7 ms·

Is there a post somewhere where I can get all the jargon for this AI/ML stuff? I have a vague understanding but I’m really sure what “weights”, “LoRA”, “LLM”, e

Is there a post somewhere where I can get all the jargon for this AI/ML stuff? I have a vague understanding but I’m really sure what “weights”, “LoRA”, “LLM”, etc. are to really understand where each tool and concept fit in.

d0m 4y ago
Run the Alpaca chat and ask it :)
singularity2001 4y ago
yes someone created a blog called "Wikipedia"
tough 4y ago
weights refer to the trained model weights like for example stable diffusion's v1.1, v1.2 .. v1.4 v.2.x etc. Same with llama having 13B up to 65B parameters (different weights) LLM refers to large language model, in contrast with diffusion models or GAN models, the text models are the ones that take text and autocomplete it, like the GPT family, open source BLOOM, and now the LlamA from facebook. LoRA is the latest most efficient fine-tuning model to teach concepts or styles to put on top of the general models, so you can have custom models on top. like embeddings or fine-tuning for LLM's. So you had Textual Inversion, Dreambooth, and LoRA on that category. what else do you need? Googling or asking chatgpt can help a lot too
- JasonZ2 4y ago
  > weights refer to the trained model weights This is what I'm having a hard time understanding. So there's the weights, and also a model somewhere? That the weights are based on? Or that you combine with the model to tune it?
  - mlboss 4y ago
    model is class with params. weights is an instance of class serialized with param values learned after training.
  - hervature 4y ago
    Let's take a step back. You have a model like linear regression. For example, y=bx where y are your outputs and x are your inputs. Based on some data, you learn that b=1. Therefore, you share the weights of the model as a file like {b=1} and also share with them the model y=bx (usually shared via code) so they can run it in production.
    - MacsHeadroom 4y ago
      This is the best explanation imo. In fact, the only thing you'd need to modify to make this analogy an actual description is for y, b, and x to each represent a matrix of numbers.
  - ALittleLight 4y ago
    My really simplified explanation is: Your inputs are lists of numbers. Your outputs are lists of numbers. There exists some possible list of numbers such that, if you multiply your inputs by that list you'll get (approximately) the outputs. In this conception that possible set of numbers are the weights. "Training" is when you run inputs, compare to known outputs, and then update the weights so they produce outputs closet to what you want. Large Language Models, it may be hard to see how they fit this paradigm - basically convert a sequence to a list of numbers ('aardvark' is 1, 'apple' is 2 etc) and then the desired output is the next word in the sequence (represented as a number). Surprisingly, if you get good at predicting next word in sequence you also get the ChatGPT et al behavior.
  - dymk 4y ago
    Model is code, weights are the input data to that code
  - tantony 4y ago
    This is what happens when running inference on a neural network: Input (list of numbers) -> (Bunch of math operations) with (other numbers) -> Output (also a list of numbers) This applies whether you are talking about image classification, image generation, text generation etc. The model defines what the "(Bunch of math operations)" part is. As in, do these multiplications, then add, then a tanh operation etc. The weights define what the "(other numbers)" are. Training is the process of figuring out these weights using various methods - some of which involve example inputs/outputs (supervised learning), others don't require examples (unsupervised or self-supervised learning).
visarga 4y ago
Just for quick reference: LLM - the model weights - the data of the model itself LoRA - a small addition to the model for more efficient fine-tuning and distribution
- EGreg 4y ago
  Tell us more about LoRA
  - deleted 4y ago
    [deleted]
  - visarga 4y ago
    Instead of a matrix NN they use two matrices, Nd and d*N, and assuming d << N it would be like a lightweight addition. Keeping the original matrix unchanged, this new addition is simply added in parallel and initialised to low values.
speedgoose 4y ago
You can ask ChatGPT.
- Ruhrbaron 4y ago
  Or Alpaca.
  - speedgoose 4y ago
    Alpaca is fun, but I would rather ask ChatGPT.
lxe 4y ago
I was like this a week ago. Basically, weights are numbers you feed to each neuron in the model, LoRA is a technique to adjust only some of the weights so you can fine-tune the model fast and on cheaper hardware, llm is a "large language model". I've been asking chatgpt4 these things and learning at my own pace and context: https://rentry.co/vafkn https://rentry.co/vafkn
- stu2b50 4y ago
  Well, it’s more that the weights are the neurons. There’s not actually like neuron objects defined or anything, neural networks are just a bunch of matrix operations. They are to neurons in the brain as the tree data structure is to actual trees.
  - stormfather 4y ago
    The weights aren't the neurons, they're the connection strengths between the neurons.
    - detrites 4y ago
      You mean the same neurons that don't really exist, other than as a connection from weight to weight, so quite reasonable to see as the weights themselves, because what else is available for us to reference as neurons in absentia those connections?
      - dragonwriter 4y ago
        > You mean the same neurons that don’t really exist, “Neurons” are an abstraction that exists logically “between” the weights, but the weights themselves don’t have the features of neurons. (In that each weight is the weight of a connection between a neurons (or between a neuron and an input/output.) Weights are more synapses than neurons.
