Was she the victim of foul play, an accident, or did she choose to disappear? In this episode of The Vanished, we retrace Diana’s last known movements, strange witness accounts, and the lingering mystery that has never been solved or.. was never meant to be solved.
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The problem of ML Model drift and decay in production
Manage episode 456086283 series 3620285
Sisällön tarjoaa David Such. David Such tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
In this episode, called “The Problem of ML Model Drift and Decay in Production,” we explore the challenges of maintaining machine learning (ML) model accuracy over time. We break down model drift, a critical issue where a model’s predictive performance degrades due to changes in data or the environment. Listeners will learn about the two main causes of drift: data drift, where input data distributions shift, and concept drift, where the relationship between inputs and outputs evolves.
We also discuss the real-world consequences of model drift, such as poor decision-making, business losses, and ethical concerns like biased predictions. To address these challenges, we outline best practices for mitigating drift, including continuous monitoring, maintaining data quality, implementing regular retraining cycles, and leveraging specialized tools and technologies. Finally, we highlight the broader business and ethical implications of neglecting model drift, emphasizing why proactive strategies are essential for ensuring long-term ML model reliability.
If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!
20 jaksoa
JaaManage episode 456086283 series 3620285
Sisällön tarjoaa David Such. David Such tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
In this episode, called “The Problem of ML Model Drift and Decay in Production,” we explore the challenges of maintaining machine learning (ML) model accuracy over time. We break down model drift, a critical issue where a model’s predictive performance degrades due to changes in data or the environment. Listeners will learn about the two main causes of drift: data drift, where input data distributions shift, and concept drift, where the relationship between inputs and outputs evolves.
We also discuss the real-world consequences of model drift, such as poor decision-making, business losses, and ethical concerns like biased predictions. To address these challenges, we outline best practices for mitigating drift, including continuous monitoring, maintaining data quality, implementing regular retraining cycles, and leveraging specialized tools and technologies. Finally, we highlight the broader business and ethical implications of neglecting model drift, emphasizing why proactive strategies are essential for ensuring long-term ML model reliability.
If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!
20 jaksoa
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Send us a text In this episode, we explore the unique nature of AI mistakes and why they differ fundamentally from human errors. Unlike people, AI systems make random, inconsistent, and unpredictable errors, often without awareness of their own limitations. This unpredictability challenges traditional security approaches, requiring new frameworks for AI reliability and risk management . The discussion delves into two potential solutions: engineering AI to make more human-like mistakes and creating specialized mistake-correcting mechanisms tailored for AI. While AI can exhibit human-like behaviors—such as prompt sensitivity and biases learned from training data —it also introduces distinct vulnerabilities that require fresh security strategies. How can we ensure AI is deployed safely in decision-making? And what do these insights mean for the future of AI security? Tune in for an eye-opening conversation on the evolving landscape of AI safety and reliability . If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we explore Apple’s strategic partnership with Alibaba , integrating the Qwen AI model into iPhones sold in China. Faced with regulatory barriers and declining sales, Apple turns to Alibaba’s powerful large language model (LLM) to bring advanced AI features to its Chinese users while ensuring compliance with local laws. We break down the implications of this move—how it strengthens Apple’s foothold in China, boosts Alibaba’s AI credibility, and reflects the broader trend of AI localization in global markets. However, challenges loom, including government scrutiny, competition from local AI firms, potential performance limitations, and privacy concerns . Is this a smart strategic play or a risky compromise? And what does it mean for US-China tech relations ? Tune in as we unpack the stakes behind Apple’s AI decision in China. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we investigate the world of AI-generated music, exploring how cutting-edge AI techniques—such as transformers, GANs, and VAEs—are revolutionizing music creation. We also take a look at traditional non-AI methods like arpeggiators and Markov chains, which continue to shape algorithmic composition. Beyond the software, we discuss an innovative MIDI controller design tailored for AI-driven music production, featuring controls specifically optimized for manipulating AI parameters and integrating seamlessly with modern music tools. But with great innovation comes great debate—what are the ethical and legal implications of AI-generated music? We tackle concerns surrounding copyright, originality, and the potential impact on human musicians. Is AI enhancing creativity, or is it replacing it? Tune in for an insightful discussion on the future of music in the age of AI. