Source Themes

The Limits of Global Inclusion in AI Development

Those best-positioned to profit from the proliferation of artificial intelligence (AI) systems are those with the most economic power. Extant global inequality has motivated Western institutions to involve more diverse groups in the development and …

Approaching Ethical Impacts in AI Research: The Role of a Graduate Student

Extant societal challenges and the problematic applications of algorithmic systems so far have motivated broader consideration of the ethical impacts of AI research. In this reflection, I explore how ethical considerations are relevant in AI research …

Inverse Policy Evaluation for Value-based Sequential Decision-making

Value-based methods for reinforcement learning lack generally applicable ways to derive behavior from a value function. Many approaches involve approximate value iteration (e.g., $Q$-learning), and acting greedily with respect to the estimates with …

Training Recurrent Neural Networks Online by Learning Explicit State Variables

Recurrent neural networks (RNNs) allow an agent to construct a state-representation from a stream of experience, which is essential in partially observable problems. However, there are two primary issues one must overcome when training an RNN: the …

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning

We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a fixed number of future time steps. To learn the value function for horizon h, these …

Automatic Prediction of Tumour Malignancy in Breast Cancer with Fractal Dimension

Breast cancer is one of the most prevalent types of cancer today in women. The main avenue of diagnosis is through manual examination of histopathology tissue slides. Such a process is often subjective and error-ridden, suffering from both inter- and …