This robot is not exactly in the cloud: Wall-E from the movie by Pixar. Bringing a new robot to market is exciting: new capability, new hardware, new services. The problem is when you get to software, where everything feels harder and takes longer than you think it should. Like Tesla's full self-driving, which has all the hardware and intelligence it needs -- with the possible exception of LIDAR -- but is perpetually just ... about ... to ... arrive ... and even so, was recently savaged by Consumer Reports as buggy and ineffective. Hardware is necessary, but software provides the animating intelligence that allows it to do useful, efficient, and safe work.

Looks like an interesting tool but without traditional wet-lab experiments why should chemists believe your suggestions? While a good chemist can often come up with a reasonable explanation why a change helps after seeing changes in assay results (e.g. If you want to try get a compound made and tested perhaps contact the team at Covid Moonshot and see if there's a feasible use case. My PI (one of the leads) says they're always interested in new candidates/triage strategies. Another issue might be that the synthetic accessibility of your candidates after large numbers of augmentation steps may become low.

Do you know how your apps work? Are you aware of what tech companies are doing in the back with your data? And what's more revealing: do you know which of your action are actually influenced by those apps? When you take a trip with Uber, buy stuff on Amazon, or watch a movie on Netflix: when are you consciously deciding and when are you being heavily influenced? Tech companies are not passively observing your behavior and acting as a consequence: they are influencing your behavior, so you become more predictable.

A visual relationship denotes a relationship between two objects in an image, which can be represented as a triplet of (subject; predicate; object). Visual relationship detection is crucial for scene understanding in images. Existing visual relationship detection datasets only contain true relationships that correctly describe the content in an image. However, distinguishing false visual relationships from true ones is also crucial for image understanding and grounded natural language processing. In this paper, we construct a visual relationship authenticity dataset, where both true and false relationships among all objects appeared in the captions in the Flickr30k entities image caption dataset are annotated. The dataset is available at https://github.com/codecreator2053/VR_ClassifiedDataset. We hope that this dataset can promote the study on both vision and language understanding.

Fact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such as FEVER. However, the challenging problem of fake news detection has not benefited from the improvement of fact verification models, which is closely related to fake news detection. In this paper, we propose a simple yet effective approach to connect the dots between fact verification and fake news detection. Our approach first employs a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. Then we use a fact verification model pre-trained on the FEVER dataset to detect whether the input news article is real or fake. Our approach makes use of the recent success of fact verification models and enables zero-shot fake news detection, alleviating the need of large-scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demonstrate the effectiveness of our proposed approach.

Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.

During the last decade, deep neural networks (DNN) have demonstrated impressive performances solving a wide range of problems in various domains such as medicine, finance, law, etc. Despite their great performances, they have long been considered as black-box systems, providing good results without being able to explain them. However, the inability to explain a system decision presents a serious risk in critical domains such as medicine where people's lives are at stake. Several works have been done to uncover the inner reasoning of deep neural networks. Saliency methods explain model decisions by assigning weights to input features that reflect their contribution to the classifier decision. However, not all features are necessary to explain a model decision. In practice, classifiers might strongly rely on a subset of features that might be sufficient to explain a particular decision. The aim of this article is to propose a method to simplify the prediction explanation of One-Dimensional (1D) Convolutional Neural Networks (CNN) by identifying sufficient and necessary features-sets. We also propose an adaptation of Layer-wise Relevance Propagation for 1D-CNN. Experiments carried out on multiple datasets show that the distribution of relevance among features is similar to that obtained with a well known state of the art model. Moreover, the sufficient and necessary features extracted perceptually appear convincing to humans.

The new FOX sci-fi show NeXt showcased a new trailer created completely by artificial intelligence. To showcase the use of artificial intelligence ahead of the premiere of the new sci-fi crime drama, "NeXt," FOX tapped space150 to create the latest trailer using only AI. Space150's engineering team used AI to create, write and edit the newest trailer with the assistance of a machine-learning algorithm used to observe the entire series and select key themes, scenes and dialogue. Facial recognition was used to analyze emotion for every frame of the show, building up an "emotion index" for scenes. Color, luminosity, contrast and sound were also observed for additional data points on each scene.

This story appeared in the November 2020 issue as "Cracking the 414s." In the 1983 techno-thriller WarGames, David Lightman, played by a fresh-faced Matthew Broderick, sits in his bedroom, plunking away on a boxy computer using an 8-bit Intel processor. As text flashes across the screen, David's face lights up; he believes he's hacking into a video game company, but the unwitting teenager is actually facing off against a military supercomputer. "Shall we play a game?" the computer asks ominously. In the film, the subsequent showdown triggers a countdown to World War III.

Songwriter's block can be a problem for even the world's most successful musicians. They can sometimes overcome it by taking breaks, seeking new forms of inspiration, or simply pushing through. And if none of that works, they could try out a new AI lyrics generator called keyword2lyrics. Sometimes I have a few ideas that I want to turn into a song, but I'm too lazy for that, so I thought it would be cool to make a program that generates lyrics from isolated keywords or phrases. Gatthi developed the tool by training OpenAI's GPT-2 language model on songs that Google lists when you search for "top artists 20th century" and "top artists 21st century," and extracted keywords from them using a tool called yake.