Ways to think about machine learning

Source: Ways to think about machine learning, by Benedict Evans

[Thinking about the invention of relational databases] is a good grounding way to think about machine learning today – it’s a step change in what we can do with computers, and that will be part of many different products for many different companies. Eventually, pretty much everything will have ML somewhere inside and no-one will care.

An important parallel here is that though relational databases had economy of scale effects, there were limited network or ‘winner takes all’ effects.

with each wave of automation, we imagine we’re creating something anthropomorphic or something with general intelligence. In the 1920s and 30s we imagined steel men walking around factories holding hammers, and in the 1950s we imagined humanoid robots walking around the kitchen doing the housework. We didn’t get robot servants – we got washing machines.

Washing machines are robots, but they’re not ‘intelligent’. They don’t know what water or clothes are. Moreover, they’re not general purpose even in the narrow domain of washing … Equally, machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company. Each of them is a piece of automation. Each of them is a washing machine.

one of my colleagues suggested that machine learning will be able to do anything you could train a dog to do, which is also a useful way to think about AI bias (What exactly has the dog learnt? What was in the training data? Are you sure? How do you ask?), but also limited because dogs do have general intelligence and common sense, unlike any neural network we know how to build. Andrew Ng has suggested that ML will be able to do anything you could do in less than one second. Talking about ML does tend to be a hunt for metaphors, but I prefer the metaphor that this gives you infinite interns, or, perhaps, infinite ten year olds.

In a sense, this is what automation always does; Excel didn’t give us artificial accountants, Photoshop and Indesign didn’t give us artificial graphic designers and indeed steam engines didn’t give us artificial horses. (In an earlier wave of ‘AI’, chess computers didn’t give us a grumpy middle-aged Russian in a box.) Rather, we automated one discrete task, at massive scale.

Dealing with Hard Problems, by Richard Rusczyk

Source: Dealing with Hard Problems, by Richard Rusczyk

We ask hard questions because so many of the problems worth solving in life are hard. If they were easy, someone else would have solved them before you got to them. … the whole point of research is to find and answer questions that have never been solved. You can’t learn how to do that without fighting with problems you can’t solve.

Six Books We Could and Should All Write – The Paris Review

Source: Six Books We Could and Should All Write – The Paris Review, by Anthony Madrid

1. The Diary of Samuel Pepys
2. Aubrey’s Brief Lives
3. Palgrave’s Golden Treasury
4. Flaubert’s Dictionary of Received Ideas
5. The Pillow Book of Sei Shōnagon
6. Li Zhi’s A Book to Burn

Six books anybody could write. You wouldn’t need any talent to produce these. All you’d have to do is stick with it.

Just to refresh: I’m saying one should compose (1) a book about oneself, (2) a book about others, (3) an anthology of favorites, (4) a book about words, and now I’m adding (5) a book of lists. [(6) a book of completely unacceptable views]

The Psychology of Money

Source: The Psychology of Money – Collaborative Fund, by Morgan Housel

Go read the whole thing.

investing is not the study of finance. It’s the study of how people behave with money. And behavior is hard to teach, even to really smart people. You can’t sum up behavior with formulas to memorize or spreadsheet models to follow. Behavior is inborn, varies by person, is hard to measure, changes over time, and people are prone to deny its existence, especially when describing themselves. … The finance industry talks too much about what to do, and not enough about what happens in your head when you try to do it.

This report describes 20 flaws, biases, and causes of bad behavior I’ve seen pop up often when people deal with money.

Optimism is a belief that the odds of a good outcome are in your favor over time, even when there will be setbacks along the way. The simple idea that most people wake up in the morning trying to make things a little better and more productive than wake up looking to cause trouble is the foundation of optimism.

If you see someone driving a $200,000 car, the only data point you have about their wealth is that they have $200,000 less than they did before they bought the car. … Wealth, in fact, is what you don’t see. It’s the cars not purchased. The diamonds not bought. The renovations postponed, the clothes forgone and the first-class upgrade declined. It’s assets in the bank that haven’t yet been converted into the stuff you see.