Chaos, tools and thoughts

It has been unsurprisingly hard to concentrate this week, but I did finish Everyday Chaos: Technology, Complexity and How We’re Thriving in a New World of Possibility by David Weinberger. Publishers love thise long subtitles and with one so long you might think there was no need to read the book. This one does the author a disservice. The book is nothing like the giddy Silicon Valley techno-optimistic tract it seems to indicate (and how badly that would have dated in current circumstances). I’d bill it as a follow-up to two much earlier books – The Cluetrain Manifesto of 2000 (of which Weinberger was a co-author) and Kevin Kelly’s 1992 Out of Control.

Everyday Chaos is concerned with the implications of AI everywhere and the always-on internet. It’s broad hypothesis is that the business environment and world more broadly need to take complexity seriously: “At last we are moving from Chaos Theory to chaos practice.” (Chaos Theory being the flapping butterfly in one place causing a hurricane across the world thanks to the non-linear complex dynamics of weather systems.) That means expecting small interventions to sometimes have huge consequences. It implies organisations need to be ‘agile’ (ie flexible), open, less hung up on causality and more willing to live with (shifting) correlations.

It’s particularly interesting on the flexibility of the concept of interoperability, which can be made to build bridges between organisations in different ways. The book advocates a “networked, permeable” view of business rather than the hard boundaries we are used to thinking about. “The knocking down of old walls that were definitional of a business is better understood as a strategic and purposive commitment to increasing a business’s interoperability with the rest of the environment.” Rather than narrowing down to a small number of strategic options, Weinberger’s advice is: “In an interoperable world in which everything affects everything else, the strategic path forward may be to open as many paths as possible.”

The book also reminded me about the very interesting work by Andy Clark and his argument that our tools – pen and paper, whiteboard, screen, spreadsheet – determine how we think (this is a terrific New Yorker profile and here’s a famous paper with Dave Chalmers on the ‘extended mind’). Knowledge is a function of what’s outside our heads. As Weinberger concludes, we are neither an effect of thing (technodeterminism) or nor to we straightforwardly cause things. new tools – machine learning – will end up with us understanding the world in a different way.

Everyday Chaos is also really well written and engaging, so it’s well worth ignoring the airport bookshop packaging. Not that there will be many chances to buy in airport bookstores for quite a while…

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Learning about (machine) learning

Last week I trotted off for my first Davos experience with four books in my bag and managed to read only one – no doubt old hands could have warned me what a full-on (and rather weird) experience it is. The one (and that read mainly on the journeys) was The Master Algorithm by Pedro Domingos. I was impressed when I heard him speak last year & have been meaning to read it ever since.

The book is a very useful overview of how machine learning algorithms work, and if you’ve been wondering, I highly recommend it. On the whole it stays non-technical, although with lapses – and I could have done without the lame jokes, no doubt inserted for accessibility. The book also has an argument to make: that there is an ultimate ‘master algorithm’, a sort of Grand Unified Theory of learning. This was a bit of a distraction, especially as there’s an early chapter doing the advocacy before the later chapters explaining what Domingos hopes will eventually be unified.

However, the flaws are minor. I learned a lot about both the history of the field and its recent practice, along with some insights as to how quickly it’s progressing in different domains and therefore what we might expect to be possible soon. Successive chapters set out the currently competing algorithmic approaches (the book identifies five), explains their history within the discipline and how they relate to each other, how they work and what they are used for. There is an early section on the importance of data.

As a by the by, I agree wholeheartedly with this observation: “To make progress, every field of science needs to have data commensurate with the complexity of the phenomena it studies.” This in my view is why macroeconomics is in such a weak state compared to applied microeconomics: the latter has large data sets, and ever more of them, but macro data is sparse. It doesn’t need more theories but more data. Nate Silver made a simliar point in his book The Signal and the Noise – he pointed out that weather forecasts improved by gathering much more data, in contrast to macro forecasting.

Another interesting point Domingos makes en passant is how much more energy machines need than do brains: “Your brain uses only as much power as a small lightbulb.” As the bitcoin environmental disaster makes plain, energy consumption may be the achilles heel of the next phase of the digital revolution.

I don’t know whether or not one day all the algorithmic approaches will be combined into one master algorithm – I couldn’t work out why unification was a better option than horses for courses. But never mind. This is a terrific book to learn about machine learning.

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