Deepfake is a deep learning-based technique that makes it easy to change or modify images and videos. In investigations and court, visual evidence is commonly employed, but these pieces of evidence may now be suspect due to technological advancements in deepfake. Deepfakes have been used to blackmail individuals, plan terrorist attacks, disseminate false information, defame individuals, and foment political turmoil. This study describes the history of deepfake, its development and detection, and the challenges based on physiological measurements such as eyebrow recognition, eye blinking detection, eye movement detection, ear and mouth detection, and heartbeat detection. The study also proposes a scope in this field and compares the different biological features and their classifiers. Deepfakes are created using the generative adversarial network (GANs) model, and were once easy to detect by humans due to visible artifacts. However, as technology has advanced, deepfakes have become highly indistinguishable from natural images, making it important to review detection methods.

A myriad of studies addressed the problem of rumor verification in Twitter by either utilizing evidence from the propagation networks or external evidence from the Web. However, none of these studies exploited evidence from trusted authorities. In this paper, we define the task of detecting the stance of authorities towards rumors in tweets, i.e., whether a tweet from an authority agrees, disagrees, or is unrelated to the rumor. We believe the task is useful to augment the sources of evidence utilized by existing rumor verification systems. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. Due to the relatively limited size of our dataset, we study the usefulness of existing datasets for stance detection in our task. We show that existing datasets are somewhat useful for the task; however, they are clearly insufficient, which motivates the need to augment them with annotated data constituting stance of authorities from Twitter.

Gro Intelligence is tackling two of the biggest problems facing the world today: food security and climate change. We understand and quantify the complex interplay between food, weather, trade, agriculture, and macroeconomic conditions in a world upended by climate change, a growing population, and more. The team at Gro has built a platform that allows businesses, non-profits, and governments to better plan for and adapt to these changes. With offices in Nairobi, New York, and Singapore Gro has the financial backing of prominent investors such as TPG Growth, Intel Capital, Data Collective, and GGV. Gro is a diverse, intellectually curious team of technologists, scientists, and business professionals united by a shared commitment to build AI that addresses agriculture, food, and our climate on the most fundamental level.

Gro Intelligence is tackling two of the biggest problems facing the world today: food security and climate change. We understand and quantify the complex interplay between food, weather, trade, agriculture, and macroeconomic conditions in a world upended by climate change, a growing population, and more. The team at Gro has built a platform that allows businesses, non-profits, and governments to better plan for and adapt to these changes. With offices in Nairobi, New York, and Singapore Gro has the financial backing of prominent investors such as TPG Growth, Intel Capital, Data Collective, and GGV. Gro is a diverse, intellectually curious team of technologists, scientists, and business professionals united by a shared commitment to build AI that addresses agriculture, food, and our climate on the most fundamental level.

Korea plans to have 30 percent of local companies adopt artificial intelligence (AI) technologies by 2030 to beef up competitiveness of local industries, the industry ministry said Friday.

The ocean can be a dangerous place, even for a shark. Despite sitting at the top of the food chain, these predators are now reeling from destructive human activities like overfishing, pollution and climate change. Researchers at UC Santa Barbara focused on a particularly troublesome issue for sharks: tangles with the longline tuna fishery. Using data from regional fisheries management organizations and machine learning algorithms, the scientists were able to map out hotspots where shark species face the greatest threat from longline fishing. The findings, published in Frontiers in Marine Science, highlight key regions where sharks can be protected with minimal impact on tuna fisheries.

Recently, fitting probabilistic models have gained importance in many areas but estimation of such distributional models with very large data sets is a difficult task. In particular, the use of rather complex models can easily lead to memory-related efficiency problems that can make estimation infeasible even on high-performance computers. We therefore propose a novel backfitting algorithm, which is based on the ideas of stochastic gradient descent and can deal virtually with any amount of data on a conventional laptop. The algorithm performs automatic selection of variables and smoothing parameters, and its performance is in most cases superior or at least equivalent to other implementations for structured additive distributional regression, e.g., gradient boosting, while maintaining low computation time. Performance is evaluated using an extensive simulation study and an exceptionally challenging and unique example of lightning count prediction over Austria. A very large dataset with over 9 million observations and 80 covariates is used, so that a prediction model cannot be estimated with standard distributional regression methods but with our new approach.

Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for deep learning can be difficult. Medical image synthesis could help mitigate this problem. However, until now, the availability of GANs capable of synthesizing longitudinal volumetric data has been limited. To address this, we use the recent advances in latent space-based image editing to propose a novel joint learning scheme to explicitly embed temporal dependencies in the latent space of GANs. This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision. We show the effectiveness of our approach on three datasets containing different longitudinal dependencies.

This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.

The XAI literature is decentralized, both in terminology and in publication venues, but recent years saw the community converge around keywords that make it possible to more reliably discover papers automatically. We use keyword search using the SemanticScholar API and manual curation to collect a well-formatted and reasonably comprehensive set of 5199 XAI papers, available at https://github.com/alonjacovi/XAI-Scholar . We use this collection to clarify and visualize trends about the size and scope of the literature, citation trends, cross-field trends, and collaboration trends. Overall, XAI is becoming increasingly multidisciplinary, with relative growth in papers belonging to increasingly diverse (non-CS) scientific fields, increasing cross-field collaborative authorship, increasing cross-field citation activity. The collection can additionally be used as a paper discovery engine, by retrieving XAI literature which is cited according to specific constraints (for example, papers that are influential outside of their field, or influential to non-XAI research).