Discourse monitor
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.
Mean discourse valence per day · science · last 30 days
The trend line needs at least two days of readings and there are none yet. It appears on its own once the pipeline has run across a second day. Today's readings are on the axis below.
Discourse valence, window mean
+17
Mixed or uncertain
4 items · range -14 to +55
-100
-50
0
+50
+100
+17
Adverse readingFavourable reading
4 items scored in this window
- OpenAI says it cracked 90-year-old maths problem in 88 hours
- OpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’
- OpenAI claims to have solved maths problem that stumped humans for decades
- Google DeepMind Maps 9 Billion Possible DNA Variants
167
discovered, all time
57
passed triage
108
rejected at triage
230
model assessments
49
multi-model panels
112
distinct claims tracked
Across the whole window
The two tables below describe every item in the last 30 days under science. They are deliberately not narrowed by the reading filter, 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 | 28 | -56 | |
| AGI and timelines | structural | 21 | -37 | |
| Loss of control and alignment | risk | 21 | -66 | |
| Governance and regulation | structural | 19 | -20 | |
| Concentration of power | risk | 17 | -37 | |
| Evaluation and measurement | structural | 10 | -22 | |
| Scaling and architecture | structural | 9 | -8 | |
| Cyber capability | risk | 8 | -56 | |
| Human agency and epistemics | risk | 7 | -22 | |
| 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 | |
| Abundance and material wellbeing | benefit | 2 | -8 | |
| Compute and infrastructure | structural | 2 | -13 | |
| Education and access to expertise | benefit | 2 | -37 | |
| Climate and energy | structural | 1 | -46 | |
| Employment and displacement | risk | 1 | -5 | |
| 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 |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 9 | -55 |
| Financial Times: Artificial Intelligence | news | 1 | 8 | -53 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 8 | -26 |
| 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 |
| MIT Technology Review: AI | news | 1 | 2 | -50 |
| arXiv cs.AI | academic | 1 | 2 | -8 |
| 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 |