Natural Language Processing
Technical foundations and modern approaches to natural language processing. From linguistic fundamentals to transformer architectures, covering methods that enable machines to understand and generate human language effectively.
6 articles
What I've Learned Shipping AI Products That Most Consulting Advice Gets Wrong
Most AI product strategy advice fails in practice because the real challenges -- architectural rewrites, evaluation infrastructure, feedback loops, nonlinear timelines, and production edge cases -- only reveal themselves through hard-won experience of actually shipping AI systems.
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.
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.
Small language models (SLMs), characterised by their efficiency and versatility, are emerging as pivotal tools for language processing, offering significant advantages in resource optimisation and accessibility, while challenging the dominance of larger models.
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.
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.