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This map of what happens when you interact with a digital assistant such as the Amazon Echo is incredible. The image is taken from a length piece of work which is trying to bring attention towards the hidden costs of using such devices.
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. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives.
It’s a tour de force. Here’s another extract:
When a human engages with an Echo, or another voice-enabled AI device, they are acting as much more than just an end-product consumer. It is difficult to place the human user of an AI system into a single category: rather, they deserve to be considered as a hybrid case. Just as the Greek chimera was a mythological animal that was part lion, goat, snake and monster, the Echo user is simultaneously a consumer, a resource, a worker, and a product. This multiple identity recurs for human users in many technological systems. In the specific case of the Amazon Echo, the user has purchased a consumer device for which they receive a set of convenient affordances. But they are also a resource, as their voice commands are collected, analyzed and retained for the purposes of building an ever-larger corpus of human voices and instructions. And they provide labor, as they continually perform the valuable service of contributing feedback mechanisms regarding the accuracy, usefulness, and overall quality of Alexa’s replies. They are, in essence, helping to train the neural networks within Amazon’s infrastructural stack.
Well worth a read, especially alongside another article in Bloomberg about what they call ‘oral literacy’ but which I referred to in my thesis as ‘oracy’:
Should the connection between the spoken word and literacy really be so alien to us? After all, starting in the 1950s, basic literacy training in elementary schools in the United States has involved ‘phonics.’ And what is phonics but a way of attaching written words to the sounds they had been or could become? The theory grew out of the belief that all those lines of text on the pages of schoolbooks had become too divorced from their sounds; phonics was intended to give new readers a chance to recognize written language as part of the world of language they already knew.
The technological landscape is reforming what it means to be literate in the 21st century. Interestingly, some of that is a kind of a return to previous forms of human interaction that we used to value a lot more.
Edge is an interesting website. Its aim is:
To arrive at the edge of the world’s knowledge, seek out the most complex and sophisticated minds, put them in a room together, and have them ask each other the questions they are asking themselves.
One recent article on the site is from Mary Catherine Bateson, a writer and cultural anthropologist who retired in 2004 from her position as Professor in Anthropology and English at George Mason University. She’s got some interesting insights into systems thinking and artificial intelligence.
We all think with metaphors of various sorts, and we use metaphors to deal with complexity, but the way human beings use computers and AI depends on their basic epistemologies—whether they’re accustomed to thinking in systemic terms, whether they’re mainly interested in quantitative issues, whether they’re used to using games of various sorts. A great deal of what people use AI for is to simulate some pattern outside in the world. On the other hand, people use one pattern in the world as a metaphor for another one all the time.
That’s such an interesting way of putting it, the insinuation being that some people have epistemologies (theories of knowledge) that are not really nuanced enough to deal with the world in all of its complexity. As a result, they use reductive metaphors that don’t really work that well. This is obviously problematic when dealing with AI that you want to do some work for you, hence the bias (racism, sexism) which has plagued the field.
One of the most essential elements of human wisdom at its best is humility, knowing that you don’t know everything. There’s a sense in which we haven’t learned how to build humility into our interactions with our devices. The computer doesn’t know what it doesn’t know, and it’s willing to make projections when it hasn’t been provided with everything that would be relevant to those projections. How do we get there? I don’t know. It’s important to be aware of it, to realize that there are limits to what we can do with AI. It’s great for computation and arithmetic, and it saves huge amounts of labor. It seems to me that it lacks humility, lacks imagination, and lacks humor. It doesn’t mean you can’t bring those things into your interactions with your devices, particularly, in communicating with other human beings. But it does mean that elements of intelligence and wisdom—I like the word wisdom, because it’s more multi-dimensional—are going to be lacking.
Something I always say is that technology is not neutral and that anyone who claims it to be so is a charlatan. Technologies are always designed by a person, or group of people, for a particular purpose. That person, or people, has hopes, fears, dreams, opinions, and biases. Therefore, AI has limits.
You don’t have to know a lot of technical terminology to be a systems thinker. One of the things that I’ve been realizing lately, and that I find fascinating as an anthropologist, is that if you look at belief systems and religions going way back in history, around the world, very often what you realize is that people have intuitively understood systems and used metaphors to think about them. The example that grabbed me was thinking about the pantheon of Greek gods—Zeus and Hera, Apollo and Demeter, and all of them. I suddenly realized that in the mythology they’re married, they have children, the sun and the moon are brother and sister. There are quarrels among the gods, and marriages, divorces, and so on. So you can use the Greek pantheon, because it is based on kinship, to take advantage of what people have learned from their observation of their friends and relatives.
I like the way that Bateson talks about the difference between computer science and systems theory. It’s a bit like the argument I gave about why kids need to learn to code back in 2013: it’s more about algorithmic thinking than it is about syntax.
