In this episode, we explore how large language models evolved from passive text generators into agentic systems that can use tools, take actions, collaborate, and operate inside dynamic environments. We explain the shift from “knowing” to “doing,” and why this transition marks one of the most significant changes since the Transformer.

We break down what defines agentic AI, how agents plan and act through tool use, and why multi-agent systems outperform single models on complex, real-world tasks. The episode also covers the emerging agent frameworks, real business impact, and the safety and governance challenges that come with autonomy.

This episode covers:

• The gap between text generation and real-world action
• What defines agentic AI: autonomy, reactivity, proactivity, learning
• Tool use as the bridge from reasoning to execution
• Agent lifecycles: planning, action, observation, refinement
• Single-agent limits and multi-agent systems (MAS)
• Popular agent frameworks (LangChain, LangGraph, AutoGen, CrewAI)
• Enterprise, science, and productivity impacts
• Safety, latency, memory, and responsibility challenges

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

In this episode, we explore how open-source large language models transformed AI by breaking proprietary barriers and making advanced systems accessible to a global community. We examine why the open movement emerged, how open LLMs are built in practice, and why transparency and reproducibility matter.

We trace the journey from large-scale pre-training to instruction tuning, alignment, and real-world deployment, showing how open models now power education, tutoring, and specialized applications—often matching or surpassing much larger closed systems.

This episode covers:

• Why open LLMs emerged and what they changed
• Model weights, transparency, and reproducibility
• Pre-training, instruction tuning, and alignment
• Open LLMs in education and specialized domains
• RAG, multi-agent systems, and trust
• Small specialized models vs large proprietary models

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

In this episode, we explore how AI crossed a critical threshold—from powerful but expert-only systems to tools anyone can use naturally. We trace the usability revolution that turned large language models into conversational, intuitive interfaces, and explain why this shift mattered as much as raw intelligence.

We walk through the technical breakthroughs behind this change—from static word embeddings and LSTMs to Transformers, scale, and RLHF—and connect them to human-centered design principles like effectiveness, efficiency, and satisfaction. The episode also examines how usability is measured, why ChatGPT succeeded despite imperfections, and how multimodal and efficient architectures are shaping the next phase of AI interaction.

This episode covers:

• Why early AI systems were hard to use
• Static vs contextual language understanding
• Transformers, scale, and zero-/few-shot learning
• RLHF and conversational alignment
• Usability metrics (SUS) and adoption drivers
• Multimodal models and efficiency-focused designs
• AI as a universal natural-language interface

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

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 instructions
• Supervised instruction tuning (SFT)
• RLHF and reward modeling
• Helpfulness, truthfulness, and safety trade-offs
• Bias, cost, and scalability of alignment
• The future of automated alignment

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

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 systems
• The scale hypothesis: how size alone unlocked new capabilities
• Zero-shot, one-shot, and few-shot learning explained
• In-context learning vs fine-tuning
• Emergent abilities in language, translation, and style
• Why GPT-3 “reasons” without symbolic logic
• Failure modes: arithmetic, logic, hallucination
• Bias, fairness, and the risks of training on the open internet
• How GPT-3 reshaped prompting, UX, and AI interaction

This 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

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 capabilities
• Instruction Tuning & RLHF — Aligning models with human intent
• ChatGPT, Gemini & Usability — Why interface design changed everything
• The Open-Source LLM Movement — How open models reshaped innovation
• Agents, Tools & Ecosystems — From models to collaborative systems

This 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

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

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

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

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