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. Scaling Laws: Data, Parameters,  Compute

    -6 H

    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.
  2. Transformer Architecture

    -3 ZILE

    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.
  3. RNNs, LSTMs & Attention

    17.12.2025

    RNNs, LSTMs & Attention

    In this episode, we trace how neural networks learned to model sequences—starting with recurrent neural networks, progressing through LSTMs and GRUs, and culminating in the attention mechanism and transformers. This journey explains how NLP moved from fragile, short-term memory systems to architectures capable of modeling global context at scale, forming the backbone of modern large language models. This episode covers: • Why feed-forward networks fail on ordered data like text and time series • The origin of recurrence and sequence memory in RNNs • Backpropagation Through Time and the limits of unrolled sequences • Vanishing gradients and why basic RNNs forget long-range dependencies • How LSTMs and GRUs use gates to preserve and control memory • Encoder–decoder models and early neural machine translation • Why recurrence fundamentally limits parallelism on GPUs • The emergence of attention as a solution to context bottlenecks • Queries, keys, and values as a mechanism for global relevance • How transformers remove recurrence to enable full parallelism • Positional encoding and multi-head attention • Real-world impact on translation, time series, and reinforcement learning 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 All referenced materials and extended resources are available at: https://adapticx.co.uk

    26 min.

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Detalii

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.