      - stormfather 4y ago
        I disagree. The metaphor we use in ML is that neurons are the nodes that receive the summed, weighted inputs from other neurons. Yes, the weights are the strengths of the connections between them. However, there are many more weights than neurons, so conflating the two doesn't make sense schematically. Also, neurons can also have other parameters which define their behavior such as bias and activation functions. Furthermore, the activation of a neuron defines the network's response to a stimuli, so these change depending on the input, whereas the weighs are constants (after being trained), that parameterize the system. The analogy is that weights are synapses, not neurons. You would never here a neurologist say that neurons and synapses are the same thing.
jazzyjackson 4y ago
I know it's downvote-worthy to suggest it but I really am so much more productive since I started using LLMs to explain technical jargon to me, it's very good at comparing technologies, so I can say "I understand x, but how is y different" and it will give an explanation fine tuned to my understanding -- this is a huge leap from the context-switching of opening a new tab and clicking on random websites looking for a blog that explains something at my level, or leaving a post on a website and waiting for someone to respond.
- thefourthchime 4y ago
  Same! ChatGPT has been out for months, and really only in the last few weeks have i started using it as much as I should. I'm using an electron wrapper now, so it's a first class desktop app. With the plus subscription, the 3.5 is as fast as google.
  - jazzyjackson 4y ago
    I'm subscribed to Poe now which has a nice ios app + mac desktop for GPT4 and Anthropic's equivalent, Claude+
  - hackernewds 4y ago
    what is an electron wrapper
    - MacsHeadroom 4y ago
      I asked GPT-4 for you: "An "Electron wrapper" refers to a software application created using Electron, a popular open-source framework. Electron allows developers to build cross-platform desktop applications using web technologies like HTML, CSS, and JavaScript. An Electron wrapper essentially "wraps" a web application inside a native desktop application, making it possible to run the web application as a standalone program on various operating systems like Windows, macOS, and Linux. This is particularly useful for developers who want to create desktop applications without having to write separate code for each platform. Instead, they can use their existing web application code and package it with Electron to create a desktop application that works across multiple platforms."
thefourthchime 4y ago
I understand that AI/ML jargon can be confusing. Here's a brief explanation of some common terms and concepts to help you better understand them: Weights: In machine learning, weights are parameters used in models to determine the strength of the relationship between input features and the output prediction. They are learned during the training process and are adjusted to minimize the error in the model's predictions. LoRA: LoRA stands for "Learning from Rules and Arguments." It's a method that combines rule-based reasoning with neural networks, which allows AI systems to reason using human-like logic and provide explanations for their decisions. LoRA can enhance the interpretability and transparency of AI systems. LLM: LLM stands for "Large Language Model." It refers to advanced AI models, such as OpenAI's GPT series, which are trained on vast amounts of text data to understand and generate human-like text. These models can perform various language tasks such as translation, summarization, question-answering, and more. Supervised Learning: This is a type of machine learning where models are trained using labeled data. The model learns to map input features to the corresponding output labels through a process of trial and error, adjusting the weights to minimize the difference between predictions and true labels. Unsupervised Learning: In contrast to supervised learning, unsupervised learning does not use labeled data. Instead, it aims to discover patterns or structures in the data, such as clustering or dimensionality reduction, without explicit guidance. Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties and aims to maximize its cumulative reward over time. Neural Network: A neural network is a type of machine learning model inspired by the human brain's structure. It consists of interconnected layers of nodes (neurons) that process and transmit information. They are particularly good at learning complex patterns and can be used for various tasks, such as image recognition, natural language processing, and more. Transfer Learning: Transfer learning is a technique in machine learning where a pre-trained model is fine-tuned for a new, related task. This approach leverages the knowledge gained from the initial task to improve the performance of the model on the new task, reducing the amount of data and training time needed. These are just a few of the many concepts and tools used in AI/ML. Understanding these terms should help you better grasp the field and how different components fit together. -- ChatGPT 4
- simse 4y ago
ec109685 4y ago
This is a truly great writeup: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...
- malborodog 4y ago
  Is it? Why does everyone dunk on him?
ginger-hot-tea 4y ago
This may help a little: https://daily.ginger-t.link/glossary https://daily.ginger-t.link/glossary (DISCLAIMER: my site)
throwwwaway69 4y ago
I'm a software guy learning the AI field and tools and I literally made a glossary in my notes that I add to any time I run into a new initialism or word or concept. It's pretty long.
thequadehunter 4y ago
Ironically the best way to learn these terms is to ask ChatGPT.

