Small Numbers, Huge Changes

In a recent inter­view, Sun­dar Pichai of Google dis­cuss­es improve­ments in the accu­ra­cy of their voice recog­ni­tion:

Just in the last three years, we have tak­en things like error in word recog­ni­tion down from about 23 per­cent to 8 per­cent.

That’s the dif­fer­ence between mis­un­der­stand­ing one word in four, to one word in twelve; the dif­fer­ence between com­plete­ly unus­able, and annoy­ing.

Andew Ng, for­mer­ly of Google and now of Baidu, expands on this:

Most peo­ple don’t under­stand the dif­fer­ence between 95 and 99 per­cent accu­rate. Nine­ty-five per­cent means you get one-in-20 words wrong. That’s just annoy­ing, it’s painful to go back and cor­rect it on your cell phone.

Nine­ty-nine per­cent is game chang­ing. If there’s 99 per­cent, it becomes reli­able. It just works and you use it all the time. So this is not just a four per­cent incre­men­tal improve­ment, this is the dif­fer­ence between peo­ple rarely using it and peo­ple using it all the time.

It’s fas­ci­nat­ing to see how these small num­bers make a huge dif­fer­ence; you might think Google’s 92% accu­rate is only a lit­tle less than Baidu’s 95% accu­rate, but in prac­ti­cal terms there’s a big gulf. And it gives me pause to think about the mon­ey, human resource and com­put­ing pow­er spent on try­ing to make those small huge increas­es in accu­ra­cy.

A conversation with a bot

It’s approach­ing 3 AM on Christ­mas Day in 2013, and a South Kore­an teenage girl who goes by the Twit­ter han­dle @jjong_gee texts her friend, Jun­myun, to con­fess a per­son­al secret. She’s depressed, and she needs sup­port. “There was a man named Osho who once said ‘don’t be too seri­ous, life is like a mov­ing pic­ture,’” replied Jun­myun. “If you treat what comes at you like a game, hap­pi­ness will come. I want to see you hap­py.” The girl tweet­ed a screen­shot of the text, thank­ing him for the kind words. But Jun­myun, with his words of wis­dom, is actu­al­ly not a real per­son. Jun­myun is actu­al­ly a bot pro­grammed inside a pop­u­lar Kore­an tex­ting app called FakeTalk, or Gaj­ja-Talk in Kore­an.

The App That Lets Depressed Teens Text with Celebri­ties and Dead Friends. Every time I read some­thing like this I remem­ber how bril­liant­ly pre­scient Black Mir­ror can be.

Of course, I had to have a go myself:

A conversation with a chat bot which ends with it declaring its love for me

What’s inter­est­ing is how, despite the bot not being Tur­ing-com­plete, I still felt com­pelled to con­tin­ue the con­ver­sa­tion, and became quite ner­vous at the abrupt turn it took at the end. After all, I don’t want to hurt the feel­ings it doesn’t have.

An interview with Andrew Ng

The Huff­in­g­ton Post series, Sophia, brings ‘life lessons from fas­ci­nat­ing peo­ple’. Their lat­est inter­view is with Andrew Ng, a Stan­ford Uni­ver­si­ty pro­fes­sor, co-founder of Cours­era, and key mem­ber of the deep learn­ing teams at first Google and now Baidu. I real­ly like some of the insights in his inter­view, the prac­ti­cal­i­ty with which he treats inno­va­tion and the easy way he explains hard con­cepts.

For exam­ple, here he talks about career advice:

I think that “fol­low your pas­sion” is not good career advice. It’s actu­al­ly one of the most ter­ri­ble pieces of career advice we give peo­ple. Often, you first become good at some­thing, and then you become pas­sion­ate about it. And I think most peo­ple can become good at almost any­thing.

On retrain­ing the work­force for a more heav­i­ly auto­mat­ed future:

The chal­lenge that faces us is to find a way to scal­ably teach peo­ple to do non-rou­tine non-repet­i­tive work. Our edu­ca­tion sys­tem, his­tor­i­cal­ly, has not been good at doing that at scale. The top uni­ver­si­ties are good at doing that for a rel­a­tive­ly mod­est frac­tion of the pop­u­la­tion. But a lot of our pop­u­la­tion ends up doing work that is impor­tant but also rou­tine and repet­i­tive. That’s a chal­lenge that faces our edu­ca­tion­al sys­tem.

On why machine learn­ing is sud­den­ly more pop­u­lar, despite the tech­nol­o­gy being around for decades in some cas­es:

I often make an anal­o­gy to build­ing a rock­et ship. A rock­et ship is a giant engine togeth­er with a ton of fuel. Both need to be real­ly big. If you have a lot of fuel and a tiny engine, you won’t get off the ground. If you have a huge engine and a tiny amount of fuel, you can lift up, but you prob­a­bly won’t make it to orbit. So you need a big engine and a lot of fuel. We final­ly have the tools to build the big rock­et engine – that is giant com­put­ers, that’s our rock­et engine. And the fuel is the data. We final­ly are get­ting the data that we need.

And the chal­lenges of talk­ing to com­put­ers:

Most peo­ple don’t under­stand the dif­fer­ence between 95 and 99 per­cent accu­rate. Nine­ty-five per­cent means you get one-in-20 words wrong. That’s just annoy­ing, it’s painful to go back and cor­rect it on your cell phone. Nine­ty-nine per­cent is game chang­ing. If there’s 99 per­cent, it becomes reli­able. It just works and you use it all the time. So this is not just a four per­cent incre­men­tal improve­ment, this is the dif­fer­ence between peo­ple rarely using it and peo­ple using it all the time.

It’s a real­ly inter­est­ing inter­view, I encour­age you to read it in full.

On filters and feelings

An inter­est­ing arti­cle from Wired on a recent study into how peo­ple use fil­ters on their dig­i­tal pho­tographs.

Seri­ous hob­by­ists” use fil­ters only to cor­rect a problem—say, cor­rect the expo­sure. “More casu­al pho­tog­ra­phers” are more like­ly to manip­u­late their images with fil­ters or adjust­ments that make them appear more “arti­fi­cial,” accord­ing to the study.

I used to be a bit of a fil­ter snob, but now I tend to think that, cer­tain­ly on Insta­gram, using a fil­ter is less about try­ing to cap­ture a moment than it is about cap­tur­ing the way that moment felt. It’s like, when you take a pic­ture on a hot day but that hot­ness doesn’t come across in the pho­to, Sier­ra or Valen­cia are tools to help com­mu­ni­cate that feel­ing to every­one else who views it.