Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the real world. Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata. By leveraging timestamp metadata, models can potentially learn from trends in past distribution shifts and extrapolate into the future. While recent works have studied distribution shifts, temporal shifts remain underexplored. To address this gap, we curate Wild-Time, a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. We use two evaluation strategies: evaluation with a fixed time split (Eval-Fix) and evaluation with a data stream (Eval-Stream). Eval-Fix, our primary evaluation strategy, aims to provide a simple evaluation protocol, while Eval-Stream is more realistic for certain real-world applications. Under both evaluation strategies, we observe an average performance drop of 20% from in-distribution to out-of-distribution data. Existing methods are unable to close this gap. Code is available at https://wild-time.github.io/.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Artificial Intelligence in Aviation Chapter 4: Presenting the Artificial Intelligence in Aviation Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Tristan Rouillard is the VP of Machine Learning Solutions at CloudFactory. In this role, he leads the company's strategy and direction related to ML products and solutions offered to CloudFactory's clients globally. Tristan was one of the cofounders of Hasty, a data-centric vision AI platform focussed on making it easier to implement the ML flywheel in production. Hasty was recently acquired by CloudFactory in late 2022. Before founding Hasty, Tristan was the Head of the Venture Development team at WATTx, a manufacturing, industry 4.0 focussed incubator, where his team built the business models and go-to-market strategies for various early-stage ventures.

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.