Recent AI developments demonstrate that traditional scaling laws may be evolving, as models like Nova-3 and Stanford's Liquid Transformer achieve competitive performance with fewer parameters, suggesting that architectural innovations can improve efficiency beyond simply increasing model size.
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AI News: Nova-3 Beats GPT-5, AlphaFold 3.5 & Liquid AI | May 19Añadido:
XAI just dropped Nova 3 on GitHub and it is beating GPT-5 and Claude 4 by 18 to 25 points on video reasoning and scientific benchmarks. This open-source move has everyone talking. Meanwhile, DeepMind released AlphaFold 3.5 and it nails protein RNA interactions at scale, which could speed up new medicines fast.
Oh, and get this, Stanford just showed off a liquid transformer that hits GPT-4 results with 40% fewer parameters and now people wonder if the old scaling rules are done. Graduates are even booing AI speeches at their college commencements this spring. Does this mean the hype is cooling or are bigger surprises still ahead?
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