Gary Marcus 在金融时报上警告AI泡沫，说超大规模投资可能是历史最大失误之一，值得一读冷静一下。
Gary Marcus 在 Financial Times 撰文指出，依赖扩大模型规模无法解决 LLM（大型语言模型）的准确性根本缺陷。他将超大规模投资比作历史上最大的金融失误之一，因为硅芯片折旧快且可能被更高效的模型取代。他还认为 LLM 行业难成科技巨头的垄断格局，更像利润微薄、竞争激烈的航空公司。文章呼吁寻找替代基础架构，而非继续押注超大规模计算。
“Scale cannot solve AI’s fundamental problem with accuracy” If my argument @financialtimes, excerpt...
“Scale cannot solve AI’s fundamental problem with accuracy” If my argument @financialtimes , excerpted below, is remotely correct, hyperscaling will prove to be among the biggest financial blunders in history. We must seek alternative foundations for AI. Gary Marcus @GaryMarcus “If there is a deflation of the AI bubble, the optimists say that the new infrastructure will remain even if the companies do not — just as railways survived the 19th-century railway bust. However, this fails to reckon with the reality of depreciation (few pieces of silicon hold their value for very long because better chips inevitably come along) and the possibility that LLMs could be displaced by more efficient models less dependent on massive numbers of expensive AI chips. In placing massive hyperscaling bets, investors are setting lavish expectations about future earnings. But LLMs are not likely to replicate the near monopolies that have made the market power of current tech giants hard to assail. A better analogy for them might be airlines, which are hobbled by small margins, intense competition, high expenses and dependence on hardware created by outside vendors.” – @garymarcus in @FinancialTimes 🔗 View Quoted Tweet 💬 4 🔄 4 ❤️ 23 👀 3683 📊 6 ⚡