Community Discussions turing lecture What needs to be improved.From the early days,theoreticians of ma- chine learning have focused on the iid “Deep Learning for AI assumption,which states that the test can neu al metwok learn the rieh cases are expected to come from the same distribution as the training ex- Communication of ACM amples.Unfortunately,this is not a re- alistic assumption in the real world: just consider the non-stationarities July,2021.Vol 64.No 7. Deep due to actions of various agents chang- ing the world,or the gradually expand- Learning ing mental horizon of a learning agent which always has more to learn and discover.As a practical consequence, for Al the performance of today's best Al sys- tems tends to take a hit when they go TURING LECTURE from the lab to the field. Our desire to achieve greater robust- ness when confronted with changes in distribution(called out-of-distribution generalization)is a special case of the mothated by the more general objective of reducing sample complexity (the number of ex- amples needed to generalize well)when Yoshua Benglo Geoffrey Hinton Yann LeCun faced with a new task-as in transfer learning and lifelong learning-or 2018 Turing Award Recipients simply with a change in distribution or Peng Zhao (Nanjing University) 10 Peng Zhao (Nanjing University) 10 Community Discussions “Deep Learning for AI” Communication of ACM July, 2021. Vol 64. No 7. 2018 Turing Award Recipients