I wanted to, made a long informative post about this but for some reason it was automatically getting removed. Got messages like "waiting for admin approval" etc.

Had to keep it short.

>What is a PPT algorithm? Describe in a teaching manner with accent on readability.

Response: A PowerPoint (PPT) algorithm is a set of instructions that can be used to automate tasks in Microsoft PowerPoint, a presentation software application. The algorithm can be written in a programming language such as Visual Basic for Applications (VBA) or JavaScript, and it can be used to automate repetitive tasks such as creating slides, inserting images, and formatting text.
To create a PPT algorithm, you need to first identify the task you want to automate. For example, you may want to create a presentation with a specific number of slides, or you may want to insert images from a specific folder. Once you have identified the task, you can start writing the code that will perform the task.
The code for a PPT algorithm should be written in a way that is easy to understand and follow. It should be clear and concise, and it should include comments that explain the purpose of each line of code. The code should also be modular and well-organized, meaning that it is broken down into smaller, manageable functions or procedures that perform specific tasks.

What are the chances that Meta will fully open source LLaMa?

Google search uses BERT, you're just calling a language model via an API.

Perhaps SpIF-T?

Right, but you're still loading the full GPT4 to do that.

The idea is that domain-specific chatbots might have better performance at a given model size. You can see this with StableDiffusion models, the ones trained on just a few styles have much higher quality than the base model - but only for those styles.

This is basically the idea behind mixture of experts.

This is /r/MachineLearning, can you actually give some quality context as to what this post is showing off.

You started from the same starting point as Stanford. What did you do differently, and why? What are your results?

Basic technical writing things here man, otherwise this post is kinda useless. I don't doubt that a bright 16 year old kid can do some great work, but I don't yet see any substance behind your claims of novelty right now.

Give us the juicy deets!

Hi, thanks for acknowledging the novelty of our work and finding our paper a good read. We look forward to releasing our code so yourself and others can experiment with the different SIFT transformations. And yes, first time sparsity is being used to improve the accuracy!

Whoa...this is the first time I've seen weight sparsity being used to actually improve accuracy! :O The paper was a pleasant read, and the method is simple but novel. Nice work.. I look forward to experimenting with these transformations in my own work once the code is out...

Prompt:
What is a PPT algorithm? Describe in a teaching manner with accent on readability.

Cool, I got the weights and see people have had some success running on single GPU's using 8bit instead of 16bit etc, but haven't tried myself yet

Wait you got that trained in one day on a single GPU? This stuff is going so fast

I used Google Colab notebook for the training, pro version. GPU, high-ram. My Macbook would probably die just by loading the model

What hardware are you running on and would it be possible to make a tutorial for how to run and do this training if it is possible on just a regular single GPU?

LIMITATIONS:

- English language only

- The code generated by this model tend to be incorrect, just like with ChatGPT

- Knowledge cutoff is in 2020

- tends to cut off longer responses (working to fix that)

​

Here is an example comparison: Question: What is the true nature of consciousness, and how does it emerge from the complex interactions of neurons in the brain? Stanford Alpaca: The true nature of consciousness is still a mystery, but research suggests that it emerges from the complex interactions of neurons in the brain. Neurons communicate with each other through electrical and chemical signals, and when these signals reach a certain threshold, consciousness is said to emerge.

My model: Consciousness refers to our subjective experience of the world around us, including thoughts, feelings, sensations, perceptions, and memories. It is often described as "what it feels like to be me," or "the inner life." The exact nature of consciousness remains a mystery, but scientists believe that it arises from the complex interactions between billions of neurons in the brain. Neurons are specialized cells that transmit electrical signals throughout the nervous system, allowing for communication between different parts of the body and the brain. These signals can create patterns of activity within specific regions of the brain, which may give rise to various mental states such as attention, memory, emotion, and awareness. Researchers have also found evidence suggesting that consciousness extends beyond the boundaries of the physical brain, suggesting that it may be more than just a product of biological processes. However, further research is needed to fully understand the intricacies of consciousness and how it emerges from the brain's neural networks.

We're really running out of acronyms at this point.

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same case，how kidding

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I think I'd look for a different acronym to SIFT, given that's a very well known feature detector and descriptor in computer vision...

Scale-Invariant Feature Transforms?

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Either it’s an easy problem where 98% - 100% accuracy on samples this size is just typical and not really worth publishing, or (not exclusive) the study is flawed.

One could get a totally independent data set of FNA images with these features extracted from different patients in different years, etc. and run their random forest on those. If it gets 98% - 100% accuracy then this is not a hard problem (the feature engineering might have been hard—not taking away from that if so!). If it fails miserably or just gets waaaay lower that 100% you know the study was flawed.

There are so many ML neophytes making “rookie mistakes” with this stuff who don’t fully grasp basic concepts that I think you always need a totally new independent test set that the authors didn’t have access to in order to really test it. That’s even a good idea for experts to be honest.

The paper’s conclusion is likely wrong either way; i.e., that Random Forests are “superior” for this application. Did they get an expert in XGBoost, neural networks, etc and put as much time and effort into those techniques using the same training and test sets to see if they also got 99% - 100%? It didn’t appear so from my cursory glance.

Good point, i'll open source the weights tomorrow as well