This year is the 10th anniversary of Lucas Pope's wrenching game about being a border officer in the functional communist country of Arstotzka, deciding when to turn desperate people away and when to risk your job but save your conscience by letting them slip by. It remains an impactful and illuminating exploration of how documentation can save or cost lives, and the moral and human cost of border enforcement. Your wife, Nour, has finally decided to risk getting out of Syria as the bombs keep falling – but you must stay behind, to look after your mother and grandfather. In this game, you are not the person fleeing but the person trying to survive back home, waiting for text message updates or an occasional photo and doing your best to give good advice from afar. An affecting work of interactive fiction told through the medium of the smartphone.

This is an edition of The Atlantic Daily, a newsletter that guides you through the biggest stories of the day, helps you discover new ideas, and recommends the best in culture. The tech world's most attention-grabbing man had a very busy week. Elon Musk launched a rocket, dealt with bad news at Tesla, stoked fear that AI could end humankind, and rolled out another controversial change on Twitter. Through it all, Musk exemplifies the danger of what happens when technology and ego collide. Earlier today, a SpaceX rocket exploded in the skies over the Gulf of Mexico, detonating itself after the booster failed to separate from the upper portion of the vehicle after launch.

The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE's ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.

This paper provides a collision avoidance perspective to maritime autonomy, in the shift towards Maritime Autonomous Surface Ships (MASS). In particular, the paper presents the developments related to the Greenhopper, Denmark's first autonomous harbour bus. The collision and grounding avoidance scheme, called the Short Horizon Planner (SHP), is described and discussed in detail. Furthermore, the required autonomy stack for facilitating safe and rule-compliant collision avoidance is presented. The inherent difficulties related to adhering to the COLREGs are outlined, highlighting some of the operational constraints and challenges within the space of autonomous ferries and harbour buses. Finally, collision and grounding avoidance is demonstrated using a simulation of the whole Greenhopper autonomy stack.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.S. fighter jets intercepted Russian bomber aircraft near Alaska Monday, according to the Alaskan Region of North American Aerospace Defense Command (NORAD). NORAD made the announcement Wednesday in an official statement. "The Alaskan Region of North American Aerospace Defense Command (NORAD) detected, tracked, positively identified and intercepted two Russian aircraft entering and operating within the Alaska Air Defense Identification Zone (ADIZ) on April 17, 2023," the defense organization said.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Navy sailed its first drone boat through the strategic Strait of Hormuz on Wednesday, a crucial waterway for global energy supplies where American sailors often faces tense encounters with Iranian forces. The trip by the L3 Harris Arabian Fox MAST-13, a 41-foot speedboat carrying sensors and cameras, drew the attention of Iran's Revolutionary Guard, but took place without incident, said Navy spokesman Cmdr. Two U.S. Coast Guard cutters, the USCGC Charles Moulthrope and USCGC John Scheuerman, accompanied the drone.

Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader.

Domain-adaptive trajectory imitation is a skill that some predators learn for survival, by mapping dynamic information from one domain (their speed and steering direction) to a different domain (current position of the moving prey). An intelligent agent with this skill could be exploited for a diversity of tasks, including the recognition of abnormal motion in traffic once it has learned to imitate representative trajectories. Towards this direction, we propose DATI, a deep reinforcement learning agent designed for domain-adaptive trajectory imitation using a cycle-consistent generative adversarial method. Our experiments on a variety of synthetic families of reference trajectories show that DATI outperforms baseline methods for imitation learning and optimal control in this setting, keeping the same per-task hyperparameters. Its generalization to a real-world scenario is shown through the discovery of abnormal motion patterns in maritime traffic, opening the door for the use of deep reinforcement learning methods for spatially-unconstrained trajectory data mining.

Elon Musk's SpaceX is gearing up for its biggest launch yet, with the maiden flight of its Starship rocket. You can watch the launch streamed live above. SpaceX is set to send its Starship rocket into near orbit for the first time at 1300 GMT today – that's 1400 BST in the UK and 0800 CDT in Boca Chica, Texas, where it will lift off. The largest and most powerful rocket system ever built. SpaceX says it will eventually ferry astronauts to the moon and Mars, if all goes according to plan.

Instruction tuning enables language models to generalize more effectively and better follow user intent. However, obtaining instruction data can be costly and challenging. Prior works employ methods such as expensive human annotation, crowd-sourced datasets with alignment issues, or generating noisy examples via LLMs. We introduce the LongForm dataset, which is created by leveraging English corpus examples with augmented instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. This approach provides a cheaper and cleaner instruction-tuning dataset and one suitable for long text generation. We finetune T5, OPT, and LLaMA models on our dataset and show that even smaller LongForm models have good generalization capabilities for text generation. Our models outperform 10x larger language models without instruction tuning on various tasks such as story/recipe generation and long-form question answering. Moreover, LongForm models outperform prior instruction-tuned models such as FLAN-T5 and Alpaca by a large margin. Finally, our models can effectively follow and answer multilingual instructions; we demonstrate this for news generation. We publicly release our data and models: https://github.com/akoksal/LongForm.