A team of engineering researchers from the University of Toronto have created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview with Eureka Alert.

PyTorch is a flexible open source framework for Deep Learning experimentation. In this post, you will learn how to train PyTorch jobs on Amazon SageMaker. As usual, you'll find my code on Github:) SageMaker provides a collection of built-in algorithms as well as environments for TensorFlow and MXNet… but not for PyTorch. Fortunately, developers have the option to build custom containers for training and prediction. This will require some changes in our PyTorch script, the well-known example of learning MNIST with a simple CNN.

Machine learning is the secret sauce that allows us to use computers to automate tasks in powerful new ways. However, there are a lot of steps that must go right for the ML to work: ideas must be mined from huge amounts of data, clean sample data must be provided to train models, and models must be managed and maintained over time. Here are three startups looking to simplify some of these aspects of the ML lifecycle. Before a data scientist can build a machine learning model, she must first identify patterns in the real world. There are various ways to identify patterns.

Within AI, deep learning is the fastest growing field and is being used to solve a wide range of problems.

If you already understand the underlying system, then machine learning maybe not be the best approach. You would do better using the physical equations for the system, and then only use parameter estimation in cases where you don't know one or more of the parameters. A physical model will be far more general than an ML model. The ML model will likely overfit to the cases you give it, and even if you manage to fix that, it will fail when it sees something qualitatively new. The physical equations will generalize to any scenario no matter what it looks like, so long as the assumptions are resonably respected.

The strange, creepy world of "deepfakes," videos (often explicit) with the faces of the subjects replaced by those of celebrities, set off alarm bells just about everywhere early this year. And in case you thought that sort of thing had gone away because people found it unethical or unconvincing, the practice is back with the highly convincing "Deep Video Portraits," which refines and improves the technique. To be clear, I don't want to conflate this interesting research with the loathsome practice of putting celebrity faces on adult film star bodies. But this application of technology is clearly here to stay and it's only going to get better -- so we had best keep pace with it so we don't get taken by surprise. Deep Video Portraits is the title of a paper submitted for consideration this August at SIGGRAPH; it describes an improved technique for reproducing the motions, facial expressions, and speech movements of one person using the face of another.

Reinforcement Learning - over the last decade we have seen a lot of progress in use of reinforcement learning algorithms in settings when labeled data doesn't exist and a supverisde learning approach is not possible. The state of the art approach to tackling RL problems are Policy Gradients, which in combination with Monte Carlo Tree Search were employed by Google DeepMind's AlphaGo system to famously beat the Go world champion Lee Sedol. The readers will love our list because it is Data-Driven & Objective. Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

TU Darmstadt Probabilistic deep learning currently receives an increased interest, as consistent treatment of uncertainty is one of the most important goals in machine learning and AI. Most current approaches, however, have severe limitations concerning inference. Sum-Product networks (SPNs), although having excellent properties in that regard, have so far not been explored as serious deep learning models, likely due to their special structural requirements. In this paper, we make a drastic simplification and use a random structure which is trained in a "classical deep learning manner" such as automatic differentiation, SGD, and GPU support. The resulting models, called RAT-SPNs, yield comparable prediction results to deep neural networks, but maintain well-calibrated uncertainty estimates which makes them highly robust against missing data. Furthermore, they successfully capture uncertainty over their inputs in a convincing manner, yielding robust outlier and peculiarity detection.

Healthcare is a natural arena for the application of machine learning, especially as modern electronic health records (EHRs) provide increasingly large amounts of data to answer clinically meaningful questions. However, clinical data and practice present unique challenges that complicate the use of common methodologies. This article serves as a primer on addressing these challenges and highlights opportunities for members of the machine learning and data science communities to contribute to this growing domain.

Concepts are the foundation of human deep learning, understanding, and knowledge integration and transfer. We propose concept-oriented deep learning (CODL) which extends (machine) deep learning with concept representations and conceptual understanding capability. CODL addresses some of the major limitations of deep learning: interpretability, transferability, contextual adaptation, and requirement for lots of labeled training data. We discuss the major aspects of CODL including concept graph, concept representations, concept exemplars, and concept representation learning systems supporting incremental and continual learning.