Sasha Luccioni

Sasha Luccioni, an AI sustainability expert, reveals the staggering environmental toll of large AI models and massive data centers. She advocates for smaller, more efficient AI that minimizes energy usage while maintaining performance. Luccioni emphasizes the importance of curated training data to reduce misinformation risks and highlights practical benefits like enhanced privacy and accessibility. With innovative applications in climate monitoring, she calls for transparency in AI's energy use and collective action to promote sustainable practices in the industry.

Artificial Intelligence seems more human-like and capable than ever before — but how did it get so good so quickly? Today, we’re pulling back the curtain to find out exactly how AI works. And we'll dig into one of the biggest problems that scientists are worried about here: The ability of AI to trick us. We talk to Dr. Sasha Luccioni and Professor Seth Lazar about the science.This episode contains explicit language.There’s also a brief mention of suicide, so please take care when listening. Here are some crisis hotlines:  United States: US National Suicide Prevention Lifeline 1-800-273-TALK (2755) (Online chat available); US Crisis Text Line Text “GO” to 741741 Australia: Lifeline 13 11 14 (Online chat available) Canada: Canadian Association for Suicide Prevention (See link for phone numbers listed by province) United Kingdom: Samaritans 116 123 (UK and ROI) Full list of international hotlines here  Find our transcript here: https://bit.ly/ScienceVsAIIn this episode, we cover:(00:00) 64,000 willies(05:13) A swag pope(06:36) Why is AI so good right now?(09:06) How does AI work? (17:43) Opening up AI to everyone(20:42) A rogue chatbot(27:50) Charming chatbots(29:42) A misinformation apocalypse?(33:16) Can you tell me something good?!(36:08) Citations, credits, and a special surprise… This episode was produced by Joel Werner, with help from Wendy Zukerman, Meryl Horn, R.E. Natowicz, Rose Rimler, and Michelle Dang. We’re edited by Blythe Terrell. Fact checking by Erica Akiko Howard. Mix and sound design by Jonathon Roberts. Music written by Bobby Lord, Peter Leonard, Emma Munger So Wylie and Bumi Hidaka. Thanks to all the researchers we spoke to including Dr Patrick Mineault, Professor Melanie Mitchell, Professor Arvind Narayanan, Professor Philip Torr, Stella Biderman, and Arman Chaudhry.Special thanks to Katie Vines, Allison, Jorge Just, the Zukerman Family and Joseph Lavelle Wilson. Science Vs is a Spotify Original Podcast. Follow Science Vs on Spotify, and if you wanna receive notifications every time we put out a new episode, tap the bell icon! Learn more about your ad choices. Visit podcastchoices.com/adchoices

Dr. Sasha Luccioni, the AI and Climate Lead at Hugging Face, discusses the hidden carbon costs of generative AI technologies like ChatGPT. She sheds light on the significant energy consumption in data centers and the collaborative efforts to analyze AI’s full lifecycle. Luccioni shares her journey from AI research to environmental advocacy, emphasizing the ethical implications of tech company's climate commitments. The conversation also tackles AI's potential role in mitigating climate change while highlighting the need for transparency and accountability in the industry.

AI researchers and climate researchers discuss the environmental impact of AI models, mining for essential minerals, reimagining transportation for sustainability, battery recycling, and using satellites and AI to monitor greenhouse gas emissions. They also explore the challenges of space debris collisions and harnessing AI for wind energy and electricity supply forecasting to mitigate climate change.

Sasha Luccioni discusses the environmental impact of generative AI, highlighting companies' rising emissions and struggles to meet climate goals. The chapter explores the energy consumption differences between traditional AI and generative AI, and the push for general AI models driven by major tech companies. It also addresses the lack of transparency in obtaining data on GPU energy usage and the tension between profit-driven tech companies and environmental responsibility.

Sasha Luccioni, AI and Climate lead at Hugging Face, dives into the environmental impact of AI models. She discusses her groundbreaking research on energy consumption, revealing stark contrasts between generative and task-specific models. The conversation highlights the importance of a standardized Energy Star rating system for AI models, aiming to guide users towards energy-efficient choices. Luccioni also tackles challenges in evaluating model performance and the need for transparency and ethical standards in AI research to promote sustainable practices.

Sasha Luccioni, an AI researcher and climate lead at Hugging Face, joins Azeem Azhar to discuss the environmental impact of AI, including energy consumption and carbon emissions. They explore the challenges of categorizing AI's climate impact and the importance of setting standards for generative AI models. They also touch on the challenges of AI infrastructure, existential risks, and the distracted focus on AI risks.

Sasha Luccioni, AI and Climate Lead, discusses the environmental impact of AI systems, quantifying emissions, efficient hardware, power-hungry processing, and biases in AI models. The podcast explores the challenges of mitigating carbon footprint in AI experimentation, hardware utilization, energy consumption, and lifecycle assessment of AI models, as well as the difficulties in measuring AI ethics and societal impacts.

Sasha Luccioni, an AI ethic researcher, discusses the current negative impacts of AI, including carbon emissions, copyright infringement, and biased information. She offers practical solutions for regulation to ensure inclusivity and transparency.

AI researchers Emma Strubell and Sasha Luccioni discuss the environmental impact of large language models, addressing carbon emissions, water and energy consumption. They emphasize the need for education, transparency, and awareness within the AI community. The podcast also covers AI's negative effects on dating apps, ethical concerns in relationship advice, debunking misconceptions about AI capabilities, and the potential negative impact of large language models in generating hateful content.