Thinking Complete

A few days ago I attended a fascinating talk by two officers in the Israel Defence Forces, Brigadier-General Amir Avivi and Sergeant Benjamin Anthony. They were representing the Miryam Institute, a recently-founded Israeli think-tank which advocates for the "New State" solution to the Israel-Palestine conflict. For those unfamiliar with the geopolitical situation, a very rough summary is that are two separate Palestinian territories, the Gaza Strip (controlled by Hamas) and the West Bank (controlled by the Palestinian Authority). The Gaza Strip is less than 10% of the size of the West Bank, and much poorer, but far more densely populated (2 million in Gaza strip vs 3 million in West Bank). Both are occupied by Israel, with significant economic repercussions, particularly in the Gaza Strip. There are also frequent outbreaks of violence between Palestinians and Israelis, including several uprisings ("intifadas"), crackdowns by the Israel Defence Forces, and regular

I've been reading a book called Happiness by Design , by Paul Dolan. Most of its material is very standard, but there was at least one thing I hadn't seen before. Dolan thinks that we should consider happiness to be a combination of the feelings of pleasure and purpose. He shows that this is a significant change in our definition of happiness because many of the most pleasurable activities, such as eating, feel the least purposeful - and vice versa. Unfortunately, Dolan doesn't ever make explicit arguments about why some states of mind should be considered 'happiness' and not others. Rather, he seems to be using an implicit definition of happiness as "the thing we value experiencing", or perhaps "the experiences which are intrinsically good for us". I'm going to simplify this and use the phrase "good experiences" instead. The claim that purposeful experiences are good experiences is not unreasonable - in hindsight I am glad to have

The last few years have seen a massive surge of interest in deep learning - that is, machine learning using many-layered neural networks. This is not unjustified - these deep neural networks have achieved impressive results on a wide range of problems. However, the core concepts are by no means widely understood; and even those with technical machine learning knowledge may find the variety of different types of neural networks a little bewildering. In this essay I'll start with a primer on the basics of neural networks, before discussing a number of different varieties and some properties of each. Three points to be aware of before we start: Broadly speaking, there are three types of machine learning: supervised, unsupervised and reinforcement. They work as follows: in supervised learning, you have labeled data. For example, you might have a million pictures which you know contain cats, and a million pictures which you know contain dogs; you can then teach a neural net to d

Inspired by Eliezer's new book , and a conversation I had with Ben Pace a few months back, I decided to write up some thoughts on the problem of trying to form opinions while accounting for the fact that other people disagree with you - in other words, epistemic modesty. After finishing, I realised that a post making very similar points had been published by Gregory Lewis on an effective altruism forum a few days beforehand. I decided to upload this anyway since I come at it from a slightly different angle. The most basic case I'm interested in would be meeting somebody who is in just as good an epistemic position are you are - someone who is exactly as intelligent, rational and well-informed as you, and who shares your basic assumptions (i.e. your 'prior') - but who nevertheless disagrees with you to a significant extent. Let's say they assign 20% confidence to a proposition you believe with 80% confidence. At least one of you is missing something; since your