algorithms

Seeing the unseen at Scarlett, Angel

Coffee Angel, Scarlett, roasters, coffee in Islington
Coffee at Scarlett, Angel

Although first alerted to Scarlett coffee in Angel by Double Skinny Macchiato last summer, we managed to visit during the one week of their summer holiday (and so we revisited Katsute100 around the corner instead). Nonetheless, it remained on the list and a few weeks ago we turned up for a mid-afternoon coffee at this inconspicuous looking venue on a side street just around the corner from Angel tube.

The roaster at the back of the cafe forms an immediate impression. With the large, communal table at the front of the cafe, backed by stairs leading up to the roaster, this is a place where coffee is taken seriously. The counter (on the left as you enter) offered a range of cakes and edibles but having recently come from lunch in Chapel Market, we passed on this on this occasion. Above the counter there were about 5 lights hanging down forming what looked like a giant Newton’s Cradle. Just too high for me to reach unfortunately.

I enjoyed my long black as I started to take in the surroundings of this cafe. Various people and regulars came and went, suggesting that this is a friendly local haunt for many. Noticing the number of different roasted coffee beans for retail, it was clear that this is a venue that you could return to for a different coffee experience each time. Each time exploring an aspect of the flavour of the coffee and building on the experience of coffee tasting that you have enjoyed before. It is definitely on the list for a repeat visit.

Interior Scarlett
One of the light fittings at Scarlett in Angel. Cube outlines drawn on paper can form an optical illusion where you can’t work out if the cube is coming out at you or going into the paper.

Above our heads, the lights were framed by the outline of a cube. Fantastic for optical illusions, these cubes offer us an opportunity to think about how we perceive depth and direction; how our eyes work and perhaps, more fundamentally, what it even means to see an object (as with Berkeley’s “New Theory of Vision”). Then, while looking through the menu, it became clear that here too there was an optical illusion of sorts. For the price list was not written on the board so much as cut out of it (see the photo below). The price you could read off the menu was, in some sense, precisely the information that was not actually on the board. Our brain makes patterns of that which we don’t see and, together with our assumptions about what should be there, we form an idea of the price we have to pay.

It is a similar thing with many algorithms in use around us now. Such tools can be immensely helpful, offering us suggestions for coffees we may like to try (based on our buying habit) or routes that we may like to take to get us to our destination. And yet, are there problems hidden in the assumptions that some of these algorithms make? What information are we getting based on elements in the programme that we do not see?

In her excellent book “Weapons of Math Destruction”, Cathy O’Neil explores some of the more dangerous ways that our biases and assumptions (particularly those that we don’t see in ourselves) can impact the results of algorithms that have been written to optimise processes from the sorting of job applications to determining the length of time a given convicted criminal will serve for an offence. In an example relevant for cafes, O’Neil related an example of how Starbucks had used an algorithm to determine which baristas and managers should work which hours, including who should close the shop at night and who should open it in the morning.

Scarlett menu
The menu at Scarlett. Apart from the filter coffee, the prices and information for each coffee is revealed by what is absent from the board rather than what is printed onto it.

The algorithm was programmed to calculate the most efficient use of the cafe’s time and money, specifically prioritising the profit that the company made. One measure of this was “revenue per employee hour”. This had the consequence that staff members were frequently in a position where they were told that they had to do both the (late night) closing and (early morning) opening of the shop and were given very few days notice of this expectation. Clearly this impacted the lives of their staff and affected their ability to arrange child care, support themselves through further education and other consequences. Eventually Starbucks was forced to amend this algorithm but change comes hard: how do you ask a computer to measure “fairness” to an employee (a subjective term) when you can use revenue per employee hour which is measurable, quantifiable and therefore ‘accurate’?

Perhaps you think that the link back to Scarlett here is obvious: That if you choose to drink your coffee in friendly neighbourhood cafes where cafe owners and baristas work to patterns formed by encounter rather than algorithm it would be better than a place which is run assuming all workers are cogs in a profit machine? Perhaps. But the link back to Scarlett in my mind is not that at all.

If you look at the front of Scarlett, or its webpage, and assume that the pink bird is a funny looking flamingo, you may make a series of assumptions about what you think the cafe will be like and why the owners have a bird on their front door. If you found out that the bird was actually a Scarlet Ibis and associated with the coffee growing regions of South America, your ideas about the cafe and the owners may be different. For a general customer, looking for somewhere to enjoy a great coffee, perhaps these assumptions and ideas do not matter so much. But if we are ever in a position to feed our assumptions into an algorithm, these hidden (to our own conscious) assumptions could matter a great deal.

Scarlett is at 30 Duncan Street, N1 8BW

“Weapons of Math Destruction – how big data increases inequality and threatens democracy” by Cathy O’Neil, Penguin Books, 2016

As quick as (a) Quarter Horse

Dog and Hat, Dog & Hat, Hundred House, Quarterhouse coffee

The package from Dog & Hat with Hundred House and Quarter Horse. Is it a particularly contemplative dog with the monocle?

