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المحتوى المقدم من DataStax and Charna Parkey. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة DataStax and Charna Parkey أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
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Using AI to Impact Performance Feedback Equity with Tacita Morway

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Manage episode 443179555 series 3604986
المحتوى المقدم من DataStax and Charna Parkey. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة DataStax and Charna Parkey أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Dive into the world of purposeful AI with Tacita Morway, CTO of Textio. Learn how Textio ensures their AI is built responsibly and ethically to transform the way teams communicate, hire, and measure their health. Discover their rigorous testing processes and the importance of having a diverse team to catch potential risks and how that helps the company develop strategies for avoiding bias and maintaining data privacy.

Episode timestamps

(02:15): Tacita's unconventional career path to becoming a CTO

(07:00): Textio's practices for building AI responsibly and ethically

(14:00) The impact of Textio's AI on performance feedback

(17:00) The importance of purpose-built vs generic AI models

(28:00) Balancing open source and proprietary data/models

(42:00) Advice for the AI industry moving forward

Quotes

Tacita Morway

“When you've got a team with different backgrounds, educational, lived experiences, identity, careers, all of those things, we have those different perspectives in the room. And we're all working off of the same expectations. We can catch each other's gaps.”

Charna Parkey

“There's an interesting conversation happening, I think, in the community right now about these purpose-built LLMs. Are they as good as generic LLMs? Sure, certainly if you're not going to apply something purpose-built to something generic or outside of its domain, it is not as good. But I think some of this shows us that unless you have something purpose-built and unless you're leveraging the data in the right way, you may just be feeding noise back into the system.”

Links

Connect with Tacita
Connect with Charna

  continue reading

103 حلقات

Artwork
iconمشاركة
 
Manage episode 443179555 series 3604986
المحتوى المقدم من DataStax and Charna Parkey. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة DataStax and Charna Parkey أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Dive into the world of purposeful AI with Tacita Morway, CTO of Textio. Learn how Textio ensures their AI is built responsibly and ethically to transform the way teams communicate, hire, and measure their health. Discover their rigorous testing processes and the importance of having a diverse team to catch potential risks and how that helps the company develop strategies for avoiding bias and maintaining data privacy.

Episode timestamps

(02:15): Tacita's unconventional career path to becoming a CTO

(07:00): Textio's practices for building AI responsibly and ethically

(14:00) The impact of Textio's AI on performance feedback

(17:00) The importance of purpose-built vs generic AI models

(28:00) Balancing open source and proprietary data/models

(42:00) Advice for the AI industry moving forward

Quotes

Tacita Morway

“When you've got a team with different backgrounds, educational, lived experiences, identity, careers, all of those things, we have those different perspectives in the room. And we're all working off of the same expectations. We can catch each other's gaps.”

Charna Parkey

“There's an interesting conversation happening, I think, in the community right now about these purpose-built LLMs. Are they as good as generic LLMs? Sure, certainly if you're not going to apply something purpose-built to something generic or outside of its domain, it is not as good. But I think some of this shows us that unless you have something purpose-built and unless you're leveraging the data in the right way, you may just be feeding noise back into the system.”

Links

Connect with Tacita
Connect with Charna

  continue reading

103 حلقات

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