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1/n It's very curious that subjectivity is an objective fact, and that our universe is evolving towards greater diversity in subjective experiences. Furthermore, in our present timeline, humans have invented subjective machines (i.e., deep learning and large language models)!

Introducing 𝚅𝚒𝚎𝚠𝙲𝚛𝚊𝚏𝚝𝚎𝚛 🥳. 𝚅𝚒𝚎𝚠𝙲𝚛𝚊𝚏𝚝𝚎𝚛 can generate high-fidelity novel views from single or sparse input images with accurate camera pose control! ✨Paper: arxiv.org/abs/2409.02048 🎯Code: github.com/Drexubery/View… 🥁Demo: huggingface.co/spaces/Doubiiu…

Here's a quick attempt at some parody. Disclaimer: The above statement is a work of parody and is not an actual quote from a well known personality. It is intended for humorous purposes only and falls under the protection of fair use. This parody does not represent the views

"Well, you see, this whole cannabis thing—it’s a sneaky, anti-capitalist ploy, right under our noses. Alcohol is the backbone of Western social structures; it’s competitive, entrepreneurial—you're out there with a drink in hand, making deals, facing challenges. But cannabis? It’s

America First! 🇺🇸 (unless Russia pays us more) 🇷🇺 #GOPutin

Interestingly, Microsoft uses RWKV (a transformer alternative with lower resource usage) to offload multilingual work for their Phi model. Microsoft has installed this in ~1.5B computers.

Automated Programming, Symbolic Computation, Machine Learning: A Personal View Programming, the art of crafting instructions for machines, stands as a cornerstone of modern automation. Yet, the very act of programming remains a laborious and error-prone endeavor, demanding

$2000/month subscription? theinformation.com/articles/opena…

The technique that drives Reflection 70B is simple, but very powerful. Current LLMs have a tendency to hallucinate, and can’t recognize when they do so. Reflection-Tuning enables LLMs to recognize their mistakes, and then correct them before committing to an answer.

Much worse than a hallucinating GPT-3 class language model!

1/n Assessing Reasoning in Large Language Models Large Language Models (LLMs) for genuine causal reasoning remains a subject of debate. This paper delves into this debate, arguing that relying solely on accuracy metrics to assess LLM reasoning is insufficient and proposing a