On-chain solutions
Block size increase
The bigger the block, the more transactions it can carry. Block size was recently increased by 8KB to 72KB (a 12.5% increase); further increases will be applied over time based on ongoing system monitoring and overall network health.
Pipelining
Improves block propagation times by coalescing validation and propagation. The goal is for blocks to be propagated to at least 95% of peers within five seconds by reducing the โdead timeโ between blocks (the block propagation overhead). This provides the headroom to make more aggressive scaling changes, such as increasing block size/increasing Plutus parameter limits.
Input Endorsers
Input endorsers improve block propagation times and throughput by allowing transactions to be separated into pre-constructed blocks. This improves the consistency of block propagation times and allows higher transaction rates.
Memory /CPU parameters for Plutus
Memory usage is more efficient across the chain. Specifically, there are memory improvements in Unspent Transaction Output (UTXO) handling, stake distribution, live stake distribution and pools, and hash representation.
Plutus script enhancements
Even more effective usage of the powerful EUTXO model through smart contract optimization, including:
- Reference inputs (CIP-0031) โ Plutus scripts can inspect transaction inputs without needing to spend them. This means that it is not necessary to create UTXOs simply to inspect the information held by an input.
- Plutus Datums (CIP-0032) โ Datums can be attached directly to outputs instead of datum hashes. This simplifies how datums are used, as a user can see the actual datum rather than having to supply the datum that matches the given hash.
- Script sharing (CIP-0033) โ Plutus script references can be associated with transaction outputs, meaning that they can be recorded on-chain for subsequent reuse. It will not be necessary to supply a copy of the script with each transaction, hugely reducing friction for developers. Reusing scripts in multiple transactions significantly reduces transaction sizes, improving throughput and reducing script execution costs.
Node enhancements
Improvements will help even distribution of stake and reward computations across the epochs, thus providing greater headroom for block size increases. Also, memory usage is now more efficient. Memory compaction reduces RSS footprint, and memory sh
... keep reading on reddit โกDeep learning-based models dominate the contemporary landscape of production recommender systems. Modern recommender systems offer a plethora of real-world applications. Thanks to deep neural network models of ever-increasing size, they have made incredible progress.
However, the training of such models is challenging even within industrial-scale data centers. This challenge stems from the training computationโs startling heterogeneityโthe modelโs embedding layer could account for more than 99.99 percent of the overall model size. The entire process is exceedingly memory-intensive, while the rest of the neural network (NN) becomes progressively computation-intensive.
PERSIA (parallel recommendation trainingย system with hybridย acceleration), an efficient distributed training system based on a revolutionary hybrid training algorithm, has been unveiled by a research team from Kwai Inc., Kuaishou Technology, and ETH Zรผrich. This approach provides training efficiency and accuracy for extensive deep learning recommender systems with up to 100 trillion parameters. The researchers have carefully co-designed the optimization method and the distributed system architecture.
Quick Read: https://www.marktechpost.com/2021/12/05/researchers-introduce-persia-a-pytorch-based-system-for-training-large-scale-deep-learning-recommendation-models-up-to-100-trillion-parameters/
Paper: https://arxiv.org/pdf/2111.05897.pdf
Github: https://github.com/persiaml/persia
I would like to have a tool that would suggest song parameters (e.g. tempo, genre, chord progression, scale, specific instruments to use, etc.) so that I could challenge myself to get out of my comfort zone when writing new music. I could just come up with a list myself but it would probably be biased and wouldnโt help me become a better producer in the long run. It would be nice if it wasnโt just totally random but threw out parameters that also made sense in a music genre sort of way.
The closest thing I could find was this Patch card game but itโs more for modular synthesis than general electronic music production. https://www.patchtcg.com
GPT-4 will be human brain scale(One hundred trillion parameters)
Unfortunately, That wonโt be ready for several years
https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/
Microsoft Research Asia has upgraded their Swin Transformer with a new version featuring three billion parameters to train images with resolutions up to 1,536 x 1,536 and advance the SOTA on four representative vision benchmarks.
Here is a quick read: Microsoft Asiaโs Swin Transformer V2 Scales the Award-Winning ViT to 3 Billion Parameters and Achieves SOTA Performance on Vision Benchmarks.
The associated code will be available on the projectโs GitHub. The paper Swin Transformer V2: Scaling Up Capacity and Resolution is on arXiv.
Hi everyone!
I'm looking to have a block where I can change an attribute within the enhanced attribute manager and have it take effect on my linear parameter.
