hello computer w6 hw



Notes from the reading  (actually, it was so well-written that I just copy/pasted excerpts that I particularly wanted to remember)

Effectiveness of Alexa’s responses is inferred by these metrics:

  • 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?)
“With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user’s commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience – be it answering a question, turning on a light, or playing a song – requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data.”
Extractive system of AI:
  • 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. 
  • 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.
Statua citofonicaStatua citofonica by Athanasius Kircher (1673)

On environmental resource extraction:
  • 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.
On human labor extraction:
  • 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
On the immensity of the supply chain:
  • 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.

AI Extractivism:

  • 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.

Hello, Computer W4 HW

Using Fulfillments, I was able to implement two guided exercises: the first was a remake of NYT’s seven minute workout (which always seems to be broken on their website), meant to trigger if the user’s input entity value is “burn out”; the second was a breathing exercise, which triggers if the entity value is “anxiety”.

I had tried to add a Fulfillment that played audio for another yet another entity, but it seems that Firebase won’t make http requests unless I have a payment method on file. I might look into a Spotify API instead.

Here’s the result:


I cleaned up the corpora I used to train the bot a little more as well, so it can now field questions about positive psychology and uh, learning effectively, slightly better. Chat with it here, although the web demo doesn’t speak, so you’ll only get the abbreviated “text” version of the Fulfillments.

It’s very confusing for me to have Dialogflow’s entity/intents, and some responses, removed from the responses in my nodejs code, so I may look into coding the entire thing at a later date.

GMO notes


  • There is a small minority of biologists raising questions about GM safety
    • Inserted genes can be transformed by several different means, and it can happen generations later, resulting in potentially toxic plants slipping through testing.
    • Funding for plant molecular biology comes mostly from companies that sell GM seeds, and favors researchers who are exploring further ways to use GM tech
      • biologists who point out risks associated with GM crops have their credibility viciously attacked, which leads to silence on the subject
  • Concerns over health risks so far remain theoretical
  • GM crops has lowered the price of food; increased farmer safety by allowing them to use less pesticides; raised the output of corn, cotton, and soy by 20-30%
  • GM crops could grow in dry and salty land, withstand high and low temperatures, and tolerate insects, disease, and herbicides
  • GM acceptance elsewhere
    • Nearly all the corn and soybeans grown in the US are GM crops, but only two crops—Monsanto’s maize and BASF’s Amflora potato—are accepted in the EU, but 10 EU countries have banned Monsanto’s maize
      • several new GM corn strains have been voted down
    • Much of Asia (including India and China) has yet to approve GMOs
    • Several African countries have refused to import GM food despite lower costs
      • Kenya has banned them altogether
    • No country has definite plans to grow Golden Rice, despite its potential to prevent death and blindness
    • only a tenth of the world’s cropland includes GMOs; four countries—US, Canada, Brazil, and Argentia—grow 90% of it
    • European resentment of American agribusiness influences global perspective
  • Humans have been breeding crops—and therefore altering their genomes—for millenia
    • wheat has long been a strictly human-engineered plant
    • for 60 years, scientists have been using “mutagenic” techniques to scramble the DNA of plants with radiation and chemicals, creating strains of wheat, rice, peanuts, and pears that have become agricultural mainstays
    • difference is that breeding and mutagenic techniques result in large swaths of genes being swapped or altered, while GM tech enables scientists to insert a single gene from another species of plant, or even bacterium, virus, or animal
      • viruses have been inserting their DNA into the genomes of crops, humans, and other animals for millions of years, and deliver the genes of other species too (human genome is loaded with sequences that originated in viruses and nonhuman species)
      • Changing a single gene, on the other hand, might turn out to be a more subversive action, with unexpected ripple effects, including the production of new proteins that might be toxins or allergens.
  • Some scientists say that objections to GMOs stem from politics rather than science, motivated by an objection to large multinational corporations having enormous influence over the food supply


more GMO notes

Are GMOs safe?

