u/Long_Window8426

Been spending the last month filming everyday household tasks (folding, cooking, object manipulation) for humanoid training pipelines. A few things that surprised us:

• Labs care way more about environment diversity than clip count
• Raw data is basically commoditized now: annotation is where the value is
• Most free datasets (Ego4D, EgoScale) miss the task-specific detail labs actually need

Happy to share our sample dataset if anyone's working on manipulation or foundation models. What data challenges are you running into?

Kintic.dev

[ Removed by Reddit on account of violating the content policy. ]

Financial Times: But company-level data suggests that between 1977 and 2016, the average dollar of R&D spending in fact became more effective at delivering patents. And those patents seemed decent at generating growth in sales per worker. If anything, the relationship strengthened later. None of which fits with the notion that we’re having to reach higher for the apples near the top of the ideas tree.

My Opinion: So the rate of innovation is not slowing down. The investment required to generate patents is decreasing. Now I expect further acceleration in innovation as scientists and technologists work with AI tools to speed up scientific discovery. What was impossible or took years before, can be done now in days or hours.

AI can also be used to generate thousands of ideas for products and simulate them, before testing them physically. 3D printers allow fast creation of prototypes, and can also be used for manufacturing complex objects.

Reference: What if ideas aren't getting harder to find after all / Financial Times