How the monitored publications were assessed
Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.
This line is the mean of every item scored on each day. It deliberately ignores the reading filter, because a daily mean of only the mixed or uncertain items would move with how many were filtered out rather than with the discourse.
6 items read as mixed or uncertain
- With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education
- Should promotion depend on how workers use AI?
- Work, Wellbeing, and Choice: Empirical Lessons for AI Futures
- California’s Governor to Sign Landmark Online Child Safety Bills
- Labor wants Australians to be able to opt out of online algorithms. How will it change your feed?
- Alternative AI Philosophy: Daoism as Method for AI in Education
Across the whole window
The two tables below describe every item in the last 30 days under human-agency. They are deliberately not narrowed by the reading filter (currently mixed or uncertain), because a mean taken only over items already selected for their score would just restate the filter.
By topic
| Topic | Branch | Items | Mean | Position |
|---|---|---|---|---|
| Existential and catastrophic risk | risk | 32 | -50 | |
| Concentration of power | risk | 23 | -28 | |
| Governance and regulation | structural | 22 | -20 | |
| Loss of control and alignment | risk | 22 | -63 | |
| AGI and timelines | structural | 21 | -37 | |
| Cyber capability | risk | 10 | -45 | |
| Evaluation and measurement | structural | 10 | -22 | |
| Human agency and epistemics | risk | 9 | -17 | |
| Scaling and architecture | structural | 9 | -8 | |
| Synthetic media and manipulation | risk | 6 | -63 | |
| Medicine and health | benefit | 5 | +0 | |
| Biological and chemical uplift | risk | 4 | -20 | |
| Scientific acceleration | benefit | 4 | +17 | |
| Education and access to expertise | benefit | 3 | -24 | |
| Abundance and material wellbeing | benefit | 2 | -8 | |
| Climate and energy | structural | 2 | -6 | |
| Compute and infrastructure | structural | 2 | -13 | |
| Employment and displacement | risk | 2 | -4 | |
| Productivity and growth | benefit | 1 | -26 |
By source
A source's mean says how the items it published were read. It is not a rating of the source.
| Source | Kind | Tier | Items | Mean |
|---|---|---|---|---|
| arXiv cs.CY (Computers and Society) | academic | 1 | 14 | -20 |
| The Guardian: Artificial Intelligence | news | 1 | 9 | -55 |
| Financial Times: Artificial Intelligence | news | 1 | 8 | -53 |
| The New York Times: Technology | news | 1 | 7 | -31 |
| BBC News: Technology | news | 1 | 6 | -36 |
| Ars Technica: AI | news | 1 | 5 | -73 |
| OpenAI | lab | 1 | 3 | -17 |
| IEEE Spectrum: AI | news | 1 | 3 | +2 |
| arXiv cs.AI | academic | 1 | 3 | +6 |
| MIT Technology Review: AI | news | 1 | 2 | -50 |
| The Economist: Science and Technology | news | 1 | 1 | -16 |
| Google DeepMind | lab | 1 | 1 | +7 |
| CSET, Georgetown | government | 1 | 1 | -22 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 1 | +4 |