kandake.africa/atmosphereinabox
All in one local private environment for operating PLC, PDS, and whatever you need for ATprotocol development
Cloud Rats and Software Roaches (Idle Words)
We are entering an epoch where it may become easy for small pieces of self-replicating code to permanently infest Internet-connected devices in a way that is hard to undo. Such a world would be annoying to live in and we should take steps to forestall it.
Attention and the Relevance Problem
ABSTRACT The relevance problem is the problem of how cognitive systems can be sensitive to what is relevant despite relevance being holistic, open‐ended, and context‐sensitive. The problem has typically been applied to higher‐order cognitive processes. In this paper, I argue that there is a hitherto unrecognized relevance problem for perceptual attention. This problem becomes apparent when considering the functions of attention: attention must not only support ongoing task performance but also remain sensitive to opportunities for task switching, latent learning, and context checking. Existing treatments of the relevance problem have largely focused on its task‐relative form: given a task, what is relevant? I argue that attention faces a more general problem that includes sensitivity to factors relevant to other interests and to cues indicating that another activity may be worth pursuing. For such features of the environment to stand out as salient, salience cannot be determined solely by bottom‐up factors and a limited set of occurrent top‐down factors, such as current goals and intentions. Rather, it must reflect the wider (if not the entire) range of an agent's latent interests in a context‐sensitive way. This motivational unencapsulation of attention poses a challenge for both contemporary accounts of attention and suggests that passive attention is much more intelligent than commonly thought.
Bayesian Inference of Social Norms as Shared Constraints on Behavior
People act upon their desires, but often, also act in adherence to implicit social norms. How do people infer these unstated social norms from others' behavior, especially in novel social contexts? We propose that laypeople have intuitive theories of social norms as behavioral constraints shared across different agents in the same social context. We formalize inference of norms using a Bayesian Theory of Mind approach, and show that this computational approach provides excellent predictions of how people infer norms in two scenarios. Our results suggest that people separate the influence of norms and individual desires on others' actions, and have implications for modelling generalizations of hidden causes of behavior.
Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents
Significance The fact that humans enforce and comply with norms is an important reason why humans enjoy higher levels of cooperation and welfare than other animals. Some norms are relatively easy to explain: They may prohibit obviously harmful or uncooperative actions. But many norms are not easy to explain. For example, most cultures prohibit eating certain kinds of foods, and almost all societies have rules about what constitutes appropriate clothing, language, and gestures. Using a computational model focused on learning shows that apparently pointless rules can have an indirect effect on welfare. They can help agents learn how to enforce and comply with norms in general, improving the group’s ability to enforce norms that have a direct effect on welfare. , How do societies learn and maintain social norms? Here we use multiagent reinforcement learning to investigate the learning dynamics of enforcement and compliance behaviors. Artificial agents populate a foraging environment and need to learn to avoid a poisonous berry. Agents learn to avoid eating poisonous berries better when doing so is taboo, meaning the behavior is punished by other agents. The taboo helps overcome a credit assignment problem in discovering delayed health effects. Critically, introducing an additional taboo, which results in punishment for eating a harmless berry, further improves overall returns. This “silly rule” counterintuitively has a positive effect because it gives agents more practice in learning rule enforcement. By probing what individual agents have learned, we demonstrate that normative behavior relies on a sequence of learned skills. Learning rule compliance builds upon prior learning of rule enforcement by other agents. Our results highlight the benefit of employing a multiagent reinforcement learning computational model focused on learning to implement complex actions.
'Ask for what you want' is a key skill for the 21st century
Takes practice, and a bit of audacity.
E. coli chemotaxis: the baffling intelligence of a single cell
I want to tell the story of a beautiful phenomenon in biology. In some sense it’s the prototype of much of the activity of life. The phenomenon is the way in which an individual cell of E. coli forages for nutrients. This process, known as “chemotaxis”—the “chemo-” for chemical and the “taxis” from the Greek τάξις, for tactics—is intelligence in one of its most elemental forms. An individual E. coli has no brain, obviously, and is even many orders of magnitude simpler than a human cell, and yet already it possesses something like a sense of smell, drive, even a memory. Chemotaxis recasts E. coli not as some aimless gut-pest but rather as an exquisitely sophisticated physical computer.
School for Poetic Computation
The School for Poetic Computation is an experimental school in New York City supporting interdisciplinary study in art, code, hardware and critical theory. It's a place for learning and unlearning.
Epistemic Vigilance
Humans massively depend on communication with others, but this leaves them open to the risk of being accidentally or intentionally misinformed. To ensure that, despite this risk, communication remain...
Social Physics in the Age of Generative AI: How, Exactly, Is Society Complex?
The aspiration for a social physics — a science that would reveal universal laws of social life analogous to those governing the natural world—has haunted the social sciences since Comte. Each new wave of quantitative and computational methods breathes fresh life into this vision. The current moment is no exception: advances in artificial intelligence and large language models have reinvigorated attempts to model society through the methods of social physics. This essay argues that these ambitions rest on a confusion about the nature of social complexity. Drawing a distinction between systems complexity and anthropological complexity, we identify the limitations of analyzing society as a complex system akin to natural systems. One crucial obstacle for social physics is the analysis of meaning and interpretation. Generative artificial intelligence (GenAI) offers important opportunities to model language and reflexivity, inviting and perhaps compelling social physics to confront meaning and interpretation as constitutive features of the social world. At the same time, GenAI leaves untouched and may even aggravate social physics’ failure to grasp power, institutions, and inequality.
Can AI slop help us save the internet?
Disrupting the status quo and opening space for new visions
Building pro-worker AI | Brookings
Daron Acemoglu, David Autor, and Simon Johnson ask: What is pro-worker AI, and how can we build it?
Gradients of intimacy and creating shelter: An interview with Erin Kissane
How might we use nature as an inspiration for creating online communities?
Unbundling Tools for Thought
Disparate thoughts on personal information management after traversing the hype cycle.
Informatics of the Oppressed
An inquiry into the rich history of radical experiments to reorganize information.
