Most teams think continual learning is a training problem. Taranjeet Singh from @mem0ai thinks they'...
TL;DR · AI 摘要
Taranjeet Singh from Mem0AI argues that improving agent memory, not training methods, is key to continual learning in production.
核心要点
- Improving memory is crucial for continual learning in agents.
- Vector Space Day on June 11 will discuss these topics.
- Joining the conference can provide insights into vector search and agent memory.
结构提纲
按章节快速跳转。
Most teams believe continual learning is a training issue, but Taranjeet Singh disagrees.
Singh argues that enhancing agent memory, not training methods, leads to better performance over time.
Vector Space Day on June 11 will focus on vector search, retrieval, and agent memory.
The conference will be held in San Francisco.
Tickets can be purchased at https://luma.com/vsd-sf.
思维导图
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- Continual Learning
金句 / Highlights
值得收藏与分享的关键句。
Taranjeet Singh from Mem0AI thinks teams are solving the wrong thing with continual learning.
Giving agents better memory, not better gradients, is what actually makes them improve over time.
Vector Space Day on June 11 will go deep on vector search, retrieval, and agent memory.
Qdrant 在 X 上发布：「大多数团队认为持续学习是一个训练问题。@mem0ai 的 Taranjeet Singh 认为他们解决的是错误的问题。在 6 月 11 日的向量空间日（Vector Space Day）上，他将展示如何通过赋予智能体更好的记忆而非更好的梯度，才能真正使它们在生产环境中随时间不断进步。」/ X
不要错过正在发生的事情
大多数团队认为持续学习是一个训练问题。来自 @mem0ai 的 Taranjeet Singh 认为他们解决的方向错了。在 6 月 11 日举行的向量空间日（Vector Space Day）大会上，他将会展示，在实际生产中让智能体随着时间推移而提升的关键在于给予它们更好的记忆能力，而不是更优的梯度。向量空间日是一场面向深入研究向量搜索、检索和智能体记忆的工程师们的全天会议。欢迎来旧金山参加活动，请在 https://luma.com/vsd-sf 获取门票。
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