LearnerBox Insights

A Question That Refuses to Stay Settled Every few months now, a new AI system produces an output so fluent, so contextually apt, so seemingly self-aware that someone, somewhere, asks the question in earnest: is it conscious? The question of AI consciousness has moved from philosophy seminar rooms into boardrooms, courtrooms, and dinner table arguments. And the honest, uncomfortable truth is that after decades of philosophical labour, we do not have a settled answer, because we do not yet have a settled account of what consciousness is in the first place, even in ourselves. This is not a failure of AI research. It is a reflection of the depth of…

This is the final part of a three-part series on AI in drug discovery. Part 1 covered molecular representations, graph neural networks, and transfer learning for QSAR modelling. Part 2 covered protein structure prediction with AlphaFold, generative molecular design, and deep learning virtual screening. Part 3 examines how AI is being applied beyond the molecule: to systems-level disease biology, clinical trial optimisation, and the open theoretical and practical challenges that remain. Beyond the Molecule Parts 1 and 2 of this series focused on AI in drug discovery at the molecular scale: representing chemical structures, predicting binding affinities, generating candidate molecules, and screening compound libraries computationally. These approaches operate primarily on…

This is Part 2 of a three-part series on AI in drug discovery. Part 1 covered the molecular foundations: chemical space, molecular representations, graph neural networks, and transfer learning for QSAR modelling. Part 2 covers protein structure prediction, generative molecular design, and deep learning-powered virtual screening. Part 3 will examine clinical trial optimisation, multi-omics integration, and the open challenges facing the field. From Representing Molecules to Understanding Targets Part 1 established how machine learning models can learn to reason about small molecules: how chemical structures are encoded as SMILES strings, molecular graphs, or 3D conformers, and how graph neural networks trained on large molecular databases can predict biological activity from…

This is Part 1 of a three-part series on AI in drug discovery. Part 1 covers the theoretical foundations: the drug discovery pipeline, molecular representation, and how machine learning models learn to reason about chemical space. Part 2 will cover protein structure prediction, generative molecular design, and virtual screening. Part 3 will examine clinical trial optimisation, multi-omics integration, and the open challenges facing the field. A Pipeline in Crisis The pharmaceutical industry operates under a brutal set of statistics. It takes an average of 12 to 15 years and over $2 billion to bring a single new drug from initial discovery to regulatory approval. Roughly 90% of drug candidates that…

The Intelligence That Did Not Come with a Manual Peer inside the mind of an AI and you will not find fully formed thoughts or intentions written in plain English. What you will find is vast arrays of numbers combining together in ways that somehow produce intelligence. How exactly that happens is, remarkably, something we genuinely do not fully understand — even the researchers who build these systems. That is the problem that AI interpretability is trying to solve: mapping meaning onto those numbers, and shining a light inside the black box. AI interpretability is, in the words of Neel Nanda, who leads the Language Model Interpretability team at Google…