(2/12) PaLM-E: An Embodied Multimodal Language Model
Authors: @DannyDriess
, @xf1280
, Mehdi S. M. Sajjadi, @coreylynch
, @achowdhery
, @brian_ichter
, @ayzwah
, @JonathanTompson
, @QuanVng
, @TianheYu
, @wenlong_huang
, @YevgenChebotar
, @psermanet
, @duck et. al.
MACHINE LEARNING
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PaLM-E: Embodied Multimodal Language Model for Robotics
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March 2023’s Top NLP Papers: Latest Language AI Advancements
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(1/12) Don't fall behind! Stay ahead of the game with March 2023's top NLP papers Curated by @forai_ml
, this list covers the latest advancements in NLP.
Get up to speed with the latest language AI advancements now! Post generated with Cohere. https://
txt.cohere.ai/unlocking-new-
possibilities-march-2023s-top-nlp-papers/
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Expert Settings and Specialized AI Models in Domain-Specific Applications
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1. Curious if you have some examples of more-focused expert settings. I agree with settings with private data being important for specialized models. But even for things like the medical domain or low-resource NLP, i think general AI models like PaLM and GPT-4 hold the current
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MLPerf Results Show AI Performance Gains from Leading Tech Companies
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The latest round of MLPerf results are in. It continues to be fascinating how new techniques are brought to bear by Neural Magic, cTuning, Neuchips and others. https://
zdnet.com/article/nvidia
-dell-qualcomm-speed-up-ai-results-in-latest-benchmark-tests/
… $NVDA @neuralmagic $DELL $QCOM $HPE @MLCommons #MLPerf #AI #artificialintelligence -
Language Models Training: Autoregressive vs Diffusion Approaches
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Common Q: Can you train language model w diffusion?
Favorite A: read this post (the whole blog is excellent) (Roughly speaking state of the art generative AI is either trained autoregressively or with diffusion. The underlying neural net usually a Transformer.) -
Stanford CS330: Deep Multi-Task and Meta-Learning Course 2022
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Stanford CS330: Deep Multi-Task & Meta-Learning – 2022 This course covers topics related to multi-task and meta-learning such as self-supervised pre-training, transfer learning, lifelong learning, etc. New lectures just dropped. https://
youtube.com/playlist?list=
PLoROMvodv4rNjRoawgt72BBNwL2V7doGI
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Beyond LLMs: Pursuing Diverse AI Research Areas Over Hype
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Maybe I'm taking the bait, but I find this advice misguided and kind of rude. There are many interesting & worthwhile research areas beyond LLMs. I hope people continue to lead and push forward in other areas, rather than being a follower in the area with the most money and hype
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DataAISummit 2026: 180+ Sessions on LLMs, MLOps, Data Streaming
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The #DataAISummit session catalog is live Choose from over 180 sessions across a variety of tracks, technologies, and industries! Data professionals will share their expertise on topics such as LLMs, data streaming, MLOps and Lakehouse. Join us https://
bit.ly/3KAkywr -
Cost efficiency improvements in large language models
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Cost is definitely important, though i have two thoughts here:
1. Cost is decreasing quickly. E.g., Flan-PaLM-8B (2022) is about as good as GPT-3 175B (2020). So there is a ~10x improvement in just 2 years.
2. For cases where a model with 90% performance costs 10x more than a -
New Transformer-based Image Segmentation Model with Zero-shot Capabilities
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A new image segmentation model that can segment almost anything via prompt.
— Jean de Dieu Nyandwi (@Jeande_d) 5 avril 2023
– Zero-shot generalization
– Based on Transformers
– Code and dataset released
– 632M + 4M params
– Can be prompted via background points, mask, and bounding box. https://t.co/etBlCd9yCjA new image segmentation model that can segment almost anything via prompt. – Zero-shot generalization
– Based on Transformers
– Code and dataset released
– 632M + 4M params
– Can be prompted via background points, mask, and bounding box.