Artificial intelligence is now increasingly present in corporate and government decision-making. And although AI tools are still largely in the hands of institutions that focus on profit before purpose, these new technologies could be equally powerful in promoting social good. A joint effort by MIT Solve and the Patrick J. McGovern Foundation shows how AI applications can be used to extend prosperity to economically marginalized groups. Already, entrepreneurs are exploring how AI can be used to address some of the world's thorniest challenges in thoughtful, creative and previously impossible ways. AI is most exciting when it can both absorb large amounts of data and identify more accurate correlations (diagnostics), while leaving the causational conclusions and ultimate decision-making to humans.

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 17/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.

Human communication often involves the use of verbal irony or sarcasm, where the speakers usually mean the opposite of what they say. To better understand how verbal irony is expressed by the speaker and interpreted by the hearer we conduct a crowdsourcing task: given an utterance expressing verbal irony, users are asked to verbalize their interpretation of the speaker's ironic message. We propose a typology of linguistic strategies for verbal irony interpretation and link it to various theoretical linguistic frameworks. We design computational models to capture these strategies and present empirical studies aimed to answer three questions: (1) what is the distribution of linguistic strategies used by hearers to interpret ironic messages?; (2) do hearers adopt similar strategies for interpreting the speaker's ironic intent?; and (3) does the type of semantic incongruity in the ironic message (explicit vs. implicit) influence the choice of interpretation strategies by the hearers?

Artificial Intelligence (AI) In Fintech Market report provides the past, present and future industry trends and the forecast information related to the expected Artificial Intelligence (AI) In Fintech Market sales revenue, growth, demand, and supply scenario. It offers in-depth data, improves variations of the worldwide Artificial Intelligence (AI) In Fintech Market to help you in deciding the final strategy. It features far-reaching information in terms of changing market dynamics, manufacturing trends, structural changes in the market, and the latest developments. Market Overview: The report begins with this section where product overview and highlights of product and application segments of the global Artificial Intelligence (AI) In Fintech Market are provided. Highlights of the segmentation study include price, revenue, sales, sales growth rate, and market share by product.

Miguel Santiago, a senior at Baruch College in Manhattan, is graduating soon and already considering his next move -- maybe to a job at Goldman Sachs or somewhere else in banking. In at least six of his interviews, he's been questioned by a computer and not a live person. "They've basically replaced the first round with the HireVue," he said, referring to the video and artificial intelligence platform increasingly being used by employers for job interviews. When a candidate applies to a job at a company that uses HireVue, they are asked to go on to the platform, allow use of their webcam and respond to interview questions on video. The candidate's answers are recorded and then saved to the platform.

Artificial intelligence is a technology that is progressing so rapidly that it's likely that scientists don't even yet know its full potential for impacting our lives. One of the areas that looks likely to gain the most from the power of AI, however, is education. AI has become such a phenomenon in the technology world that colleges, universities, and other institutions have established educational programs surrounding it so that people can learn about it and how to use it to better their lives. It's believed that artificial intelligence will impact education at both the K-12 level and in higher ed. It will impact both teachers and students and it could cause a whole lot of disruption in classrooms, making artificial intelligence something that educational leaders need to really start thinking about.

A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, i.e., the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram.

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.