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.
The trend line needs at least two days of readings and there is one so far. It appears on its own once the pipeline has run across a second day. Today's readings are on the axis below.
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.
17 items read as mixed or uncertain
- Scalable Oversight for AI in Mental Health: Lessons from 350,000 AI Coaching Conversations between Therapy Sessions
- With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education
- OpenAI's apparent maths breakthrough raises profound questions
- OpenAI says it cracked 90-year-old maths problem in 88 hours
- Watermarks Without Verification: AI Text Watermarking After the EU AI Act
- Should promotion depend on how workers use AI?
- California’s Governor to Sign Landmark Online Child Safety Bills
- China’s Regulators Take Aim at “AI Boyfriends”
- AI Models Are Watermarking Text—Will You Notice?
- New AI health tools need ‘L-plates’, says UK review
- Labor wants Australians to be able to opt out of online algorithms. How will it change your feed?
- Democracy Needs Reach: Political Equality, Online Speech, and Algorithmic Recommendation
- OpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’
- UK needs new laws for AI in healthcare, says watchdog
- Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity
- AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome
- Early Data Indicates an A.I.-Generated Drug Could Slow Aging
Across the whole window
The two tables below describe every item in the last 180 days. 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 | 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 |