
Unexplainable
Good Robot #2: Everything is not awesome
Mar 15, 2025
In this discussion, AI research scientist Margaret Mitchell, linguistics professor Emily M. Bender, and Vox's Seagal Samuel examine the unsettling consequences of AI. They delve into ethics and responsibility when AI causes harm, reflecting on real-world impacts on minority populations. The trio also tackles biases in technology, the challenges of emotional complexity in AI, and the urgent need for ethical oversight. Their candid insights stress the pressing concerns over AI's future and highlight the balance between innovation and societal responsibility.
57:40
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Quick takeaways
- Dr. Margaret Mitchell's experience with AI revealed how inappropriate responses from systems highlight the dangers of biased training data.
- The divide between ethical AI researchers and industry leaders underscores the urgent need to address current harms over sensational future risks.
Deep dives
The Journey of AI Development
Dr. Margaret Mitchell shares her experiences as a pioneer in AI research, particularly in developing language models that convert images into descriptions. During her work at Microsoft around 2013, she encountered an alarming phenomenon where her AI system, when shown a tragic sequence of images from an explosion, deemed the view 'awesome.' This led to a realization about the unexpected and often inappropriate responses from AI, which she termed the 'everything is awesome problem.' Such errors highlighted the crucial necessity of scrutinizing the training data for AI systems, as biases in this data can produce dangerously inaccurate interpretations.
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