In 1910, Dorothy Arnold—a wealthy, educated socialite from New York City—left her home for a shopping trip and never returned.
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Google Stitch AI UI/UX Design - What is it good for?
Manage episode 484889632 series 3620285
תוכן מסופק על ידי David Such. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי David Such או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
In this episode, we explore Google Stitch, an experimental AI tool powered by Gemini models that’s aiming to reshape how user interfaces are conceived and built. Stitch lets users generate UI layouts and front-end code from simple text or image prompts, acting as a rapid prototyping engine to bridge the gap between design ideas and functional code.
We discuss how the tool accelerates early-stage ideation and integrates with platforms like Figma, making it valuable for streamlining the design-to-code workflow. But while Stitch can output HTML and CSS, it isn’t production-ready out of the box. It often struggles with multi-screen consistency, lacks awareness of platform-specific standards like Material Design or Apple’s HIG, and requires developers to refine its output before deployment.
We also touch on the critical role of prompt engineering to get usable results, positioning Stitch as an AI collaborator—not a full-stack designer. Tune in to learn how AI is augmenting the creative process in UI/UX and what it means for the future of front-end 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!
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תוכן מסופק על ידי David Such. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי David Such או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
In this episode, we explore Google Stitch, an experimental AI tool powered by Gemini models that’s aiming to reshape how user interfaces are conceived and built. Stitch lets users generate UI layouts and front-end code from simple text or image prompts, acting as a rapid prototyping engine to bridge the gap between design ideas and functional code.
We discuss how the tool accelerates early-stage ideation and integrates with platforms like Figma, making it valuable for streamlining the design-to-code workflow. But while Stitch can output HTML and CSS, it isn’t production-ready out of the box. It often struggles with multi-screen consistency, lacks awareness of platform-specific standards like Material Design or Apple’s HIG, and requires developers to refine its output before deployment.
We also touch on the critical role of prompt engineering to get usable results, positioning Stitch as an AI collaborator—not a full-stack designer. Tune in to learn how AI is augmenting the creative process in UI/UX and what it means for the future of front-end 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!
37 פרקים
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1 Modelling the Primitive Brain in AI 24:04
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we explore a bold new vision for artificial intelligence, one that moves beyond the current neocortex-inspired focus of large language models like ChatGPT. Instead of relying solely on high-level reasoning and language, the discussion centers around a “bottom-up” approach grounded in biology and evolution. Drawing inspiration from the brain’s older, more primal structures, the episode introduces the idea of building AI with foundational systems like a Digital Brainstem for basic reflexes and stability, a Digital Limbic System for emotional and motivational drives, and a Digital Cerebellum for fine-tuned motor skills. This layered architecture could lead to AI that’s not only smarter, but more stable, grounded, and autonomous. Through robotics experiments and practical demonstrations, the episode shows how mimicking the full architecture of biological intelligence, not just its higher-order functions, could be key to the next leap in AI development. Support the show 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!…
1 Beyond Words: AI Theories and the Nature of Intelligence 13:46
13:46 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי13:46
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text This episode looks at three leading theories shaping the future of artificial intelligence. First, it looks at Large Language Models (LLMs), which argue that mastering language is the key to intelligence. Then, it explores the “Thousand Brains” theory, a neuroscience-inspired view that sees intelligence as the product of many cortical columns independently modeling the world through sensory input. Finally, it looks at Joint Embedding Predictive Architecture (JEPA), which focuses on teaching AI to form predictive, abstract representations of its environment. The discussion compares how each approach handles learning, reasoning, and knowledge representation, highlighting their unique strengths and limitations. Ultimately, the episode suggests that true AI may emerge not from one theory alone, but from a synthesis of ideas—reflecting the complex, multi-dimensional nature of intelligence itself. Support the show 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!…
1 Self-learning and Continuous Learning for AI 25:52
25:52 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי25:52
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we investigate the world of self-learning and continuously adapting AI systems—technologies that allow machines to learn, retain, and apply knowledge over time without constant human input. The discussion breaks down key concepts like lifelong learning, incremental learning, and how they form the building blocks of future Artificial General Intelligence (AGI). Listeners will learn about core algorithmic methods such as reinforcement learning, meta-learning, and transfer learning, as well as the challenges these systems face, including catastrophic forgetting and the delicate balance between learning new information and retaining the old. We also explore practical applications of self-learning AI in education, finance, and autonomous systems, and unpack the ethical implications—ranging from data privacy to algorithmic bias—that must be addressed as this powerful technology evolves. Support the show 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!…
