Episodes

  • 16: Infini-Attention: Google's Solution for Infinite Memory in LLMs

    16: Infini-Attention: Google's Solution for Infinite Memory in LLMs
    May 22 2024
    In this episode of the AI Paper Club Podcast, hosts Rafael Herrera and Sonia Marques welcome Leticia Fernandes, a Deeper Insights Senior Data Scientist and Generative AI Ambassador. Together, they explore the groundbreaking "Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention" paper from Google. This paper addresses the challenge of fitting infinite context into large language models, introducing the Infini-attention method. The trio discusses how this approach works, including how it uses linear attention and employs compressive memory to store key-value pairs, enabling models to handle extensive contexts.

    We also extend a special thank you to the research team Google for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/pdf/2404.07143.pdf

    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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    23 mins
  • 15: The AI Composer: Meta AI’s Innovations in Music and Melody

    15: The AI Composer: Meta AI’s Innovations in Music and Melody
    Apr 23 2024
    This month's episode of the AI Paper Club Podcast covers AI Music! Hosts Rafael and Sonia welcome Deeper Insights Data Scientist Joan Rosello to discuss the paper "Simple and Controllable Music Generation" from Meta AI. He introduces us to Meta AI's model that not only generates audio from text prompts but also introduces a novel feature—melody conditioning. This allows the creation of music that adheres to a provided melody, pushing the boundaries of AI in music generation. We also explore the technical concepts involved in creating AI music today, including residual vector quantization used in this model.

    We also extend a special thank you to the research teams at Meta AI for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/pdf/2306.05284.pdf

    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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    24 mins
  • 14: Using Gemini for Enterprise with Googler Jupiter Angulo

    14: Using Gemini for Enterprise with Googler Jupiter Angulo
    Mar 18 2024
    In this month’s episode of the AI Paper Club Podcast, hosts Rafael Herrera and Sonia Marques welcome special guest Jupiter Angulo from Google. He helps us explore the innovations behind Gemini, its applications in enterprise settings, and Google's latest advancements in multimodal models.

    Developed by Google's think tank, DeepMind, Gemini represents a significant leap forward, offering efficient, scalable, and cost-effective solutions for both enterprise and consumer applications. With Jupiter's insights, we get a behind-the-scenes look at the challenges and opportunities presented by AI integration across various enterprise sectors, exploring the model's versatility and Google's commitment to responsible deployment.

    We also extend a special thank you to the research teams at Google’s DeepMind for developing this month's paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/abs/2312.11805.

    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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    28 mins
  • 13: AI, Copyright, and the Future of Creativity

    13: AI, Copyright, and the Future of Creativity
    Feb 21 2024
    In this episode of the AI Paper Club Podcast, hosts Rafael Herrera and Marcia Oliveira, alongside special guest Senior Data Scientist, Matt Kidd, delve into the thought-provoking realm of generative AI and copyright law. They explore the innovative paper "Talkin’ ‘Bout AI Generation: Copyright and the Generative-AI Supply Chain" from Cornell Law University, bridging computer science and law to unravel the complex interplay between cutting-edge AI technologies and legal frameworks. 

    This discussion not only illuminates the challenges posed by AI-generated content in the legal domain but also sparks a crucial conversation about the future of intellectual property rights in the digital age. The episode deeply explores how generative AI is reshaping our understanding of creativity, authorship, and copyright in a rapidly evolving technological landscape.

    We also send a huge thank you to the team at Cornell Law University for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4523551

    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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    32 mins
  • 11: Understanding Deep Learning with Simon Prince

    11: Understanding Deep Learning with Simon Prince
    Dec 12 2023
    This month’s episode of the Paper Club Podcast, host Rafael Herrera is joined by special guest co-host Dr. Tom Heseltine, Deeper Insights’ Chief Technical Officer. On this special show, we are talking to Professor Simon Prince, an authority in deep learning, who literally wrote a book on the subject. We discuss his newly released book “Understanding Deep Learning” while delving into the complex world of deep learning, exploring its mechanisms, the phenomenon of double descent, and the balancing act of network depth and capacity. The discussion also touches on AI ethics, the impact of AI advancements, and the responsibilities of engineers in this rapidly evolving field.

