The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...

For years, it seemed obvious that the best way to scale up artificial intelligence models was to throw more upfront computing resources at them. The theory was that performance improvements are ...

Given the high costs and slow speed of training large language models (LLMs), there is an ongoing discussion about whether spending more compute cycles on inference can help improve the performance of ...

MIT researchers achieved 61.9% on ARC tasks by updating model parameters during inference. Is this key to AGI? We might reach the 85% AGI doorstep by scaling and integrating it with COT (Chain of ...

You're currently following this author! Click to unsubscribe from email alerts. OpenAI cofounder Ilya Sutskever announced something at a recent conference that should have had the AI industry ...