Notes from the reading (actually, it was so well-written that I just copy/pasted excerpts that I particularly wanted to remember)
- Is the same question uttered again? (Did the user feel heard?)
- Was the question reworded? (Did the user feel the question was understood?)
- Was there an action following the question? (Did the interaction result in a tracked response: a light turned on, a product purchased, a track played?)
- As Liam Young and Kate Davies observe, “your smart-phone runs on the tears and breast milk of a volcano. This landscape is connected to everywhere on the planet via the phones in our pockets; linked to each of us by invisible threads of commerce, science, politics and power.” 8
- Our exploded view diagram combines and visualizes three central, extractive processes that are required to run a large-scale artificial intelligence system: material resources, human labor, and data…
- Vincent Mosco has shown how the ethereal metaphor of ‘the cloud’ for offsite data management and processing is in complete contradiction with the physical realities of the extraction of minerals from the Earth’s crust and dispossession of human populations that sustain its existence. 9
- Sandro Mezzadra and Brett Nielson use the term ‘extractivism’ to name the relationship between different forms of extractive operations in contemporary capitalism, which we see repeated in the context of the AI industry. 10
- Thinking about extraction requires thinking about labor, resources, and data together.
- The Echo user is simultaneously a consumer, a resource, a worker, and a product.
- Reflecting upon media and technology as geological processes enables us to consider the profound depletion of non-renewable resources required to drive the technologies of the present moment. Each object in the extended network of an AI system, from network routers to batteries to microphones, is built using elements that required billions of years to be produced. Looking from the perspective of deep time, we are extracting Earth’s history to serve a split second of technological time, in order to build devices than are often designed to be used for no more than a few years.
- Amazon CEO Jeff Bezos, at the top of our fractal pyramid, made an average of $275 million a day during the first five months of 2018, according to the Bloomberg Billionaires Index. 17 A child working in a mine in the Congo would need more than 700,000 years of non-stop work to earn the same amount as a single day of Bezos’ income
- As a semiconductor chip manufacturer, Intel supplies Apple with processors. In order to do so, Intel has its own multi-tiered supply chain of more than 19,000 suppliers in over 100 countries providing direct materials for their production processes, tools and machines for their factories, and logistics and packaging services. 20
Dutch-based technology company Philips has also claimed that it was working to make its supply chain ‘conflict-free’. Philips, for example, has tens of thousands of different suppliers, each of which provides different components for their manufacturing processes. 21 Those suppliers are themselves linked downstream to tens of thousands of component manufacturers that acquire materials from hundreds of refineries that buy ingredients from different smelters, which are supplied by unknown numbers of traders that deal directly with both legal and illegal mining operations.
- Apple’s supplier program reveals there are tens of thousands of individual components embedded in their devices, which are in turn supplied by hundreds of different companies. In order for each of those components to arrive on the final assembly line where it will be assembled by workers in Foxconn facilities, different components need to be physically transferred from more than 750 supplier sites across 30 different countries. 24This becomes a complex structure of supply chains within supply chains, a zooming fractal of tens of thousands of suppliers, millions of kilometers of shipped materials and hundreds of thousands of workers included within the process even before the product is assembled on the line.
On environmental damage/effects on human health:
- In recent years, shipping boats produce 3.1% of global yearly CO2 emissions, more than the entire country of Germany. 27 In order to minimize their internal costs, most of the container shipping companies use very low grade fuel in enormous quantities, which leads to increased amounts of sulphur in the air, among other toxic substances. It has been estimated that one container ship can emit as much pollution as 50 million cars, and 60,000 deaths worldwide are attributed indirectly to cargo ship industry pollution related issues annually. 28Typically, workers spend 9 to 10 months in the sea, often with long working shifts and without access to external communications. The most severe costs of global logistics are born by the atmosphere, the oceanic ecosystem and all it contains, and the lowest paid workers.
- David Abraham describes the mining of dysprosium and Terbium used in a variety of high-tech devices in Jianxi, China. He writes, “Only 0.2 percent of the mined clay contains the valuable rare earth elements. This means that 99.8 percent of earth removed in rare earth mining is discarded as waste called “tailings” that are dumped back into the hills and streams,” creating new pollutants like ammonium. 31 In order to refine one ton of rare earth elements, “the Chinese Society of Rare Earths estimates that the process produces 75,000 liters of acidic water and one ton of radioactive residue.” 32Furthermore, mining and refining activities consume vast amount of water and generate large quantities of CO2 emissions.
- Availability of open-source tools for doing so in combination with rentable computation power through cloud superpowers such as Amazon (AWS), Microsoft (Azure), or Google (Google Cloud) is giving rise to a false idea of the ‘democratization’ of AI. While ‘off the shelf’ machine learning tools, like TensorFlow, are becoming more accessible from the point of view of setting up your own system, the underlying logics of those systems, and the datasets for training them are accessible to and controlled by very few entities. In the dynamic of dataset collection through platforms like Facebook, users are feeding and training the neural networks with behavioral data, voice, tagged pictures and videos or medical data. In an era of extractivism, the real value of that data is controlled and exploited by the very few at the top of the pyramid.
Every form of biodata – including forensic, biometric, sociometric, and psychometric – are being captured and logged into databases for AI training. That quantification often runs on very limited foundations: datasets like AVA which primarily shows women in the ‘playing with children’ action category, and men in the ‘kicking a person’ category. The training sets for AI systems claim to be reaching into the fine-grained nature of everyday life, but they repeat the most stereotypical and restricted social patterns, re-inscribing a normative vision of the human past and projecting it into the human future.
While Shiva is referring to enclosure of nature by intellectual property rights, the same process is now occurring with machine learning – an intensification of quantified nature. The new gold rush in the context of artificial intelligence is to enclose different fields of human knowing, feeling, and action, in order to capture and privatize those fields. When in November 2015 DeepMind Technologies Ltd. got access to the health records of 1.6 million identifiable patients of Royal Free hospital, we witnessed a particular form of privatization: the extraction of knowledge value. 53 A dataset may still be publicly owned, but the meta-value of the data – the model created by it – is privately owned.
Response to the reading:
So I had been the president of my high school’s Environmental Club, obsessively sorting the building’s recycling bins, running e-waste collection drives, and begging my parents to buy low-flow shower heads and compact fluorescent lightbulbs. To their despair, I applied to colleges with the intent of majoring in Environmental Science, and spent the first semester at UMich loading up on such courses and joining every environmentally-themed club I could find. All this led to nothing but my rapid disillusionment—most people just don’t care. Like, at all. And just like that, I completely lost hope that humanity could ever curb its materialism for the sake of our planet’s health, and transferred to art school the next year.
Like the article pointed out, not just economies but even early forms of trade are based on the extraction of resources for gain. Capitalism fundamentally depends on the pillaging of the planet and exploitation of large swaths of society. The tech industry may seem relatively innocuous, providing mostly intangible services rather than physical products, but they still pillage resources—namely, our time, attention, and data, as well as the materials necessary for the server farms and physical products they do sell—and profit to a degree that is astronomical.
Unfortunately, all of it is here to stay, and it’s still painful for me to be complicit in such an extravagantly wasteful system when I was so sensitive to it before, but the reality is that opting out would effectively disable me from engaging and participating in society in any meaningful way—technology is the platform on which our society organizes itself nowadays. Even in 2013, before I finally acquiesced to buying my first smart phone, I already had the feeling that I slipping to the margins. To even attempt to opt out is to render yourself irrelevant.