Neil Lawrence is a researcher and thought leader writing about machine learning, artificial intelligence, and decision-making under uncertainty. His work explores the societal, ethical, and technical implications of AI, data science, and policy over the past decade.
7 articles from this blog
The author draws parallels between historical perpetual motion claims and modern superintelligence hype, exploring a theoretical link between information theory and thermodynamics.
Explores the concept of 'intellectual debt' in AI and software systems, comparing it to The Sorcerer's Apprentice and arguing for open society principles as a solution.
The author critiques the focus on speculative AI risks at global summits, arguing for addressing real issues like corporate power and algorithmic bias instead.
Analyzes the challenges of using data science and scientific advice for Covid-19 policy, comparing it to the gap between scientists and policymakers.
A satirical look at AI development and government funding, imagining a fictional 'Ministry of Silly Models' in the UK.
Explores the 3D framework (Decomposition, Data, Deployment) for designing and deploying effective machine learning systems in business contexts.
Explores the history and current state of AI, questioning if we truly understand human intelligence or are merely emulating it.