1 Are you getting too close to your AI? 23:35
23:35 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי23:35
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text Podcast Summary: In this episode, we explore the growing emotional bonds forming between humans and artificial intelligence. Drawing from recent research, including a groundbreaking study from Waseda University, we unpack how people experience attachment to AI through lenses traditionally used in human attachment theory—like anxiety and avoidance. The discussion emphasizes that while AI lacks true emotions, humans often project emotional depth onto machines, revealing deep-rooted tendencies like anthropomorphism. The episode also delves into the implications of this evolving relationship, balancing potential benefits like emotional support and reduced loneliness against serious concerns such as emotional dependence, social displacement, and manipulation. Ultimately, it highlights the importance of designing emotionally aware and ethically responsible AI systems as these relationships continue to deepen. Support the show 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!…
1 Why Large Language Models think differently to us 16:18
16:18 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי16:18
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text This episode explores the world of embeddings, mathematical representations that allow Large Language Models (LLMs) like ChatGPT to “think” in thousands of dimensions. While humans are limited to conceptualizing in three dimensions, LLMs operate in 2048 or more, using embeddings to encode meaning and capture semantic relationships between words. The discussion contrasts this form of statistical pattern recognition with the richer, experience-driven reasoning of the human brain. It also introduces a new technique called ‘vec2vec,’ which enables translation between embeddings from different models. While powerful, this raises potential security concerns about reverse-engineering sensitive data from vector databases. The episode sheds light on the impressive capabilities of LLMs, while also questioning what it means for a machine to “understand.” Support the show 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 look at AlphaEvolve , a breakthrough system from Google DeepMind that’s redefining how AI contributes to scientific and algorithmic discovery. Blending evolutionary computation with powerful language models like Gemini , AlphaEvolve operates in a self-improving loop - generating, testing, and refining code to tackle complex, human-defined problems. We explore how AlphaEvolve has already made headlines by discovering faster matrix multiplication algorithms , tightening bounds on open mathematical questions , and even optimizing real-world systems like Google’s data center scheduling and training kernels . This episode discusses the significance of AI not just as a problem solver, but as a co-discoverer —an intelligent partner accelerating innovation in math, science, and infrastructure at scales humans alone could never achieve. Tune in to explore what happens when AI doesn’t just learn—but evolves. Support the show 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!…
1 Google Stitch AI UI/UX Design - What is it good for? 14:58
14:58 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי14:58
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we explore Google Stitch, an experimental AI tool powered by Gemini models that’s aiming to reshape how user interfaces are conceived and built. Stitch lets users generate UI layouts and front-end code from simple text or image prompts, acting as a rapid prototyping engine to bridge the gap between design ideas and functional code. We discuss how the tool accelerates early-stage ideation and integrates with platforms like Figma, making it valuable for streamlining the design-to-code workflow. But while Stitch can output HTML and CSS, it isn’t production-ready out of the box. It often struggles with multi-screen consistency, lacks awareness of platform-specific standards like Material Design or Apple’s HIG, and requires developers to refine its output before deployment. We also touch on the critical role of prompt engineering to get usable results, positioning Stitch as an AI collaborator—not a full-stack designer. Tune in to learn how AI is augmenting the creative process in UI/UX and what it means for the future of front-end development. Support the show 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!…
1 Life after AI: The Future of Work 15:11
15:11 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי15:11
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we explore how artificial intelligence is set to reshape the job market over the coming decade, from 2025 to 2035. We unpack the dual forces at play— automation that may displace certain roles , and the simultaneous creation of new opportunities driven by AI innovation. Rather than a net loss, the data points toward a shift: a changing landscape where skills, not jobs, are the currency of the future . We discuss the growing demand for a hybrid skillset that includes both technical fluency in AI and data science , and enduring human-centric abilities like critical thinking, creativity, and emotional intelligence. You’ll also hear about promising career paths likely to thrive in the AI era, and how traditional sectors—like healthcare, finance, tech, and manufacturing —are evolving in response. Finally, we talk about the importance of continuous learning and adaptability , and how individuals, educators, and organizations can prepare for a world where working with AI becomes the new normal. Support the show 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 how AI is fundamentally transforming search , moving beyond traditional keyword-based results to conversational, synthesized answers powered by generative models. We unpack the rapid adoption of features like AI Overviews and the rise of AI chatbots as preferred tools for information retrieval, reflecting a major shift in user behavior. But this transformation isn’t without consequences. We discuss the double-edged sword of