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 ...
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 [email protected].
--------
26:25
20: Technical Debt and Its Hidden Costs in Machine Learning Development
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 [email protected].
--------
23:29
19: Unlocking Explainable Machine Learning in Manufacturing
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 [email protected].
--------
26:24
18: The Future of Sport: AI-Generated Soccer Commentary
In the latest AI Paper Club Podcast episode, hosts Rafael Herrera and Sonia Marques welcome Dr. Catarina Carvalho, Senior Data Scientist and Computer Vision SME from Deeper Insights, to discuss, "Match Time: Towards Automatic Soccer Game Commentary Generation". This paper introduces a method for generating engaging soccer commentary from video sequences using advanced AI techniques.
The episode explores the importance of data quality, the innovative pipeline for dataset curation, and the perceiver-like architecture ensuring temporal coherence. It also covers broader applications, such as in cooking shows or assisting the hearing impaired. Tune in to discover how AI is revolutionising sports commentary and how you can try these techniques at home.
We also extend a special thank you to the research teams from Shanghai University and Shanghai AI Laboratory for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/abs/2406.18530
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 [email protected].
--------
24:27
17: Meta’s Chameleon: Redefining Data Integration with Mixed-Modal AI
In this episode of the AI Paper Club Podcast, hosts Rafael Herrera and Sonia Marques are joined by Andrew Eaton, an AI Solutions Consultant from Deeper Insights, to explore Meta’s latest paper, “Chameleon: Mixed Modal Early Fusion Foundation Models.” This paper marks Meta’s first steps into the mixed modal AI space, combining text, images, and other data types from the start for a more integrated understanding.
The podcast explores how, unlike traditional models that process text and images separately before combining them, Chameleon integrates these modalities right from the beginning. This early fusion method promises enhanced performance in tasks like image captioning and interleaved text-image outputs, setting new benchmarks in the field.
We also extend a special thank you to the research team at Meta for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/abs/2405.09818.
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 [email protected].
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.
Colombia