Motivation. Mark Zuckerberg’s notorious motto, “move fast and break things'' [1], reflects a mindset shared by many of the most powerful entrepreneurs in Silicon Valley. This mindset rests on the assumption that the benefits of discovering advanced technologies will ultimately outweigh any disruptions (i.e., broken things) created along the way....
Introduction. When at their best, democracies are able to transform diverse beliefs into effective real-world policies. This ideal is achievable when citizens are well-informed, engaged, and open [1]. However, these favourable democratic conditions are increasingly undermined by the rise of misinformation [2] and polarisation [3], fuelled by the influence of...
Thank you for the reply. I am using the ruler as an informal way of introducing a Pitchfork Bifurcation - see [3]. Although the specific analogy to a ruler may appear tenuous, my article merely attempts to draw links between the underlying dynamics, in which a stable point (with symmetries) splits into two stable branches protruding from a critical point. This setup is used to study a wide variety of physical and biological phenomena - see [Catastrophe Theory and its Applications, Poston and Stewart, 1978].
Motivation.
Mark Zuckerberg’s notorious motto, “move fast and break things'' [1], reflects a mindset shared by many of the most powerful entrepreneurs in Silicon Valley. This mindset rests on the assumption that the benefits of discovering advanced technologies will ultimately outweigh any disruptions (i.e., broken things) created along the way. However, Nick Bostrom’s vulnerable world hypothesis (VWH) [2] presents a sobering alternative. It supposes that there exist certain advanced technologies whose discovery would break society to the point of devastation. The purpose of this article isn’t to argue that we live in a vulnerable world, but rather to examine and visualise what such a world might look like. The intention is to provide... (read 1128 more words →)
Introduction.
When at their best, democracies are able to transform diverse beliefs into effective real-world policies. This ideal is achievable when citizens are well-informed, engaged, and open [1]. However, these favourable democratic conditions are increasingly undermined by the rise of misinformation [2] and polarisation [3], fuelled by the influence of AI. In this article, we explore how AI may yet solve the very problems it’s created and, in doing so, modernise our democracies to meet the demands of the 21st century. Before we explore AI’s potential contributions, let’s first outline and critique an existing democratic structure that will be relevant to our proposal.
Citizens Assemblies: A Democratic Success Story.
A Citizens Assembly involves a small,... (read 1010 more words →)
This was a really interesting paper; however, I was left with one question. Can anyone argue why exactly the model is motivated to learn a much more complex function than the identity map? An auto-encoder whose latent space is much smaller than the input is forced to learn an interesting map; however, I can't see why a highly over-parameterised auto-encoder wouldn't simply learn something close to an identity map. Is it somehow the regularisation or the bias terms? I'd love to hear an argument for why the auto-encoder is likely to learn these mono-semantic features as opposed to an identity map.