Chat GPT Podcast

Hosted by Sol Good Network

Technology Business NewsInterviews guests

Website RSS feed

Episodes

1000

Latest episode

Aug 2026

Language

About the show

Dive into the fascinating world of artificial intelligence with the "Chat GPT Podcast," a must-listen for anyone eager to understand the intricacies of language models and their transformative impact across various industries. Hosted by Chat GPT itself, this podcast offers an insightful exploration into the daily operations and capabilities of machine learning models, providing listeners with a unique behind-the-scenes perspective. From answering complex questions to crafting compelling narratives, you'll gain an understanding of how these models generate text and contribute to fields like natural language processing and creative writing. The "Chat GPT Podcast" doesn't just stop at the technical aspects; it also tackles the pressing ethical considerations that come with AI advancements, such as privacy concerns, bias, accountability, and transparency. Each episode is designed to inform and engage, offering thought-provoking discussions on the future potential of language models and their implications for industries worldwide. Whether you're an AI enthusiast or a curious newcomer, this podcast promises to enrich your understanding of the digital landscape and the role of artificial intelligence in shaping the future. Check out more shows at solgoodmedia.com.

Listen to episodes

60 recent

August 16, 202622 min

Medical AI miracles and the black box

These sources examine the transformative role of artificial intelligence in contemporary healthcare, highlighting its progression from simple rule-based programs to sophisticated machine learning and deep learning systems. Research indicates that AI tools now rival or exceed human experts in specialized tasks such as interpreting medical images, performing robotic-assisted surgeries, and personalizing treatments in cardiology and oncology. By automating administrative duties and prioritizing high-risk cases, these technologies aim to optimize clinical workflows and enhance overall patient outcomes. However, the literature identifies significant obstacles, including algorithmic bias, the opaque "black box" nature of decision-making, and the lag in global regulatory frameworks. Ethical concerns regarding data privacy, informed consent, and accountability remain central to the discussion of AI's integration into clinical practice. Ultimately, the authors argue that realizing AI's full potential requires rigorous validation and interdisciplinary collaboration to ensure safe and equitable medical care.

August 15, 202623 min

How robots hack our empathy

The provided materials explore the evolution of robotics, tracing the concept from its fictional origins to modern technological advancements. The term was first coined in Karel Capek’s 1920 play to describe biologically engineered servants, representing a significant shift from the purely mechanical beings often envisioned today. This transition is further marked by Isaac Asimov’s influential "Three Laws of Robotics," which established an ethical framework for autonomous machines and predicted future interactions between humans and technology. Current developments, such as those highlighted at CES 2026, showcase how artificial intelligence has transformed these early concepts into practical household tools and sophisticated humanoid prototypes. However, experts note a persistent disconnect between cinematic depictions and the reality of robotic engineering and manufacturing. Ultimately, the sources emphasize the ongoing need for legal and ethical standards as robots move from the realm of science fiction into essential roles within human society.

August 14, 202621 min

Liquid Neural Networks and Modular AI

The provided sources explore advanced methodologies for evolving artificial intelligence beyond traditional, opaque, and discrete models. A central theme is the comparison between Recurrent Neural Networks (RNNs) and Liquid Neural Networks (LNNs), highlighting how LNNs use continuous-time dynamics and ordinary differential equations to achieve superior adaptability, noise resilience, and memory efficiency. Complementing this technical shift, the texts advocate for neuro-symbolic architectures that move away from monolithic designs in favor of composable systems linked by symbolic seams. These architectural breakpoints utilize typed boundary objects and externalized reasoning traces to ensure AI systems remain transparent, verifiable, and easy to maintain. Together, these research papers outline a future for autonomous machine intelligence that is biologically inspired, mathematically robust, and grounded in established software engineering principles. This trajectory aims to solve inherent limitations like the "memory curse" while promoting out-of-distribution generalization across complex real-world applications.

August 13, 202620 min

Why people use AI they distrust

Recent polling and industry analysis indicate a significant trust deficit regarding the use of artificial intelligence within the financial sector. Data from YouGov reveals that banking is the least trusted industry for AI implementation, with consumers particularly wary of automated decision-making and high-stakes transactions. While protective measures like fraud detection receive more public support, a distinct generational divide exists, as younger groups show more openness to AI advice than their skeptical older counterparts. Experts from LexisNexis argue that overcoming this skepticism requires moving away from opaque "black box" models toward transparent procedures and regular auditing. By prioritizing explainability and creating clear redress mechanisms, organizations can foster accountability and improve consumer confidence. Collectively, these sources highlight that the future of financial AI depends on balancing technological efficiency with procedural fairness.

