Researchers from Purdue University and the Georgia Institute of Technology argue that the growing energy demands of artificial‑intelligence models stem from the longstanding "memory wall"—the gap between fast processors and slower memory. By adopting brain‑inspired architectures that integrate processing and memory, such as spiking neural networks and compute‑in‑memory (CIM) designs, they claim AI can become far more efficient. The study highlights potential benefits for devices ranging from medical equipment to drones, suggesting a shift away from traditional von Neumann designs could dramatically cut power consumption while preserving performance. Leia mais →

Researchers from Texas A&M, the University of Texas and Purdue University have introduced the “LLM brain rot hypothesis,” suggesting that continual pre‑training on low‑quality web text can cause lasting cognitive decline in large language models. Their pre‑print paper analyzes a HuggingFace dataset of 100 million tweets, separating “junk” tweets—identified by high engagement yet short length or superficial, click‑bait content—from higher‑quality samples. Early results show a 76 percent agreement between automated classifications and graduate‑student evaluations, highlighting the potential risks of indiscriminate data ingestion for AI systems. Leia mais →