Collaborating Authors


Deep Learning for Air Quality Prediction


Traditional neural networks can't do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It's unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

Over a Decade of Social Opinion Mining Artificial Intelligence

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

Artificial Intelligence Explained in Simple Terms


There are many great articles about Artificial Intelligence (AI) and its benefits for business and society. However, many of these articles are too technical for the average reader. I love reading about AI, but I sometimes think to myself, 'Gee, I wish the author had explained this in simple English.' I will try and explain AI and its related technologies in simple terms, using real-life examples, as though I were talking to someone at a party. Your colleagues or your (close) friends may tolerate your endless and complex ramblings, but I guarantee you that people at parties are far less forgiving.

Lookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images Artificial Intelligence

Autoencoders are a class of artificial neural networks which have gained a lot of attention in the recent past. Using the encoder block of an autoencoder the input image can be compressed into a meaningful representation. Then a decoder is employed to reconstruct the compressed representation back to a version which looks like the input image. It has plenty of applications in the field of data compression and denoising. Another version of Autoencoders (AE) exist, called Variational AE (VAE) which acts as a generative model like GAN. Recently, an optimizer was introduced which is known as lookahead optimizer which significantly enhances the performances of Adam as well as SGD. In this paper, we implement Convolutional Autoencoders (CAE) and Convolutional Variational Autoencoders (CVAE) with lookahead optimizer (with Adam) and compare them with the Adam (only) optimizer counterparts. For this purpose, we have used a movie dataset comprising of natural images for the former case and CIFAR100 for the latter case. We show that lookahead optimizer (with Adam) improves the performance of CAEs for reconstruction of natural images.

Gunrock 2.0: A User Adaptive Social Conversational System Artificial Intelligence

Gunrock 2.0 is built on top of Gunrock with an emphasis on user adaptation. Gunrock 2.0 combines various neural natural language understanding modules, including named entity detection, linking, and dialog act prediction, to improve user understanding. Its dialog management is a hierarchical model that handles various topics, such as movies, music, and sports. The system-level dialog manager can handle question detection, acknowledgment, error handling, and additional functions, making downstream modules much easier to design and implement. The dialog manager also adapts its topic selection to accommodate different users' profile information, such as inferred gender and personality. The generation model is a mix of templates and neural generation models. Gunrock 2.0 is able to achieve an average rating of 3.73 at its latest build from May 29th to June 4th.

Text Classification with RNN


RNN is a famous supervised Deep Learning methodology. Other commonly used Deep Learning neural networks are Convolutional Neural Networks and Artificial Neural Networks. The main goal behind Deep Learning is to reiterate the functioning of a brain by a machine. ANN stores data for a long time, so does the Temporal lobe. So it is linked with the Temporal Lobe.

Video SemNet: Memory-Augmented Video Semantic Network Artificial Intelligence

Stories are a very compelling medium to convey ideas, experiences, social and cultural values. Narrative is a specific manifestation of the story that turns it into knowledge for the audience. In this paper, we propose a machine learning approach to capture the narrative elements in movies by bridging the gap between the low-level data representations and semantic aspects of the visual medium. We present a Memory-Augmented Video Semantic Network, called Video SemNet, to encode the semantic descriptors and learn an embedding for the video. The model employs two main components: (i) a neural semantic learner that learns latent embeddings of semantic descriptors and (ii) a memory module that retains and memorizes specific semantic patterns from the video. We evaluate the video representations obtained from variants of our model on two tasks: (a) genre prediction and (b) IMDB Rating prediction. We demonstrate that our model is able to predict genres and IMDB ratings with a weighted F-1 score of 0.72 and 0.63 respectively. The results are indicative of the representational power of our model and the ability of such representations to measure audience engagement.

Create your first Text Generator with LSTM in few minutes


What if I tell you that an entire short sci-fi film has been written by an AI bot built on LSTM recurrent neural network, and it has even received positive reviews and critics, Surprised?! well, I'm sure you are because that's what I felt watching "Sunspring" for the first time, I mean I know it can't be compared to Steven Spielberg's or Alex Garland's screenwriting quality but no wonder if in the next few years AI bots will compete against them in the Academy Awards. Indeed, we should no longer be surprised by what artificial intelligence is capable of in order to flip our world upside down, making it a better, "easier", and most comfortable place to live in. From all of the AI subfields, in my opinion, NLP has the coolest and most exciting applications. One of them is text generation that we should have a deep look at it. In this article, I will briefly explain how RNN and LSTM work and how we can generate texts using LSTM in Python.

Fact Checking via Path Embedding and Aggregation Artificial Intelligence

Knowledge graphs (KGs) are a useful source of background knowledge to (dis)prove facts of the form (s, p, o). Finding paths between s and o is the cornerstone of several fact-checking approaches. While paths are useful to (visually) explain why a given fact is true or false, it is not completely clear how to identify paths that are most relevant to a fact, encode them and weigh their importance. The goal of this paper is to present the Fact Checking via path Embedding and Aggregation (FEA) system. FEA starts by carefully collecting the paths between s and o that are most semantically related to the domain of p. However, instead of directly working with this subset of all paths, it learns vectorized path representations, aggregates them according to different strategies, and use them to finally (dis)prove a fact. We conducted a large set of experiments on a variety of KGs and found that our hybrid solution brings some benefits in terms of performance.

Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems Artificial Intelligence

Goal-oriented dialog systems enable users to complete specific goals like requesting information about a movie or booking a ticket. Typically the dialog system pipeline contains multiple ML models, including natural language understanding, state tracking and action prediction (policy learning). These models are trained through a combination of supervised or reinforcement learning methods and therefore require collection of labeled domain specific datasets. However, collecting annotated datasets with language and dialog-flow variations is expensive, time-consuming and scales poorly due to human involvement. In this paper, we propose an approach for automatically creating a large corpus of annotated dialogs from a few thoroughly annotated sample dialogs and the dialog schema. Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal. We validate our approach by generating data and training three different downstream conversational ML models. We achieve 18 ? 50% relative accuracy improvements on a held-out test set compared to a baseline dialog generation approach that only samples natural language and entity value variations from existing catalogs but does not generate any novel dialog flow variations. We also qualitatively establish that the proposed approach is better than the baseline. Moreover, several different conversational experiences have been built using this method, which enables customers to have a wide variety of conversations with Alexa.