conclusions the future of deep learning this is more speculative section aimed at opening horizons for people who want to join research program or begin doing independent research given what we know of how deep nets worktheir limitationsand the current state of the research landscapecan we predict where things are headed in the medium termfollowing are some purely personal thoughts note that don' have crystal ballso lot of what anticipate may fail to become reality ' sharing these predictions not because expect them to be proven completely right in the futurebut because they're interesting and actionable in the present at high levelthese are the main directions in which see promisemodels closer to general-purpose computer programsbuilt on top of far richer primitives than the current differentiable layers this is how we'll get to reasoning and abstractionthe lack of which is the fundamental weakness of current models new forms of learning that make the previous point possibleallowing models to move away from differentiable transforms models that require less involvement from human engineers it shouldn' be your job to tune knobs endlessly greatersystematic reuse of previously learned features and architecturessuch as metalearning systems using reusable and modular program subroutines additionallynote that these considerations aren' specific to the sort of supervised learning that has been the bread and butter of deep learning so far--ratherthey're applicable to any form of machine learningincluding unsupervisedself-supervisedand reinforcement learning it isn' fundamentally important where your labels come from or what your training loop looks likethese different branches of machine learning are different facets of the same construct let' dive in models as programs as noted in the previous sectiona necessary transformational development that we can expect in the field of machine learning is move away from models that perform purely pattern recognition and can only achieve local generalizationtoward models capable of abstraction and reasoning that can achieve extreme generalization current ai programs that are capable of basic forms of reasoning are all hardcoded by human programmersfor instancesoftware that relies on search algorithmsgraph manipulationand formal logic in deepmind' alphagofor examplemost of the intelligence on display is designed and hardcoded by expert programmers (such as monte carlo tree search)learning from data happens only in specialized submodules (value networks and policy networksbut in the futuresuch ai systems may be fully learnedwith no human involvement what path could make this happenconsider well-known type of networkrnns it' important to note that rnns have slightly fewer limitations than feedforward networks that' because rnns are bit more than mere geometric transformationslicensed to