"Global Artificial Intelligence in Chatbot Security Market Size, Status and Forecast 2019-2026"offers a primary overview of the Artificial Intelligence in …

Amazon Web Services debuted a keyboard called DeepComposer this week, claiming it's "the world's first musical keyboard powered by generative AI." It has 32 keys, costs $99, and connects to a software interface that uses machine learning and cloud computing to generate music based on what you play. It's been unclear who this is for, and many have latched on to the fact that the music it creates just sounds bad. It looks like a consumer product, and Amazon used an over-the-top presentation to hype it, which included what AWS claimed was "the first hybrid AI human pop acoustic collaboration." But actually, the keyboard is intended to be a beginning tool for developers to get into machine learning and music.

Year 2020 does have a sci-fi ring to it. Perhaps that's one reason AI has so many people nervous. From your smart phone to your Google search to your Netflix and Spotify recommendations, AI shapes modern US work and life in many ways. Despite its growing ubiquity, fear of AI remains. The 2019 Artificial Intelligence--American Attitudes and Trends report from the Future of Humanity Institute at the University of Oxford found that "more Americans think that high-level machine intelligence will be harmful to humanity" than those who think it will be beneficial.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

Once the model is loaded, you can extract its input and output schema. The schemas are displayed for interest and learning only. The input schema is the fixed-length array of integer encoded words. The output schema is a float array of probabilities indicating whether a review's sentiment is negative, or positive . These values sum to 1, as the probability of being positive is the complement of the probability of the sentiment being negative.

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.

The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This quickly shifting panorama of technologies and applications is challenging for builders and practitioners alike to follow. In this paper, we set out to capture this panorama through a wide-angle lens, by performing the largest analysis of DS projects to date, focusing on questions that can help determine investments on either side. Specifically, we download and analyze: (a) over 6M Python notebooks publicly available on GITHUB, (b) over 2M enterprise DS pipelines developed within COMPANYX, and (c) the source code and metadata of over 900 releases from 12 important DS libraries. The analysis we perform ranges from coarse-grained statistical characterizations to analysis of library imports, pipelines, and comparative studies across datasets and time. We report a large number of measurements for our readers to interpret, and dare to draw a few (actionable, yet subjective) conclusions on (a) what systems builders should focus on to better serve practitioners, and (b) what technologies should practitioners bet on given current trends. We plan to automate this analysis and release associated tools and results periodically.

We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user's training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature.

The tool uses deep-learning algorithms: a type of machine learning algorithm which processes data through successive layers to extract increasingly meaningful and complex information. This algorithm – which the researchers were motivated to create by the proliferation of politically motivated viral deception online – determines whether claims made in news stories or social media posts are supported by other content on the same subject. "If they are: great, it's probably a real story," said Professor Alexander Wong, a systems design engineering expert at the University of Waterloo. The algorithm was trained with tens of thousands of claims paired with stories that either supported or rejected them. The researchers tested their system using a dataset created for the 2017 Fake News Challenge.