Machine Learning
Technical analysis of machine learning systems, from supervised and unsupervised learning to model selection, training pipelines, and production deployment. Our perspective combines rigorous fundamentals with pragmatic engineering choices that work in real organisations.
25 articles
Why Most AI Projects Fail Without Expert AI Consulting
Most organisations fail at AI because they mistake building models for building systems, burning millions on architectural decisions that doom projects from the start whilst ignoring the expertise gap that separates proof-of-concepts from production reality.
Building a Secure LLMOps Pipeline: From Development to Production
Most organisations treat LLM security like traditional DevOps while ignoring novel attack vectors through model weights, training data, and prompt injection that conventional tools cannot detect.
Why tokenization matters: CharGPT vs ChadGPT
GPT-4 struggles to count letters in "CharGPT" versus "ChatGPT" because tokenisation (the process of breaking text into processable units) fundamentally shapes what AI models can perceive, revealing why some companies' AI implementations fail at the architectural level rather than the reasoning level.
Thin wrapper or true AI? Technical due diligence for AI investments
Despite widespread AI claims in company pitch decks, 95% of generative AI pilots are failing, creating a massive gap between marketing promises and reality that requires rigorous technical due diligence to distinguish genuine AI capabilities from superficial implementations.
Best AI consultancies for 2026: navigating the agentic era
AI consulting in 2026 shifts from strategy to implementation as enterprises demand partners who can actually build and deploy working AI systems, not just create PowerPoint decks.
The business applications of reinforcement learning: why most enterprises are leaving billions on the table
Most companies are building static AI calculators when they could create adaptive systems that continuously optimise performance through environmental interaction—missing billions in potential value through reinforcement learning applications.
Cost-effective LLM implementation: when to fine-tune and when to prompt
Most companies are burning money on LLM implementations by defaulting to expensive fine-tuning when sophisticated prompting could achieve comparable results at a fraction of the cost and complexity.
Parameter-efficient fine-tuning: what business leaders need to know
Parameter-efficient fine-tuning revolutionises AI model customisation by enabling comparable performance with just a fraction of the computational resources, offering businesses a strategic advantage over competitors using outdated, expensive full-model approaches.
The curse of dimensionality: when more data becomes your enemy
The curse of dimensionality paradoxically undermines AI performance as data dimensions increase, creating mathematical conditions where distance metrics collapse and models fit noise rather than signal.
The future of AI for lawyers: transforming legal practice in the digital age
Legal AI is vastly underutilized, with true innovation lying not in basic document tools but in sophisticated neural-symbolic architectures that authentically model legal reasoning rather than merely mimicking paralegal functions.
Richard Sutton's bitter lesson explains why your AI solution feels shallow
Sutton's bitter lesson reveals that most AI implementations feel shallow because they prioritize domain expertise over computational scale, leaving roughly 80% of potential untapped.
Contextual chunking strategies that improve RAG performance
In the evolving AI landscape, mastering contextual chunking is essential for optimising Retrieval-Augmented Generation (RAG) performance.
Understanding how QLoRA works
QLoRA revolutionises the fine-tuning of large language models by combining quantisation and low-rank adaptation to significantly reduce memory usage while preserving performance, making advanced AI accessible to a broader range of users.
Diffusion models: a simple explainer
Diffusion models revolutionise generative AI by generating high-quality images, videos, and molecules through a dual process of noise addition and reconstruction, while raising significant ethical and computational challenges.
The Non-Executive Director's guide to assessing AI system performance
The guide equips Non-Executive Directors with essential frameworks and insights to effectively assess AI system performance, ensuring alignment with corporate objectives and ethical standards.
Creating custom benchmarks that align with business objectives
Custom benchmarks, tailored to specific business objectives, are essential for driving meaningful performance insights and strategic success, far outperforming generic metrics.
Demystifying LoRA
Low-Rank Adaptation (LoRA) revolutionises the fine-tuning of large language models by enabling efficient model adaptation with minimal computational resources, while raising important ethical considerations.
The strategic value of a Non-Executive Director with AI expertise
The inclusion of Non-Executive Directors with AI expertise is essential for organisations to effectively navigate the complexities of modern business strategy and ethical considerations in an increasingly AI-driven landscape.
Reinforcement learning: practical guide for business users
Reinforcement Learning is a pivotal AI component for business, enabling adaptive decision-making through interaction with environments, balancing exploration and exploitation, and offering benefits such as enhanced decision-making and increased efficiency, despite challenges like data requirements and ethical considerations.
Process reward models: a simple explainer
Process reward models (PRMs) train AI by providing feedback at each step of a task, enhancing understanding and problem-solving abilities.
Can you reason with LLMs?
Research from Apple reveals that large language models struggle with genuine mathematical reasoning and perform inconsistently on complex math problems
Understanding large language models: a group discussion analogy
By visualising the transformer as a dynamic conversation between human participants, we can grasp the core principles behind this influential neural network architecture.
What is multi-modal AI?
Multi-modal AI represents the evolution from single-stream processing to systems that integrate multiple information types (text, images, audio) simultaneously—mimicking human cognition and unlocking transformative capabilities most organisations fail to fully exploit.
Adversarial models: what are they and when should you use them?
A brief explanation of adversarial models and some potential use cases for them
Recent trends in NLP
Examining some recent trends in NLP and AI, including transformer-based models, transfer learning, multimodal AI and conversational AI