In the US, today is Inauguration Day, and as Joe Biden prepares to take the oath as our 46th president, it's worth taking a look back at the discussions four years ago. Back then, the "most tech-savvy" president exited as all eyes turned to Donald Trump trading in his Android Twitter machine for a secure device. We know how things went after that. Donald Trump isn't tweeting anymore (at least not from his main accounts), and the country is struggling through a pandemic. The outgoing president just saw his temporary YouTube ban extended and, in one of his last official acts, pardoned Anthony Levandowski for stealing self-driving car secrets from Google's subsidiary Waymo.

In the movie All The President's Men, Woodward and Bernstein meet their informant in a parking garage who tells them: "Follow the money." If you want to know which technologies are hot in banking, you should do the same. The truly "hot" technologies in banking are the ones that financial institutions invest in--not necessarily the ones the pundits talk about. At the end of the past seven years, Cornerstone Advisors has surveyed financial institutions to find out where their technology dollars will go in the coming year. In Cornerstone's What's Going On in Banking 2021 study, the top five technologies for 2021 are: 1) Digital account opening; 2) Application programming interfaces (APIs); 3) Video collaboration; 4) P2P payments; and 5) Cloud computing.

"As you identify more and more examples of covert payment the AI learns on the fly. That's the beauty and the magic of AI," says Mr Mason. A scoring system was set up, with points added for certain attributes. Any score above a certain number was deemed worthy of further investigation. The machine-learning technology became better and better as it progressed.

The human ability of deep cognitive skills are crucial for the development of various real-world applications that process diverse and abundant user generated input. While recent progress of deep learning and natural language processing have enabled learning system to reach human performance on some benchmarks requiring shallow semantics, such human ability still remains challenging for even modern contextual embedding models, as pointed out by many recent studies. Existing machine comprehension datasets assume sentence-level input, lack of casual or motivational inferences, or could be answered with question-answer bias. Here, we present a challenging novel task, trope detection on films, in an effort to create a situation and behavior understanding for machines. Tropes are storytelling devices that are frequently used as ingredients in recipes for creative works. Comparing to existing movie tag prediction tasks, tropes are more sophisticated as they can vary widely, from a moral concept to a series of circumstances, and embedded with motivations and cause-and-effects. We introduce a new dataset, Tropes in Movie Synopses (TiMoS), with 5623 movie synopses and 95 different tropes collecting from a Wikipedia-style database, TVTropes. We present a multi-stream comprehension network (MulCom) leveraging multi-level attention of words, sentences, and role relations. Experimental result demonstrates that modern models including BERT contextual embedding, movie tag prediction systems, and relational networks, perform at most 37% of human performance (23.97/64.87) in terms of F1 score. Our MulCom outperforms all modern baselines, by 1.5 to 5.0 F1 score and 1.5 to 3.0 mean of average precision (mAP) score. We also provide a detailed analysis and human evaluation to pave ways for future research.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Dating is about to get political again. After briefly disabling the feature, Bumble is reportedly allowing users to once again filter matches based on their political stance. This option was temporarily disabled following the riot at the U.S. Capitol "to prevent misuse," Bumble previously said.

If you're listening to music right now, chances are you didn't choose what to put on--you outsourced it to an algorithm. Such is the popularity of recommendation systems that we've come to rely on them to serve us what we want without us even having to ask, with music streaming services such as Spotify, Pandora, and Deezer all using personalized systems to suggest playlists or tracks tailored to the user. This story originally appeared on WIRED UK. Generally, these systems are very good. The problem, for some, is that they're perhaps really too good.

Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in local appearance. The results show that this is a brittle approach since it relies on accurate body parts/objects detection. In this work, we argue that there exist local discriminative semantic regions, whose "informativeness" can be evaluated by the attention mechanism for inferring fine-grained gestures/actions. To this end, we propose a novel end-to-end \textbf{Regional Attention Network (RAN)}, which is a fully Convolutional Neural Network (CNN) to combine multiple contextual regions through attention mechanism, focusing on parts of the images that are most relevant to a given task. Our regions consist of one or more consecutive cells and are adapted from the strategies used in computing HOG (Histogram of Oriented Gradient) descriptor. The model is extensively evaluated on ten datasets belonging to 3 different scenarios: 1) head pose recognition, 2) drivers state recognition, and 3) human action and facial expression recognition. The proposed approach outperforms the state-of-the-art by a considerable margin in different metrics.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Drew Barrymore is among the legion of fans who have been wrapped up in Netflix's latest hit, "Bridgerton." The "50 First Dates" and "Ever After" star invited Phoebe Dynevor and Regé-Jean Page on to her talk show to discuss the period drama. During their appearance on Friday, Barrymore revealed that the show's steamier scenes inspired the 45-year-old to try her hand once again at dating apps.

Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current work in image simulation either fail to be photorealistic or do not model the 3D environment and the dynamic objects within, losing high-level control and physical realism. In this paper, we present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses. Towards this goal, we first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. During simulation, we perform a novel geometry-aware simulation-by-composition procedure which 1) proposes plausible and realistic object placements into a given scene, 2) renders novel views of dynamic objects from the asset bank, and 3) composes and blends the rendered image segments. The resulting synthetic images are photorealistic, traffic-aware, and geometrically consistent, allowing image simulation to scale to complex use cases. We demonstrate two such important applications: long-range realistic video simulation across multiple camera sensors, and synthetic data generation for data augmentation on downstream segmentation tasks.

Despite the recent attention to DeepFakes and other forms of image manipulations, one of the most prevalent ways to mislead audiences is the use of unaltered images in a new but false context. To address these challenges and support fact-checkers, we propose a new method that automatically detects out-of-context image and text pairs. Our core idea is a self-supervised training strategy where we only need images with matching (and non-matching) captions from different sources. At train time, our method learns to selectively align individual objects in an image with textual claims, without explicit supervision. At test time, we check for a given text pair if both texts correspond to same object(s) in the image but semantically convey different descriptions, which allows us to make fairly accurate out-of-context predictions. Our method achieves 82% out-of-context detection accuracy. To facilitate training our method, we created a large-scale dataset of 203,570 images which we match with 456,305 textual captions from a variety of news websites, blogs, and social media posts; i.e., for each image, we obtained several captions.