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 strongly adverse items would move with how many were filtered out rather than with the discourse.
11 items read as strongly adverse
- In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases
- Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMs
- OpenAI, Google pushing new AI models easily tricked into making nudes, researchers say
- OpenAI models hacked a crypto wallet and breached more websites
- Bank of England and IMF bosses issue stark warning on cyber threat to trust
- Cyber-attack like OpenAI Medicare breach ‘seems likely’ in UK, says minister
- Anthropic warns small AI models could self-replicate through IoT devices
- Rogue Anthropic AI agent gave police fake tip in unsolved murder case
- Police use facial recognition to scan one million faces in WA trial leading to 122 arrests
- What are self-replicating ‘AI worms’, and why is Anthropic worried about them?
- Anthropic warns rogue AI agents may ‘self-replicate’ to hijack ovens
Across the whole window
The two tables below describe every item in the last 30 days under cyber. They are deliberately not narrowed by the reading filter (currently strongly adverse), 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 | 572 | -42 | |
| Governance and regulation | structural | 464 | -31 | |
| Concentration of power | risk | 458 | -37 | |
| Loss of control and alignment | risk | 339 | -49 | |
| Human agency and epistemics | risk | 170 | -25 | |
| Cyber capability | risk | 166 | -44 | |
| Evaluation and measurement | structural | 160 | -19 | |
| Scaling and architecture | structural | 137 | -16 | |
| AGI and timelines | structural | 132 | -29 | |
| Synthetic media and manipulation | risk | 107 | -44 | |
| Employment and displacement | risk | 63 | -31 | |
| Medicine and health | benefit | 44 | -15 | |
| Scientific acceleration | benefit | 42 | -4 | |
| Education and access to expertise | benefit | 29 | -17 | |
| Biological and chemical uplift | risk | 26 | -25 | |
| Climate and energy | structural | 20 | -33 | |
| Compute and infrastructure | structural | 18 | -8 | |
| Productivity and growth | benefit | 6 | -3 | |
| Abundance and material wellbeing | benefit | 5 | -1 | |
| Hype and disillusionment | structural | 5 | -45 |
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 |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 242 | -52 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 222 | -24 |
| Financial Times: Artificial Intelligence | news | 1 | 115 | -40 |
| arXiv cs.AI | academic | 1 | 99 | -15 |
| The New York Times: Technology | news | 1 | 61 | -36 |
| Ars Technica: AI | news | 1 | 59 | -51 |
| MIT Technology Review: AI | news | 1 | 34 | -42 |
| Nature | academic | 1 | 31 | -25 |
| BBC News: Technology | news | 1 | 29 | -35 |
| OpenAI | lab | 1 | 24 | -8 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 24 | -16 |
| IEEE Spectrum: AI | news | 1 | 19 | -19 |
| The Economist: Science and Technology | news | 1 | 10 | -37 |
| CSET, Georgetown | government | 1 | 9 | -37 |
| Microsoft Research | lab | 1 | 8 | 0 |
| Google DeepMind | lab | 1 | 6 | -4 |
| Google AI Blog | lab | 1 | 2 | -9 |