    We're grateful to have had Simon Prince on our podcast. To discover more about his work and insights, visit his platforms:

    Purchase 'Understanding Deep Learning' by Simon Prince wherever books are sold:
    https://mitpress.mit.edu/9780262048644/understanding-deep-learning/   

    For student and instructor resources, including Python Notebooks, visit his website:
    www.udlbook.com

    Follow him on X: 
    @SimonPrinceAI

    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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    39 mins
  • 10: Translating the Visual Dialogue of AI with Transformers

    10: Translating the Visual Dialogue of AI with Transformers
    Nov 8 2023
    Join us on the Paper Club Podcast where our hosts, Rafael Herrera and Marcia Oliveira, delve into the cutting-edge world of data science. This episode features Sonia Marques, a seasoned data scientist and Generative AI Ambassador from Deeper Insights, as they explore the transformative paper "Vision Transformers Need Registers'' from the FAIR team at META and the INRIA research group in France.

    The podcast examines the intricacies of vision transformers, traditionally used in natural language processing, now making waves in computer vision. The discussion illuminates the paper's innovative analysis of how these transformers handle complex visual data, revealing some of the processes that occur in the AI black box.
    We also extend a special thank you to the research teams at FAIR, Meta, and INRIA for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/pdf/2309.16588.pdf. 

    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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    28 mins
  • 9: Simplifying Chest X-ray Diagnosis with AI

    9: Simplifying Chest X-ray Diagnosis with AI
    Oct 24 2023
    In this month's episode of the Paper Club Podcast, your hosts Rafael Herrera and Sónia Marques welcome a special guest, Joana Rocha, a PhD candidate at the University of Porto's Engineering School. Joana joins us to discuss her seminal paper, 'Attention Driven Spatial Transformer Network for Abnormality Detection in Chest X-ray Images,' for which she is the lead author.

    The paper introduces a groundbreaking approach to computer-aided diagnosis in the medical field, specifically focusing on chest X-ray images. Unlike traditional methods that often require two separate models—one for selecting the thoracic region and another for the actual classification of abnormalities—the paper presents an end-to-end architecture that simplifies this process. 

    During the podcast, we explore the challenges and necessities of implementing AI models in healthcare. Joana's model addresses these by not only improving diagnostic performance but also offering insights into what the model is focusing on. This is crucial in a clinical setting, where understanding the model's decision-making process can be a matter of life and death. The model's ability to focus on the relevant anatomical features ensures that it gains the trust of medical professionals, a critical factor for its effective implementation in clinical practice.

    We thank Joana and her co-authors for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://ieeexplore.ieee.org/abstract/document/9867115  

    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 mins
  • 8: Exploring The Future of Multi-Modal Embeddings with ImageBind

    8: Exploring The Future of Multi-Modal Embeddings with ImageBind
    Sep 25 2023
    On this month's episode of the Paper Club Podcast, hosts Rafael Herrera and Marcia Oliveira, welcome Joan Rossello, data scientist at Deeper Insights. The focus of the discussion is the paper "ImageBind: One Embedding Space To Bind Them All", published by the MetaAI Research team, which introduces a revolutionary approach to multimodal learning representation. ImageBind is the first AI model capable of binding data from six modalities at once, without the need for explicit supervision, and is part of Meta’s efforts to create multimodal AI systems that learn from all possible types of data around them. 

    The paper presents a methodology for learning a unified embedding across various data modalities, such as images, text, audio, depth, thermal, and IMU data. The podcast discusses the challenges of conventional multimodal representation learning approaches, and how ImageBind was able to overcome those challenges by leveraging the binding property of images. The approach reduces the need for large, cumbersome datasets, where all combinations of data modalities are present together, thus making it a transformative tool in the realm of artificial intelligence. 

    We also send a huge thank you to the team MetaAI Research for developing this month’s paper. If you are interested in reading the paper for yourself, please check this link: https://arxiv.org/pdf/2305.05665.pdf   

    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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    24 mins