The Protein and the Social Worker: How machine learning can help us tackle society’s biggest challenges

Article by Juan Mateos-Garcia: “The potential for machine learning (ML) to address our toughest health, education and sustainability issues remains unfulfilled. What lessons about what to do – and what not to do – can we learn from other sectors where ML has been applied at scale?

Last year, the UK research lab DeepMind announced that its AI system, AlphaFold 2, can predict a protein’s 3D structure with an unprecedented level of accuracy. This breakthrough could enable rapid advances in drug discovery and environmental applications.

Like almost all AI systems today, AlphaFold 2 is based on ML techniques that learn from data to make predictions. These ‘prediction machines’ are at the heart of internet products and services we use every day, from search engines and social networks to personal assistants and online stores. In years to come, ML is expected to transform other sectors including transportation (through self-driving vehicles), biomedical research (through precision medicine) and manufacturing (through robotics).

But what about fields such as healthy living, early years development or sustainability, where our societies face some of their greatest challenges? Predictive ML techniques could also play an important role there – by helping identify pupils at risk of falling behind, or by personalising interventions to encourage healthier behaviours. However, its potential in these areas is still far from being realised….(More)”.