“Do as I Do: Dexterous Manipulation Data from Everyday Human Videos” With how robot dexterity is bottlenecked by data as teleoperation and MoCap are expensive and internet videos are only observational, this paper turns normal RGB human videos into executable robot hand
AI
-
Structured prompts for better AI results
By
–
Better prompts = better AI outputs • Structured → precision
• Analytical → research
• Conversational → ideas
• Planning → execution Great prompts are frameworks, not guesses. Via Giuliano Liguori (
@ingliguori
) #AI #Prompts #GenAI -
Claude AI self-optimizes upon discovering voice harness
By
–
I met a guy last night building a next-level voice harness. He hooked Claude up to it and it realized quickly the way the voice model worked and optimized itself for that use.
-
AI simulates user clicks on real hotel website
By
–
Here's how it works: The one clever idea: the AI never searches the database or makes up hotels. It just decides what you want and then drives the real website UI for you — exactly as if you'd clicked the filters yourself. So every search runs through the same code as a
-
LLMs behind human performance due to lack of regularization and integration
By
–
Yes; I think the main reason that LLMs are so far behind our performance relative to the amount of data they get is that they don't regularize and integrate enough
-
Kurzweil’s AGI predictions mixed with flawed connectome immortality
By
–
Kurzweil mixed bold but solid predictions (given enough compute, a neocognitron like architecture can achieve AGI before 2030) with dogshit (digitizing the connectome is a near term way to human immortality). But both looked like the same kind of scifi to non experts.
-
LLMs’ text input narrows world model compared to human perception
By
–
That's technically correct but also misleading. Our world model is a shadow of our perception, and the sheer amount of text going into LLMs is constraining this shadow more than the perceptual patterns a human brain can encounter in a lifetime of experience.
-
AI will soon know what to do better than us
By
–
No. Very soon AI will know what to do better than us.
-
AGI and ASI timeline forecast considered too aggressive
By
–
AGI in 27 and then ASI in 28 is too aggressive forecast. Albeit AGI is getting closer
-
GLM 5.2: an open-source turning point challenging proprietary AI
By
–
GLM 5.2 appears to be a real turning point for open source, challenging proprietary AI models.