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    ai energy consumption

    Explore "ai energy consumption" with insightful episodes like "Exploring the Mind: John Vervaeke on Relevance Realization and Consciousness", "Delving into the Frontiers of Artificial General Intelligence with Sam Tideman" and "#150: Yann LeCun on World Models, AI Threats and Open-Sourcing" from podcasts like ""John Vervaeke", "John Vervaeke" and "Eye On A.I."" and more!

    Episodes (3)

    Exploring the Mind: John Vervaeke on Relevance Realization and Consciousness

    Exploring the Mind: John Vervaeke on Relevance Realization and Consciousness

    In Episode 3 of the "Active Inference Insights" series, host Darius Parvizi-Wayne welcomes John Vervaeke for an insightful discussion bridging cognitive science and philosophy. The episode delves into topics like relevance realization, evolutionary processes in cognition, and understanding cultural variations in self-modeling. Verveke articulates the dynamic nature of cognition and its relationship with the environment, challenging traditional views on consciousness and the subjective-objective divide. Listeners will better understand how computational models and philosophical frameworks can synergistically enhance our comprehension of the mind and its processes. This episode is a thought-provoking journey that connects cognitive science theories with philosophical inquiries, offering listeners nuanced perspectives on the complexity of human cognition and its implications for meaning in life.

     

    Glossary of Terms

     

    4E Cognitive Science: A view of cognition as embodied, embedded, enacted, and extended.

    Relevance Realization: The ability to focus on salient information in a complex environment.

    Predictive Processing: A framework in cognitive science that describes how the brain makes predictions about incoming sensory information.

    Opponent Processing: A concept in biology where two subsystems work in opposition to regulate functions like arousal.

     

    Resources and References:

     

    Dr. John Vervaeke: Website | YouTube | Patreon | X | Facebook

    Darius Parvizi: X | Active Inference Institute | Active Inference Insights

     

    The Vervaeke Foundation

    Awaken to Meaning



    John Vervaeke YouTube

    Awakening from the Meaning Crisis

    After Socrates

    The Crossroads of Predictive Processing and Relevance Realization | Leiden Symposium




    Books, Articles, Publications, and Videos

    Heidegger, Neoplatonism, and the History of Being: Relation as Ontological Ground - James Filler

    Predictive processing and relevance realization: exploring convergent solutions to the frame problem. Phenomenology and the Cognitive Sciences. Andersen, B. P., Miller, M., & Vervaeke, J. (2022)

    The Self‐Evidencing Brain. Noûs Hohwy, Jakob (2016).

    Attenuating oneself. Philosophy and the Mind Sciences. Limanowski, Jakub & Friston, Karl (2020).

    'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference. Frontiers in psychology. Limanowski, J., & Friston, K. (2018).

    Deeply Felt Affect: The Emergence of Valence in Deep Active Inference. Neural computation.  Forgetting Ourselves in Flow: An Active Inference Account of Flow States. Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021). Parvizi-Wayne, D., Sandved-Smith, L., Pitliya, R. J., Limanowski, J., Tufft, M. R. A., & Friston, K. (2023, December 7).

    Cognitive effort and active inference. Neuropsychologia. Parr, T., Holmes, E., Friston, K. J., & Pezzulo, G. (2023).

    "The Theory of Affordances" The Ecological Approach to Visual Perception. Boston: Houghton Mifflin, Gibson, James J. (1979).

    Karl Friston ~ Active Inference Insights 001 ~ Free Energy, Time, Consciousness 

     

    Quotes

     

    "Relevance realization inverts the way common sense works." - John Verveke

     "The deeper your temporal model, the more critical relevance realization becomes." - Darius Parvizi Wayne



    Chapters with Timestamps

     

    Introduction and Overview [00:00:00]

    Evolution and Function in Cognition [00:06:17]

    Opponent Processing in Biology [00:09:42]

    Problem-Solving and Anticipation [00:14:22]

    Relevance Realization and Evolution [00:31:34]

    Consciousness and Subject-Object Distinction [00:53:00]

    Cultural and Historical Perspectives on Cognition [00:56:35]

    Ontological Self and Phenomenal Self Modeling [01:11:19]

    Self-Modeling and Cultural Perspectives [01:14:00]

    Agency and Selfhood in Cognitive Processes [01:18:16]

    Self-Modeling Under flow States [01:22:01]

    Arousal and Metamotivational Theory [01:35:54]

    Predictive Processing Symposium and Relevance Realization [01:46:26]

    Episode Conclusion and Future Plans [01:48:20]



    Timestamped Highlights

     

    [00:00:00] - Darius Parvizi Wayne introduces the episode and guest John Verveke, highlighting John's expertise in psychology, cognitive science, and Buddhist philosophy​​.

