With empathy, do you mean the ability to model the hidden states of another agent?
SAFETY
-
Jack Clark: 60% probability of RSI by end of 2028
By
–
Anthropic co-founder Jack Clark says 60% chance of RSI by end of 2028:
-
AI Transparency and Cognitive Impact on Developing Brains Needed
By
–
At the minimum, transparency in what queries lead to which outputs (interpretability), how this connects to educational principles/pedagogy, more evidence of how this may impact cognition, especially of developing brains, in the short, medium and long term.
-
GPT-4o and Llama 3.3 Pose Low Harm Risk Study Suggests
By
–
I think the fact that GPT-4o and Llama 3.3-80B did no significant harm is just as important as whether AI helped. If older (less accurate & more sycophantic) chatbots essentially did nothing for people who followed their advice, it means that there is less risk of harm as well.
-
Concerns Over Unregulated AI Technology Deployed to Children
By
–
How have we got to the point where we are rolling out untested/unregulated AI technology to kids while they are literally in the process of learning?!
-
Stuart Russell Testifies at Musk-OpenAI Trial Despite Exclusion Attempts
By
–
Well-known, well-respected AI expert Stuart Russell is now testifying at the Musk-OpenAI trial – despite OpenAI’s BS attempts to exclude him. Read the backstory here:
-
Anthropic’s Jack Clark Predicts 60% Chance of Recursive Self-Improvement by 2028
By
–
Anthropics Jack Clarke now believes that recurse self-improvement has a 60% change of happening by end of 2028.
-
Detecting AI Drift Across Hundreds of Edge Nodes With Intermittent Connectivity
By
–
How do you spot AI drift across hundreds of edge nodes when connectivity is intermittent?
-
Governance Frameworks Must Design for Continuous AI Model Updates
By
–
Governance frameworks must assume models need updates, conditions shift, fleets evolve. Design for continuous change rather than treating initial deployment as finished state.
-
Model Drift Goes Undetected Without Centralized Monitoring
By
–
Seasonal patterns alter data profiles. Market rules shift. New OEMs join fleets with different protocols. A model accurate in January quietly produces unreliable outputs by June with no centralized dashboard warning.