We are as gods and might as well get good at it

Photo some rights reserved by Michael Coté

That was the opening line of the first issue of the Whole Earth Catalogue back in 1968. Last night I went along to one of the Long Now Seminars in San Francisco — organised by Stewart Brand, the person who penned that first line — to hear astrobiologist David Grinspoon give a talk.

Grinspoon’s talk applied the principles of how we look for life and potential civilisations in the rest of the universe to the Earth. If we were alien astrobiologists looking at our planet, what would we be able to tell? Grinspoon thinks it would be obvious that we’re an intelligent civilisation rather than just simple life and that’s mainly because of the effect that humans have had on land and the atmosphere. We’d quite obviously be in an anthropocene to outside observers or a period when life is having an intentional macrosopic effect on the planet. We are, to some extent, in control.

Grinspoon is an optimist that we will eventually control climate change — we have the tools, we just need to get good at using them. He points to the way that the hole in the ozone layer has now almost been reversed as a point of hope that it is possible to reverse unintentional man-made change in the atmosphere.

The best Five Books on anything

I stumbled across Five Books this morning. It’s a fantastic archive of interviews with people who recommend the best five books on their chosen subject. It ranges from Diane Coyle talking about the best economics books of 2016, through to Tyler Cowen on the best books about information theory or Jeremy Mynott on the best books about birdwatching. Whether or not you agree with their picks, it’s a real treasure trove with over a thousand topics covered so far.

It got me thinking about what my five books would be. I think I’d choose ‘values and invention’ as my topic at the moment (with a bias towards understanding how we can create the most positive social change from digital technologies) and my five would be:

  • The Lunar Men by Jenny Uglow is the story of the pioneers of the industrial revolution who met on the full moon each month in Birmingham in the 1760s to swap ideas and invent the modern world.
  • Where Good Ideas Come From by Steven Johnson is the best explanation of how we think innovation happens. Spoiler alert: it’s not the way that governments and big companies think it does.
  • What the Dormouse Said by John Markoff explains why Silicon Valley is a pretty confused place ethically. The mixture of military money and 60s counter-culture made for some strange ideas.
  • The Making of the Atomic Bomb by Richard Rhodes is the ultimate history of how the brightest and best scientists and engineers of a generation found their skills put to work on something that almost none of them thought was a good idea in the end.
  • Microserfs by Douglas Coupland is an ordinary (and very funny) tale of what it’s like to work for a technology company when nobody really asks why you’re doing what you’re doing.

Labor in the twenty-first century

I’m in America so in honour of my hosts I’ll skip the ‘u’ in labour for this post. Yesterday was Labor Day here which — because I had the day off — got me thinking about what work and labor mean today.

The nature of work and the way it’s organised are two of the biggest issues we face in the twenty-first century. Both are hugely intertwined with technology because very few jobs have been untouched by the information age and we’re now really starting to see changes in the way that work is organised, particularly because of the ubiquity of mobile phones.

This throws up some big questions about the negative impacts we’re seeing like conditions for workers in the gig economy, the debate about automation and the inequality created by tech companies themselves.

The gig economy companies know that they’re in the front line of the upcoming wave of regulation of tech companies that will almost certainly come. If that’s done well (big ‘if’ there) and we avoid monopolistic behaviour amongst the platforms I think things could improve.

Gavin Kelly has done a great corrective job on media hyperbole on how many jobs will disappear because of automation. It’s a risk of course but I agree with Gavin that it won’t happen as dramatically as some reports have said. There’s a big opportunity for automation to create better jobs if it’s done well.

Inequality is a much more difficult issue with no simple answer. I was struck by this graph which is an example of correlation rather than causation but striking nonetheless.

Fred Wilson has written about Union 2.0 and that’s an area I’m really interested in. At the moment though I’m not convinced that existing large unions are where the change is going to come from. They seem to feel they have a lot to lose and are unwilling to take big risks with new services. Ideally new unions should provide services for workers that have network effects.

I’m still to be convinced that UBI is an answer to rising inequality. I get the appeal of it but, as soon as you get into the detail, unintended consequences abound. I think the experiments in Oakland, Finland and Canada are great but I’m not sure they’ll give answers that are particularly transferrable.

At BGV we’ve been searching for and funding startups in ‘workertech’ for almost a year now and have found all kinds of interesting ideas. It’s been really great working with the Resolution Trust who care so much about the issue and have access to amazing data, particularly on the economics of modern work. It’s made me an optimist that things will change for the better as I’ve met so many people who want to make a difference in this arena but there’s still so much more to be done.

Today I learned that a Dutch Baby is a bit like a Yorkshire Pudding

Image some rights reserved by Dale Cruse.

We’re staying with friends in San Francisco and this morning they made a Dutch Baby. Actually they also called it a German Pancake which gave me a bit more of a clue as to what it was but as it came out of the oven I realised it’s basically a Yorkshire Pudding. The main difference is that you eat it for breakfast with sweet things like fruit and maple syrup rather than for lunch with beef and gravy but it has the same ingredients and it definitely looks the same.