    [00:06:17] - John Verveke discusses the evolution of cognitive functions and the role of evolution in shaping cognition​​.

    [00:11:40] - Explanation of the autonomic nervous system, detailing how its two subsystems with opposite biases work together to regulate bodily functions.

    [00:14:43] - The conversation delves into the nature of problem-solving, exploring how organisms predict and prepare for future states. 

    [00:22:23] - The concept of hyperbolic discounting in cognition is examined, analyzing its impact on decision-making and goal pursuit.

    [00:26:20] - Discussion on the role of affordances in predictive processing, exploring how environments offer action possibilities to organisms.

    [00:31:34] - Conversation on the analogy between relevance realization and evolutionary processes, highlighting the dynamic nature of cognitive adaptation​​.

    [00:38:00] - The existential imperative is clarified in the context of the free energy principle, exploring its implications in cognitive science​​.

    [00:53:00] - Consciousness and the subject-object distinction are addressed, challenging traditional cognitive models and exploring interrelational perspectives​​.

    [00:56:35] - Cultural and historical influences on cognitive processes are explored, examining how these factors shape our understanding of cognition​​.

    [00:57:13] - John Verveke discusses the hermeneutics of suspicion in cognitive science, questioning the distinction between appearance and reality​​.  

    [01:04:49] - The role of perception and its function in cognitive processes are discussed, emphasizing the interconnectedness of perception and cognition​​.

    [01:11:19] - The concepts of ontological and phenomenal self-modeling are delved into, discussing how these models influence cognitive processes​​.

    [01:14:00] - Self-modeling and its cultural variations are discussed, highlighting the diversity in conceptualizing the self across different cultures​​.

    [01:18:16] - Agency and selfhood in cognitive processes are examined, focusing on how these concepts enhance predictive agency in the world​​.

    [01:22:01] - Exploration of self-modeling under flow states and their impact on cognitive processes​​.

    [01:35:54] - Analysis of arousal in the context of meta motivational theory, discussing how arousal is framed differently based on goals and motivations​​.

    [01:38:04] - Discussion of the intersection of philosophical concepts and computational models in cognitive science, emphasizing the importance of integrating these approaches to enhance understanding without oversimplifying complex phenomena.

    [01:46:26] - Overview of a talk integrating predictive processing and relevance realization theory, offering insights into their combined impact on cognitive science​​.

     

    Delving into the Frontiers of Artificial General Intelligence with Sam Tideman

    Delving into the Frontiers of Artificial General Intelligence with Sam Tideman

    John Vervaeke and guest Sam Tideman delve into the intricate world of artificial general intelligence (AGI) and its intersection with healthcare. Sam, an expert in biostatistics, machine learning, and AI, shares valuable insights from his professional experiences, particularly in healthcare system optimization. The conversation navigates the ethical and moral challenges of applying AI in complex environments like emergency departments, the intricacies of predictive modeling, and the broader societal implications of AI, including its energy consumption and public perception. This episode is essential listening for anyone interested in understanding the nuanced interplay between technology, healthcare, and ethics, offering a comprehensive perspective on the current and future potential of AI to transform lives and systems.

     

    Sam Tideman, an accomplished healthcare data scientist with an MS in Biostatistics, blends his analytical acumen with a passion for theology in his podcast, "Transfigured." The podcast features long-form discussions exploring the identity of Jesus, reflecting Sam's unique intersection of scientific expertise and spiritual inquiry.

     

    Glossary of Terms

     

    AGI (Artificial General Intelligence): An AI that has the ability to understand, learn, and apply its intelligence to a wide range of problems, much like human intelligence.

    Biostatistics: The application of statistics to a wide range of topics in biology.

     

    Resources and References:

     

    Dr. John Vervaeke: Website | YouTube | Patreon | X | Facebook

    Sam Tideman: YouTube

     

    The Vervaeke Foundation



    John Vervaeke YouTube

    Awakening from the Meaning Crisis - series

    Artificial Intelligence - series

    The Crossroads of Predictive Processing and Relevance Realization | Leiden Symposium

     

    Books, Articles, Publications, and Videos

    Mentoring the Machines: Orientation - Part One: Surviving the Deep Impact of the Artificially Intelligent Tomorrow - John Vervaeke, Shawn Coyne 

    Mentoring the Machines: Origins - Part 2: Surviving the Deep Impact of the Artificially Intelligent Tomorrow - John Vervaeke, Shawn Coyne 

    Predictive processing and relevance realization: Exploring convergent solutions to the frame problem. Phenomenology and the Cognitive Sciences. Andersen, B., Miller, M., & Vervaeke, J. (2022).

