com.openai.unity VS mup

I am trying to block the OpenAI website through Defender web filtering for Windows and macOS. It seems to block fine on Edge, but not for Chrome when trying to block the subdomains like chat.openai.com. If we try openai.com then this gets blocked fine on both Chrome and Edge.

NeuralPit platform allows team members to collaborate on projects and seamlessly upload and analyze data in various formats, such as PDF, DOCX, Excel, CSV, video, audio, website URLs, YouTube links, and images. Allow teams to unearth insights, brainstorm ideas, and visualize and interact with data anytime, anywhere. NeuralPit provides small and medium businesses, as well as professionals, with access to features similar to those of Microsoft Copilot and OpenAI, but at significantly lower costs, particularly for teams.

If you have been keeping up to date with the Spring ecosystem, you may have heard about the Spring AI Project, It is currently in its pre-release state but it provides an innovative abstraction toolkit fostering AI integration across applications. The experimental Spring AI project was introduced during the SpringOne conference and allows the creation of AI applications by using common concepts of Spring. Currently, the project integrates Azure OpenAI and OpenAI as AI backends. Use cases like content generation, code generation, semantic search, and summarization are supported by the project.

The official Todoist website can be found at https://todoist.com, and information about OpenAI is available at https://openai.com. The names Todoist and OpenAI as well as related names, marks, emblems, and images are registered trademarks of their respective owners.

A Generative Pre-Trained Transformer (GPT) is a type of Large Language Model (LLM) and they are the hot topic in the technology world this year and many companies are scrambling to add this technology to their products. Creating and training these large models can be a very complex, time consuming and expensive. You may think that you cannot use this technology since it is so complex and expensive but companies like OpenAI have done a ton of work to create useful models and setup platforms exposing APIs to use them. If you have ever used an API where you send some data in, it does some magic behind the scenes and you get some data to use in a response, then you can integrate this cutting edge technology into your application. Let’s take a look at how we can setup a Full stack web app which lets us ask questions sent to OpenAI and stream the response.

OpenAI

Our team is led by Elon Musk, CEO of Tesla and SpaceX. We have previously worked at DeepMind, OpenAI, Google Research, Microsoft Research, Tesla, and the University of Toronto. Collectively we contributed some of the most widely used methods in the field, in particular the Adam optimizer, Batch Normalization, Layer Normalization, and the discovery of adversarial examples. We further introduced innovative techniques and analyses such as Transformer-XL, Autoformalization, the Memorizing Transformer, Batch Size Scaling, and μTransfer. We have worked on and led the development of some of the largest breakthroughs in the field including AlphaStar, AlphaCode, Inception, Minerva, GPT-3.5, and GPT-4.

I believe tuning hyper parameters well without a lot of waste for the largest models was only figured out by Greg Yang/Microsoft Research around 2022 (cited in GPT-4 paper):

https://arxiv.org/abs/2203.03466

Also part of how they predicted the loss ahead of time so well.

This is the first time I have seen muP applied by the third party. See Cerebras Model Zoo, where muP models have scale-invariant constant LR.

I guess, but its actually not simple to do that, in my experience. There’s another paper on that: https://arxiv.org/abs/2203.03466

Why isn’t chinchilla running google AI chat or whatever then?

Well, but it isn't like this kind of research is new. Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (2022) tuned hyperparameters in 40M model, transferred it to 6.7B model, and beat OpenAI's 6.7B run. It is likely what OpenAI did is perfecting this kind of research. I note that four authors of that paper (Igor Babuschkin, Szymon Sidor, David Farhi, Jakub Pachocki) are credited for pretraining optimization & architecture at https://openai.com/contributions/gpt-4.

Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes: https://arxiv.org/abs/1910.12478 Tensor Programs II: Neural Tangent Kernel for Any Architecture: https://arxiv.org/abs/2006.14548 Tensor Programs III: Neural Matrix Laws: https://arxiv.org/abs/2009.10685 Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks: https://proceedings.mlr.press/v139/yang21c.html Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer: https://arxiv.org/abs/2203.03466

I think there remains an immense amount of such suboptimality still hanging from the tree, so to speak.

For example, our recent paper "Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer"[1] shows that even learning rate and initialization used by existing models are deeply wrong. By just picking them correctly (which involves some really beautiful mathematics), we can effectively double the model size of the GPT-3 6.7B model (to be comparable in quality to the 13B model across the suite of benchmark tasks).

Large neural networks behave in a way we are only beginning to understand well just because each empirical probe of any such model is so much more expensive and time consuming than typical models. But principled theory here can have a lot of leverage by pointing out the right direction to look, as it did in our work.

[1] http://arxiv.org/abs/2203.03466

On the hyperparameter front there seems to be some overlap with the recent hyperparameter transfer paper, which I get the impression Microsoft is going to try to scale, and which was referenced (and so is known) by the authors of this DeepMind paper. Which is to say, there's a good chance we'll be seeing models of this size trained with more optimal hyperparameters pretty soon.