Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.

IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.

Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence. Because its population is so large -- and because its privacy norms put fewer restrictions on the sharing of digital data -- it may be easier for Chinese companies and researchers to build and train the "deep learning" systems that are rapidly changing the trajectory of health care. On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this "American A.I. Initiative," the administration will encourage federal agencies and universities to share data that can drive the development of automated systems. Pooling health care data is a particularly difficult endeavor.

This function used varies with the application, but some of the most used ones are the Sigmoid, ReLU and Leaky ReLU shown in this picture. More details about these activation functions are linked in below if that interests you. This transformed output is then sent to the next layer of neurons as input. They help to decide if the neuron would fire or not its information to the next layer. This output is then sent to a pooling layer.

For example, in machine translation, the input is an English sentence, and the output is the French translation. The Encoder will unroll each word in sequence and forms a fixed-length vector representation of the input English sentence. Then, the Decode will take the fixed-length vector representation as input, and produce each French word one after another, forming the translated English sentence. However, RNN models have some problems, they are slow to train, and they can't deal with long sequences. The input data needs to be processed sequentially one after the other.

Since the beginnings of artificial intelligence, researchers have long sought to test the intelligence of machine systems by having them play games against humans. It is often thought that one of the hallmarks of human intelligence is the ability to think creatively, consider various possibilities, and keep a long-term goal in mind while making short-term decisions. If computers can play difficult games just as well as humans then surely they can handle even more complicated tasks. From early checkers-playing bots developed in the 1950s to today's deep learning-powered bots that can beat even the best players in the world at games like chess, Go and DOTA, the idea of machines that can find solutions to puzzles is as old as AI itself, if not older. As such, it makes sense that one of the core patterns of AI that organizations develop is the goal-driven systems pattern.

It's a NASA tradition that stretches back decades: astronauts waking to tunes piped up from the ground. The tradition began in 1965, when the wake-up song was "Hello Dolly" by Jack Jones during Gemini 6, and continued during the Apollo program "when astronauts returning from the Moon were serenaded by their colleagues in mission control with lyrics from popular songs that seemed appropriate to the occasion," according to a history of NASA wake-up music compiled by Colin Fries, a NASA historian. "Several crews have awakened on their final day in space to Dean Martin's popular song'Going Back to Houston,'" Fries wrote.

Generative adversarial network (GAN) has been shown to be useful in various applications, such as image recognition, text processing and scientific computing, due its strong ability to learn complex data distributions. In this study, a theory-guided generative adversarial network (TgGAN) is proposed to solve dynamic partial differential equations (PDEs). Different from standard GANs, the training term is no longer the true data and the generated data, but rather their residuals. In addition, such theories as governing equations, other physical constraints and engineering controls, are encoded into the loss function of the generator to ensure that the prediction does not only honor the training data, but also obey these theories. TgGAN is proposed for dynamic subsurface flow with heterogeneous model parameters, and the data at each time step are treated as a two-dimensional image. In this study, several numerical cases are introduced to test the performance of the TgGAN. Predicting the future response, label-free learning and learning from noisy data can be realized easily by the TgGAN model. The effects of the number of training data and the collocation points are also discussed. In order to improve the efficiency of TgGAN, the transfer learning algorithm is also employed. Numerical results demonstrate that the TgGAN model is robust and reliable for deep learning of dynamic PDEs.

Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling -action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019). After training agents with these predictive objectives in a visually-rich, 3D environment with an assortment of objects, colors, shapes, and spatial configurations, we probe their internal state representations with synthetic (English) questions, without backpropagating gradients from the question-answering decoder into the agent. The performance of different agents when probed this way reveals that they learn to encode factual, and seemingly compositional, information about objects, properties and spatial relations from their physical environment. Our approach is intuitive, i.e. humans can easily interpret responses of the model as opposed to inspecting continuous vectors, and model-agnostic, i.e. applicable to any modeling approach. By revealing the implicit knowledge of objects, quantities, properties and relations acquired by agents as they learn, question-conditional agent probing can stimulate the design and development of stronger predictive learning objectives.

Deep reinforcement learning has led to many recent-and groundbreaking-advancements. However, these advances have often come at the cost of both the scale and complexity of the underlying RL algorithms. Increases in complexity have in turn made it more difficult for researchers to reproduce published RL algorithms or rapidly prototype ideas. To address this, we introduce Acme, a tool to simplify the development of novel RL algorithms that is specifically designed to enable simple agent implementations that can be run at various scales of execution. Our aim is also to make the results of various RL algorithms developed in academia and industrial labs easier to reproduce and extend. To this end we are releasing baseline implementations of various algorithms, created using our framework. In this work we introduce the major design decisions behind Acme and show how these are used to construct these baselines. We also experiment with these agents at different scales of both complexity and computation-including distributed versions. Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.