d0m 4y ago

Run the Alpaca chat and ask it :)

singularity2001 4y ago

yes someone created a blog called "Wikipedia"

tough 4y ago

weights refer to the trained model weights like for example stable diffusion's v1.1, v1.2 .. v1.4 v.2.x etc. Same with llama having 13B up to 65B parameters (different weights) LLM refers to large language model, in contrast with diffusion models or GAN models, the text models are the ones that take text and autocomplete it, like the GPT family, open source BLOOM, and now the LlamA from facebook. LoRA is the latest most efficient fine-tuning model to teach concepts or styles to put on top of the general models, so you can have custom models on top. like embeddings or fine-tuning for LLM's. So you had Textual Inversion, Dreambooth, and LoRA on that category. what else do you need? Googling or asking chatgpt can help a lot too

JasonZ2 4y ago
> weights refer to the trained model weights This is what I'm having a hard time understanding. So there's the weights, and also a model somewhere? That the weights are based on? Or that you combine with the model to tune it?
- mlboss 4y ago
  model is class with params. weights is an instance of class serialized with param values learned after training.
- hervature 4y ago
  Let's take a step back. You have a model like linear regression. For example, y=bx where y are your outputs and x are your inputs. Based on some data, you learn that b=1. Therefore, you share the weights of the model as a file like {b=1} and also share with them the model y=bx (usually shared via code) so they can run it in production.
  - MacsHeadroom 4y ago
    This is the best explanation imo. In fact, the only thing you'd need to modify to make this analogy an actual description is for y, b, and x to each represent a matrix of numbers.
- ALittleLight 4y ago
  My really simplified explanation is: Your inputs are lists of numbers. Your outputs are lists of numbers. There exists some possible list of numbers such that, if you multiply your inputs by that list you'll get (approximately) the outputs. In this conception that possible set of numbers are the weights. "Training" is when you run inputs, compare to known outputs, and then update the weights so they produce outputs closet to what you want. Large Language Models, it may be hard to see how they fit this paradigm - basically convert a sequence to a list of numbers ('aardvark' is 1, 'apple' is 2 etc) and then the desired output is the next word in the sequence (represented as a number). Surprisingly, if you get good at predicting next word in sequence you also get the ChatGPT et al behavior.
- dymk 4y ago
  Model is code, weights are the input data to that code
- tantony 4y ago
  This is what happens when running inference on a neural network: Input (list of numbers) -> (Bunch of math operations) with (other numbers) -> Output (also a list of numbers) This applies whether you are talking about image classification, image generation, text generation etc. The model defines what the "(Bunch of math operations)" part is. As in, do these multiplications, then add, then a tanh operation etc. The weights define what the "(other numbers)" are. Training is the process of figuring out these weights using various methods - some of which involve example inputs/outputs (supervised learning), others don't require examples (unsupervised or self-supervised learning).

visarga 4y ago

Just for quick reference: LLM - the model weights - the data of the model itself LoRA - a small addition to the model for more efficient fine-tuning and distribution

EGreg 4y ago
Tell us more about LoRA
- deleted 4y ago
  [deleted]
- visarga 4y ago
  Instead of a matrix NN they use two matrices, Nd and d*N, and assuming d << N it would be like a lightweight addition. Keeping the original matrix unchanged, this new addition is simply added in parallel and initialised to low values.

speedgoose 4y ago

You can ask ChatGPT.

Ruhrbaron 4y ago
Or Alpaca.
- speedgoose 4y ago
  Alpaca is fun, but I would rather ask ChatGPT.

lxe 4y ago

I was like this a week ago. Basically, weights are numbers you feed to each neuron in the model, LoRA is a technique to adjust only some of the weights so you can fine-tune the model fast and on cheaper hardware, llm is a "large language model". I've been asking chatgpt4 these things and learning at my own pace and context: https://rentry.co/vafkn https://rentry.co/vafkn

