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Sisällön tarjoaa Ronak Nathani, Guang Yang, Ronak Nathani, and Guang Yang. Ronak Nathani, Guang Yang, Ronak Nathani, and Guang Yang tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
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Pete Warden - On launching "AI in a Box" and building a hardware edge AI company - #24

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Manage episode 380617317 series 2838288
Sisällön tarjoaa Ronak Nathani, Guang Yang, Ronak Nathani, and Guang Yang. Ronak Nathani, Guang Yang, Ronak Nathani, and Guang Yang tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

What's "AI in a Box"? Pete Warden joins the show to share a new project he recently launched that encapulates Language Transcription/Translation and Question Answering capabilities into a wallet-sized board running locally without internet, as well as stories and learnings from building his new company, Useful Sensors, after 7 years of leading the tensorflow mobile project at Google.

Pete is the CEO of Useful Sensors. After founding his own company Jetpac and selling it to Google in 2014, he became a staff research engineer at Google, where he led the TensorFlow Mobile team. Pete is also the author of two well-received books: "Public Data Sources" and "Big Data Glossary" and builder of OpenHeatMap.

Show Notes:

Stay in touch:

✉️ Subscribe to our newsletter: https://softwaremisadventures.com

👋 Send feedback or say hi: hello@softwaremisadventures.com

Segments:

[0:00:00] Failing and trying again, experiential learning

[0:03:13] AI-in-a-box demo

[0:07:28] Animatronics?

[0:10:12] Privacy and trust

[0:12:04] Talk to your appliances

[0:15:22] How to fit the LLM into such a small chip?

[0:16:50] Quantization

[0:20:07] Disposable ML frameworks

[0:24:33] Updating model on shipped hardware

[0:28:34] LLMs with specialized domain knowledge

[0:30:08] Founding Useful Sensors

[0:37:07] scaling training vs inference

  continue reading

53 jaksoa

Artwork
iconJaa
 
Manage episode 380617317 series 2838288
Sisällön tarjoaa Ronak Nathani, Guang Yang, Ronak Nathani, and Guang Yang. Ronak Nathani, Guang Yang, Ronak Nathani, and Guang Yang tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

What's "AI in a Box"? Pete Warden joins the show to share a new project he recently launched that encapulates Language Transcription/Translation and Question Answering capabilities into a wallet-sized board running locally without internet, as well as stories and learnings from building his new company, Useful Sensors, after 7 years of leading the tensorflow mobile project at Google.

Pete is the CEO of Useful Sensors. After founding his own company Jetpac and selling it to Google in 2014, he became a staff research engineer at Google, where he led the TensorFlow Mobile team. Pete is also the author of two well-received books: "Public Data Sources" and "Big Data Glossary" and builder of OpenHeatMap.

Show Notes:

Stay in touch:

✉️ Subscribe to our newsletter: https://softwaremisadventures.com

👋 Send feedback or say hi: hello@softwaremisadventures.com

Segments:

[0:00:00] Failing and trying again, experiential learning

[0:03:13] AI-in-a-box demo

[0:07:28] Animatronics?

[0:10:12] Privacy and trust

[0:12:04] Talk to your appliances

[0:15:22] How to fit the LLM into such a small chip?

[0:16:50] Quantization

[0:20:07] Disposable ML frameworks

[0:24:33] Updating model on shipped hardware

[0:28:34] LLMs with specialized domain knowledge

[0:30:08] Founding Useful Sensors

[0:37:07] scaling training vs inference

  continue reading

53 jaksoa

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