I spent 3 months over the summer on a break from work (if you’re feeling pretentious, feel free to call it a sabbatical) mostly to keep a promise I made to myself a decade ago: when I was interviewing at Google my recruiter had warned me that without a degree (which I lacked) I might succeed at interview and fall at the final hurdle. So I booked onto a MSc at Sussex, absolutely loved it, and ended it swearing I’d take a year off every ten thereon to focus on lifelong learning. Then I blinked, ten years had passed, and I found myself thinking awkwardly about how to keep both this promise and my job. I’m exceptionally fortunate to have an employer with a generous view of taking time out, so that’s what I did.

Common advice online was to start any break with a complete change of scene and habits, so I spent the first month in Tuscany, tucked away in the countryside near Figline Valdarno (between Florence and Siena) with a pile of books, some walking boots and my running shoes. I slept voraciously, read indulgently, and hiked around the local countryside until I was sick of the sight of terracotta and lush greenery. Towards the end my dad came out to visit and we continued in this vein together.

Then it was back to the Bay Area, where to scratch that educational itch I’d signed up for a course at Berkeley University as part of their summer program, Computational Models of Cognition. I figured that being in a university environment would be stimulating, I’d meet a load of like-minded people, the topic would give me an opportunity to look at biological aspects of intelligence (the day job focusing on silicon), and going a bit deeper on machine learning theory couldn’t hurt.

In practice my experiences were mixed. Where I’d expected to be one of many mature students along for the ride (Bay Area! Cognition! AI! 2022!), I seemed to be the only attendee over the age of 27. This was OK and everyone was very sweet, but I’d met That Guy during my brief undergraduate time at Reading, and been That Guy once already at Sussex. On the ML theory side of things, we didn’t go as deep as I’d hoped - building a simple feedforward network from scratch but not much more. The biological side was much more interesting, forcing me to revisit GCSE chemistry as we looked into exactly how synapses fire and impressing upon me the overwhelming complexity of the brain as we looked at key circuits in the hippocampus.

To pad this out, I took the excellent Brain Inspired Neuro AI course, an online effort from Paul Middlebrooks who runs a podcast of the same name. This course examined the same subjects, a little more broadly and superficially than the Berkeley course, and very capably. I particularly liked how Paul had extended the prerecorded lectures with regular calls for questions which he then answered in follow-up videos; and after subscribing to his Patreon, I’ve been enjoying the Discord-based community around the podcast.

Lots of real life happened during this time too: visits from my dad and an old friend from Brighton, both of which triggered some explorations of California countryside; a bout of COVID which passed through me and Kate, but (thanks perhaps to diligent masking and extreme ventilation) avoided our daughter B; we lost a much-loved cat; and B and I started learning to roller-skate in GGP. I also resurrected my Birdweather station - for reasons work-related and personal, ambient birdsong tracking has become interesting to me - and indulged an interest in bee navigation which I wrote about previously.

I read a lot. Here are some books I particularly enjoyed during this time; all are excellent and recommended.

• Don’t Point That Thing at Me, by Kyril Bonfiglioli - laugh-out-loud 70s cloak-and-dagger novel, starring an alcoholic middle-aged art historian.
• Lanny, by Max Porter - failing marriages, ancient spirits and the abduction of a child from an English village. Beautifully written from the author of the incredible Grief Is The Thing With Feathers.
• The self-assembling brain, Peter Robin Hiesinger - how do brains develop? Our models are static but biological systems grow, and the mechanism of their unfolding growth seem important to the end-results (perhaps but perhaps not essentially) and their efficient genetic coding (definitely).
• Seeing like a state, by James C Scott - the need for society to be legible to a state apparatus leads to an imposition of top-down order; the models needed are wrong, even if some are useful, and forcing your reality to conform to a model doesn’t work. Hard not to see the connections between the emancipatory nature of high modernism and modern tech culture - “we’re building better worlds”…
• Build, by Tony Fadell - on the topic of building software/hardware products, and excellent. I started finding this a bit trite, but as I worked through it I loved it more and more. Chapter 6 in particular was an astonishing record of what it’s like to actually sell to Big Tech.
• The Prince Of The Marshes, Rory Stewart, an account of the author’s time as an acting governor in post-war Iraq, with a theme of the importance of devolution throughout. I became a bit of a Rory Stewart fanboy after reading The Places In Between (on James’ recommendation) and have really been enjoying his podcast with Alastair Campbell, The Rest is Politics
• Head, hand, heart by David Goodhart, follow-up to the excellent Road To Somewhere, about the over-privileging of cognitive ability (relative to manual and caring skills) and how it’s damaged society.
• The Upswing, Robert Puttnam, a follow-up to Bowling Alone which seems to throw its predecessor under the bus in looking back further and plotting many aspects of American society on an inverted U-curve during the 20th century… with a common sentiment that while things are bad, but we’ve been here before and prevailed. Exhaustive, fascinating, ultimately optimistic.
• What is life, Addy Pross: how does chemistry become biology? A similar question to Gödel Escher Bach I guess, but lays out evolutionary principles: replication of simple molecules in a finite environment leads to competition and thus efficiency; then energy-harvesting arrives, and you have life.