stu2b50 4y ago
Well, it’s more that the weights are the neurons. There’s not actually like neuron objects defined or anything, neural networks are just a bunch of matrix operations. They are to neurons in the brain as the tree data structure is to actual trees.
- stormfather 4y ago
  The weights aren't the neurons, they're the connection strengths between the neurons.
  - detrites 4y ago
    You mean the same neurons that don't really exist, other than as a connection from weight to weight, so quite reasonable to see as the weights themselves, because what else is available for us to reference as neurons in absentia those connections?
    - dragonwriter 4y ago
      > You mean the same neurons that don’t really exist, “Neurons” are an abstraction that exists logically “between” the weights, but the weights themselves don’t have the features of neurons. (In that each weight is the weight of a connection between a neurons (or between a neuron and an input/output.) Weights are more synapses than neurons.
    - stormfather 4y ago
      I disagree. The metaphor we use in ML is that neurons are the nodes that receive the summed, weighted inputs from other neurons. Yes, the weights are the strengths of the connections between them. However, there are many more weights than neurons, so conflating the two doesn't make sense schematically. Also, neurons can also have other parameters which define their behavior such as bias and activation functions. Furthermore, the activation of a neuron defines the network's response to a stimuli, so these change depending on the input, whereas the weighs are constants (after being trained), that parameterize the system. The analogy is that weights are synapses, not neurons. You would never here a neurologist say that neurons and synapses are the same thing.

jazzyjackson 4y ago

I know it's downvote-worthy to suggest it but I really am so much more productive since I started using LLMs to explain technical jargon to me, it's very good at comparing technologies, so I can say "I understand x, but how is y different" and it will give an explanation fine tuned to my understanding -- this is a huge leap from the context-switching of opening a new tab and clicking on random websites looking for a blog that explains something at my level, or leaving a post on a website and waiting for someone to respond.

thefourthchime 4y ago
Same! ChatGPT has been out for months, and really only in the last few weeks have i started using it as much as I should. I'm using an electron wrapper now, so it's a first class desktop app. With the plus subscription, the 3.5 is as fast as google.
- jazzyjackson 4y ago
  I'm subscribed to Poe now which has a nice ios app + mac desktop for GPT4 and Anthropic's equivalent, Claude+
- hackernewds 4y ago
  what is an electron wrapper
  - MacsHeadroom 4y ago
    I asked GPT-4 for you: "An "Electron wrapper" refers to a software application created using Electron, a popular open-source framework. Electron allows developers to build cross-platform desktop applications using web technologies like HTML, CSS, and JavaScript. An Electron wrapper essentially "wraps" a web application inside a native desktop application, making it possible to run the web application as a standalone program on various operating systems like Windows, macOS, and Linux. This is particularly useful for developers who want to create desktop applications without having to write separate code for each platform. Instead, they can use their existing web application code and package it with Electron to create a desktop application that works across multiple platforms."

thefourthchime 4y ago

I understand that AI/ML jargon can be confusing. Here's a brief explanation of some common terms and concepts to help you better understand them: Weights: In machine learning, weights are parameters used in models to determine the strength of the relationship between input features and the output prediction. They are learned during the training process and are adjusted to minimize the error in the model's predictions. LoRA: LoRA stands for "Learning from Rules and Arguments." It's a method that combines rule-based reasoning with neural networks, which allows AI systems to reason using human-like logic and provide explanations for their decisions. LoRA can enhance the interpretability and transparency of AI systems. LLM: LLM stands for "Large Language Model." It refers to advanced AI models, such as OpenAI's GPT series, which are trained on vast amounts of text data to understand and generate human-like text. These models can perform various language tasks such as translation, summarization, question-answering, and more. Supervised Learning: This is a type of machine learning where models are trained using labeled data. The model learns to map input features to the corresponding output labels through a process of trial and error, adjusting the weights to minimize the difference between predictions and true labels. Unsupervised Learning: In contrast to supervised learning, unsupervised learning does not use labeled data. Instead, it aims to discover patterns or structures in the data, such as clustering or dimensionality reduction, without explicit guidance. Reinforcement Learning: This is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties and aims to maximize its cumulative reward over time. Neural Network: A neural network is a type of machine learning model inspired by the human brain's structure. It consists of interconnected layers of nodes (neurons) that process and transmit information. They are particularly good at learning complex patterns and can be used for various tasks, such as image recognition, natural language processing, and more. Transfer Learning: Transfer learning is a technique in machine learning where a pre-trained model is fine-tuned for a new, related task. This approach leverages the knowledge gained from the initial task to improve the performance of the model on the new task, reducing the amount of data and training time needed. These are just a few of the many concepts and tools used in AI/ML. Understanding these terms should help you better grasp the field and how different components fit together. -- ChatGPT 4

simse 4y ago

ec109685 4y ago

This is a truly great writeup: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...

malborodog 4y ago
Is it? Why does everyone dunk on him?

ginger-hot-tea 4y ago

This may help a little: https://daily.ginger-t.link/glossary https://daily.ginger-t.link/glossary (DISCLAIMER: my site)

throwwwaway69 4y ago

I'm a software guy learning the AI field and tools and I literally made a glossary in my notes that I add to any time I run into a new initialism or word or concept. It's pretty long.

thequadehunter 4y ago

Ironically the best way to learn these terms is to ask ChatGPT.