您提供的内容与“什么是 AI Agent 以及程序员学习相关内容能否获得额外收入”的问题无关。AI Agent 是指具有自主决策和行动能力的智能体，能够在特定环境中感知、学习、推理和执行任务。作为程序员，如果您想学习与 AI Agent 相关的知识，建议您掌握以下方面：
至于能否获得额外收入，这取决于多种因素，如您所开发的 AI Agent 应用的市场需求、您的技术水平和创新能力等。如果您能够开发出具有实用价值和创新性的 AI Agent 应用，并找到合适的商业机会，是有可能获得额外收入的。
in terms of SIFT features.But today all this is discarded.Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances,and perform much better.This is a big lesson.As a field,we still have not thoroughly learned it,as we are continuing to make the same kind of mistakes.To see this,and to effectively resist it,we have to understand the appeal of these mistakes.We have to learn the bitter lesson that building in how we think we think does not work in the long run.The bitter lesson is based on the historical observations that 1)AI researchers have often tried to build knowledge into their agents,2)this always helps in the short term,and is personally satisfying to the researcher,but 3)in the long run it plateaus and even inhibits further progress,and 4)breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning.The eventual success is tinged with bitterness,and often incompletely digested,because it is success over a favored,human-centric approach.One thing that should be learned from the bitter lesson is the great power of general purpose methods,of methods that continue to scale with increased computation even as the available computation becomes very great.The two methods that seem to scale arbitrarily in this way are search and learning.The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously,irredeemably complex;we should stop trying to find simple ways to think about the contents of minds,such as simple ways to think about space,objects,multiple agents,or symmetries.All these are part of the arbitrary,intrinsically-complex,outside world.They are not what should be built in,as their complexity is endless;instead we should build in only the meta-methods that can find and capture this arbitrary complexity.Essential to these methods is that they can find good approximations,but the search for them should be by our methods,not by us.We want AI agents that can discover like we can,not which contain what we have discovered.Building in our discoveries only makes