Firstly; a disclaimer. I don’t intend to give a comprehensive synopsis of the talk, I’m not sure I could even do it justice, I intend to highlight some of the points which really spoke to me, and the consequent thinking that they started up in my head.
New vs. News
Alan’s talk was titled New vs. News and was more of a journey than a discourse, covering immense ground without meandering into unrelated territory. At its core was innovation and invention and how to tap into that je ne sais quoi which leads to something truly different. More importantly, it was about how to get people to recognise what you have created and get it adopted.
The theory is that news - something incremental to what we already have today - is easy for most people to grok because of context. We already know so much about the general subject it builds on, and what’s being introduced is easy to understand relative to that. Example; google instant. We know all about searching the web, a lot of us have even been ‘trained’ into a new syntax to coax better results out of that magic little window; I mean come on, the word google has even become a verb! Getting an ever changing list of those tasty results back in real time as we typed was pretty easy to get our heads around. We also possess the necessary passing appreciation of the other prerequisite concepts, like speed and relevancy and refining.
New on the other hand takes imagination and vision to appreciate, because we lack the tools necessary to understand it - we have no common understanding of something else so similar that we can readily make comparisons and build on that knowledge. Example; the humble mouse. When that inseparable companion of the GUI was first rolling around PARC desks in the early 70s it was hard for people to see the application. Computers were big scary things which occupied whole rooms, were fed punched cards or magnetic tape, and standard out was still something very mechanical indeed. What would one ever really point to? Or click on? We have the opposite problem now - it is hard imagine a time when mousing around wasn’t core to the experience of computing. Luckily, nearly 10 years later, a guy you might have heard of incorporated these concepts into the computers he wanted to build, and the rest is history as they say.
“If I had asked people what they wanted, they would have told me faster horses.” probably not, but frequently attributed to, Henry Ford.
My favourite takeaways
- Argue more, debate less. Arguing is a constructive process of trying to find out and to illuminate. Debating is trying to win. Organisations need to take a hard look at their behaviour here; as soon as you form an ‘organization’ you create a set of agendas which are irrelevant to problem solving. A group that can put all that aside - along with their personal preferences on the matter - and have rational discussions is powerful indeed. I have always like this sentiment “The mark of intellectual honesty is the solicitation of opposing points of view.” from a Tom Clancy novel (believe it or not).
- Science is the structure which solves the problem of people loving their own ideas. You have to want to engage in a process which puts some structure into how you conceptualise a problem, explore the ideas, and figure out the best solutions. Science is that framework. As a scientist myself I always identified with the ‘study of/organised knowledge’ definitions, but I really like this one because it speaks to how you apply science and why you apply science. To remove noise.
- Further to defining science; in some follow up with me Alan has pointed out yet another way to look at this; the idea of ideas. It is a disciplined way to free yourself from today, from the constraints of How It Works now. It is a shame that "thinking outside the box" has become a cliche - this is the prerequisite of thinking outside the box. It is acknowledging that there is a box, so that one is able to zoom out enough to see the container one was in, and then start to discover what's been excluded by that container.
- 7 ± 2 and how people think. George A. Miller’s theory which deals with how people absorb information. Wikipedia explains this more eloquently than I will, so I will stick to emphasis. Effectively communicating ideas is a critical skill in science so, along with strong writing and presentation, a solid understanding of how people learn and how brains process input is essential knowledge.
- Learn to think. Professional tennis players practice their game for 8 hours a day, what training do we do to expand our mental toolkit and keep it sharp? We probably think that we do this a lot - because we’re always thinking - but that stuff is the Wimbledon Open, not the preparation and training and learning you need to do to make sure that you’re good enough when you get there...
- Almost all ideas are mediocre to bad, which is why you get no points for them, points are awarded for the successful execution and adoption of one.
- IQ is less important than you think. In terms of what you’re able to actually achieve, the impact that you can have on the world, context [coupled with being smart enough] is far more important. Example; Leonardo da Vinci invented an incredible number of machines, vehicles, siege weapons, ways to automate industrial tasks, but was unable to manifest any of those creations. Not because he wasn’t smart enough, but because of the environment. Back in the late 1400’s and early 1500’s the world around da Vinci lacked the rigid construction materials and the understanding of chemistry etc which needed to exist to be able to build and to supply motive power to those inventions. Conversely, it took Karl Benz in 1881 to build a practical, working motor vehicle and Henry Ford in 1914 to shape mass production and make it accessible. Clever men indeed but were they da Vinci smart, or did they have an environment around them which had the right collection of primitives solved that they were able to build on?
- Convenience is a seldom recognised barrier to progress. Most people really struggle to give up something near and convenient in order to reach something else bigger and far more beneficial but further away. Twinkies for you insiders!
- User experience and the 250ms timeout. A quarter of a second is how long it takes for a brain to get bored - to have seen an image and processed and interpreted it and be ready to act on the meaning - and feel like it ought to be doing something. Even if that something is make-work (such as superfluous navigation) because that improves the perception of speed.
- TEMS. Tinkering Engineering Math Science. A kind of sophistication curve societies move through when they start to play with technology (and haven’t most of us have moved through this in our education and careers too?)
- Don’t lose sight of your mission. Companies always start off with a mission, but can make the mistake of becoming too attached to a specific instantiation of that mission and then start to believe that was the real mission all along. Example; railroad companies in the US started off in the transportation business, using the technology at the time (rail). They became attached to rail to the exclusion of progress elsewhere and were outflanked by competitors that they hadn’t even considered to be competition (air and road travel).
Threads this started for me
My overall impression was of how much more there is yet to do in my chosen field. Invigorated. And a little bit humbled by the distance yet to travel. And specifically;
- So much of the world is just point solutions. Tiny increments on what we already do and already know.
- Humans like stories, we’re just wired that way. Things that can be wrapped up in a narrative are much more digestible by most people.
- Simplicity is always worth the continual investment and stretch that it requires to achieve.
- Having a vision in technology is 90% imagination and 10% science; then the job is to transform that into 10% imagination and 90% science so that it becomes buildable today.
- You have to invest so much more of yourself than you think into understanding the problem if you’re really going to solve for it (or even know what you’re solving for).
- Good science is timeless. I have met a lot of other entrepreneurs/innovators/inventors (in the ‘have a patent’ sense of the word) in my few short years on this planet and the majority of them have been lucky tinkerers. They usually have an interesting story which matches the times but no fundamental underlying lesson which could be applied anywhere. I have always believed that a rational scientific approach can take anything from point A onward, anytime and in any situation, because it transcends circumstances and specifics. And this is exactly what was proven out to me through Dr Kay’s talk, in terms more eloquent than I am capable of reciting.
- And finally; it was eye opening to see how much of this was about humans - the intersection of anthropology and computer science. If you truly understand people and technology then that’s when you’ll be able to really change the world, not just build things.
That all took 2 weeks to cook in my head. I hope it is consumable and I hope it starts threads in you that lead you somewhere else too. Something Dr Kay reiterated several times in his half day session was that his message - the specifics he was trying to communicate to us - was not nearly as important as how that message makes you think for yourself. And that makes a lot of sense when you reflect upon it from a distance.
Special thanks to the man himself for kindly validating my notes (oh yeah, and for radically expanding my horizons, that too I guess).