The tragedy of the cybernetic revolution, which had two phases, the computer science side and the systems theory side, has been the neglect of the systems theory side of it. We chose marketable gadgets in preference to a deeper understanding of the world we live in.
The article is worth reading in its entirety, as Bateson goes off at tangents that make it difficult to quote sections here. It reminds me that I need to revisit the work of Donella Meadows.
Another fantastic article from Tim Carmody, a.k.a. Dr. Time:
An Echo or an iPhone is not a friend, and it is not a pet. It is an alarm clock that plays video games. It has no sentience. It has no personality. It’s a string of canned phrases that can’t understand what I’m saying unless I’m talking to it like I’m typing on the command line. It’s not genuinely interactive or conversational. Its name isn’t really a name so much as an opening command phrase. You could call one of these virtual assistants “sudo” and it would make about as much sense.
I have also watched a lot (and I mean a lot) of Star Trek: The Next Generation. And while I feel pretty comfortable talking about “it” in the context of the speaker that’s sitting on the table across the room—there’s even a certain rebellious jouissance to it, since I’m spiting the technology companies whose products I use but whose intrusion into my life I resent—I feel decidedly uncomfortable declaring once and for all time that any and all AI assistants can be reduced to an “it.” It forecloses on a possibility of personhood and opens up ethical dilemmas I’d really rather avoid, even if that personhood seems decidedly unrealized at the moment.
I’m really enjoying his new ‘column’ as well as Noticing, the newsletter he curates.
Algorithms and artificial intelligence are an increasingly-normal part of our everyday lives, notes this article, so the next step is in the workplace:
Each one of us is becoming increasingly more comfortable being advised by robots for everything from what movie to watch to where to put our retirement. Given the groundwork that has been laid for artificial intelligence in companies, it’s only a matter of time before the $60 billion consulting industry in the U.S. is going to be disrupted by robotic advisors.
I remember years ago being told that by 2020 it would be normal to have an algorithm on your team. It sounded fanciful at the time, but now we just take it for granted:
Robo-advisors have the potential to deliver a broader array of advice and there may be a range of specialized tools in particular decision domains. These robo-advisors may be used to automate certain aspects of risk management and provide decisions that are ethical and compliant with regulation. In data-intensive fields like marketing and supply chain management, the results and decisions that robotic algorithms provide is likely to be more accurate than those made by human intuition.
I’m kind of looking forward to this becoming a reality, to be honest. Let machines do what machines are good at, and humans do what humans are good at would be my mantra.
Source: Harvard Business Review
Cory Doctorow writes:
There is a war for your attention, and like all adversarial scenarios, the sides develop new countermeasures and then new tactics to overcome those countermeasures.
Using a metaphor from virology, he notes that we become to immune to certain types of manipulation over time:
When a new attentional soft spot is discovered, the world can change overnight. One day, everyone you know is signal boosting, retweeting, and posting Upworthy headlines like “This video might hurt to watch. Luckily, it might also explain why,” or “Most Of These People Do The Right Thing, But The Guys At The End? I Wish I Could Yell At Them.” The style was compelling at first, then reductive and simplistic, then annoying. Now it’s ironic (at best). Some people are definitely still susceptible to “This Is The Most Inspiring Yet Depressing Yet Hilarious Yet Horrifying Yet Heartwarming Grad Speech,” but the rest of us have adapted, and these headlines bounce off of our attention like pre-penicillin bacteria being batted aside by our 21st century immune systems.
However, the thing I’m concerned about is the kind of AI-based manipulation that is forever shape-shifting. How do we become immune to a moving target?
Source: Locus magazine
Email is an awesome system. It’s open, decentralised, and you can pick whoever you want to provide your emails. The trouble is, of course, that if you decide you don’t want a certain company, say Google, to read your emails, you only have control of your half of the equation. In other words, it doesn’t matter if you don’t want to use GMail, if most of your contacts do.
The same is true of AI assistant. You might not want an Amazon Echo device in your house, but you don’t spend all your life at home:
Amazon wants to bring Alexa to more devices than smart speakers, Fire TV and various other consumer electronics for the home, like alarm clocks. The company yesterday announced developer tools that would allow Alexa to be used in microwave ovens, for example – so you could just tell the oven what to do. Today, Amazon is rolling out a new set of developer tools, including one called the “Alexa Mobile Accessory Kit,” that would allow Alexa to work Bluetooth products in the wearable space, like headphones, smartwatches, fitness trackers, other audio devices, and more.
The future isn’t pre-ordained. We get to choose the society and culture in which we’d like to live. Huge, for-profit companies having listening devices everywhere sounds dystopian to me.