Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaus- sian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many authors have so far con- centrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial probit estimation by using an adaptation of the diagonal orthant multinomial probit in the topic models context, resulting in the ability of our topic modelling scheme to handle corpuses with a large number of latent topics. An additional and very important benefit of our method lies in the fact that unlike with the logistic normal model whose non-conjugacy leads to the need for sophisticated sampling schemes, our ap- proach exploits the natural conjugacy inherent in the auxiliary formulation of the probit model to achieve greater simplicity. The application of our proposed scheme to a well known Associated Press corpus not only helps discover a large number of meaningful topics but also reveals the capturing of compellingly intuitive correlations among certain topics. Besides, our proposed approach lends itself to even further scalability thanks to various existing high performance algorithms and architectures capable of handling millions of documents.

Since 2019 the FDA's crosscutting work has implemented artificial intelligence (AI) as part of the its New Era of Smarter Food Safety initiative. This new application of available data sources can strengthen the agency's public health mission with the goal using AI to improve capabilities to quickly and efficiently identify products that may pose a threat to public health by impeding their entry into the U.S. market. On February 8 the FDA reported the initiation of their succeeding phase for AI activity with the Imported Seafood Pilot program. Running from February 1 through July 31, 2021, the pilot will allow FDA to study and evaluate the utility of AI in support of import targeting, ultimately assisting with the implementation of an AI model to target high-risk seafood products--a critical strategy, as the United States imports nearly 94% of its seafood, according to the FDA. Where in the past, reliance on human intervention and/or trend analysis drove scrutiny of seafood shipments such as field exams, label exams or laboratory analysis of samples, with the use of AI technologies, FDA surveillance and regulatory efforts might be improved.

The use of artificial intelligence (AI) in life sciences, or "Life Tech", has increased at a rapid pace. According to World Intellectual Property Organization (WIPO), there has been "a shift from theoretical research to the use of AI technologies in commercial products and services," as reflected in the change in ratio of scientific papers to patent applications over the past decade.1 Indeed, while research into AI began in earnest in the 1950s, more than 1.6 million scientific papers have been published on AI, with more than half of identified AI inventions in the last six years alone.2,3 A review article in Nature Medicine reported last year that despite few peer-reviewed publications on use of machine learning technologies in medical devices, FDA approvals of AI as medical devices have been accelerating.4 Many of these FDA approvals relate to image analysis for diagnostic purposes, such as QuantX, the first AI platform to evaluate breast abnormalities; Aidoc, which detects acute intracranial hemorrhages in head CT scans, assisting radiologists to prioritize patient injuries; and IDx-DR, which analyzes retinal images to detect diabetic retinopathy.

The developing regions of the world contain most of the human population and the planet's natural resources, and hence are particularly important to the study of sustainability. Despite some difficult problems in such places, a period of enormous technology-driven change has created new opportunities to address poor management of resources and improve human well-being.

Policy making is an extremely complex process occurring in changing environments and affecting the three pillars of sustainable development: society, economy and the environment. Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life-Cycle, and we identify some potential research challenges.

Participants Intelligence (AAAI-15) and the Twenty-Seventh Conference in the AAAI-15 Robotics Exhibition and the on Innovative Applications of Artificial Intelligence AAAI-15 Video Competition are encouraged to contribute (IAAI-15) will be held January 25-29 at the to the Demonstration Program with their systems, Hyatt Regency Austin in Austin, Texas, USA. AAAI is working October 8 (Papers Due) closely with the local AI community to create opportunities The Senior Member Track provides an opportunity for attendees to experience AI in Texas! Attendees for established researchers in the AI community to can also enjoy nearly 200 music venues that feature give a broad talk on a well-developed body of everything from rock and blues to country and research, an important new research area, or a promising jazz every night of the week. Austin cuisine has new topic. This year, new "Blue Sky Ideas" track expanded from barbecue and Tex-Mex to award-winning is seeking presentations aimed at presenting ideas and inventive international cuisine, and blossomed and visions that can stimulate the research community beyond brick-and-mortar restaurants to a to pursue new directions, such as new problems, vibrant, citywide food truck movement.

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2014 Spring Symposium Series, held Monday through Wednesday, March 24–26, 2014. The titles of the eight symposia were Applied Computational Game Theory, Big Data Becomes Personal: Knowledge into Meaning, Formal Verification and Modeling in Human-Machine Systems, Implementing Selves with Safe Motivational Systems and Self-Improvement, The Intersection of Robust Intelligence and Trust in Autonomous Systems, Knowledge Representation and Reasoning in Robotics, Qualitative Representations for Robots, and Social Hacking and Cognitive Security on the Internet and New Media). This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine.

We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.