Googling the difference between them led me down a rabbit hole of finding out about the science of Yorkshire Puddings (sometimes I think most of the internet is people arguing about recipes). This includes an official press release (and recipe) from the Royal Society of Chemistry on the matter and this excellent article by J. Kenji López-Alt which tests some of the techniques people swear make a better pudding pretty scientifically. His conclusion is that the only one that makes a significant difference is leaving the batter to rest overnight.

Go figure

I’m a terrible Go player. Perhaps that’s why I hadn’t quite understood AlphaGo until recently reading more about it in Erik Brynjolfsson and Andrew McAfee’s book Machine, Platform, Crowd.

Machines became better than humans at chess a while back as increasing computing power enabled Deep Blue and the like to calculate possible moves and rank whether they were likely to help the computer win. But there are more possible positions in Go than there are atoms in the universe. In fact there are enough possible positions for there to be a universe of atoms for every atom in the universe (that’s 10 to the power 82 in case you’re wondering).

Even today cracking that by brute force would require more computing power than we have available. But what I hadn’t understood was that the best human Go players don’t know why they’re so good.

“How do the top human Go players navigate this absurd complexity and make smart moves? Nobody knows — not even the players themselves. Go players learn a group of heuristics and tend to follow them. Beyond these rules of thumb, however top players are often at a loss to explain their own strategies. As Michael Redmond, one of the few Westerners to reach the game’s highest rank, explains, “I’ll see and move and be sure it’s the right one, bit won’t be able to tell you exactly how I know. I just see it.”

So programming a computer to play Go is tricky because we don’t know what to teach it. What AlphaGo did was teach itself. Back in the 1950s when the theoretical basis for artificial intelligence was being laid out there were two branches — one was rule-based (a bit like the way adults learn a new language) while the other was essentially statistical (like a child learning to talk by trying things over and over again). What AlphaGo shows is just how powerful this second version of AI has now become.

What would Michael Young do?

I’ve been thinking about Michael Young a lot recently. Not least because it’s all change at the Young Foundation with the appointment of Helen Goulden as Chief Executive there which is great news. But when we topped 90 ventures supported at Bethnal Green Ventures, I remembered that people say Michael helped to create that number of organisations.

Perhaps it’s easier these days to start a new venture as technology allows you to prototype and test services in a way that wasn’t possible for much of Michael’s life. According to one (tongue in cheek) account of Michael’s method, it doesn’t sound like it was too hard though:

“So here’s what you do: you spot a problem, imagine a solution and give it a working title. Then you write to everyone who might conceivably have an interest in it, and many who don’t; produce a paper taking in the resulting comments, without once losing sight of the original notion; form a steering committee; set up a charitable trust or a company limited by guarantee (preferably both); meet someone by chance on a train outside Basingstoke and invite him or her to become the unpaid director of the new organization; launch the new body at a press conference, couple this with an article in the Guardian, carpet-bomb the charitable foundations with grant applications, stick with the fledgling organisation for precisely as long as is necessary and then push it out of the crow’s nest to make room for the other six institutions which you are waiting to hatch that week.”

Michael was notoriously single-minded and tricky to work with but he always tried to make sure there was a strong link with the people who might benefit from the organisations he created. It was no accident that the Institute for Community Studies wasn’t an ivory towered university or shiny research park in the green belt, it was in Bethnal Green. Michael’s introduction to starting to work there always stays in my mind:

“The fog became thicker as I crossed the canal from Bow and by the time I left the housing office I could not see on the ground … I abandoned the old London taxi … and that was when the enquiry began. Waiting until I heard some steps, I put my first question: I asked the way to the nearest Tube station. ‘Search me, mate,’ came back the voice, curiously loud in the fog. Then a woman spoke from nearer me. ‘The Tube? Yes, dearie, you go straight on till you get to the traffic lights. You turn left and you’ll see it right in front of you. What a game, eh?’ With the help of other faceless friends, I felt my way, tapping my foot against the kerbstones as I went. I am still tapping. So I know when the enquiry began. What I am much less clear about is why. What brought me to the housing office? So far as I can remember, the point of departure for my journey into the fog was an interest in the social services, particularly in housing.”

I worry that it’s too easy for us at BGV to be disconnected from the people who benefit from the ventures we help get going. I spend a lot of time with the ventures we invest in but I know I should really be spending more time with the people who face the problems they’re trying to solve. That’s a bit harder in Bethnal Green than when Michael was based here. I’ve never seen a ‘pea-souper’ and while there is still a great deal of poverty, it’s becoming a very different place.

Michael made another contribution that is often misunderstood. He coined the term ‘meritocracy’ and his book ‘The Rise of the Meritocracy’ is a very strong part of the narrative in Europe and the US at the moment, and even more so in the tech industry. But Michael thought a pure meritocracy was an insidious thing. He abhorred the idea of a society based on a narrow notion of merit where the winners see their success as validating themselves (and not based on luck which is more often the case), and the failures of others as just being about their shortcomings rather than based on problems beyond their control.