DecagonAI的CTO说，别迷信顶级模型，微调开源小模型做具体任务，又便宜又快还更好用，值得一看。
DecagonAI CTO Ashwin Sreenivas在a16z访谈中表示，前沿大模型与廉价小模型之间的取舍是虚假的。他说，通过针对特定任务微调较'笨'的模型，这些模型在该任务上反而能超过大型前沿模型。目前DecagonAI约90%的推理负载运行在开源模型上，并以此服务银行、航空和电信客户。他举例称，年付费10万美元的客户享受白手套服务，而年付费10美元的客户只能看帮助中心，AI可缩小这种差距。
.@DecagonAI co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models an...
. @DecagonAI co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models and cheaper, less capable ones is a false trade-off: "Even if you have a 'dumber model,' you can get it to higher performance on that specific task." "When we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models." "So we end up getting all three things. It is better at the task, it is cheaper, and it is faster." @AshwinSreenivas Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z A $100K/year customer gets white-glove treatment while a $10/year customer gets a help center. @DecagonAI 's bet is that AI closes that gap, and enterprise is buying it. Co-founders Jesse Zhang and Ashwin Sreenivas sit down with a16z's Kimberly Tan and Sarah Wang to discuss what they've learned running agents inside the biggest banks, airlines, and telcos: - Smart-vs-cheap models are a false trade-off - Start with frontier models on new use cases, then migrate to open-source after they mature - Fine-tuning per use case works at the application layer - Support demand always outran supply: make it cheaper and companies buy more of it 00:00 Intro 01:07 90% of Decagon runs on open-source 05:00 The smart vs. cheap trade-off is false 09:26 Building a model factory in-house 15:07 Are the labs the last startups? 21:21 Are forward deployed engineers a trap? 28:36 The agent that builds the agent 37:02 Glass box beats black box 47:55 From help desk to AI concierge 1:14:45 Jevons paradox in customer support @thejessezhang @AshwinSreenivas @kimberlywtan @sarahdingwang Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 3 🔄 3 ❤️ 13 👀 8280 📊 4 ⚡