     

    Related Resources

    Chicagoland Bridges of Meaning Meetup

     

    Chapters with Timestamps

     

    [00:00:00] Introduction of Sam Tiedemann and Episode Overview 

    [00:01:15] Sam’s Background and Intersection with AI 

    [00:04:11] The Role of AI in Healthcare and Emergency Departments 

    [00:14:26] The Limitations of AI in Morally Complex Environments 

    [00:24:34] Discussion on AI's Capability to Predict vs. Normative Decision-Making 

    [00:53:06] The Energy Consumption and Environmental Impact of Training AI Models 

     

    Timestamped Highlights

     

    [00:00:00] John opens the discussion by welcoming Sam and introducing the topic of artificial general intelligence (AGI).

    [00:01:15] Sam shares his diverse background, which spans theology, philosophy, and artificial intelligence.

    [00:06:15] The conversation focuses on AI's potential and dangers, setting the stage for the day's discussion.

    [00:09:28] Sam reflects on the complexities he faced while trying to implement AI in emergency department forecasting.

    [00:14:53] Sam points out the practical limitations of AI in real-world applications.

    [00:21:38] Sam criticizes the inflated expectations surrounding AI in healthcare projects.

    [00:26:26] John and Sam discuss how predictive processing and relevance realization can be integrated into AI.

    [00:29:37] They delve into the potential of AI to emulate human qualities like intentionality and care.

    [00:34:11] John emphasizes the need to recognize the limitations of AI in solving complex real-world problems.

    [00:38:30] Sam's parable features an AI model in healthcare that prescribes drugs probabilistically and learns from outcomes, hinting at AI's emerging agency.

    [00:42:10] The feasibility of AI replicating human intuition and judgment in complex scenarios is questioned.

    [00:46:15] John highlights the importance of a multidisciplinary approach to understanding and developing AI.

    [00:49:57] Philosophical aspects of AI, such as intentionality and consciousness, are explored in-depth.

    [00:53:30] Sustainability concerns in AI development, especially compared to the human brain's efficiency, are discussed.

    [01:06:40] The episode concludes with a discussion on AI's inability to align with human normativity and the limitations of its social, cultural, and biological understanding.

     

    #150: Yann LeCun on World Models, AI Threats and Open-Sourcing

    #150: Yann LeCun on World Models, AI Threats and Open-Sourcing

    This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere.

    If you want to do more and spend less like Uber, 8x8, and Databricks Mosaic - take a free test drive of OCI at https://oracle.com/eyeonai

     

    Welcome to episode 150 of the ‘Eye on AI’ podcast. In this episode, host Craig Smith sits down with Yann LeCun, a Turing Award winner who has been instrumental in advancing convolutional neural networks and whose work spans machine learning, computer vision, and more.

    Tune is as Craig and Yann explore the intricacies of AI, world models, and the challenges of continuous learning.

    In this episode, Yann delves deep into the concept of a "world model" - systems that can predict the world's future states, allowing agents to make informed decisions. The discussion transitions to the challenges of training these models, particularly when dealing with diverse data like text and images. We then discuss the computational demands of modern AI models, with Yann highlighting the nuances between generative models for videos and language. 

    He also touches upon the idea of the "Embodied Turing Tests" and how augmented language models can bridge the gap between human-like behavior and computational efficiency.The spotlight then shifts to pressing concerns surrounding the open-source nature of AI models, with Yann articulating the legal ramifications and the future of open-source AI. Drawing from global perspectives, including China's stance on open-source, Yann underscores the imperative for a collaborative approach in the AI space, ensuring it's reflective of diverse global needs.

     

    Craig Smith Twitter: https://twitter.com/craigss

    Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

     

    (00:00) Preview, Oracle and Introduction

    (02:42) Decoding The World Model and Gaia 1 

    (07:43) Energy and Computational Demands of AI

    (08:06) Video vs. Text Processing & True AI Capabilities

    (11:17) Embodied Turing Test & Augmented LLMs

    (15:38) Is AI a Threat To Society?

    (25:04) Where is AI Development Headed?

    (31:06) Interplay of Neuroscience and AI**

    (33:33) Yann's Vision, JEPA, and Learning Challenges

    (39:05) Yann's Career, AI Progress, and Challenges

    (44:47) The Open Source Debate in AI

    (55:30) Oracle Cloud Infrastructure