12 Posts
Itching to get your hands on a fully trained large language model? The wait is over. Meta introduced the OPT family of transformer-based language models with nearly unfettered access to source code and trained weights.
Itching to get your hands on a fully trained large language model? The wait is over. Meta introduced the OPT family of transformer-based language models with nearly unfettered access to source code and trained weights.
The trend toward ever-larger models crossed the threshold from immense to ginormous. Google kicked off 2021 with Switch Transformer, the first published work to exceed a trillion parameters, weighing in at 1.6 trillion.
The trend toward ever-larger models crossed the threshold from immense to ginormous. Google kicked off 2021 with Switch Transformer, the first published work to exceed a trillion parameters, weighing in at 1.6 trillion.
The cost of training top-performing machine learning models has grown beyond the reach of smaller companies.
The cost of training top-performing machine learning models has grown beyond the reach of smaller companies.
Google will upgrade its search engine with a new model that tracks the relationships between words, images, and, in time, videos — the first fruit of its latest research into multimodal machine learning and multilingual language modeling.
Google will upgrade its search engine with a new model that tracks the relationships between words, images, and, in time, videos — the first fruit of its latest research into multimodal machine learning and multilingual language modeling.
A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture...
A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture...
Performance in language tasks rises with the size of the model — yet, as a model’s parameter count rises, so does the time it takes to render output. New work pumps up the number of parameters without slowing down the network.
Performance in language tasks rises with the size of the model — yet, as a model’s parameter count rises, so does the time it takes to render output. New work pumps up the number of parameters without slowing down the network.
Stay updated with weekly AI News and Insights delivered to your inbox