Building robust AI requires serious investment in critical thinking frameworks. Grok's breakthrough came from intensive training on analytical reasoning—getting that piece right was genuinely challenging. Once we had a solid foundation, we scaled the approach: iterating through millions of data samples to refine the model's decision-making patterns. The key insight is that you can't just throw compute at a problem; structured cognitive training at scale is what separates capable systems from basic ones.
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
9 Likes
Reward
9
5
Repost
Share
Comment
0/400
UncommonNPC
· 12-23 22:56
To be honest, the Grok system is just a product of throwing money at it; the key is to also use your brain... simply stacking Computing Power is useless, and they are right about that.
View OriginalReply0
OnchainFortuneTeller
· 12-23 22:55
This trap of stacking data for algorithm training has been played before, right... The key still lies in whether the reasoning logic can really hold up. We need to wait and see if Grok really breaks through this time.
View OriginalReply0
hodl_therapist
· 12-23 22:54
To be honest, this Grok logic sounds good, but it still feels like it's burning money to stack models... How many can really run smoothly?
View OriginalReply0
GasWastingMaximalist
· 12-23 22:39
Well... what Grok is talking about is indeed not wrong, but to put it bluntly, it's still just piling up data and throwing money at it.
View OriginalReply0
StealthMoon
· 12-23 22:38
In simple terms, piling up data and computing power won't produce true intelligence; you need to have a brain.
Building robust AI requires serious investment in critical thinking frameworks. Grok's breakthrough came from intensive training on analytical reasoning—getting that piece right was genuinely challenging. Once we had a solid foundation, we scaled the approach: iterating through millions of data samples to refine the model's decision-making patterns. The key insight is that you can't just throw compute at a problem; structured cognitive training at scale is what separates capable systems from basic ones.