Links with science can be found everywhere, from the café to the coffee roaster. A couple of weeks ago a delivery from Dog and Hat coffee gave me an opportunity to explore the random thought paths that may occur if you stop to ponder your coffee at home rather than in a café. The first coffee, an Ethiopian from Hundred House prompted thoughts on star gazing. But the second coffee, a Mexican from Quarter Horse coffee was equally thought provoking.

Finding time to prepare a V60 and sit with the SCAA “flavor wheel” as a guide, I was rewarded with a sweet, well rounded and perfectly enjoyable brew. I found fruity notes of blueberry and cherry/pineapple though the tasting notes on the packaging say “green grape, toffee and cocoa”. Sadly I missed the cocoa but this offers a good excuse for another slow brew with the coffee wheel at hand.

Thinking about the name of the coffee, I started to consider how you could quarter a horse. Perhaps not a literal horse given the ethical considerations but rather an irregularly shaped volume. How would you divide, into equal portions, an irregularly shaped object such as a horse? It seemed related to the question of finding the shortest route between two locations, how would you calculate the best route to take from A to B? In the 1950s a computer scientist called Edsger Dijkstra (1930-2002) came up with an algorithm to calculate precisely this problem. Originally designed to show the shortest routes between 64 cities in the Netherlands, Dijkstra’s algorithm is now ubiquitous in our lives.

Quarter Horse but how would you

A close up of the Quarter Horse Coffee Bag.

One of the ways in which we have started to rely on such algorithms is in car GPS devices or even on our phones trying to navigate to our destinations. Or at least, many of us do. London taxi drivers however have been shown to have developed a different brain structure from the general population that means that, for them, Dijkstra’s algorithm may be unnecessary. A few years ago, a study compared brain scans of people who had been driving London’s “black cabs” for a number of years to those of us in the general population. A follow-up study followed three sets of people over several years. A control group of people in the general population and a second group of people who studied the “Knowledge”, the navigational test that London taxi drivers have to pass in order to become cabbies. The Knowledge tests the driver’s ability to recall tens of thousands of London’s streets and the prospective cabbie can be asked to navigate between two points anywhere within a 6 mile radius of Charing Cross. Typically it takes years to acquire the Knowledge and not everyone who starts on the Knowledge will pass (the pass rate is only about 50%). This means that this second group of people splits into two groups; those who studied and passed the Knowledge and those who studied but did not pass.

The studies proved illuminating. One particular part of the brain, the posterior hippocampus had a greater volume of “grey matter” (the brain processing cells) in taxi drivers who had studied, and passed, the Knowledge compared with the general population. Moreover, those that had been taxi drivers for longer, showed larger posterior hippocampi. The changes in the brain seemed to lead to the cabbies having not only better navigational ability than the general population but better memory for London based information. The study of the trainees moreover confirmed that these brain changes occurred as a result of learning the Knowledge, showing that our brains are adaptable and still able to develop well into adulthood. While the brains of all the study participants started off similarly, those that went on to pass the Knowledge had a larger posterior hippocampus than those who either didn’t study or studied but hadn’t passed. However it was not all good news for the cabbies. The growth of the posterior hippocampus seemed to occur at the expense of the anterior hippocampus in long serving taxi drivers (but not newly qualified ones). The improved memory for London based information shown by the taxi driving group was also accompanied by a poorer ability to learn other visual information/memory related tasks in those that passed the Knowledge compared to the general population.

taxi and motorcycle, London

London black cab drivers have been shown to have a larger volume of grey matter in the posterior hippocampus area of their brains, demonstrating that our brains remain adaptable well into adulthood.

Perhaps the ability of the cabbies to navigate quickly around London’s streets suggests a second connection with Quarter Horse. A Quarter Horse is a breed of horse that can sprint very quickly over short (less than a quarter of a mile) distances. Which goes faster, the cabbie with the Knowledge or us with our smartphones once we have plugged in our destination? We are reminded of the tale of the hare and the tortoise. But I think a different tale is more appropriate. A tale that in reality was only ever a snippet of an ancient saying but has been developed into tales by thinkers such as Isaiah Berlin and Ronald Dworkin.

“The fox knows many things but the hedgehog one important thing”.

What does this mean? It seems there is a connection here between coffee roasting and taxi drivers, between algorithms and personal development, between coffee science and writing about coffee science. Is this connection really there or is it a meaningless statement that leads us into blind alleys of coffee consideration? It may be time to stretch our brains, grow our grey matter a bit and contemplate. Am I a fox or a hedgehog and where do London cabbies and coffee roasters fit in?

Quarter Horse coffee is online at https://quarterhorsecoffee.com

You can find out more about the coffee subscription site Dog and Hat on their website https://dogandhat.co.uk

You can read more about the taxi driver study on the Wellcome Trust’s press release about it here.

Enjoy your coffee, have fun thinking, grow your grey matter.