So far I have my attribute and my linear parameter separated, which means i have to enter 2 different values. This tree block is only concentric circles.
I'm wondering if this is possible, otherwise I might drop the attribute and stick with the parameter. I may be overthinking my way here. Let me know what you guys have to say!
Deep learning is one area of technology where ambitiousness has no barriers. According to a recent announcement byย The Beijing Academy of Artificial Intelligence (BAAI), in China, yet another milestone has been achieved in the field with its โWu Daoโ AI system. Theย GPT 3ย brought in new interest for all the AI researchers, the super scale pre training models. By this approach and making use of 175 billion parameters, it managed to achieve exceptional performance results across the natural language processing tasks (NLP). However, the lacking component is its inability to have any form of cognitive abilities or common sense. Therefore, despite the size, even these models cannot indulge in tasks such as open dialogues, visual reasoning, and so on. With Wu Dao, the researchers plan to address this issue. This is Chinaโs first attempt at a home-grown super-scale intelligent model system.
Deep learning-based models dominate the contemporary landscape of production recommender systems. Modern recommender systems offer a plethora of real-world applications. Thanks to deep neural network models of ever-increasing size, they have made incredible progress.
However, the training of such models is challenging even within industrial-scale data centers. This challenge stems from the training computationโs startling heterogeneityโthe modelโs embedding layer could account for more than 99.99 percent of the overall model size. The entire process is exceedingly memory-intensive, while the rest of the neural network (NN) becomes progressively computation-intensive.
PERSIA (parallel recommendation trainingย system with hybridย acceleration), an efficient distributed training system based on a revolutionary hybrid training algorithm, has been unveiled by a research team from Kwai Inc., Kuaishou Technology, and ETH Zรผrich. This approach provides training efficiency and accuracy for extensive deep learning recommender systems with up to 100 trillion parameters. The researchers have carefully co-designed the optimization method and the distributed system architecture.
Quick Read: https://www.marktechpost.com/2021/12/05/researchers-introduce-persia-a-pytorch-based-system-for-training-large-scale-deep-learning-recommendation-models-up-to-100-trillion-parameters/
Paper: https://arxiv.org/pdf/2111.05897.pdf
Github: https://github.com/persiaml/persia
GPT-4 will be human brain scale(One hundred trillion parameters)
Unfortunately, That wonโt be ready for several years.
https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/
Microsoft Research Asia has upgraded their Swin Transformer with a new version featuring three billion parameters to train images with resolutions up to 1,536 x 1,536 and advance the SOTA on four representative vision benchmarks.
Here is a quick read: Microsoft Asiaโs Swin Transformer V2 Scales the Award-Winning ViT to 3 Billion Parameters and Achieves SOTA Performance on Vision Benchmarks.
The associated code will be available on the projectโs GitHub. The paper Swin Transformer V2: Scaling Up Capacity and Resolution is on arXiv.
Microsoft Research Asia has upgraded their Swin Transformer with a new version featuring three billion parameters to train images with resolutions up to 1,536 x 1,536 and advance the SOTA on four representative vision benchmarks.
Here is a quick read: Microsoft Asiaโs Swin Transformer V2 Scales the Award-Winning ViT to 3 Billion Parameters and Achieves SOTA Performance on Vision Benchmarks.
The associated code will be available on the projectโs GitHub. The paper Swin Transformer V2: Scaling Up Capacity and Resolution is on arXiv.
GPT-4 will be human brain scale(One hundred trillion parameters)
Unfortunately, That wonโt be ready for several years.
https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/
GPT-4 will be human brain scale(One hundred trillion parameters)
Unfortunately, That wonโt be ready for several years
https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/
GPT-4 will be human brain scale(One hundred trillion parameters)
Unfortunately, That wonโt be ready for several years.
https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/
Deep learning is one area of technology where ambitiousness has no barriers. According to a recent announcement byย The Beijing Academy of Artificial Intelligence (BAAI), in China, yet another milestone has been achieved in the field with its โWu Daoโ AI system. Theย GPT 3ย brought in new interest for all the AI researchers, the super scale pre training models. By this approach and making use of 175 billion parameters, it managed to achieve exceptional performance results across the natural language processing tasks (NLP). However, the lacking component is its inability to have any form of cognitive abilities or common sense. Therefore, despite the size, even these models cannot indulge in tasks such as open dialogues, visual reasoning, and so on. With Wu Dao, the researchers plan to address this issue. This is Chinaโs first attempt at a home-grown super-scale intelligent model system.
