Omslagafbeelding van de show AI for Good: Transforming Communities
GoodSam Podcast • Inspiring Hope with Douglas Liles

AI for Good: Transforming Communities GoodSam Podcast • Inspiring Hope with Douglas Liles

Podcast door A.I. Powered Hope with Douglas Liles

Engels

Nieuws & Politiek

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Over AI for Good: Transforming Communities GoodSam Podcast • Inspiring Hope with Douglas Liles

🌟 GoodSam: Where AI Meets Social Impact | Journey into the world of transformative technology changing lives and communities. Each episode explores groundbreaking AI innovations in healthcare, education, and sustainability, featuring tech visionaries and community leaders. From ethical AI to smart cities, discover how artificial intelligence is building a more equitable world. Perfect for innovators, changemakers, and anyone passionate about tech for good. Get exclusive insights on green tech, digital transformation, and grassroots innovations that matter. #AIForGood #TechForChange #GreenTech

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132 afleveringen

aflevering Safe AI implementation for Florida Special Districts artwork

Safe AI implementation for Florida Special Districts

This field guide provides a strategic framework for special districts looking to integrate artificial intelligence into their operations responsibly. Rather than focusing on technical hype, the material emphasizes a governance-first approachthat prioritizes established policies and human oversight before selecting specific tools. The author identifies five key areas where the technology serves as a force multiplier, including financial oversight, infrastructure maintenance, and the management of public records. Essential precautions are outlined to ensure compliance with transparency standardsand data retention laws. Ultimately, the guide serves as a practical roadmap for leaders to enhance administrative efficiency while protecting the district from legal and ethical risks.

1 jun 2026 - 47 min
aflevering AIUC-1_and_the_Agentic_Resilience_Gap artwork

AIUC-1_and_the_Agentic_Resilience_Gap

This podcast discusses AI agents and the necessary governance frameworks required to manage their unique autonomous risks. A primary focus is the launch of the Artificial Intelligence Underwriting Company (AIUC) and its AIUC-1 standard, a certifiable framework designed to provide a "SOC-2 for AI agents" through independent audits and specialized insurance. Organizations like NIST are simultaneously introducing the AI Agent Standards Initiative to foster secure, interoperable protocols across the digital landscape. Technical research from MLCommons and Vectra AI highlights critical vulnerabilities such as jailbreaking and memory poisoning, noting that traditional security is often insufficient for agentic architectures. To address these threats, we propose multilayered defense-in-depth strategies and zero-trust governance, moving beyond simple model integrity to monitor real-world behavioral impact. Ultimately, these initiatives aim to build enterprise confidence by standardizing how autonomous systems are developed, insured, and held accountable.

25 feb 2026 - 24 min
aflevering What Is Neuromorphic Computing and Why Does It Matter? artwork

What Is Neuromorphic Computing and Why Does It Matter?

Neuromorphic computing is an approach to processor design that mimics the structure and function of biological neural networks, using analog circuits and spiking patterns instead of traditional digital logic. Unlike conventional computers that separate memory and processing (the Von Neumann architecture), neuromorphic chips perform computation directly within memory arrays, eliminating the data-transfer bottleneck that limits modern AI efficiency. The practical significance is energy efficiency. Traditional deep learning models consume enormous power during both training and inference. Data centers running AI workloads consume megawatts of electricity. Brain-inspired chips target the efficiency of biological neurons, which process information using approximately 20 watts for the entire human brain. This efficiency advantage makes neuromorphic computing critical for edge AI applications, autonomous systems, and sustainable AI infrastructure.

21 dec 2025 - 14 min
aflevering DeepSeek_3.2_Sparse_Attention_Changes_Agent_Economic artwork

DeepSeek_3.2_Sparse_Attention_Changes_Agent_Economic

detailed overview of the DeepSeek-V3.2 large language model, positioning it as an open-weight solution specifically engineered for agentic workloads. Its key architectural innovation is DeepSeek Sparse Attention (DSA), which efficiently manages extremely long 128K context windows by only attending to a small, relevant subset of tokens, dramatically reducing computational costs from O(L²) to O(L·k). The model also relies on scaled reinforcement learning and extensive agentic task synthesis to enhance reasoning and generalization, addressing historical weaknesses in open models regarding robust agent behavior. Operationally, the model is designed to be economically disruptive, with its release tied to 50%+ API price cuts, enabling developers to run complex, long-horizon agent loops that were previously too expensive.

15 dec 2025 - 15 min
aflevering DeepSeek_3.2_AI_Half_Cost_Breakthrough artwork

DeepSeek_3.2_AI_Half_Cost_Breakthrough

Architecture, performance, and impact of DeepSeek 3.2, a new open-source large language model that aims to redefine efficient AI development. The model achieves benchmark performance comparable to frontier proprietary systems like GPT-5 and Claude 4.5 Sonnet, while operating at significantly lower computational cost, primarily through the introduction of DeepSeek Sparse Attention. This novel attention mechanism dramatically reduces resource usage by retaining only the approximately 2,000 most relevant tokens, regardless of the total input length. DeepSeek 3.2 also introduces sophisticated training innovations, including an unprecedented allocation of its compute budget to reinforcement learning (RL), alongside techniques like mixed RL training and keep routing operations to maintain stability in its mixture-of-experts (MoE) architecture. The release is positioned as evidence that the AI industry is shifting from an "age of scaling" to an "age of research," prioritizing architectural efficiency over raw compute to achieve state-of-the-art results. The model’s known limitations, such as verbose output and reduced breadth of world knowledge, are also acknowledged in comparison to more extensively trained closed-source competitors.

15 dec 2025 - 13 min
Super app. Onthoud waar je bent gebleven en wat je interesses zijn. Heel veel keuze!
Super app. Onthoud waar je bent gebleven en wat je interesses zijn. Heel veel keuze!
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