Topic: overfitting

John Platt discusses how Google’s applied-science team chooses problems where AI can make a useful contribution. Topics include satellite-based detection and prediction of aircraft contrails, evaluation of rerouting trials, limitations of physical simulation, and the role of AI agents in scientific modelling and data work. The conversation also examines overfitting, action incentives, gradient-free optimisation, SMO and support-vector machines, the physical constraints on scaling, and opportunities for robotics and manufacturing to support climate technology. It concludes with FireSat’s approa

Artificial neural networks (ANNs) form the basis of most successful Deep Learning (DL) algorithms$^1$, which are capable of solving complex problems such as image recognition and natural language processing$^{2,3}$. However, unlike biological brains, which efficiently solve similar problems, DL algorithms require a large number of adjustable parameters, making them energy-intensive and susceptible to overfitting. In this study, a new ANN architecture is introduced that incorporates the structured connectivity and restricted sampling properties of biological dendrites, aiming to overcome these