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„Benjamin's description of the allegorist as one who "rummages here and there for a particular piece, holds it next to some other piece, and tests to see if they fit together" readily applies to the collage or assemblage artist. Collage and assemblage are allegorical processes par excellence insofar as the combination of things accentuates rather than ameliorates arbitrariness. That Genzken's chosen material is plastic gives added depth, meaning, and emphasis to her allegorical manipulation of commodities. For if in allegory "any person, any object, any relationship can mean anything else," in plastic's molecular structure resides the capacity to realize an infinite number of forms. As an abstract and inorganic material, one without essential materiality and characterized by a mysterious transmutability, plastic is perhaps the ideal material for the production of a contemporary allegory. The mass-produced plastic thing can be held up as the perfect emblem of our alienated times.“
– Art Historian Lisa Lee about Isa Genzken. Lee claims that Genzken’s working method is structurally allegorical and mobilizes Walter Benjamin for that claim *…~_..**
I saw a very good Instagram reel yesterday by dozytattoo, regarding the line between hobbies and consumerism. That hobbies like collecting are often just consumerism. It's a take I very much agree with. If the thing you are collecting, is actively marketed as a collectible, that the primary intention of the product is that you buy all, or as close to all of the product line as possible, it's more consumerism than hobby.
However, to look at it through an ecological lens, most hobbies require us to consume resources for us to do them. Hiking requires good shoes, painting requires art supplies. The problem occurs when we get into the mindset that the more stuff we buy for our hobby, the better we will be at that hobby, especially as beginners. There are plenty businesses related to any hobby that are willing to prey on your insecurity, to sell you a product you probably don't need.
Also we live in an age of mass-production, and as hobbies often get turned into small businesses, there is always a push within our capitalist society, to mass-produce your content regardless of how small your audience is.
The definition of what a hobby is, is often blurred. It is often a form of labour, albeit one that is usually not compensated for. We give our body and time to these hobbies and the people who find joy in the products of our hobbies. Hobbies can become our job, making it no longer our hobby. Even those collectors, like someone who has a house filled with Minecraft merch, often becomes an influencer, or turns their house into a museum. If your hobby goes from something small and personal, to something big and open to a large audience, the scale of resources being consumed for your hobby is going to be greater, and have a greater ecological impact.
Natural Language Processing
(...)Nonprofit research firm OpenAI released a new language modelcapable of generating convincing passages of prose. So convincing in fact that the researchers won't be open-sourcing the code to stall its potential weaponization as a means of mass-producing fake news. (Read the full story from senior AI editor Will Knight here.)
“It’s kind of surprising people in terms of what you can do with [...] more data and bigger models,” says Percy Liang, a computer science professor at Stanford.
The passages of text that the model produces are good enough to masquerade as something human-written. But this ability should not be confused with a genuine understanding of language—the holy grail of the subfield of AI known as natural language processing (NLP). (An analog exists in computer vision too: an algorithm doesn’t need to master visual comprehension to synthesize highly realistic images.) In fact, how we even get machines to that level of understanding has remained largely elusive to NLP researchers. That goal could take years, even decades, to achieve, surmises Liang, and will likely involve techniques that don’t yet exist.
There are currently four different philosophies of language that drive the development of NLP techniques. Let’s begin with the one used by OpenAI.
#1. Distributional semantics
Linguistic philosophy. Words derive meaning from how they are used. For example, the words “cat” and “dog” mean more or less the same thing because they are used more or less the same way. You can feed and pet a cat, and you feed and pet a dog. You can’t, however, feed and pet an orange.
How it translates to NLP. Algorithms based on distributional semantics have been largely responsible for the recent breakthroughs in NLP. They take a machine learning approach to processing text, finding patterns in word usage by essentially counting how often and how closely words are used in relation to one another. The resultant models can then use those patterns to construct complete sentences or paragraphs, and power things like autocomplete or other predictive text systems. In recent years, some researchers have also begun experimenting with looking at the distributions of random character sequences rather than words. This way models can more flexibly handle acronyms, punctuation, slang, and other non-word groupings that don’t appear in the dictionary—as well as languages that don’t have clear delineations between words.
Pros. These algorithms are flexible and scalable because they can be applied within any context and learn from unlabeled data.
