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As language models show increasing power, a parallel trend has received less notice: The vogue for naming models after characters in the children’s TV show Sesame Street.
The previous state-of-the-art image classifier was trained on the ImageNet dataset plus 3.5 billion supplemental images from a different database. A new method achieved higher accuracy with one-tenth as many supplemental examples — and they were unlabeled, to boot.
More and more companies are developing machine learning models for internal use. But many are still struggling to bridge the gap to practical deployments.
The number of patents issued for deep learning has doubled every year since 2013. Inventor, engineer, and lawyer Nick Brestoff tracks deep learning patents. He detailed his findings in a blog on InsideBigData and offers advice on how to get patent applications approved.
As language models show increasing power, a parallel trend has received less notice: The vogue for naming models after characters in the children’s TV show Sesame Street.
Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. But what if one doctor is handier with a scalpel while another excels at suturing?
I’ve been reflecting on the NeurIPS 2019 conference, which ended on Saturday. It’s always a wonderful event, but this year I found it a bittersweet experience. Bitter because the conference has grown so much that we no longer focus on a handful of ideas.
The number of patents issued for deep learning has doubled every year since 2013. Inventor, engineer, and lawyer Nick Brestoff tracks deep learning patents. He detailed his findings in a blog on InsideBigData and offers advice on how to get patent applications approved.
I’ve been reflecting on the NeurIPS 2019 conference, which ended on Saturday. It’s always a wonderful event, but this year I found it a bittersweet experience. Bitter because the conference has grown so much that we no longer focus on...
Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. But what if one doctor is handier with a scalpel while another excels at suturing?
The previous state-of-the-art image classifier was trained on the ImageNet dataset plus 3.5 billion supplemental images from a different database. A new method achieved higher accuracy with one-tenth as many supplemental examples — and they were unlabeled, to boot.
More and more companies are developing machine learning models for internal use. But many are still struggling to bridge the gap to practical deployments.
I’ve been reflecting on the NeurIPS 2019 conference, which ended on Saturday. It’s always a wonderful event, but this year I found it a bittersweet experience. Bitter because the conference has grown so much that we no longer focus on a handful of ideas.
Some politicians view international competition in AI as an arms race. That mindset could lead to escalating conflict, experts said.
Some politicians view international competition in AI as an arms race. That mindset could lead to escalating conflict, experts said.
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