This post looks at some key ideas for artificial intelligence systems. It acts as a guide to the landmarks on our path to improved computing.
I often find myself thinking deeply about probability. What *is* it? Let’s start by assuming there is some form of local reality at a point of interest. Let’s then assume that the “true” nature of this local reality is unknowable. This may be due to the limits of our senses or the limits of time … Continue reading Probability as Humble Knowledge
In this post, I go back to basics with reinforcement learning and consider the stupidest form of intelligence.
The rise of machine learning and developments in neuroscience hint that prediction is key to how brains navigate the world. But how could this work in practice?
The more we design intelligent systems the more we creep up against the concepts of free will and determinism. These concepts underlie the stories we tell ourselves and underpin our legal systems. But what does free will mean? How does it influence our actions? And can we get rid of it? The approach of this … Continue reading Free Will: Do We Have It?
Some things have recently been bugging me when applying deep learning models to natural language generation. This post contains my random thoughts on two of these: sampling and prediction. By writing this post, I hope to try to tease these apart in my head to help improve my natural language models. Sampling Sampling is the … Continue reading Sampling vs Prediction