Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic text because they cannot ground input context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in text generation systems, namely knowledge-enhanced text generation. The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into generation process. This survey gives a comprehensive review of knowledge-enhanced text generation systems, summarizes research progress to solving these challenges and proposes some open issues and research directions.

Explainability in AI is gaining attention in the computer science community in response to the increasing success of deep learning and the important need of justifying how such systems make predictions in life-critical applications. The focus of explainability in AI has predominantly been on trying to gain insights into how machine learning systems function by exploring relationships between input data and predicted outcomes or by extracting simpler interpretable models. Through literature surveys of philosophy and social science, authors have highlighted the sharp difference between these generated explanations and human-made explanations and claimed that current explanations in AI do not take into account the complexity of human interaction to allow for effective information passing to not-expert users. In this paper we instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations. We illustrate how we can utilize this framework through two very different examples: an artificial neural network and a Prolog solver and we provide a possible implementation for both examples.

Interactive search sessions often contain multiple queries, where the user submits a reformulated version of the previous query in response to the original results. We aim to enhance the query recommendation experience for a commercial image search engine. Our proposed methodology incorporates current state-of-the-art practices from relevant literature -- the use of generation-based sequence-to-sequence models that capture session context, and a multitask architecture that simultaneously optimizes the ranking of results. We extend this setup by driving the learning of such a model with captions of clicked images as the target, instead of using the subsequent query within the session. Since these captions tend to be linguistically richer, the reformulation mechanism can be seen as assistance to construct more descriptive queries. In addition, via the use of a pairwise loss for the secondary ranking task, we show that the generated reformulations are more diverse.

Generalizing outside of the training distribution is an open challenge for current machine learning systems. A weak form of out-of-distribution (OoD) generalization is the ability to successfully interpolate between multiple observed distributions. One way to achieve this is through robust optimization, which seeks to minimize the worst-case risk over convex combinations of the training distributions. However, a much stronger form of OoD generalization is the ability of models to extrapolate beyond the distributions observed during training. In pursuit of strong OoD generalization, we introduce the principle of Risk Extrapolation (REx). REx can be viewed as encouraging robustness over affine combinations of training risks, by encouraging strict equality between training risks. We show conceptually how this principle enables extrapolation, and demonstrate the effectiveness and scalability of instantiations of REx on various OoD generalization tasks. Our code can be found at https://github.com/capybaralet/REx_code_release.

Understanding the role of (stochastic) gradient descent (SGD) in the training and generalisation of deep neural networks (DNNs) with ReLU activation has been the object study in the recent past. In this paper, we make use of deep gated networks (DGNs) as a framework to obtain insights about DNNs with ReLU activation. In DGNs, a single neuronal unit has two components namely the pre-activation input (equal to the inner product the weights of the layer and the previous layer outputs), and a gating value which belongs to $[0,1]$ and the output of the neuronal unit is equal to the multiplication of pre-activation input and the gating value. The standard DNN with ReLU activation, is a special case of the DGNs, wherein the gating value is $1/0$ based on whether or not the pre-activation input is positive or negative. We theoretically analyse and experiment with several variants of DGNs, each variant suited to understand a particular aspect of either training or generalisation in DNNs with ReLU activation. Our theory throws light on two questions namely i) why increasing depth till a point helps in training and ii) why increasing depth beyond a point hurts training? We also present experimental evidence to show that gate adaptation, i.e., the change of gating value through the course of training is key for generalisation.

"Read enough so you start developing intuitions and then trust your intuitions and go for it!" If you find a course that fits in any of the above categories (i.e. DL, ML, RL, CV, NLP), and the course has lecture videos (with slides being optional), then please raise an issue or send a PR by updating the course according to the above format.

There is a great deal of speculation whether or not artificial intelligence and machine learning will take over the job market. Artificial intelligence will be able to automate jobs that many people thought could only be done by humans. However, there will be many new jobs created by artificial intelligence, machine learning and deep learning. In case you aren't aware machine learning is a subset of artificial intelligence and deep learning is a subset of machine learning. Companies are investing millions upon millions of dollars in artificial intelligence.

Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I'll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.

In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning. Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning. Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays. But still, there is a big misconception among many people about the meaning of these terms. In the worst case, one may think that these terms describe the same thing -- which is simply false.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. This week, Google introduced Meena, a chatbot that can "chat about… anything." Meena is the latest of many efforts by large tech companies trying to solve one of the toughest challenges of artificial intelligence: language. "Current open-domain chatbots have a critical flaw -- they often don't make sense. They sometimes say things that are inconsistent with what has been said so far, or lack common sense and basic knowledge about the world," Google's researcher wrote in a blog post.