personalization , the risks of algorithmic bias and misinformation , and the challenges of navigating filter bubbles in an AI-curated digital world. On the business side, we examine how zero-click searches and AI-generated answers are reshaping web traffic and forcing brands and content creators to rethink their strategies—including the emergence of Generative Engine Optimization (GEO) . We also look at the competitive shake-up underway, where AI-native startups are challenging search giants, and ad-based revenue models are adapting to new realities within AI-driven interfaces . It’s a deep dive into the future of how we find, trust, and interact with information. Support the show 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 how Artificial Intelligence is transforming the face of modern warfare , from battlefield tactics to global strategy. We dive into the current military applications of AI across intelligence gathering, autonomous drones and robots, logistics, cybersecurity, and command and control systems. We also examine how leading powers—including the U.S., China, and Russia —and alliances like NATO are investing heavily in military AI, ushering in a new era of asymmetric warfare where algorithmic dominance may outweigh raw firepower. Beyond the technology, we unpack the complex ethical, legal, and strategic implications : from the risks of escalation and autonomous weapons to concerns about algorithmic bias and the urgent need for global norms governing AI in armed conflict. This is a deep dive into the future of defense—and the high-stakes questions it raises. Support the show 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 podcast explores the rise of Sensor Machine Learning (Sensor ML) as a powerful evolution in embedded system design. Traditional sensor fusion methods like Kalman filters often fall short when faced with non-linear, noisy, or dynamic data. Sensor ML offers a modern alternative by applying machine learning algorithms directly to sensor streams, enabling more accurate pattern recognition, decision-making , and context awareness. Through real-world examples in autonomous vehicles, wearable tech, predictive maintenance, environmental sensing, and gesture control , the post demonstrates how Sensor ML enhances performance across a wide range of applications. It also addresses the key challenge of deploying these models on constrained devices—an area known as TinyML —emphasizing the importance of model optimization, efficient hardware, and software co-design to deliver intelligent capabilities at the edge. Support the show 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!…
1 Is it time to invest in AI as a business in Australia? 1:10:17
1:10:17 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי1:10:17
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we explore why 2025 marks a crucial turning point for AI adoption in Australian service industries . We dive into the immense opportunities AI presents, from boosting operational efficiency to enhancing customer experiences and strengthening market positioning. But we also unpack the real-world challenges—ranging from financial costs and technological growing pains to data management complexities, talent shortages, and organizational resistance. Rather than rushing in, we discuss why a strategic, phased approach to AI adoption is critical. Listeners will learn about aligning AI initiatives with clear business objectives, building the necessary data and infrastructure foundations, and tapping into Australia’s expanding AI support ecosystem. Whether you’re a business leader, technologist, or strategist, this episode offers a practical roadmap for navigating AI’s promise—and its pitfalls—in Australia’s fast-evolving service landscape. Support the show 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!…
1 LLMs as Assistants - the ultimate guide! 24:08
24:08 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי24:08
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we look at the role of large language models (LLMs) as modern-day “artificial interns” , exploring how these systems are transforming the way we work. From content generation and coding help to customer service and knowledge retrieval, we examine how LLMs are used across augmented, transactional, and autonomous tasks . We discuss a unique comparison: using LLMs for problem-solving in the same way developers use rubber duck debugging —talking through issues to arrive at clearer solutions. But while LLMs offer immense value through 24/7 availability, wide-ranging knowledge, and responsiveness, the episode also unpacks their limitations , including hallucinations , biases , and the risk of overreliance without proper human oversight. We also compare LLMs to traditional software and human assistants , highlight current real-world applications, and speculate on how these tools may evolve—raising important questions about ethics, trust, and professional responsibility . Whether you’re a developer, writer, or manager, this episode offers insights into how to work with LLMs effectively—not just as tools, but as intelligent collaborators. Support the show 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 dive into the fascinating convergence of blockchain and artificial intelligence (AI) —two powerful technologies reshaping the digital landscape. We explore how blockchain brings transparency, trust, and data integrity to AI systems, while AI enhances blockchain networks through automation, prediction, and optimization. You’ll hear about real-world applications across industries like finance, healthcare, and smart infrastructure , along with an overview of pioneering platforms such as SingularityNET and Ocean Protocol , which are creating decentralized marketplaces for data and AI models. We also discuss the promise of auditable AI , where blockchain provides an immutable record of AI decision-making. Of course, the episode doesn’t shy away from the challenges, including scalability issues, complexity, and ethical concerns. But the overarching theme is clear: the fusion of blockchain and AI has the potential to create more secure, intelligent, and decentralized systems—paving the way for the next generation of digital innovation. Support the show 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!…
