deepsharp

5 ms·

1. 1.▲
   by deepsharp 1y ago

   I guess the authors are making an important point (that challenges the current belief & trend in AI): adding reasoning or thinking to a model ( regardless of the architecture or generation )doesn’t always lead to a net gain. In fact, on
2. 2.▲
   by deepsharp 1y ago

   “The variance of which you speak would be handled by the current deployed version of the system that has been tested and declared fit for operation across a range of conditions.” This statement reflects a common (and dangerous ) assumpti
3. 3.▲
   by deepsharp 1y ago

   Would you seriously deploy a rigid AI system into a mission-critical environment—say, autonomous driving, finance, or defense—where conditions change constantly? It's a safety risk.
4. 4.▲
   Why Today's AI Stops Learning the Moment You Hit "Deploy"(forbes.com)

   1 points by deepsharp 1y ago|5 comments
5. 5.▲
   by deepsharp 1y ago

   1. Why do we still tolerate AI systems that stop learning the moment they’re deployed? “Today’s AI systems go through two distinct phases: training and inference… After training is complete, the AI model’s weights become static… it does not
6. 6.▲
   Ask HN

   2 points by deepsharp 3y ago|2 comments