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 *
  • 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.