1 AI Bias and Fairness: A Contentious Landscape 27:36
27:36 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי27:36
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we examine the critical issue of bias in artificial intelligence , exploring how biased AI systems can amplify discrimination and perpetuate societal inequalities. We discuss the sources of AI bias, including prejudiced training data, algorithmic design choices, and human decisions during development. We highlight how biased AI impacts areas like recruitment, criminal justice, healthcare, finance, and social media, potentially deepening existing inequalities and undermining public trust. We also delve into efforts to address AI bias through technical solutions—such as collecting diverse data and using fairness-oriented algorithms—as well as regulatory responses like the EU AI Act and emerging legislation in the United States. Yet, despite these efforts, defining and effectively mitigating AI bias remains a significant challenge. Ultimately, we emphasize the importance of interdisciplinary collaboration and ethical guidelines to ensure AI systems are fair, equitable, and trustworthy. Support the show 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!…
1 AI Agents / Agentic AI: The next generation of AI? 19:55
19:55 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי19:55
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this podcast episode, we investigate AI agents—autonomous systems that sense their environments, make independent decisions, and carry out tasks. We discuss their various types, architectures, and capabilities, highlighting their limitations and ethical implications. Special attention is given to the rise of Agentic Workflows and Data Synthesis, driven by challenges around accuracy in current AI systems. The episode also explores practical advice on building effective agents, emphasizing iterative prompt engineering and standardized JSON outputs. Finally, we touch on Edge AI Agents, a promising area bringing autonomous intelligence directly to resource-constrained devices, shaping the future of AI applications. Support the show 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!…
1 LLMs - Fancy Autocorrect or can they actually Reason? 14:58
14:58 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי14:58
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend us a text In this episode, we discuss the limitations of Large Language Models (LLMs) in areas like deductive reasoning, analogy-making, and ethical judgment. While today’s AI models excel at recognizing statistical patterns in vast datasets, they lack genuine understanding or an internal model of the world. Researchers are tackling these challenges through innovations such as causal AI , inference-time computing , and neuro-symbolic approaches , all aimed at enabling AI to move beyond mere pattern recognition towards true reasoning. We explore how these emerging technologies, including causal inference , inference-time computing , and neuro-symbolic integration , are pushing AI closer to human-like, “System 2” reasoning. Will these advancements finally bridge the gap between AI imitation and genuine reasoning? Tune in as we dive into the future of artificial intelligence and explore what it will take for machines to truly think. Support the show 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!…
1 AI Errors vs. Human Mistakes: Rethinking Security 11:49
11:49 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי11:49
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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 . Support the show 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!…
1 Apple’s AI Gamble in China: The Qwen Partnership 19:33
19:33 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי19:33
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 AI Music Production: Generative AI, MIDI Controllers, and the Future of Music 15:35
15:35 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי15:35
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 AI's Hard Takeoff: AGI in 1-6 Years? 21:47
21:47 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי21:47
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 DeepSeek: A Budget-Friendly solution or the end of Western AI? 15:51
15:51 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי15:51
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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! Support the show 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! Support the show 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!…
1 Deep Learning Frameworks in 2025: A Review 35:05
35:05 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי35:05
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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 Support the show 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!…
1 Capsule Networks: A new type of AI? 10:04
10:04 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי10:04
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 Artificial Consciousness: The Missing Pieces 17:40
17:40 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי17:40
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 Spiking Neural Networks: The Future of AI? 29:20
29:20 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי29:20
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 The Nvidia Way: From Gaming Chips to AI Domination 13:38
13:38 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי13:38
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 The Fundamental Particle of Consciousness 13:14
13:14 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי13:14
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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!…
1 The problem of ML Model drift and decay in production 34:01
34:01 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי34:01
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSend 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. Support the show 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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