GPT Reviews
Google's AI Overviews are improving to provide accurate and helpful information.
Nvidia's new embedding model, NV-Embed-v1, ranks number one on the Massive Text Embedding Benchmark.
Matryoshka Query Transformer (MQT) offers flexibility to Large Vision-Language Models (LVLMs) by encoding an image into a variable number of visual tokens during inference.
Contextual Position Encoding (CoPE) improves the position encoding method in Large Language Models (LLMs) and solves tasks where popular position embeddings fail.Β
Contact:Β Β sergi@earkind.com
Timestamps:
00:34 Introduction
01:35Β AI Overviews: About last week
03:58Β Nvidia Releases Embedding Model NV-Embed-v1
04:53Β Multi-camera YOLOv5 on Zynq UltraScale+ with Hailo-8 AI Acceleration
06:31 Fake sponsor
08:28Β Matryoshka Query Transformer for Large Vision-Language Models
10:24Β Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
11:51Β Contextual Position Encoding: Learning to Count What's Important
13:30 Outro