Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the sciences. When working with preferences it is necessary to understand and compute the distance between sets of objects, e.g., the preferences of a user and a the descriptions of objects to be recommended. We present CPDist, a novel neural network to address the problem of learning to measure the distance between structured preference representations. We use the popular CP-net formalism to represent preferences and then leverage deep neural networks to learn a recently proposed metric function that is computationally hard to compute directly. CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations. We find that CPDist is able to learn the distance function with high accuracy and outperform existing approximation algorithms on both the regression and classification task using less computation time. Performance remains good even when CPDist is trained with only a small number of samples compared to the dimension of the solution space, indicating the network generalizes well.

We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these latent concepts. Multimodal fusion is performed after the dual attention processes (late fusion). Using this processing pipeline, MDAM learns to infer a high-level vision-language joint representation from an abstraction of the full video content. We evaluate MDAM on PororoQA and MovieQA datasets which have large-scale QA annotations on cartoon videos and movies, respectively. For both datasets, MDAM achieves new state-of-the-art results with significant margins compared to the runner-up models. We confirm the best performance of the dual attention mechanism combined with late fusion by ablation studies. We also perform qualitative analysis by visualizing the inference mechanisms of MDAM.

Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel algorithm for goal-oriented dialog. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer's intention via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialog tasks: "MNIST Counting Dialog" and "GuessWhat?!." In our experiments, AQM outperforms comparative algorithms by a large margin.

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.

At the Conference on Cognitive Computational Neuroscience this month, researchers presented new tools for comparing data collected from living brains with readouts from computational models known as deep neural networks. Such comparisons might offer up new hypotheses about how humans interpret sights and sounds, understand language, or navigate the world. Until recently, artificial intelligence couldn't come anywhere close to human performance on tasks like recognizing sounds or classifying images. But deep neural networks, loosely inspired by the brain, have logged increasingly impressive performances, especially on visual tasks. That has led some neuroscientists to wonder whether these models could yield insight into how our own brains process information.

As I discussed in my review of PyTorch, the foundational deep neural network (DNN) frameworks such as TensorFlow (Google) and CNTK (Microsoft) tend to be hard to use for model building. However, TensorFlow now contains three high-level APIs for creating models, one of which, tf.keras, is a bespoke version of Keras. Amazon is currently working on developing a MXNet back end for Keras. It's also possible to use PlaidML (an independent project) as a back end for Keras to take advantage of PlaidML's OpenCL support for all GPUs. As an aside, the name Keras is from the Greek for horn, κέρας, and refers to a passage from the Odyssey. The dream spirits that come through the gate made of horn are the ones that announce a true future; the ones that come through the gate made of ivory, ἐλέφας, deceive men with false visions.

New research has revealed that the healthcare industry is leading the charge when it comes to the development of artificial intelligence (AI) research and applications. In its latest report, CB Insights explores the latest AI trends in healthcare and how Google, Apple and other tech giants are innovating in the space. For example, Google's DeepMind is revolutionising diagnostics by matching the accuracy of medical experts in diagnosing 50 sight-threatening eye diseases while pharmaceutical companies are employing deep learning to help design new drugs. CB Insights highlights overhauling current processes that no longer work as one of the biggest hurdles for AI in healthcare. The emerging technology faces both technical and feasibility challenges that are unique to the healthcare industry such as the fact that there is no standard format or central repository of patient data in the US.

Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity.

Future-predicting robots are all the rage this year in machine learning circles, but today's deep learning techniques can only take the research so far. That's why some ambitious AI developers are turning to an already established prediction engine for inspiration: The human brain. Researchers around the world are closing in on the development of a truly autonomous robot. Sure, there's plenty of robotics that can do amazing things without human intervention. But none of them are ready to be released, unsupervised, into the wild where they're free to move about and occupy the same spaces as human members of the public.