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jonathanbgn.com
Since the deep learning wave started in the early 2010s, there has been much hype and disappointments. I feel that a big part of this is due to high expectations driven by research progress that do not translate so well in real-world applications. Hopefully, self-supervised learning might be able to close the gap between these two worlds. This learning paradigm is not new, but it has seen a resurgence of interest over the past few years thanks to mediatized successes like GPT-3 or BERT. Many AI pundits h... |
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bergum.medium.com
Three mistakes when introducing embeddings and vector search Representing unstructured data as embedding vectors and embedding-based retrieval (EBR) using vector search is more popular than ever ... |
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haifengl.wordpress.com
Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree... |
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