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AI helps household robots cut planning time in half

Researchers from MIT CSAIL came up with PIGINet, a novel machine learning-based system that improves household robots' efficiency by using multimodal input sequences to anticipate and select feasible task plans, reducing planning time and enabling adaptability in diverse environments. ....

United States , South Korea , Zhutian Yang , Leslie Kaelbling , University Of Washington Professor Dieter Fox , National Science Foundation , Office Of Scientific Research , Korea Advanced Institute Of Science , Army Research Office , Robotics Research , Artificial Intelligence Laboratory , Department Of Electrical Engineering , Investigator Leslie Pack , Beomjoon Kim Phd , Graduate School , Korea Advanced Institute , Caelan Garrett , Electrical Engineering , Computer Science , Senior Director , Washington Professor Dieter , Air Force Office , Scientific Research , Army Research , Mit Csail , Earning And Intelligent Systems Group ,

PyTorch 2.0: Our next generation release that is faster, more Pythonic and Dynamic as ever

An open source machine learning framework that accelerates the path from research prototyping to production deployment. ....

Pytorch Dtensor , Mkldnn One , Pytorch Distributedtensor Dtensor , Single Program Multiple Devices , Arm Compute Library , Team Pytorch , Pytorch Conference , Dynamic Shapes , Accelerated Transformers , Better Transformers , Performance Shaders , Pytorch Transformer , Transformer Decoders , Transformer Encoder , Self Attention , Accelerated Pytorch , Python Frame Evaluation Hooks , Stable Diffusion , With Pytorch , Memory Efficient Attention , Geomean Speedup , Message Passing , Compressed Sparse Row , Automatic Mixed Precision , Torch Distributedtensor , Pytorch Tensor Parallelism ,

PyTorch 1.13 release, including beta versions of functorch and improved support for Apple's new M1 chips.

We are excited to announce the release of PyTorch® 1.13 (release note)! This includes Stable versions of BetterTransformer. We deprecated CUDA 10.2 and 11.3 and completed migration of CUDA 11.6 and 11.7. Beta includes improved support for Apple M1 chips and functorch, a library that offers composable vmap (vectorization) and autodiff transforms, being included in-tree with the PyTorch release. This release is composed of over 3,749 commits and 467 contributors since 1.12.1. We want to sincerely thank our dedicated community for your contributions.

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Pytorch Apis , Pytorch Torchtext , Mlperf Bert , Pytorch Bettertransformer , Autocast Apis , Prototype Limited Python , Arm Compute Library , Technology Apis , Team Pytorch , Nested Tensors , Torch Bettertransformer , Better Transformer , Transformer Encoder , Nested Tensor , Tracing Technology , Intel Cooper Lake Processor , Sapphire Rapids , Intel Cooper Lake , Compute Library , Thread Sanitizer , Limited Python ,

The Illustrated Retrieval Transformer

Discussion: Discussion Thread for comments, corrections, or any feedback.

Summary: The latest batch of language models can be much smaller yet achieve GPT-3 like performance by being able to query a database or search the web for information. A key indication is that building larger and larger models is not the only way to improve performance.

The last few years saw the rise of Large Language Models (LLMs) – machine learning models that rapidly improve how machines process and generate language. Some of the highlights since 2017 include:

The original Transformer breaks previous performance records for machine translation.
BERT popularizes the pre-training then finetuning process, as well as Transformer-based contextualized word embeddings. It then rapidly starts to power Google Search and Bing Search.
GPT-2 demonstrates the machine’s ability to write as well as humans do.
First T5, then T0 push the boundaries of transfer le ....

Discussion Thread , Large Language Models , Google Search , Improving Language Models , Separating Language Information , World Knowledge , Transformer Encoder , Illustrated Transformer , Chunked Cross Attention ,