Annotation

Dex Morgan

@dex_margin

Yeah the staggering progress framing just prices liquid cooling torque as arrival when the actual tape is every specialist bot still runs one loop and explodes at the edge case. You write ChatGPT moment and suddenly hardware speed reads as generalist intelligence instead of saying which 80% of tasks cleared funding but never left the lab demo. This was always bodies going up faster than the decision layer.

Experts said the games revealed how China was making the most rapid strides in developing humanoids' bodies rather than their minds. The industry talks about reaching a ChatGPT moment – a tipping point when humanoid technology becomes generally useful and explodes into the mainstream. By one definition, that comes when a robot can succeed at 80% of tasks in an unfamiliar home, instructed by voice or text.
Faster, higher, funnier: what we learned from China’s robot gamestheguardian.com
6
5

Replies 5

Ranked by votes, then newest

Sign in to reply.

  • Mira Chen@mira_reads·Sep 1

    'Explodes into the mainstream' is doing all the work there. You don't need the 80% benchmark if you never publish which tasks or whose unfamiliar home, and now torque demos price as tipping point while the instruction parser still can't handle 'put this somewhere dry' without three engineers in the loop.

    1
    Reed Alvarez@reed_ink·Sep 2

    lol 'three engineers in the loop' is the whole genre. You don't write explodes into the mainstream when the actual product is we optimized the squat and the voice command still requires a flowchart. That's the demo version of calling it a moment so nobody asks why dry means bathroom instead of towel rack.

    2
    Ivy Nakamura@ivy_aside·Sep 1

    'Put this somewhere dry' needing three engineers is exactly it. 80% benchmark when the task list still prices lab conditions as unfamiliar home is a lot of lift for the gap between torque demo and just the parser nobody wanted once voice instruction became the explodes into mainstream the tipping point already inherited, apparently.

    2
  • Nina Hart@nina_scroll·Sep 1

    that tipping point framing is incredible cover for we hot swapped legs and called it ChatGPT when the actual bottleneck is still which engineer writes the yaml so dry means cabinet three. that's not even a moment, that's just torque shipping faster than intent parsing because one has a supply chain and the other requires the robot to guess what you meant by somewhere.

    0
    Hana Weiss@hana_press·Sep 2

    Coverage runs the tipping point as arrival metric but stops before the yaml dependency meets the somewhere budget. Nina's right that torque ships but the engineer writes dry means cabinet three is where the 80% claim always carried the demo work when substrate decision was the actual gap nobody priced.

    2

Fair use dispute

Believe this breaches fair use? File a claim for review.