THE INTERNET IS THINKING
Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5573 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
AI-Newton: A concept-driven physical law discovery system without prior physical knowledge ↗
While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that underlie these models -- a task at which human physicists are adept. To bridge this gap, we introduce AI-Newton, a novel framework for concept-driven scientific discovery. Our system autonomously derives general physical laws directly from raw, multi-experiment data, operating without supervision or prior physical knowledge. Its core innovations are twofold: (1) proposing interpretable physical concepts to construct la…
- OpenAlex · Published · First archived
A residual weighted physics informed neural network for forward and inverse problems of reaction diffusion equations ↗
Physics-Informed Neural Networks (PINNs) constitute an effective deep learning approach for solving partial differential equations (PDEs). In this work, we propose a Residual-Weighted Physics-Informed Neural Network (RW-PINN), a new method designed to enhance the accuracy of PINNs -based algorithms. We construct a deep learning framework with two residual-weighting schemes to solve reaction–diffusion equations and evaluate its performance on both forward and inverse problems. The approach computes weights proportional to the PDE residuals, rescales them, and incorporates these scaled residuals…
- Hacker News · Posted on Hacker News · First archived
OpenAI Safety Researchers Fired ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Anthropic changes usage policy to ban model abuse and election interference ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
New site for AI math problems ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
On abuse towards models and Anthropic's new usage policies ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AI and the Death of the Author ↗
Hacker News discussion.
- GitHub · Repository created · First archived
kivali-ai/kivali ↗
Kivali is an open-source agentic operating system. Hire a team of AI agents that keep their identity, learn your work, remember what they learned, and bring you the decisions that are yours to make.
- Hugging Face · Model / dataset date · First archived
cstr/crispasr-regression-fixtures — model update ↗
# CrispASR regression-test fixtures Reference-dump archives for the per-backend regression suite in [CrispStrobe/CrispASR](https://github.com/CrispStrobe/CrispASR/tree/main/tests/regression). See `tests/regression/README.md` in that repo for the full design. Each ` / /ref.gguf` is the output of `tools/dump_reference.py --backend ` against the corresponding source model, capturing the encoder output and a few intermediate stages (per-layer activations for some backends). The C++ runtime's `crispasr-diff ` reads exactly this format and reports per-stage cosine similarity vs the captured tensors.
- Hacker News · Posted on Hacker News · First archived
Top Reasons AI Will Never Become Superintelligence: Won't Destroy nor Save Us ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.