  • no adverse health effects among consumers have been found
  • about 90% of scientists believe GMOs are safe, but only slightly more than 1/3 of consumers think the same
  • commonly expressed concerns (unwanted changes in nutritional content, creation of allergens, and toxic effects on organs) have not been clearly demonstrated yet; but benefits (higher yield, lower toxins) have been well-established
    • yields of corn, cotton, and soybeans are said to have risen by 20-30%
    • animal health and growth have improved on genetically engineered feed
    • could greatly increase food supply
    • Golden Rice: GMO that has more vitamin A than spinach and could prevent blindness and more that a million deaths a year in African and Asian countries
      • has genes added to it which allow it to produce beta-carotene



Soft Sensing HW

For our last homework assignment, I wanted to make my breath sensor a little nicer. Luckily, Alexandra was around to give me a refresher on how to use the sewing machine, and showed me a neat trick that keeps the conductive thread on the underside of the fabric, and therefore unexposed:

front, back

I then used this technique to sew along the length of the straps, and used another trick Alexandra showed me to sew terminals for alligator clips:

Then I added velcro at the ends of the straps:

When it came to actually test on a person, the analog read range was quite small, so I had to map the min and max reads to the digital write range to make the LED fade discernible:

Apparently, there are no alligator clips at ITP, so I had to improvise with wire, copper tape, and paper clips.

Soft Sensing Day 2

As we were all intrigued by the possibilities of conductive thread, our “research group” decided to explore making the stroke sensor we saw on How to Get What You Want.

Instead of using a piece of conductive fabric on the backside, we opted to use the Eeonyx StaTex Conductive Fiber as filling for an amorphous plushie. This way, it would be able to detect pressure as well. So many fun things!

Here we are, hard at work:

Not pictured: Alan, who was doing the documenting. Thanks Alan!

Here’s what we ended up with:

Soft Sensors Day 1

For the materials testing lab, Erin and I were able to experiment with different conductive materials: Velostat, Eeonyx Pressure Sensing Fabric, Eeonyx Stretch Sensing Fabric, and Eeonyx Conductive Fiber.

Velostat and the pressure sensing fabric needed to be “sandwiched” in a non-conductive material, with conductive terminals for power and ground to be connected to the multimeter; the stretch sensing fabric and conductive fiber only needed to be clamped with alligator clips.

At rest, the velostat had a resistance of 24.5k. To test it, we 1) applied pressure, which yielded a resistance of ~26k, 2) bend it, which yielded a resistance of ~30k, and 3) twisted it, which yielded a resistance of 35k.

The pressure sensing fabric had a resistance of about 3.5k at rest. To test it, we 1) applied pressure, which yielded a resistance of ~3.1k, 2) bent it, which yielded a resistance of ~2.5k, and 3) twisted it, which yielded a resistance of 2.2k.

The stretch fabric had a resistance of ~140k at rest. When we 1) applied pressure, the resistance dropped to ~129k, 2) stretched it, the resistance dropped to ~95k, and 3) twisted it, the resistance dropped to 123k.

The conductive fiber had a resistance of ~2.2k at rest. When we pressed it, it dropped to a value of 1.5k.


When we convened for our group exploration, one of our group members had an idea that she wanted to work on alone, so we ended up doing our own thing in close proximity to each other.

I’d never worked with a stretch sensor before, so I attempted to integrate it into a chest strap to make a breath tracker. Since we were limited on time and resources, I quickly sewed together an extremely rough prototype:

I had initially used the grey mystery fabric—since there was more of it—but it didn’t yield very good results: the analog read ranged only from 1014 at a relaxed state, to 1016 at a fully stretched state, using a 220ohm resistor.

The Eeonyx fabric was much more responsive, giving an analog reading from 870 in a relaxed state, to 950 in a fully stretched state, using a 1M ohm resistor.

cloud computing + data architecture

ML as a Service (Comprehend, Rekognition)

import boto3

AWS_client = boto3.client('comprehend', region_name='us-east-1')

AWS_sentiment_response = AWS_client.detect_sentiment(Text='i am so tired',LanguageCode='en')


AWS Lambda: functions as a service

  • run code with Lambda > analyze with Comprehend > store on S3 > serve with EC2
  • can use AWS Lambda as an endpoint
  • EC2 has tensorflow instance
  • can ask alexa to run lambda
  • cloudwatch events: trigger at a certain time