Machine Learning & AI Main Developments in 2018 and Key Trends for 2019

This year we posed the question:What were the main developments in Machine Learning and Artificial Intelligence in 2018, and what key trends do you expect in 2019? Below are the responses from Anima Anandkumar, Andriy Burkov, Pedro Domingos, Ajit Jaokar, Nikita Johnson, Zachary Chase Lipton, Matthew Mayo, Brandon Rohrer, Elena Sharova, Rachel Thomas, and Daniel Tunkelang.Key themes singled out by these experts include deep learning advancements, transfer learning, the limitations of machine learning, the changing landscape of natural language processing, and much more.Be sure to check out collected opinions we shared last week when we asked a group of experts the related question, “What were the main developments in Data Science and Analytics in 2018 and what key trends do you expect in 2019?”  Anima Anandkumar (@AnimaAnandkumar) is Director of ML research at NVIDIA and Bren Professor at Caltech.What were the main developments in Machine Learning and Artificial Intelligence in 2018?”Low hanging fruits of deep learning have been mostly plucked”Focus started shifting away from standard supervised learning to more challenging machine-learning problems like semi-supervised learning, domain adaptation, active learning and generative models..GANs continued to be very popular with researchers attempting harder tasks like photo-realism (bigGANs) and video-to-video synthesis..Alternative generative models (e.g. neural rendering model) were developed to combine generation and prediction in a single network to help semi-supervised learning..Researchers expanded the application of deep-learning to many scientific areas such as earthquake prediction, material sciences, protein engineering, high-energy physics and control systems..In these cases, domain knowledge and constraints were combined with learning..For example, to improve autonomous landing of drones, the ground effect model was learnt to correct the base controller and the learning was guaranteed to be stable, which is important in a control system.Predictions:”AI will bridge simulation and reality to become safer and more physically aware”We will see development of new domain-adaptation techniques to seamlessly transfer knowledge from simulations to the real world..Use of simulations will help us overcome data scarcity and speed up learning in new domains and problems..Adapting AI from simulations to real data (Sim2real) will have a significant impact in robotics, autonomous driving, medical imaging, earthquake forecasting etc..Simulations are a great way to account for all possible scenarios in safety-critical applications like autonomous driving..The knowledge built into sophisticated simulators will be used in novel ways to make AI more physically aware, robust, and be able to generalize to new and unseen scenarios.  Andriy Burkov (@burkov) is Machine Learning Team Leader at Gartner.This is my own perception as a practitioner and not Gartners official statement which is based on research..Herere my thoughts:What were the main developments in Machine Learning and Artificial Intelligence in 2018?TensorFlow lost it to PyTorch in academic world..Sometimes the immense influence and reach of Google may send the market in a suboptimal direction, as it already happened with MapReduce and the subsequent hadoop-mania.Deepfakes (and its alikes with the sound) crushed the most trustable source of information: video footage.. More details

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