5 stories tagged Scaling
Cerebras Systems CEO Andrew Feldman will discuss the challenges of scaling AI at TechCrunch Disrupt 2026, focusing on compute, energy, and infrastructure constraints and how Cerebras' wafer-scale architecture offers a different approach.
Researchers have identified a new scaling law, the contextual persistence function P(d), that governs how word arrangement affects meaning in human language. Using LLMs as probes across 10 languages, they found the impact of prior context follows a power-law decay, which could improve AI language models.
Researchers have developed a new method called KV-PRM that makes multi-agent AI systems more efficient. This could lead to faster, more capable AI collaborations in complex scenarios. The technique reduces the computational cost of evaluating long interactions, making it practical for real-world applications.
Enterprises are moving beyond AI experiments to widespread adoption. They're focusing on trust, governance, and workflow design to make AI truly useful. This shift could mean better AI tools for all of us.
Researchers find that the primary bottleneck in scaling multimodal large language models (MLLMs) is knowledge density in training data, not task format. Task-specific supervision like Visual Question Answering (VQA) adds little incremental semantic information beyond image captions.