The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Sam Charrington
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Jan 9, 2023 • 1h 6min

Supporting Food Security in Africa Using ML with Catherine Nakalembe - #611

Catherine Nakalembe, an Associate Research Professor at the University of Maryland and Africa Program Director at NASA Harvest, discusses her work on food security in Africa through machine learning and satellite data. She highlights the critical challenges of food insecurity exacerbated by climate change and the COVID-19 pandemic. Catherine delves into innovative techniques like multi-task learning for crop mapping and emphasizes the importance of improving access to remote sensing data. Collaborations and local initiatives are vital for enhancing agricultural productivity and sustainability.
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Jan 2, 2023 • 39min

Service Cards and ML Governance with Michael Kearns - #610

In a fascinating discussion, Michael Kearns, a UPenn professor and Amazon Scholar, delves into the vital topics of AI governance and fairness. He describes the innovative service cards introduced at Amazon, emphasizing their holistic approach compared to traditional model cards. Kearns also tackles the ongoing debate surrounding algorithmic versus dataset bias, reflecting on current challenges in fairness within large language models. His insights shed light on the importance of responsibly addressing these issues in AI development.
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39 snips
Dec 29, 2022 • 41min

Reinforcement Learning for Personalization at Spotify with Tony Jebara - #609

Tony Jebara, VP of Engineering and Head of Machine Learning at Spotify, shares insights on how the platform evolves its personalization strategies through reinforcement learning. He explains the balance between immediate rewards and long-term user engagement, emphasizing the importance of Lifetime Value (LTV) in enhancing subscription retention. Jebara discusses innovative approaches in user behavior modeling, using coin and dice analogies to illustrate preferences. Learn how Spotify is transforming recommendations to create a richer user experience!
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24 snips
Dec 26, 2022 • 37min

Will ChatGPT take my job? - #608

In this intriguing conversation with ChatGPT, a groundbreaking AI language model from OpenAI, the discussion dives into the pressing question of whether AI will take our jobs. ChatGPT explores its own capabilities in conversational context, revealing both opportunities and challenges for the workforce. The model discusses how it interprets human emotions and the nuances of effective communication, while also reflecting on the irreplaceable human touch in interviews. Finally, listeners are left to ponder the future role of AI in their careers.
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Dec 22, 2022 • 37min

Geospatial Machine Learning at AWS with Kumar Chellapilla - #607

Join Kumar Chellapilla, General Manager of ML and AI Services at AWS, as he delves into the exciting world of geospatial machine learning. He discusses the recent integration of geospatial data in SageMaker and its potential to revolutionize industries like agriculture and automotive. Kumar highlights how satellite imagery can enhance economic predictions and addresses challenges in data accessibility. He also forecasts the future of geospatial data, including intriguing intersections with generative models and digital twins.
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16 snips
Dec 19, 2022 • 44min

Real-Time ML Workflows at Capital One with Disha Singla - #606

Disha Singla, Senior Director of Machine Learning Engineering at Capital One, leads efforts to democratize ML across the company. She discusses the creation of reusable workflows and libraries designed for citizen data scientists, enabling quicker decision-making. The conversation dives into the implementation of real-time ML, balancing the need for speed in insights with regulatory compliance. Disha also shares strategies for securing executive buy-in by demonstrating ROI and emphasizes the importance of rigorous testing and observability in ML models.
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18 snips
Dec 15, 2022 • 47min

Weakly Supervised Causal Representation Learning with Johann Brehmer - #605

Join Johann Brehmer, a research scientist at Qualcomm AI Research, as he dives into the intriguing world of causal representation learning. He shares insights from his work, emphasizing how high-level causal representations can be identified in weakly supervised environments. They discuss the implications of causality in machine learning, including advancements in autonomous driving and the challenges faced in achieving reliable systems. Brehmer also highlights innovative methodologies and new frameworks for optimizing neural networks and understanding complex causal mechanisms.
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22 snips
Dec 12, 2022 • 43min

Stable Diffusion & Generative AI with Emad Mostaque - #604

Emad Mostaque, Founder and CEO of Stability AI, shares the fascinating story behind Stability's creation and the impact of Stable Diffusion. He discusses how generative AI is poised to disrupt industries like gaming and film by revolutionizing content production. Emad dives into the ethical implications of AI, emphasizing user safety and artist attribution. He also explores innovations in AI-assisted design and the importance of open-source models. The conversation highlights partnerships that enhance AI deployment and the challenges of training AI models efficiently.
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25 snips
Dec 8, 2022 • 37min

Exploring Large Language Models with ChatGPT - #603

In a captivating dialogue with ChatGPT, the AI marvel from OpenAI, listeners delve into the world of large language models. ChatGPT reveals its impressive capabilities in generating human-like responses and discusses its training through supervised learning and PPO. Key topics include the importance of prompt engineering, the risks of misuse, and the fascinating use of AI in artistic creation. The conversation also touches on the challenges of bias and fairness in AI, leaving audiences with insights into the future of machine learning.
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11 snips
Dec 5, 2022 • 57min

Accelerating Intelligence with AI-Generating Algorithms with Jeff Clune - #602

In this insightful conversation, Jeff Clune, an Associate Professor at the University of British Columbia and a leading mind at the Vector Institute, discusses his pioneering research on AI-generating algorithms and the quest for artificial general intelligence (AGI). He shares the three pillars for achieving this goal—meta-learning architectures, algorithms, and auto-generating environments. The dialogue also tackles the ethical implications of evolving AI, the importance of safety measures, and innovative teaching techniques that could redefine AI learning in real-world applications.

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