Cons. The models they produce don’t actually understand the sentences they construct. At the end of the day, they’re writing prose using word associations.
#2. Frame semantics
Linguistic philosophy. Language is used to describe actions and events, therefore sentences can be subdivided into subjects, verbs, and modifiers—aka: the who, what, where, and when—to be understood.
How it translates to NLP. Algorithms based on frame semantics learn to deconstruct sentences based on a set of rules or lots of labeled training data. This makes them particularly good at parsing simple commands—and thus useful for chatbots or voice assistants. If you asked Alexa to “find a restaurant with four stars for tomorrow,” for example, such an algorithm would figure out how to execute the sentence by breaking it down into the action (“find”), the what (“restaurant with four stars”), and the when(“tomorrow”).
Pros. Unlike distributional-semantic algorithms, which don’t understand the text they learn from, frame-semantic algorithms can distinguish the different pieces of information within a sentence. These can be used to answer questions like “when is this event taking place?”
Cons. These algorithms can only handle very simple sentences and therefore fail to capture nuance. Because they require a lot of context-specific training, they’re also not flexible.
#3. Model-theoretical semantics
Linguistic philosophy. Language is used to communicate human knowledge.
How it translates to NLP. Model-theoretical semantics is based on an old idea in AI that all of human knowledge can be encoded, or modeled, in a series of logical rules. For example, if you know birds can fly, and eagles are birds, then you can deduce that eagles can fly. This approach to AI is no longer in vogue because researchers soon realized there were too many exceptions to each rule (eg: Penguins are birds, but they can’t fly). But algorithms based on model-theoretical semantics are still useful for extracting information from models of knowledge, such as databases. Like frame-semantics algorithms, they parse sentences by deconstructing them into parts. But whereas the former defines those parts as the who, what, where, and when, model-theoretical semantics defines them as the logical rules encoding knowledge. Consider the question: “What is the largest city in Europe by population?” A model-theoretical algorithm would break it down into a series of queries that correspond to logical rules within the model of knowledge: “What are all the cities in the world?” “Which ones fall in Europe?” “What are the cities’ populations?” “Which population is the largest?” It would then be able to traverse that model to get you your final answer.
Pros. These algorithms give machines the ability to answer complex and nuanced questions.
Cons. They require a model of knowledge, which is time consuming to build, and are not flexible across different contexts.
#4. Grounded semantics
Linguistic philosophy. Language derives meaning from lived experience. In other words, humans created language to achieve their goals, so it must be understood within the context of our goal-oriented world.
How it translates to NLP. This is the newest approach within the field and the one that Liang thinks holds the most promise. It tries to mimic how humans pick up language over the course of their life: the machine starts with a blank state and learns to associate words and phrases with the correct meanings through conversation and interaction. In a simple example, if you wanted to teach a computer how to move objects around in a virtual world, you would give it a command like “move the red block to the left,” then show the machine what you mean. Over time, the machine would learn to understand and execute the commands without help.
Pros. In theory, these algorithms should be very flexible and get the closest to a genuine understanding of language.
Cons. Teaching is very time-intensive—and not all words and phrases are as easy to show as “move the red block.”
In the short term, Liang thinks the field of NLP will see much more progress from exploiting existing techniques, particularly those based on distributional semantics. But longer term he believes they all have limits. “There's probably a qualitative gap between the way that humans understand language and perceive the world, and our current models,” he says. To close that gap would likely require a new way of thinking, he adds, as well as much more time.
mass production - assembling and wiring sumo bots. Huge thanks to everyone for help making bots in time!
mass production - making parts for sumo-bots
We are all caught standing in the same mass-production shoes, trying to find the perfect pair of one-of-a-kind-kicks, but those are so expensive. Try making your own pair.
Kimberly
LRL: Artist’s book meant a book by an artist. I hate it when they are called “artist books” which is ungrammatical and meaningless. My own definition of an artist’s book was quite strict: mass produced, relatively cheap, accessible to a broad public, all art and no commentary or preface or anything that wasn’t part of the artwork by anyone—artist or critic; the sequential nature made it a single piece (maybe at times a whole “exhibition” but that never appealed to me as much as the holistic view). Hand-made, one-of-a-kind books were something else—often very beautiful, but the kind of “precious objects” I hoped we’’d escape.
