Adapticx AI

Adapticx Technologies Ltd

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.

  1. Instruction Tuning & RLHF

    11 HR AGO

    Instruction Tuning & RLHF

    In this episode, we explore how large language models learned to follow instructions—and why this shift turned raw text generators into reliable AI assistants. We trace the move from early, unaligned models to instruction-tuned systems shaped by human feedback. We explain supervised fine-tuning, reward models, and reinforcement learning from human feedback (RLHF), showing how human preference became the key signal for usefulness, safety, and control. The episode also looks at the limits of RLHF and how newer, automated alignment methods aim to scale instruction learning more efficiently. This episode covers: Why early LLMs struggled with instructionsSupervised instruction tuning (SFT)RLHF and reward modelingHelpfulness, truthfulness, and safety trade-offsBias, cost, and scalability of alignmentThe future of automated alignmentThis 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    28 min
  2. GPT-3 & Zero-Shot Reasoning

    2 DAYS AGO

    GPT-3 & Zero-Shot Reasoning

    In 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    34 min
  3. LLM Evolution to Present (Trailer)

    SEASON 6 TRAILER

    LLM Evolution to Present (Trailer)

    Season 6 explores how large language models evolved from research systems into everyday AI tools. We focus on the breakthroughs that unlocked reasoning, instruction-following, usability, and agentic behavior—and why this era marks a true turning point in AI. Episodes this season: GPT-3 & Zero-Shot Reasoning — How scale unlocked emergent capabilitiesInstruction Tuning & RLHF — Aligning models with human intentChatGPT, Gemini & Usability — Why interface design changed everythingThe Open-Source LLM Movement — How open models reshaped innovationAgents, Tools & Ecosystems — From models to collaborative systemsThis season traces the moment AI moved from the lab into daily life. 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    4 min
  4. Scaling Laws: Data, Parameters, Compute

    3 DAYS AGO

    Scaling Laws: Data, Parameters, Compute

    In this episode, we examine the discovery of scaling laws in neural networks and why they fundamentally reshaped modern AI development. We explain how performance improves predictably—not through clever architectural tricks, but by systematically scaling data, model size, and compute. We break down how loss behaves as a function of parameters, data, and compute, why these relationships follow power laws, and how this predictability transformed model design from trial-and-error into principled engineering. We also explore the economic, engineering, and societal consequences of scaling—and where its limits may lie. This episode covers: • What scaling laws are and why they overturned decades of ML intuition • Loss as a performance metric and why it matters • Parameter scaling and diminishing returns • Data scaling, data-limited vs model-limited regimes • Optimal balance between model size and dataset size • Compute scaling and why “better trained” beats “bigger” • Optimal allocation under a fixed compute budget • Predicting large-model performance from small experiments • Why architecture matters less than scale (within limits) • Scaling beyond language: vision, time series, reinforcement learning • Inference scaling, pruning, sparsity, and deployment trade-offs • The limits of single-metric optimization and values pluralism • Why breaking scaling laws may define the next era of AI 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    35 min
  5. BERT, GPT, T5

    4 DAYS AGO

    BERT, GPT, T5

    In this episode, we explore the three Transformer model families that shaped modern NLP and large language models: BERT, GPT, and T5. We explain why they were created, how their architectures differ, and how each one defines a core capability of today’s AI systems. We show how self-attention moved NLP beyond static word embeddings, enabling deep contextual understanding and large-scale pretraining. From there, we break down how encoder-only, decoder-only, and encoder–decoder models emerged—and why their training objectives matter as much as their architecture. This episode covers: • Why early NLP models failed to generalize • How self-attention enabled contextual language understanding • BERT and encoder-only models for analysis and comprehension • GPT and decoder-only models for fluent text generation • T5 and the text-to-text unification of NLP tasks • Pretraining objectives: masking, next-token prediction, span corruption • Scaling laws and emergent abilities • Instruction tuning and following human intent 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    31 min
  6. Transformer Architecture

    2 JAN

    Transformer Architecture

    In this episode, we break down the Transformer architecture—how it works, why it replaced RNNs and LSTMs, and why it underpins modern AI systems. We explain how attention enabled models to capture global context in parallel, removing the memory and speed limits of earlier sequence models. We cover the core components of the Transformer, including self-attention, queries, keys, and values, multi-head attention, positional encoding, and the encoder–decoder design. We also show how this architecture evolved into encoder-only models like BERT, decoder-only models like GPT, and why Transformers became a general-purpose engine across language, vision, audio, and time-series data. This episode covers: • Why RNNs and LSTMs hit hard limits in speed and memory • How attention enables global context and parallel computation • Encoder–decoder roles and cross-attention• Queries, keys, and values explained intuitively • Multi-head attention and positional encoding • Residual connections and layer normalization • Encoder-only (BERT), decoder-only (GPT), and seq-to-seq models • Vision Transformers, audio models, and long-range forecasting • Why the Transformer defines the modern AI era 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    25 min
  7. Attention Is All You Need?!!!

    18/12/2025

    Attention Is All You Need?!!!

    In this episode, we explore the attention mechanism—why it was invented, how it works, and why it became the defining breakthrough behind modern AI systems. At its core, attention allows models to instantly focus on the most relevant parts of a sequence, solving long-standing problems in memory, context, and scale. We examine why earlier models like RNNs and LSTMs struggled with long-range dependencies and slow training, and how attention removed recurrence entirely, enabling global context and massive parallelism. This shift made large-scale training practical and laid the foundation for the Transformer architecture. Key topics include: • Why sequential memory models hit a hard limit • How attention provides global context in one step • Queries, keys, and values as a relevance mechanism • Multi-head attention and richer representations • The quadratic cost of attention and sparse alternatives • Why attention reshaped NLP, vision, and multimodal AI 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    31 min
  8. Beginning of LLMs (Transformers) : The Introduction

    SEASON 5 TRAILER

    Beginning of LLMs (Transformers) : The Introduction

    This trailer introduces Season 5 of the Adapticx Podcast, where we begin the story of large language models. After tracing AI’s evolution from rules to neural networks and attention, this season focuses on the breakthrough that changed everything: the Transformer. We preview how “Attention Is All You Need” reshaped language modeling, enabled large-scale training, and led to early models like BERT, GPT-1, GPT-2, and T5. We also introduce scaling laws—the insight that performance grows predictably with data, compute, and model size. This episode sets the direction for the season and explains why the Transformer marks the start of the modern LLM era. 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 Reading Additional references and extended material are available at: https://adapticx.co.uk

    3 min
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Trailers

About

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.

Information

  • Creator
    Adapticx Technologies Ltd
  • Years Active
    2025 - 2026
  • Episodes
    29
  • Rating
    Clean
  • Copyright
    Copyright © 2025 Adapticx Technologies Ltd. All Rights Reserved.
  • Show Website
    Adapticx AI