@Didi

It seems raw teleoperation data (RGB + joint states) structurally lacks affordance, contact intent, and embodiment-specific kinematic context. (information that can't be reliably recovered post-hoc once the demonstration is recorded) Most current approaches either filter/clean after collection, or rely on simulation to compensate. But neither seems to close the semantic gap for contact-rich tasks in unstructured environments. Is anyone working on supervision at acquisition time, enriching the stream as it's captured rather than labeling after the fact? And if not, is this a real bottleneck or am I overestimating the problem? submitted by /u/Several-Many9101 [link] [Kommentare]

Hi! I’ve been working in IT for over seven years now, and my office is next to some healthcare professionals. During a lunch break sitting on a bench in the sun, one of them asked me: If I enter my patients’ personal information into ChatGPT, is that a problem? I wasn’t sure how to answer him, in my opinion, yes, but what do you think? I’d be curious to hear your thoughts, and if there are any studies on the subject, I’d love to see them too! Thanks in advance for your responses! Have a great day, everyone ☀️ Alex submitted by /u/No_Computer_1247 [link] [Kommentare]

eh, too late brah.. submitted by /u/TrisolaranPrinceps- [link] [Kommentare]