Tag Archives: AI

Machine Research @ Transmediale

The results of the Machine Research workshop from back in October were launched at Transmediale: the zine, and a studio talk.

During the workshop, we explored the use of various writing machines and ways in which research has become machine-like. The workshop questioned how research is bound to the reputation economy and profiteering of publishing companies, who charge large amounts of money to release texts under restrictive conditions. Using Free, Libre, and Open Source collaboration tools, Machine Research participants experimented with collective notetaking, transforming their contributions through machine authoring scripts and a publishing tool developed by Sarah Garcin. (The image accompanying this post is a shot of the PJ, or Publication Jockey, with some text it laid out on a screen in the back). The print publication, or ‘zine, was launched at transmediale is one result of this process. You can read the zine online

The studio talk brought together one half of our research group that talked about’infrastructures’. Listen to it here: (I’m speaking at 44:09)

Machine Research workshop w/ Constant, Aarhus U, Transmediale

I’m in Brussels with a group of fellow PhDs, academics, artists and technologists, at a workshop called Machine Research organised by Constant, Aarhus University’s Participatory IT centre, and Transmediale.

The workshop aims to engage research and artistic practice that takes into account the new materialist conditions implied by nonhuman techno-ecologies including new ontologies of learning and intelligence (such as algorithmic learning), socio-economic organisation (such as blockchain), population management and tracking (such as datafied borders), autonomous or semi-autonomous systems (such as bots or drones) and other post-anthropocentric reconsiderations of agency, materiality and autonomy.

I wanted to work on developing a subset of my ‘ethnography of ethics’ with a focus on error, and trying to think about what error means and is managed in the context of driverless car ethics. It’s been great to have this time to think with other people working on related – and very unrelated – topics. It is the small things that count,really; like being able to turn around and ask someone: “what’s the difference between subjection, subjectivity, subjectification, subjectivization?”. The workshop was as much about researching the how of machines as it was about the how of research. I appreciated some encouraging thoughts and questions about what an ‘ethnography’ means as it relates to ethics and driverless cars; as well as a fantastic title for the whole thing (thanks Geoff!!).

Constant’s work involves a lot of curious, cool, interesting publishing and documentation projects, including those of an Oulipo variety. So one of the things they organised for us was etherpads. I use etherpads a lot at work, but for some people this was new. It was good seeing pads in “live editing” mode, rather than just for storage and sharing. We used the pads to annotate everyone’s presentations with comments, suggestions, links, and conversation. They had also made text filters that performed functions like deleting prepositions (the “stop words” filter), or based on Markov chains (Markov filter):

“by organizing the words of a source text stream into a dictionary, gathering all possible words that follow each chunk into a list. Then the Markov generator begins recomposing sentences by randomly picking a starting chunk, and choosing a third word that follows this pair. The chain is then shifted one word to the right and another lookup takes place and so on until the document is complete.”

This is the basis of spam filters too.

In the course of the workshop people built new filters, like Dave Young (who is doing really fascinating research on institutionality and network warfare in the US during the Cold War through the study of its grey literature like training manuals) who made an “Acronymizer”, a filter that searches for much-used phrases in a text and creates acronyms from them.

We’ve also just finished creating our workshop “fanzine” using Sarah Garcin’s Publication Jockey, an Atari-Punk, handmade, publication device made with a Makey Makey and crocodile clips. The fanzine is a template and experiment for what we will produce at Transmediale. Some people have created entirely new works based on applying their machine research practices to pieces of their own text. Based on the really great inputs I got, I rewrote my post as a series of seven scenarios to think about how ethics may be produced in various sociotechnical contexts. There’s that nice ‘so much to think about’ feeling! (And do, of course).

Sarah Garcin's Publication Jockey, PJ
Sarah Garcin’s Publication Jockey, PJ

The Tesla Crash

It’s happened. A person has died in a an accident involving a driverless car, raising difficult questions about what it means to regulate autonomous vehicles, to negotiate control and responsibility with software that may one day be very good, but currently is not.

