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merve's
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about 1 month ago
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3742
Meta AI vision has been cooking
@facebook
They shipped multiple models and demos for their papers at @ECCV 🤗
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
They shipped multiple models and demos for their papers at @ECCV 🤗
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
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singhsidhukuldeep's
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2 months ago
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1146
1 hour of OpenAi o1, here are my thoughts...
Here are my few observations:
- Slower response times: o1 can take over 10+ seconds to answer some questions, as it spends more time "thinking" through problems. In my case, it took over 50 seconds.
- Less likely to admit ignorance: The models are reported to be less likely to admit when they don't know the answer to a question.
- Higher pricing: o1-preview is significantly more expensive than GPT-4o, costing 3x more for input tokens and 4x more for output tokens in the API. With more thinking and more tokens, this could require houses to be mortgaged!
- Do we need this?: While it's better than GPT-4o for complex reasoning, on many common business tasks, its performance is just equivalent.
- Not a big deal: No comparisons to Anthropic or Google DeepMind Gemini are mentioned or included.
- This model tries to think and iterate over the response on its own! Think of it as an inbuilt CoT on steroids! Would love a technical review paper on the training process.
A must-read paper: https://cdn.openai.com/o1-system-card.pdf
Here are my few observations:
- Slower response times: o1 can take over 10+ seconds to answer some questions, as it spends more time "thinking" through problems. In my case, it took over 50 seconds.
- Less likely to admit ignorance: The models are reported to be less likely to admit when they don't know the answer to a question.
- Higher pricing: o1-preview is significantly more expensive than GPT-4o, costing 3x more for input tokens and 4x more for output tokens in the API. With more thinking and more tokens, this could require houses to be mortgaged!
- Do we need this?: While it's better than GPT-4o for complex reasoning, on many common business tasks, its performance is just equivalent.
- Not a big deal: No comparisons to Anthropic or Google DeepMind Gemini are mentioned or included.
- This model tries to think and iterate over the response on its own! Think of it as an inbuilt CoT on steroids! Would love a technical review paper on the training process.
A must-read paper: https://cdn.openai.com/o1-system-card.pdf
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Jaward's
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6 months ago
Post
1562
Very Insightful Read!!!
A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)
It outperforms current “cheat” RAG:)
This is how we achieve human-level intelligence, by modeling natural intelligence correctly!
Paper: https://arxiv.org/abs/2405.14831
A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)
It outperforms current “cheat” RAG:)
This is how we achieve human-level intelligence, by modeling natural intelligence correctly!
Paper: https://arxiv.org/abs/2405.14831
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