"The new appointments reflect growth in core areas of Data Science such as Machine Learning, Mobile Health, Privacy, and Statistics but also …

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

At NBCUniversal, we believe in the talent of our people. It's our passion and commitment to excellence that drives NBCU's vast portfolio of brands to succeed. From broadcast and cable networks, news and sports platforms, to film, world-renowned theme parks and a diverse suite of digital properties, we take pride in all that we do and all that we represent. It's what makes us uniquely NBCU. Here you can create the extraordinary.

"AI image generators use two neural networks. The first neural network creates an image while the second judges how close to the real thing the image is, based on real-life examples from the internet. Once scoring the image for accuracy is complete, the data is sent back to the original AI system. That system then learns from the feedback and sends back an altered image for further scoring until the AI-generated image matches the control/template image. "We recognize that work involving generative models has the potential for significant, broad societal impacts. In the future, we plan to analyze how models like DALL·E relate to societal issues like economic impact on certain work processes and professions, the potential for bias in the model outputs, and the longer term ethical challenges implied by this technology."-openai.com AI-generated image results are made from a collection of images it has no right to use. It does not create as artists do. Artists did not opt-in their work for this. AI is sourcing from portfolio sites like Behance, Art Station, Deviantart, Dribbble, and Pinterest without the original author's consent. The text below is taken from a now-suspended Kickstarter by Unstable Diffusion. The 2nd paragraph is especially telling. It's as much a tool as a robotic arm is on an assembly line. It's not meant for artists but as a replacement for artists. AI companies want amateurs to produce artwork without the need for further editing. It is marketed toward amateurs with the promise that they can create art without being an artist. Making good art is harder still. It is the very antithesis of what AI companies are claiming to stand for. And as it stands today, illegal and unethical. Why are they doing this? To unleash your creative power? If you believe that, I have some NFTs to sell you. "Our hope is that DALL·E 2 will empower people to express themselves creatively.

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I$^2$MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental leaning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the ontological schema of KG. Extensive evaluation on multiple benchmarks has shown the effectiveness of techniques involved in RMPI and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes and data are available at https://github.com/zjukg/RMPI.

6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficiency in dense deployments of such subnetworks. However, existing solutions for centralized power control may require full channel state information (CSI) of all the desired and interfering links, which may be cumbersome and time-consuming to obtain in dense deployments. This paper presents a novel solution for centralized power control for industrial subnetworks based on Graph Neural Networks (GNNs). The proposed method only requires the subnetwork positioning information, usually known at the central controller, and the knowledge of the desired link channel gain during the execution phase. Simulation results show that our solution achieves similar spectral efficiency as the benchmark schemes requiring full CSI in runtime operations. Also, robustness to changes in the deployment density and environment characteristics with respect to the training phase is verified.

With 2022 almost over it's almost time to ring in a new year of movies and one of the biggest projects being released at the beginning of 2023 will be M3gan. M3gan is the story of a robotic doll with artificial intelligence that serves as a protector to the child that owns her, but things go haywire and she begins killing. With the film releasing right around the corner we're beginning to see more and more from M3gan and now we have a brand new clip that shows off the doll horrific forest kill. You can check out the clip below! There have been rumblings that Anabelle and M3gan could go toe to toe in a battle of the dolls, but nothing has been official.

He spends most of his time working on projects aimed at harnessing the power of machine learning.

A novel artificial intelligence-based tool predicted treatment failure with … Their next study showed that a deep learning approach using 2D MRI …