Month: January 2017

High-skill robots, low-skill workers

Some notes on what I think I understand about technology and inequality.

Let’s start with an obvious big question: is technology destroying jobs faster than they can be replaced? On the long term the evidence isn’t strong. Humans always appear to invent new things to do. There is no reason this time around should be any different.

But in the short term technology has contributed to an evaporation of mid-skilled jobs. Parts of these jobs are automated entirely, parts can be done by fewer people because of higher productivity gained from tech.

While productivity continues to grow, jobs are lagging behind. The year 2000 appears to have been a turning point. “Something” happened around that time. But no-one knows exactly what.

My hunch is that we’ve seen an emergence of a new class of pseudo-monopolies. Oligopolies. And this is compounded by a ‘winner takes all’ dynamic that technology seems to produce.

Others have pointed to globalisation but although this might be a contributing factor, the evidence does not support the idea that it is the major cause.

So what are we left with?

Historically, looking at previous technological upsets, it appears education makes a big difference. People negatively affected by technological progress should have access to good education so that they have options. In the US the access to high quality education is not equally divided.

Apparently family income is associated with educational achievement. So if your family is rich, you are more likely to become a high skilled individual. And high skilled individuals are privileged by the tech economy.

And if Piketty’s is right, we are approaching a reality in which money made from wealth rises faster than wages. So there is a feedback loop in place which only exacerbates the situation.

One more bullet: If you think trickle-down economics, increasing the size of the pie will help, you might be mistaken. It appears social mobility is helped more by decreasing inequality in the distribution of income growth.

So some preliminary conclusions: a progressive tax on wealth won’t solve the issue. The education system will require reform, too.

I think this is the central irony of the whole situation: we are working hard to teach machines how to learn. But we are neglecting to improve how people learn.

Move 37

Designers make choices. They should be able to provide rationales for those choices. (Although sometimes they can’t.) Being able to explain the thinking that went into a design move to yourself, your teammates and clients is part of being a professional.

Move 37. This was the move AlphaGo made which took everyone by surprise because it appeared so wrong at first.

The interesting thing is that in hindsight it appeared AlphaGo had good reasons for this move. Based on a calculation of odds, basically.

If asked at the time, would AlphaGo have been able to provide this rationale?

It’s a thing that pops up in a lot of the reading I am doing around AI. This idea of transparency. In some fields you don’t just want an AI to provide you with a decision, but also with the arguments supporting that decision. Obvious examples would include a system that helps diagnose disease. You want it to provide more than just the diagnosis. Because if it turns out to be wrong, you want to be able to say why at the time you thought it was right. This is a social, cultural and also legal requirement.

It’s interesting.

Although lives don’t depend on it, the same might apply to intelligent design tools. If I am working with a system and it is offering me design directions or solutions, I want to know why it is suggesting these things as well. Because my reason for picking one over the other depends not just on the surface level properties of the design but also the underlying reasons. It might be important because I need to be able to tell stakeholders about it.

An added side effect of this is that a designer working with such a system is be exposed to machine reasoning about design choices. This could inform their own future thinking too.

Transparent AI might help people improve themselves. A black box can’t teach you much about the craft it’s performing. Looking at outcomes can be inspirational or helpful, but the processes that lead up to them can be equally informative. If not more so.

Imagine working with an intelligent design tool and getting the equivalent of an AlphaGo move 37 moment. Hugely inspirational. Game changer.

This idea gets me much more excited than automating design tasks does.

Books I’ve read in 2016

I’ve read 32 books, which is four short of my goal and also four less than the previous year. It’s still not a bad score though and quality wise the list below contains many gems.

I resolved to read mostly books by women and minority authors. This lead to quite a few surprising experiences which I am certainly grateful for. I think I’ll continue to push myself to seek out such books in the year to come.

There are only a few comics in the list. I sort of fell off the comics bandwagon this year mainly because I just can’t seem to find a good place to discover things to read.

Anyway, here’s the list, with links to my reviews on Goodreads. A * denotes a particular favourite.

Favourite music albums of 2016

I guess this year finally marked the end of my album listening behaviour. Spotify’s Discover and Daily Mix features were the one-two punch that knocked it out. In addition I somehow stopped scrobbling to Last.fm in March. It’s switched back on now but the damage is done.

So the data I do have is incomplete. I did still deliberately put on a number of albums this year. But I won’t post them in order of listens like I did last year. This is subjective, unsorted and hand-picked. I will even sneak in a few albums that were published towards the end of 2015.

My sources included Pitchfork’s list of best new albums which used to be how I discovered new music and still wields some influence. I cross-referenced with Spotify’s top songs of 2016.

So first Spotify tells me what to listen to and then it gives me a list of things I actually listened to. This is getting weird…

Anyway, here they are. A * marks a particular favourite.

  • A Tribe Called Quest – We Got It From Here… *
  • Solange – A Seat At the Table
  • Hamilton Leithauser + Rostam – I Had A Dream That You Were Mine
  • The Avalanches – Wildflower *
  • Blood Orange – Freetown Sound
  • Whitney – Light Upon the Lake
  • Car Seat Headrest – Teens Of Denial *
  • Chance The Rapper – Coloring Book *
  • ANOHNI – HOPELESSNESS
  • Moodymann – DJ-Kicks *
  • Grimes – Art Angels *
  • Floating Points – Elaenia
  • The Range – Potential *
  • Sepalcure – Folding Time
  • Jamila Woods – HEAVN

Here’s a playlist which includes a couple of more albums if you want to have a listen.