Podcast
Adapticx AI
Hosted by Adapticx Technologies Ltd · EN
Adapticx AI is a podcast designed to make advanced AI understandable, practical, and inspiring.
We explore the evolution of intelligent systems with the goal of empowering innovators to build responsible, resilient, and future-proof solutions.
Clear, accessible, and grounded in engineering reality—this is where the future of intelligence becomes understandable.
12episodes
Episodes
Newest firstAll episodes
GPT-3 & Zero-Shot Reasoning
Jan 700:33:53Tap to summarizeIn this episode, we examine why GPT-3 became a historic turning point in AI—not because of a new algorithm, but because of scale. We explore how a single model trained on internet-scale data began performing tasks it was never explicitly trained for, and why this forced researchers to rethink what “reasoning” in machines really means.We unpack the scale hypothesis, the shift away from fine-tuning toward task-agnostic models, and how GPT-3’s size unlocked zero-shot and few-shot learning. This episode also looks beyond the hype, examining the limits of statistical reasoning, failures in arithmetic and logic, and the serious risks around hallucination, bias, and misinformation.This episode covers:Why GPT-3 marked the shift from specialist models to general-purpose systemsThe scale hypothesis: how size alone unlocked new capabilitiesZero-shot, one-shot, and few-shot learning explainedIn-context learning vs fine-tuningEmergent abilities in language, translation, and styleWhy GPT-3 “reasons” without symbolic logicFailure modes: arithmetic, logic, hallucinationBias, fairness, and the risks of training on the open internetHow GPT-3 reshaped prompting, UX, and AI interactionThis episode is part of Season 6: LLM Evolution to the Present of the Adapticx AI Podcast.This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.Sources and Further ReadingAdditional references and extended material are available at:https://adapticx.co.uk
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Frameworks & Foundation Models
Dec 10Tap to summarizeIn this episode, we explore how modern AI frameworks and foundation models have reshaped the entire lifecycle of building, training, and applying large-scale neural systems. We trace the shift from bespoke, task-specific models to massive general-purpose architectures—trained with self-supervision at unprecedented scale—that now serve as the universal substrate for most AI applications. We discuss how frameworks like TensorFlow and PyTorch enabled this transition, how transformers unlocked true scalability, how representation learning and multimodality extend these models across domains, and how techniques such as LoRA make fine-tuning accessible. We also examine the hidden systems engineering behind trillion-parameter training, the rise of retrieval-augmented generation, and the profound ethical risks created by model homogenization, bias propagation, security vulnerabilities, environmental impact, and the limits of interpretability.This episode covers:• Why modern frameworks enabled rapid experimentation and automated differentiation• ReLU, attention, and the architectural breakthroughs that enabled scale • What defines a foundation model and why emergent capabilities appear only at extreme size• Representation learning, transfer learning, and self-supervised objectives like contrastive learning • Multimodal alignment across text, images, audio, and even brain signals• Parameter-efficient fine-tuning: LoRA and the democratization of model adaptation • Distributed training: data, pipeline, and tensor parallelism; Megatron and DeepSpeed • Inference efficiency and retrieval-augmented generation • Environmental costs, societal risks, systemic bias, data poisoning, dual-use harms • Black-box models, interpretability challenges, and the need for responsible governanceThis episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.Sources and Further ReadingAll referenced materials and extended resources are available at:https://adapticx.co.uk
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