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we explore the concept of AI hard takeoff —the moment when artificial intelligence rapidly surpasses human intelligence, triggering an unstoppable acceleration in its capabilities. We break down the difference between a hard takeoff and a soft takeoff , weighing the potential risks and benefits of AI evolving beyond human control. We also examine recent breakthroughs in AI, uncovering evidence that suggests we may be closer than ever to Artificial General Intelligence (AGI) and even Artificial Superintelligence (ASI) . With insights from leading AI experts, we discuss the growing concerns over the speed of AI development and whether organizations and governments are truly prepared for what comes next. Is humanity on the brink of an AI revolution, or are we rushing into unknown dangers? Tune in to find out. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we investigate DeepSeek AI , the cost-effective yet high-performing Chinese AI model that is making waves in the industry. We compare its capabilities to leading American models like OpenAI’s, uncovering how DeepSeek achieves impressive reasoning and coding performance at a fraction of the training cost. We break down the innovative techniques behind its success, including the Mixture-of-Experts architecture and Multi-Head Latent Attention mechanism , which contribute to its efficiency. But what does this mean for the global AI landscape? We explore the potential disruption to major tech companies, the implications of DeepSeek’s open-source nature, and the ripple effects on the US stock market and the future of AI development . Is DeepSeek the beginning of a new AI era, or a major challenge to Western AI dominance? Tune in to find out! If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text This episode talks about the essentials of exploratory data analysis (EDA) for image recognition. We discuss key techniques—descriptive, diagnostic, and predictive EDA—and outline recommended steps such as image visualization, statistical analysis, anomaly removal, and feature engineering, along with ethical considerations in the process. We also explore how EDA enhances model accuracy, focusing on the person detection model MCUNet-VWW2 and the Wake Vision dataset. Learn how label correction, data augmentation, and preprocessing improved performance while addressing dataset features, limitations, and the impact of EDA in real-world applications. Join us for an insightful guide to mastering EDA in image recognition! If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we investigate the state of deep learning frameworks in 2025. We review the leading contenders—TensorFlow, PyTorch, JAX, MXNet, and LightningAI—analyzing their strengths, latest features, performance benchmarks, and the size of their user communities. We also explore key trends shaping the field, including the sustained dominance of established frameworks and the growing popularity of specialized options like LightningAI, known for its focus on performance, scalability, and usability. To wrap up, we discuss future directions in deep learning frameworks, from integrating quantum computing to improving model interpretability. Tune in for a forward-looking discussion on the tools driving the future If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we explore capsule networks (CapsNets), an innovative advancement in artificial neural networks designed to overcome the limitations of traditional convolutional neural networks (CNNs). CapsNets introduce “capsules,” groups of neurons that encode richer information about features, such as their position and orientation, enabling a deeper understanding of spatial hierarchies. We break down the concept of dynamic routing, a key mechanism that intelligently connects capsules and allows CapsNets to effectively recognize hierarchical relationships and maintain viewpoint invariance. The episode compares CapsNets to CNNs, highlighting their advantages in handling complex spatial features, while addressing challenges like their higher computational cost. We also dive into the latest research and exciting applications of CapsNets, including breakthroughs in image recognition and medical image analysis. Join us as we unravel the potential of capsule networks to transform the landscape of machine learning. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we explore the fascinating and complex concept of artificial consciousness (AC). We dive into its definition, the current state of research, and the philosophical and ethical questions surrounding the creation of conscious machines. The discussion highlights the immense challenges in replicating subjective experience and measuring consciousness in artificial systems, while also examining diverse perspectives on whether AC is possible—or even desirable. We analyze groundbreaking research shaping the field and tackle pressing ethical concerns, such as the potential for AI to experience suffering and the urgent need for ethical frameworks to guide its development. As the debate over artificial consciousness continues to evolve, we reflect on the implications of this emerging frontier and what it means for the future of technology and humanity. Tune in for a thought-provoking discussion that uncovers the missing pieces of artificial consciousness. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we get into spiking neural networks (SNNs), a cutting-edge AI model inspired by the brain’s biological processes. Unlike traditional neural networks, SNNs are energy-efficient and optimized for neuromorphic hardware, making them ideal for tasks involving temporal or sequential data. We explore their event-driven approach, potential to revolutionize AI, and their promise as a stepping stone toward artificial general intelligence. While challenges in training and hardware adoption persist, the discussion highlights the need for innovative architectures that replicate the brain’s complexity, positioning SNNs as a foundation for next-generation AI systems. