This article really made me pauze and think about the critical barriers for scaling AI, especially concerning energy consumption. Could you perhaps elaborate more on the practical challenges for wider adoption of in-memory computing at scale?
In-memory computing might be necessary for scaling AI. However, it’s probably not sufficient for crossing the threshold from sophisticated pattern recognition to genuine intelligence. That likely requires not just architectural innovation, but algorithmic breakthroughs we haven’t yet conceived. Energy consumption is not really the angle I am coming at this problem. I am more into learning and metacognition, but there does seem to be a horizon of diminishing returns with energy input and AI’s ability to identify its hallucinations.
This article really made me pauze and think about the critical barriers for scaling AI, especially concerning energy consumption. Could you perhaps elaborate more on the practical challenges for wider adoption of in-memory computing at scale?
In-memory computing might be necessary for scaling AI. However, it’s probably not sufficient for crossing the threshold from sophisticated pattern recognition to genuine intelligence. That likely requires not just architectural innovation, but algorithmic breakthroughs we haven’t yet conceived. Energy consumption is not really the angle I am coming at this problem. I am more into learning and metacognition, but there does seem to be a horizon of diminishing returns with energy input and AI’s ability to identify its hallucinations.