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Practical AI with Chris Benson (Repeat)

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Manage episode 280296388 series 1433944
内容由Machine Learning Archives - Software Engineering Daily提供。所有播客内容(包括剧集、图形和播客描述)均由 Machine Learning Archives - Software Engineering Daily 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Originally published December 9, 2019

Machine learning algorithms have existed for decades. But in the last ten years, several advancements in software and hardware have caused dramatic growth in the viability of applications based on machine learning.

Smartphones generate large quantities of data about how humans move through the world. Software-as-a-service companies generate data about how these humans interact with businesses. Cheap cloud infrastructure allows for the storage of these high volumes of data. Machine learning frameworks such as Apache Spark, TensorFlow, and PyTorch allow developers to easily train statistical models.

These models are deployed back to the smartphones and the software-as-a-service companies, which improves the ability for humans to move through the world and gain utility from their business transactions. And as the humans interact more with their computers, it generates more data, which is used to create better models, and higher consumer utility.

The combination of smartphones, cloud computing, machine learning algorithms, and distributed computing frameworks is often referred to as “artificial intelligence.” Chris Benson is the host of the podcast Practical AI, and he joins the show to talk about the modern applications of artificial intelligence, and the stories he is covering on Practical AI. On his podcast, Chris talks about everything within the umbrella of AI, from high level stories to low level implementation details.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Practical AI with Chris Benson (Repeat) appeared first on Software Engineering Daily.

  continue reading

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Manage episode 280296388 series 1433944
内容由Machine Learning Archives - Software Engineering Daily提供。所有播客内容(包括剧集、图形和播客描述)均由 Machine Learning Archives - Software Engineering Daily 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Originally published December 9, 2019

Machine learning algorithms have existed for decades. But in the last ten years, several advancements in software and hardware have caused dramatic growth in the viability of applications based on machine learning.

Smartphones generate large quantities of data about how humans move through the world. Software-as-a-service companies generate data about how these humans interact with businesses. Cheap cloud infrastructure allows for the storage of these high volumes of data. Machine learning frameworks such as Apache Spark, TensorFlow, and PyTorch allow developers to easily train statistical models.

These models are deployed back to the smartphones and the software-as-a-service companies, which improves the ability for humans to move through the world and gain utility from their business transactions. And as the humans interact more with their computers, it generates more data, which is used to create better models, and higher consumer utility.

The combination of smartphones, cloud computing, machine learning algorithms, and distributed computing frameworks is often referred to as “artificial intelligence.” Chris Benson is the host of the podcast Practical AI, and he joins the show to talk about the modern applications of artificial intelligence, and the stories he is covering on Practical AI. On his podcast, Chris talks about everything within the umbrella of AI, from high level stories to low level implementation details.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Practical AI with Chris Benson (Repeat) appeared first on Software Engineering Daily.

  continue reading

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