With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world. In this work, we present a new evaluation framework entitled Attributable to Identified Sources (AIS) for assessing the output of natural language generation models, when such output pertains to the external world. We first define AIS and introduce a two-stage annotation pipeline for allowing annotators to appropriately evaluate model output according to AIS guidelines. We empirically validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset) via human evaluation studies that suggest that AIS could serve as a common framework for measuring whether model-generated statements are supported by underlying sources. We release guidelines for the human evaluation studies.

Recent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text prompts. Such text-based synthesis methods are particularly appealing to humans who are used to verbally describe their intent. Therefore, it is only natural to extend the text-driven image synthesis to text-driven image editing. Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome. State-of-the-art methods mitigate this by requiring the users to provide a spatial mask to localize the edit, hence, ignoring the original structure and content within the masked region. In this paper, we pursue an intuitive prompt-to-prompt editing framework, where the edits are controlled by text only. To this end, we analyze a text-conditioned model in depth and observe that the cross-attention layers are the key to controlling the relation between the spatial layout of the image to each word in the prompt. With this observation, we present several applications which monitor the image synthesis by editing the textual prompt only. This includes localized editing by replacing a word, global editing by adding a specification, and even delicately controlling the extent to which a word is reflected in the image. We present our results over diverse images and prompts, demonstrating high-quality synthesis and fidelity to the edited prompts.

Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval. Nevertheless, existing algorithms to compute NTD are mostly designed for the Euclidean loss. This work proposes a multiplicative updates algorithm to compute NTD with the beta-divergence loss, often considered a better loss for audio processing. We notably show how to implement efficiently the multiplicative rules using tensor algebra. Finally, we show on a music structure analysis task that unsupervised NTD fitted with beta-divergence loss outperforms earlier results obtained with the Euclidean loss.

The Houma Alliance Book is one of the national treasures of the Museum in Shanxi Museum Town in China. It has great historical significance in researching ancient history. To date, the research on the Houma Alliance Book has been staying in the identification of paper documents, which is inefficient to identify and difficult to display, study and publicize. Therefore, the digitization of the recognized ancient characters of Houma League can effectively improve the efficiency of recognizing ancient characters and provide more reliable technical support and text data. This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters. In the database, 297 classes and 3,547 samples of Houma Alliance ancient handwritten characters are collected from the original book collection and by human imitative writing. Furthermore, the decision-level classifier fusion strategy is applied to fuse three well-known deep neural network architectures for ancient handwritten character recognition. Experiments are performed on our new database. The experimental results first provide the baseline result of the new database to the research community and then demonstrate the efficiency of our proposed method.

India's tigers roam across vast areas, so artificial intelligence is being developed to help track them.

AI or Artificial Intelligence has been around for a while, but after recent advancements and developments, it's rapidly becoming a part of our daily lives. Not just that, it's being said that AI is likely to eliminate almost half of the present jobs by 2025. Well, in this article, we will try to explain it by discussing 5 unbelievable tasks that today's AI can perform. Today's AI (SummarizeBot) can read articles, emails, documents, audio, images, web links, E-books, and much more. Not only that, but it can also report back and highlight the most essential information from a given material.

There's a perfectly good reason to break open the secrets of social-media giants. Over the past decade, governments have watched helplessly as their democratic processes were disrupted by misinformation and hate speech on sites like Meta Platforms Inc.'s Facebook, Alphabet Inc.'s YouTube and Twitter Inc. Now some governments are gearing up for a comeuppance. In the next two years, Europe and the UK are preparing laws that will rein in the troublesome content that social-media firms have allowed to go viral. There has been much skepticism over their ability to look under the hood of companies like Facebook.

While artificial intelligence movies aren't as popular as comedies or thrillers, they still rank well. And I feel confident you'll recognize at least one movie from this list. The article covers the top eleven artificial intelligence movies to watch right now, spanning everything from timeless classics to breakthrough titles that have redefined the genre. You'll also find the IMDb rating for each one, helping you judge if a movie's for you. Let's see which AI movies are worth a watch in 2022.

The latest report titled Global Artificial Intelligence-Emotion Recognition Market covering industry growth rate, competitive landscape, …

In a world where seeing is increasingly no longer believing, experts are warning that society must take a multi-pronged approach to combat the potential harms of computer-generated media. As Bill Whitaker reports this week on 60 Minutes, artificial intelligence can manipulate faces and voices to make it look like someone said something they never said. The result is videos of things that never happened, called "deepfakes." Often, they look so real, people watching can't tell. Even Justin Bieber has been tricked by a series of deepfake videos on the social media video platform TikTok that appeared to be of Tom Cruise.