enonimal
this was more a slam on python packaging in general, than it is on the OpenAI implementation.
I wouldn't be surprised if many of the issues under this topic are more related to Python package version nightmares, than OpenAI's Python implementation itself.
> # 1 Result: Python Packaging
Checks out
In Machine Learning, we might think of this idea as "setting your Learning Rate too high"
prompt engineering before it was cool
For instance, if there are three conversations that you can use to test if your AI is working correctly:
(1) HUMAN: "Please say hello"
AI: "Hello!"
AI: "Goodbye!"
AI: "4!"
Because Embeddings allow us to determine how similar in vibes things are, we have a tool with which we can automatically search over our dataset for things that have very different vibes, meaning that each evaluation run is more likely to return new information about how well the model is doing.
My question to the OP was mostly about whether or not this "vibe differentiated dataset" was constructed prior to the evaluation run, or populated gradually, based on each individual test case result.
so anyway it's just vibes man
XD
> Those pointers point to undocumented functions forming CUDA Dark API. It's impossible to tell how many of them exist, but debugging experience suggests there are tens of function pointers across tens of tables. A typical application will use one or two most common. Due to they undocumented nature they are exclusively used by Runtime API and NVIDIA libraries (and in by CUDA applications in turn). We don't have names of those functions nor names or types of the arguments. This makes implementing them time-consuming. Dark API functions are are reverse-engineered and implemented by ZLUDA on case-by-case basis once we observe an application making use of it.
1. Sam Altman started this company
2. He and other founders would benefit enormously if this was the way to solve the issue that AI raises, namely, "are you a human?"
3. Their mission statement:
> The rapid advancement of artificial intelligence has accelerated the need to differentiate between human- and AI-generated content online Proof of personhood addresses two of the key considerations presented by the Age of AI: (1) protecting against sybil attacks and (2) minimizing the spread of AI-generated misinformation World ID, an open and permissionless identity protocol, acts as a global digital passport and can be used anonymously to prove uniqueness and humanness as well as to selectively disclose credentials issued by other parties Worldcoin has published in-depth resources to provide more details about proof of personhood and World ID
1. Sam Altman started this company
2. He and other founders would benefit enormously if this was the way to solve the issue that AI raises, namely, "are you a human?"
3. Their mission statement:
> The rapid advancement of artificial intelligence has accelerated the need to differentiate between human- and AI-generated content online Proof of personhood addresses two of the key considerations presented by the Age of AI: (1) protecting against sybil attacks and (2) minimizing the spread of AI-generated misinformation World ID, an open and permissionless identity protocol, acts as a global digital passport and can be used anonymously to prove uniqueness and humanness as well as to selectively disclose credentials issued by other parties Worldcoin has published in-depth resources to provide more details about proof of personhood and World ID
1. Homomorphic Encryption
2. Self-Hosted AI Systems
Homomorphic encryption is the idea that you can perform ML operations on encrypted data -- so that the providers of powerful AI need not know precisely what data you have.
Self-Hosted AI is just what it sounds like -- what if your Alexa processed on-device, and never left your home? What if your mobile devices could "phone home" to it from wherever you were, using an encrypted tunnel?
The second point is possible today, but the issue is keeping your local model to pace with the cloud offerings.
TL;DR