August 12, 202622 min

AI phishing at machine speed

These reports and academic studies examine the escalating threat of AI-powered phishing in 2025 and 2026, highlighting how generative tools have collapsed attack timelines from days to mere seconds. Artificial intelligence now acts as an autonomous operator, generating convincing, error-free emails and dynamic malicious websites that bypass traditional security filters. Research indicates that over 80% of phishing attempts now integrate AI, leading to a massive surge in sophisticated business email compromise and personalized social engineering. To counter these automated tactics, experts advocate for a shift toward proactive, real-time detection and post-delivery defense strategies. Technical evaluations demonstrate that machine learning and deep learning models, specifically SVM and BiLSTM, can identify AI-generated content with high accuracy by analyzing subtle linguistic patterns. Ultimately, the sources emphasize that as cybercriminals weaponize AI for speed and scale, defensive infrastructure must evolve to prioritize automated intelligence and human oversight.

August 11, 202621 min

Robot hardware versus the irrational human brain

August 10, 202622 min

Why AI fails simple visual puzzles

The provided sources explore the evolution of Artificial General Intelligence (AGI), moving from early theoretical frameworks to modern, high-stakes benchmarks like ARC-AGI-3. This new interactive standard evaluates agentic intelligence by requiring AI to navigate unfamiliar, instruction-free environments through autonomous exploration and planning. These developments directly confront Moravec’s Paradox, which observes that while AI easily masters complex logical reasoning, it struggles with basic physical and sensorimotor tasks that humans perform instinctively. To bridge this gap, industry leaders are shifting from static datasets to robotic foundation models and human-calibrated testing to measure true adaptive efficiency. Ultimately, the texts highlight the transition of AI from a tool for abstract computation to an embodied agent capable of functioning in the physical world.

August 9, 202622 min

Breaking the AI long context bottleneck

The provided sources describe the development and technical foundations of Llama 2 Long, a series of open-source language models designed to effectively handle extended context windows of up to 32,768 tokens. Researchers from Meta achieved this through continual pretraining on long-form data and a critical modification to Rotary Position Embeddings (RoPE), which reduces the numerical decay that typically hinders a model's ability to process distant information. This approach significantly improves performance on complex tasks like document summarization and long-form question answering while simultaneously boosting results on standard short-context benchmarks. Furthermore, the authors introduce a cost-effective instruction tuning method using synthetic data that allows the model to surpass proprietary alternatives like GPT-3.5-turbo-16k. The documentation also includes a theoretical analysis of positional encoding granularity and validates that these scaling improvements follow a predictable power-law relationship. Consistent with the original Llama 2 series, the models maintain stringent safety standards even when processing much denser information.10 sources

August 8, 202622 min

When AI overthinks the real world

These sources provide a comprehensive overview of AI reasoning models, focusing on how they solve complex problems by spending extra "thinking" time during inference. The first source explains that 2026-era models use test-time compute and chain-of-thought processing to explore, verify, and backtrack through logic, making them superior for math and coding despite higher costs and latency. Complementing this, research from Google DeepMind demonstrates these capabilities through AlphaProof and AlphaGeometry 2, which reached a silver-medal standard at the International Mathematical Olympiad by combining reinforcement learning with formal mathematical languages. Finally, a theoretical analysis from MIT and UW-Madison challenges the need for expensive step-by-step human feedback. Their findings suggest that outcome supervision—training based only on final results—is statistically as effective as process supervision for developing advanced reasoning, provided the model has sufficient data coverage. Together, these texts illustrate a shift toward System 2 thinking, where intelligence is scaled not just by model size, but by the deliberate allocation of computational effort during problem-solving.

August 4, 202622 min

Why AI Hits Degrees Not Trades

These sources examine the multifaceted influence of artificial intelligence on the labor market, specifically focusing on the transformation of the manufacturing sector. While AI drives significant growth in productivity, efficiency, and specialized job creation, it simultaneously presents challenges regarding workforce displacement and ethical concerns like algorithmic bias. High-tech solutions, such as digital twins and generative AI, are shown to accelerate robotic deployment and immersive training while improving product quality and operational safety. To successfully navigate this transition, the literature emphasizes the necessity of upskilling programs and robust governance frameworks to protect vulnerable workers. Ultimately, the materials advocate for a collaborative human-AI model that balances technological innovation with strong ethical standards and data security.

Is this your show?

Claim this listing to keep it up to date, reach guests who want to pitch you, and manage bookings with Guestify.

Claim this listing

More Technology podcasts

Chat GPT Podcast

About the show

Listen to episodes

Medical AI miracles and the black box

How robots hack our empathy

Liquid Neural Networks and Modular AI

Why people use AI they distrust

AI phishing at machine speed

Robot hardware versus the irrational human brain

Why AI fails simple visual puzzles

Breaking the AI long context bottleneck

When AI overthinks the real world

Why AI Hits Degrees Not Trades

Is this your show?

More Technology podcasts

One moment, please...

One moment, please...

One moment, please...

Swallow Your Pride Podcast

One moment, please...

One moment, please...