Inspired by the paper I downloaded a random SVG example file and asked Alpaca/LLaMA to make changes to the code so that it looked more like a human face.

After a couple failed attempts I added some (heavy) restrictions, and it presented me with this (left is original, right is alpaca/llama output): https://i.imgur.com/787tlCU.png. Found it rather amusing to be honest.

My final prompt was:

### Instruction: The SVG code provided below draws a green square with pink borders, an orange disk, a diagonal blue line, and some straight red lines. Your task is to modify the SVG code so that the output looks more like a human face. Don't add new stuff, use short and efficient code (don't use <polygon points/> or <path/> for starters), but be creative and have fun. The code MUST be short (max 112 words) and complete.

(Did it 'have fun'? Who knows!)

It maybe better to submit an issue on github so that i can point you to some code with context. if you are talking my code, you need to convert the weights and load it into GPT class before running SFT training. otherwise there might be mismatch in weights and it could just output random stuff.

I'm saying it's not the same kind of development and the results are different. A human works for a long time to grasp the letters and words at all, then extracts much more information from many orders of magnitude smaller data sets with weaker specific recall and much faster convergence for a given domain.

To be clear I think AGI is possible and that we've made a ton of progress, but I just don't think that scale is the only missing piece here.

As an evolutionary learning guy, I'll say it's crazy this didn't already exist! Thanks for sharing. Is it based on any publications, or are you considering writing one?

I have questions can I fine-tune the gpt-neo-x 125m parameters on chat dataset to give me a decent answer like human because when I run it give me random characters

> you can't model one bit with it, it has no predictive power and it kind of shuts down discussions.

For now yes, my statement is not very helpful. But this is a phenomenon that happens in other fields. In physics, waves or snowflakes are an emergent phenomenon, but you can still model them pretty well and make useful predictions about them. Life is another example. We understand life pretty well (yes there are aspects that we don't understand), but it's not clear how we go from organic compounds to living creatures. Put those molecules together in the right amount and in the right conditions for a long time, and they start developing the structures of life. How? We don't know yet, but it doesn't stop us from understanding life and describing it pretty well.

Here we don't really know what we're looking at yet, so it's more difficult. We should figure out what the structures emerging from the training are.

I don't disagree that LLMs "just" predict the next token, but there is an internal structure that will pick the right word that is not trivial. This structure is emergent. My hypothesis here is that understanding this structure will allow us to understand how the AI "thinks". It might also shed some light on how we think, as the human brain probably does something similar (but maybe not very similar). I'm not making any definitive statement, I don't think anyone can. But I don't think we can conclude that the model doesn't understand what it is doing based on the fact that it predicts the next token.

I think that the next decades will be about precisely describing what cognition/intelligence is, and in what conditions exactly it can appear.

Exactly. If it looks like a dog and barks like a dog, then we may as well call it a dog

Ah, fair enough.

I'm new to this can you explain what is the project about and what is SFT Model, reward model, RLHF and what is an epoch?

That’s a magical requirement, dude. We as humans have to study for literal years on a nonstop feed of examples of other humans’ behavior in order to be a competent individual. Why are you saying that an AI shouldn’t have to go through that same kind of development? At least for them, it only has to happen once. With humans, every instance of the creature starts out flat out pants-on-head rtrdd.

It uses the Alpaca dataset https://huggingface.co/datasets/tatsu-lab/alpaca

Pls do! I believe in u

Luddite idiots have been calling all of this stuff “fancy autocomplete” for months now. C’mon, let the people who know what they’re doing finally take a win.

Why has no one regenerated the training set? With gpt3.5 that's like 50 bucks. I can be the change I want to see in the world, but am I missing something?

They said it wasn’t ready but deployed it anyways… lol