『The AI Paper Club』のカバーアート

The AI Paper Club

The AI Paper Club

著者： Deeper Insights
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 Are you interested in the fascinating world of data science and artificial intelligence? Then you have come to the right podcast! We've hand-picked some of the most knowledgeable and experienced experts from Deeper Insights, a UK AI consultancy, to join our hosts in discussing the latest academic papers, research, and theories in the field. From hands-on experience developing and working with machine learning models to mastering unstructured data management and computer vision, our guests have it all. And with their in-depth insights and knowledge, The AI Paper Club is the perfect podcast for anyone looking to expand their understanding of AI.Deeper Insights 科学
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  • 21: AI Unleashes Doom with Stable Diffusion

    21: AI Unleashes Doom with Stable Diffusion
    2024/10/29
    In the latest AI Paper Club podcast, hosts Rafael Herrera and Sonia Marques are joined by João Costa, Senior Machine Learning Software Engineer at Deeper Insights. Together, they explore the paper “Diffusion Models are Real-Time Game Engines,” produced by researchers at Google. This episode delves into the intriguing evolution of AI as it replicates the iconic game Doom using stable diffusion—an AI model typically associated with image generation.

    The team discusses the paper’s innovative methodology, detailing how stable diffusion models were adapted to generate frame-by-frame gameplay, capturing Doom’s game logic through AI. João unpacks the technical nuances behind the real-time generation of 20 frames per second using powerful TPU processors and explores the research’s practical applications and limitations.

    We also extend a special thank you to the Google DeepMind team for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://gamengen.github.io.

    For more information on all things artificial intelligence, machine learning, and engineering for your business, please visit www.deeperinsights.com or reach out to us at thepaperclub@deeperinsights.com.
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    26 分
  • 20: Technical Debt and Its Hidden Costs in Machine Learning Development

    20: Technical Debt and Its Hidden Costs in Machine Learning Development
    2024/09/26
    In this episode of the AI Paper Club Podcast, hosts Rafael Herrera and Sonia Marques sit down with senior machine learning engineer Bernardo Ramos from Deeper Insights. Together, they explore the classic 2015 paper "Hidden Technical Debt in Machine Learning Systems". The paper highlights the often-overlooked issue of technical debt in machine learning projects and how it silently accumulates over time, much like financial debt.

    The discussion delves into the nuances of technical debt, particularly how data dependencies differ from code dependencies and why they are harder to detect. The podcast also covers unstable data signals, feedback loops, and the unique challenges faced by large language models (LLMs) in today's data-driven world. Bernardo shares potential mitigation strategies to help manage these technical debts effectively.

    A special thank you to the authors D. Sculley, G. Holt, D. Golovin, and their team for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://dl.acm.org/doi/10.5555/2969442.2969519.

    For more information on artificial intelligence, machine learning, and engineering solutions for your business, please visit www.deeperinsights.com or contact us at thepaperclub@deeperinsights.com.
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    23 分
  • 19: Unlocking Explainable Machine Learning in Manufacturing

    19: Unlocking Explainable Machine Learning in Manufacturing
    2024/08/22
    This month’s episode of the AI Paper Club Podcast welcomes Dr. Diogo Ribeiro, a senior machine learning engineer at Deeper Insights. Diogo presents a research paper he co-developed, focusing on the industrial application of AI, titled "Isolation Forest and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection." The podcast explores the intricacies of combining traditional machine learning models with deep learning techniques to address a critical problem in industrial manufacturing: detecting anomalies in screw tightening processes.

    The conversation highlights the importance of explainability in AI, particularly in industrial settings where safety and cost are paramount. The episode also touches on the broader implications of machine learning in AI, contrasting it with the current excitement surrounding generative AI models.

    We also extend a special thank you to Diogo and his team of researchers for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://www.mdpi.com/2073-431X/11/4/54.

    For more information on all things artificial intelligence, machine learning, and engineering for your business, please visit www.deeperinsights.com or reach out to us at thepaperclub@deeperinsights.com.
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    26 分

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