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text Welcome to The Nvidia Way: From Gaming Chips to AI Domination. In today’s episode, we explore the fascinating story behind Nvidia’s rise, inspired by Tae Kim’s groundbreaking new book, The Nvidia Way. This is the first comprehensive account of Nvidia’s history and the visionary leadership of Jensen Huang. From the company’s early struggles to its risky yet brilliant decisions, Kim takes us through the journey that transformed Nvidia from a niche player in gaming graphics to a dominant force in artificial intelligence. We’ll chat about the unique corporate culture and Huang’s distinctive management style that fueled this meteoric rise. While the book captures Nvidia’s path to market dominance, it also leaves room for debate on the company’s recent strategies, like the controversial Arm acquisition attempt. Are Nvidia’s successes a product of strategic genius, or were they simply in the right place at the right time? And what does the future hold for this tech giant as it looks beyond Huang’s leadership? Join us as we unpack these questions and examine the lessons from Nvidia’s remarkable ascent. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, we explore the intricate relationship between neurons, intelligence, and consciousness. We talk about traditional views that associate consciousness with specific brain structures, such as the midline brain regions, and contrast them with emerging theories that highlight the role of the thalamocortical system. Key frameworks like the Global Workspace Theory and Integrated Information Theory provide insights into how consciousness might arise. The episode also ventures into alternative perspectives, discussing potential “fundamental particles” of consciousness, from neuronal networks to quantum phenomena. We clarify the distinction between consciousness, as subjective experience, and intelligence, as a measure of cognitive ability, examining how these concepts apply to both biological and artificial systems. Ultimately, we tackle the enduring mystery of consciousness and its connection to intelligence, acknowledging the limitations of current scientific understanding. It is a thought-provoking exploration of one of the most profound questions in science and philosophy. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this episode, called “The Problem of ML Model Drift and Decay in Production,” we explore the challenges of maintaining machine learning (ML) model accuracy over time. We break down model drift , a critical issue where a model’s predictive performance degrades due to changes in data or the environment. Listeners will learn about the two main causes of drift: data drift , where input data distributions shift, and concept drift , where the relationship between inputs and outputs evolves. We also discuss the real-world consequences of model drift, such as poor decision-making, business losses, and ethical concerns like biased predictions. To address these challenges, we outline best practices for mitigating drift, including continuous monitoring , maintaining data quality, implementing regular retraining cycles, and leveraging specialized tools and technologies. Finally, we highlight the broader business and ethical implications of neglecting model drift, emphasizing why proactive strategies are essential for ensuring long-term ML model reliability. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text In this podcast, “Quantum Computing and AI: A Symbiotic Leap Forward,” we discuss the intersection of quantum computing and artificial intelligence, exploring their combined potential to reshape industries and redefine innovation. The episode begins by breaking down the fundamentals of quantum computing, shedding light on the various types of quantum computers and the unique capabilities they bring to problem-solving. It then examines how quantum computing amplifies AI applications, with a focus on groundbreaking advancements in quantum machine learning, natural language processing, and computer vision. We also spotlight the leading players driving this convergence and discuss the immense opportunities it presents, as well as the significant challenges that must be addressed. Finally, the conversation turns to the ethical implications of this powerful synergy, raising important questions about its societal impact and the need for responsible development. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
Send us a text Welcome to Is AI the End of Coding or the Start of Something Else? Today, we explore the impact of artificial intelligence on the coding profession. AI is revolutionizing how programmers work by automating routine tasks, integrating seamlessly into tools like VS Code through GitHub Copilot, and powering advanced Modular Development Environments like Cursor. While these advancements free developers to focus on high-level problem-solving, they also raise significant concerns. Over-reliance on AI coding tools could lead to skill atrophy, particularly for students and junior developers, and increase the risk of subtle bugs. Ethical challenges, such as intellectual property and copyright issues stemming from AI training data, further complicate the equation. One theme emerges: the need for adaptation. Reskilling programmers to thrive in a world of AI-augmented development is essential, requiring investments from companies, governments, and educational institutions. In this episode, we’ll discuss whether AI signals the demise of traditional coding or the beginning of an era where human creativity and AI capabilities come together to redefine the future of programming. If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!…
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