Navigating Data Rights In AI

Compiler

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اضافه شده در four سال پیش

محتوای ارائه شده توسط Red Hat. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Red Hat یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

The Knowledge Project with Shane Parrish

1
Katharine Graham: The Woman Who Took Down a President [Outliers] 57:36

11 روز پیش57:36

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لیست ها

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دوست داشته شد

57:36

When Katharine Graham took over the Washington Post in 1963, she was a shy socialite who'd never run anything. By retirement, she'd taken down a president, ended the most violent strike in a generation, and built one of the best-performing companies in American history. Graham had no training, no experience, not even confidence. Just a newspaper bleeding money and a government that expected her to fall in line. When her editors brought her stolen classified documents, her lawyers begged her not to publish. They said it would destroy the company. She published them anyway. Nixon came after her, attacking her with the full force of the executive. Then Watergate. For nearly a year she was ridiculed and isolated while pursuing the story that would eventually bring down the president. Graham proved that you can grow into a job that initially seems impossible and no amount of training can substitute for having the right values and the courage to act on them. ------ 10 Lessons from Katharine Graham: ⁠⁠ ⁠https://fs.blog/knowledge-project-podcast/outliers-katharine-graham/ ------ Approximate timestamps: (00:00) Start (02:19) The Making of an Unlikely Heiress (10:15) The Education of a Publisher’s Wife (22:16) Learning to Lead (30:46) Becoming a Media Titan (44:12) Legacy (47:59) Reflections + Lessons ------ Thanks to ReMarkable for sponsoring this episode. Get your paper tablet at reMarkable.com today ------ Upgrade : Get a hand edited transcripts and ad free experiences along with my thoughts and reflections at the end of every conversation. Learn more @ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/membership⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠ ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it’s completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish X ⁠@ShaneAParrish⁠ Insta ⁠@farnamstreet⁠ ⁠LinkedIn⁠ ------ This episode is for informational purposes only and contains the lessons I learned reading her memoir, Personal History and watching Becoming Katharine Graham. ------ Check out our website for all stock video and photo credits. Episode photo sourced from: iwmf.org/community/katharine-graham/ Learn more about your ad choices. Visit megaphone.fm/adchoices…

حدود یک سال پیش 38:42

MP3•خانه قسمت

Copyright infringement is a huge issue for AI training and use. Can LLMs give you copyrighted content? What data can you use to train and tune your own model? In this episode of Compiler, we explore who owns what when AI models learn from protected content—and why it matters.

77 قسمت

Navigating Data Rights In AI

Compiler

11,488 subscribers

published حدود یک سال پیش

اشتراک گذاری

MP3•خانه قسمت

77 قسمت

همه قسمت ها

1
Context And The True "Cost" Of AI 41:12

۹ weeks پیش41:12

41:12

Sure, AI has made a splash. And it's on us to level up, learn the ropes, and roll with it. But how do we even do that? And what cool human stuff might we accidentally ditch along the way? The Compiler team ends the season discussing the importance of context, creativity, and applied knowledge.

1
Agentic AI: Working As Instructed 37:36

11 weeks پیش37:36

37:36

Agentic AIs are showing promise for tedious work. But it’s hard to explain exactly how you want it done—and getting it wrong could create big problems. This episode of Compiler investigates how Agentic AIs could carry out their tasks and how some agents are taking their baby steps in the wide world. The team also considers the difficulties humans have expressing what we want computers to do for us, and how that could create unintended consequences.…

1
Breaking Down AI Biases 34:27

13 weeks پیش34:27

34:27

Does your health insurance chatbot need to tell jokes? No. Does it need to be accurate? Absolutely. That's hard when biases get in the way. The introduction of bias into a model can be unintentional, but it can have significant consequences for those relying on its guidance. The Compiler team examines the ways bias can creep in, and what steps can be taken to address it.…

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Diagnosing and Dispelling AI Hallucinations 33:43

15 weeks پیش33:43

33:43

AI is notorious for making stuff up. But it doesn’t always tell you when it does. That’s a problem for users who may not realize hallucinations are possible. This episode of Compiler investigates the persistent problem of AI Hallucination. Why does AI lie? Do these AI models know they’re hallucinating? What can we do to minimize hallucinations—or at least get better at seeing them?…

1
Chasing Its Own Tail 36:43

17 weeks پیش36:43

36:43

With the massive flow of AI-generated content onto the internet, it was only a matter of time until all of those bits of data found their way back into AI models. But what do you get when generative AI models start getting their answers from that content? The Compiler team digs into AI feedback loops, and the unique challenges they present for technologists...and everyone else.…

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