In tracing an ethnography of error in driverless cars, I’m particularly interested in how error happens, is recorded, understood, regulated and then used as feedback in further development and learning. So the news of any and every crash or accident becomes a valuable moment to document.

What we know from various news reports is that 40 year old Joshua Brown was, supposedly, watching a Harry Potter DVD while test-driving his Tesla with the autopilot mode enabled, when the car slammed into the under-side of a very large trailer truck. Apparently the sensors on the car could not distinguish between the bright sky, and the white of the trailer truck. The top of the car was sheared off as it went fast under the carriage of the trailer and debris was scattered far.

Here’s an excerpt from a Reuters report of the crash from the perspective of a family whose property parts of the car landed in:

“Van Kavelaar said the car that came to rest in his yard next to a sycamore tree looked like a metal sardine can whose lid had been rolled back with a key. After the collision, he said, the car ran off the road, broke through a wire fence guarding a county pond and then through another fence onto Van Kavelaar’s land, threaded itself between two trees, hit and broke a wooden utility pole, crossed his driveway and stopped in his large front yard where his three daughters used to practice softball. They were at a game that day and now won’t go in the yard. His wife, Chrissy VanKavelaar, said they continue to find parts of the car in their yard eight weeks after the crash. “Every time it rains or we mow we find another piece of that car,” she said.”

People in the vicinity of a crash get drawn into without seeming to have a choice in the matter. Their perspective provides all kinds of interesting details and parallel narratives.

Joshua Brown was a Tesla enthusiast and had signed up to be a test driver. This meant he knew he was testing software; it wasn’t ready for the market yet. From Tesla’s perspective, what seems to count is how many millions of miles their car logged before an accident occurred, which may have not been the best way to lead with a report on Brown’s death.

Key for engineers is perhaps the functioning of the sensors that could not distinguish between a bright sky and a bright, white trailer. Possibly, the code analysing the sensor data hasn’t been trained well enough to make the distinction between the bright sky and a bright, white trailer. Interestingly, this is the sort of error a human being wouldn’t make; just as we know that humans can distinguish between a Labrador and a Dalmatian but computer programs are only just learning how to. Clearly, miles to go ….

A key detail in this case is about the nature of auto-pilot and what it means to engage this mode. Tesla clearly states that its auto pilot mode means that a driver is still in control and responsible for the vehicle:

“[Auto pilot] is an assist feature that requires you to keep your hands on the steering wheel at all times,” … you need to maintain control and responsibility for your vehicle while using it. Additionally, every time that auto pilot is engaged, the car reminds the driver to “Always keep your hands on the wheel. Be prepared to take over at any time.”

What Tesla is saying is that they’re not ready to hand over any responsibility or autonomy to machines. The human is still very much ‘in the loop’ with autopilot.

I suspect the law will need to wrangle over what auto pilot means in the context of road transport and cars as opposed to auto pilot in aviation; this history has been traced bt Madeleine Elish and Tim Hwang. They write that they “observe a counter intuitive focus on human responsibility even while human action is increasingly replaced by automation.”

There is a historical tendency to ‘praise the machine and punish the human’ for accidents and errors. Their recommendation is for a reframing of the question of accountability to increase the web of actors and their agencies rather than just vehicle and driver. They “propose that the debate around liability and autonomous systems be reframed more precisely to reflect the agentive role of designers and engineers and the new and unique kinds of human action attendant to autonomous systems. The advent of commercially available autonomous vehicles, like the driverless car, presents an opportunity to reconfigure regimes of liability that reflect realities of informational asymmetry between designers and consumers.”

This is so important and yet I find it difficult to see how, even as a speculative exercise, how you’d get Elon Musk to acknowledge his own role or that of his engineers and developers in accountability mechanisms. It will be interesting to watch how this plays out in the American legal system, because eventually there are going to have to be laws that acknowledge shared responsibility between humans and machines, just as robots needs to be regulated differently from humans.