Adversarial Spaces - Pardy Time DevLog

Adversarial internecting - permablog

How to move data to the Atmosphere and screw over someone else's valuation in the process

Adversarial internecting - permablog

How to move data to the Atmosphere and screw over someone else's valuation in the process

Adversarial internecting - permablog

How to move data to the Atmosphere and screw over someone else's valuation in the process

Adversarial internecting - permablog

How to move data to the Atmosphere and screw over someone else's valuation in the process

Adversarial construction as a potential solution to the experiment design problem in large task spaces

Despite decades of work, we still lack a robust, task-general theory of human behavior even in the simplest domains. In this paper we tackle the generality problem head-on, by aiming to develop a unified model for all tasks embedded in a task-space. In particular we consider the space of binary sequence prediction tasks where the observations are generated by the space parameterized by hidden Markov models (HMM). As the space of tasks is large, experimental exploration of the entire space is infeasible. To solve this problem we propose the adversarial construction approach, which helps identify tasks that are most likely to elicit a qualitatively novel behavior. Our results suggest that adversarial construction significantly outperforms random sampling of environments and therefore could be used as a proxy for optimal experimental design in high-dimensional task spaces.

Scaffolding, Hard and Soft: Infrastructures as Critical and Generative Structures

Words in Space is the work of Shannon Mattern.

Auto-grading decade-old Hacker News discussions with hindsight

A vibe coding thought exercise on what it might look like for LLMs to scour human historical data at scale and in retrospect.

Latent Spacecraft: Brains, GANs, Finnegans.

Latent Spacecraft combines computational linguistics, neuroscience, and literary analysis to investigate latent space, i.e. the hidden internal structure that enables both humans and machines to produce language. Peeking into AI’s hidden interiority, we parallel speech generation in humans and speech-trained generative adversarial networks (GANs), as well as in the language of Joyce’s Finnegans Wake and the GAN model trained on the novel, FinneGAN.

FlowLog - Efficient and Extensible Datalog | FlowLog

FlowLog: Efficient and Extensible Datalog via Incrementality

The Copernican Shift of Data

The Copernican Shift of Data And Why Generative AIs Make It Inevitable https://komoroske.com/copernican-shift points here Status: Random thoughts burped up while the kids were napping Author: Alex Komoroske Riffing on by The computing world as we know it is an increasingly untenable state....

Meandering on Manifolds: The Neural Geometry of Stories Over Time

To fully understand LLM representations, we must understand how they change dynamically, over the course of a prompt or conversation. We investigate these temporal dynamics with a simple case study: how do LLMs represent human emotions while reading short stories, both geometrically (in activation space) and temporally (changing from sentence to sentence)?

Meandering on Manifolds: The Neural Geometry of Stories Over Time

To fully understand LLM representations, we must understand how they change dynamically, over the course of a prompt or conversation. We investigate these temporal dynamics with a simple case study: how do LLMs represent human emotions while reading short stories, both geometrically (in activation space) and temporally (changing from sentence to sentence)?

Datalog

Datalog is a declarative logic programming language. While it is syntactically a subset of Prolog, Datalog generally uses a bottom-up rather than top-down evaluation model. This difference yields significantly different behavior and properties from Prolog. It is often used as a query language for deductive databases. Datalog has been applied to problems in data integration, networking, program analysis, and more.