The Role of Bio-Inspired Modularity in General Learning

One goal of general intelligence is to learn novel information without overwriting prior learning. The utility of learning without forgetting (CF) is twofold: first, the system can return to previously learned tasks after learning something new. In addition, bootstrapping previous knowledge may allow for faster learning of a novel task. Previous approaches to CF and bootstrapping are primarily based on modifying learning in the form of changing weights to tune the model to the current task, overwriting previously tuned weights from previous tasks.However, another critical factor that has been largely overlooked is the initial network topology, or architecture. Here, we argue that the topology of biological brains likely evolved certain features that are designed to achieve this kind of informational conservation. In particular, we consider that the highly conserved property of modularity may offer a solution to weight-update learning methods that adheres to the learning without catastrophic forgetting and bootstrapping constraints. Final considerations are then made on how to combine these two learning objectives in a dynamical, general learning system.

A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle

Self-driving technology is advancing rapidly --- albeit with significant challenges and limitations. This progress is largely due to recent developments in deep learning algorithms. To date, however, there has been no systematic comparison of how different deep learning architectures perform at such tasks, or an attempt to determine a correlation between classification performance and performance in an actual vehicle, a potentially critical factor in developing self-driving systems. Here, we introduce the first controlled comparison of multiple deep-learning architectures in an end-to-end autonomous driving task across multiple testing conditions. We compared performance, under identical driving conditions, across seven architectures including a fully-connected network, a simple 2 layer CNN, AlexNet, VGG-16, Inception-V3, ResNet, and an LSTM by assessing the number of laps each model was able to successfully complete without crashing while traversing an indoor racetrack. We compared performance across models when the conditions exactly matched those in training as well as when the local environment and track were configured differently and objects that were not included in the training dataset were placed on the track in various positions. In addition, we considered performance using several different data types for training and testing including single grayscale and color frames, and multiple grayscale frames stacked together in sequence. With the exception of a fully-connected network, all models performed reasonably well (around or above 80\%) and most very well (~95\%) on at least one input type but with considerable variation across models and inputs. Overall, AlexNet, operating on single color frames as input, achieved the best level of performance (100\% success rate in phase one and 55\% in phase two) while VGG-16 performed well most consistently across image types.