In the news | The Register
There’s a new giant AI language model in town: enter Microsoft’s Turing-NLG system, which apparently contains a whopping 17 billion parameters, making it the largest publicly known model of its class yet.
In the news | Neowin
Transformer-based language generation models have enabled better conversational applications. Though they still have their shortcomings, which were recently exposed by a team at MIT, researchers continue improving them to build better, larger, and more robust models.
In the news | WinBuzzer
Microsoft has developed a Transformer-based language generation model that it describes as the largest ever made. This week, Microsoft AI & Research announced Turing NLG, which is twice the size of its nearest competitor.
In the news | WinBuzzer
Microsoft has released a new open-source library called DeepSpeed, which, when combined with its ‘ZeRO’ module can train 100 billion parameter models without using the resources traditionally associated with that.
In the news | ITPro
Microsoft has revealed its largest deep learning language model, the Turing Natural Language Generation (T-NLG), which is claimed to have a record-breaking 17 billion parameters. The T-NLG, according to Microsoft, outperforms the largest deep learning models to date: the University of Washington’s Grover-Mega and Nvidia’s MegatronLM, which…
In the news | MSPoweruser
Microsoft Research today announced DeepSpeed, a new deep learning optimization library that can train massive 100-billion-parameter models. In AI, you need to have larger natural language models for better accuracy. But training larger natural language models is time consuming and…
In the news | Future Decoded Mumbai CEO Summit
In the news | VentureBeat
Microsoft AI & Research today shared what it calls the largest Transformer-based language generation model ever and open-sourced a deep learning library named DeepSpeed to make distributed training of large models easier.
In the news | InfoWorld
Microsoft has released DeepSpeed, a new deep learning optimization library for PyTorch, that is designed to reduce memory use and train models with better parallelism on existing hardware.