Thinking Complete

Note: the material in this essay was largely derived from Burton Stein's   A History of India , Nandan Nilekani's   Imagining India , and various online sources . Most dates and statistics were drawn from the first two (some modern statistics from Nilekani's book may be a little out of date, as it was published in 2008). Feedback and errata greatly appreciated. The more large-scale history I read, the more vital geography seems. The collision of the Indian subcontinent with the rest of Asia many millions of years ago created the Himalayas, blocking India off from substantial interactions with China. For most of its history, India's channels to the rest of the world were roughly the areas corresponding to modern Pakistan and Bangladesh (which, for most of this essay, I will include when talking about "India"). Those channels were formative, but in a skewed way: India has very disproportionately been influenced from the west and, in turn, exerted influence

In a previous post , I talked about some of the biases which skew the evaluations of our memories carried out by our "remembering selves". One domain in which these biases are particularly prevalent is romantic relationships. The most emotionally charged period of a relationship is usually the acrimony which accompanies its demise; the peak-end effect ensures that this negative affect is one of the main things we remember. Then there's duration neglect: our memories discount long periods of uneventful happiness compared with sudden changes. There's also a great deal of cognitive dissonance involved in reflecting on past relationships: we don't want to think that we were the reason a happy relationship failed, so it's easiest to conclude that it probably wasn't happy, and that this unhappiness was the other person's fault! Lastly, there's a comparison effect, where we constantly hold in our minds certain ideals to which past relationships haven&

I've had a fascinating day in Delhi, India. Let me tell you about it. This morning, I went to the Akshardham, a temple complex dedicated to the Hindu sage Swaminarayan. How can I describe it? It's a cross between a mega-church and a theme park. The central attraction is a huge domed temple; I have never seen anything so extravagantly ornate. Every inch of its surface, inside and out, is decorated with carvings. The exterior is girded, at its base, with several layers of engraved animals - each layer must contain over a thousand individual carvings stretching around the whole perimeter. The entire edifice rests on a plinth decorated with hundreds of metre-tall elephants; this is in turn surrounded by a moat containing "water from 151 holy springs and rivers from around India". The complex around it contains perhaps half a dozen more buildings in the same style. One has the biggest screen I've ever seen, used to play a panegyric movie about Swaminarayan's saint

I struggle a lot with motivating myself to be productive. I've tried a bunch of standard strategies, but also a few slightly unusual arguments to try and motivate me. No guarantees that they work, though, and some might actually make you feel more guilty. Sunk costs. Humans are prone to the sunk cost fallacy, where we irrationally take into account costs that we've already incurred when making decisions. For example, when we've already bought a movie ticket, but then find out about something else we'd rather be doing, we may still go to that movie, because we feel that otherwise the ticket has been "wasted". Perhaps we can think the same way about time that we've spent procrastinating. We've already "spent" that time, but it wouldn't have been entirely wasted if it inspires us to do better in the future. So we can use the instinct behind sunk cost fallacies to push ourselves to do productive things even when we don't feel like it,

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