These gentle, 400-pound giants are splashing back from the brink of extinction. Breakthroughs, discoveries, and DIY tips sent every weekday. In an ocean conservation victory, green sea turtles () have been brought from the brink of extinction. The International Union for Conservation of Nature (IUCN) elevated the keystone species from Endangered to Least Concern . The global conservation organization moves species between categories once new data indicates changes in their population, threat levels, or habitat.

Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments. In particular, the predator-prey dynamics have captured substantial interest and various simulations been tailored to unique requirements. To prevent further time-intensive developments, we introduce Aquarium, a comprehensive Multi-Agent Reinforcement Learning environment for predator-prey interaction, enabling the study of emergent behavior. Aquarium is open source and offers a seamless integration of the PettingZoo framework, allowing a quick start with proven algorithm implementations. It features physics-based agent movement on a two-dimensional, edge-wrapping plane. The agent-environment interaction (observations, actions, rewards) and the environment settings (agent speed, prey reproduction, predator starvation, and others) are fully customizable. Besides a resource-efficient visualization, Aquarium supports to record video files, providing a visual comprehension of agent behavior. To demonstrate the environment's capabilities, we conduct preliminary studies which use PPO to train multiple prey agents to evade a predator. In accordance to the literature, we find Individual Learning to result in worse performance than Parameter Sharing, which significantly improves coordination and sample-efficiency.

The widespread use of Text-to-Image (T2I) models in content generation requires careful examination of their safety, including their robustness to adversarial attacks. Despite extensive research into this, the reasons for their effectiveness are underexplored. This paper presents an empirical study on adversarial attacks against T2I models, focusing on analyzing factors associated with attack success rates (ASRs). We introduce a new attack objective - entity swapping using adversarial suffixes and two gradient-based attack algorithms. Human and automatic evaluations reveal the asymmetric nature of ASRs on entity swap: for example, it is easier to replace "human" with "robot" in the prompt "a human dancing in the rain." with an adversarial suffix but is significantly harder in reverse. We further propose probing metrics to establish indicative signals from the model's beliefs to the adversarial ASR. We identify conditions resulting in a 60% success probability for adversarial attacks and others where this likelihood drops below 5%.

Here are the AIContentFly OTO links. Generate marketing content in seconds for any industry with this incredible suite of 60 AI writing tools, then scale your business, increase your search engine rankings, and boost your productivity and profits. Here we are in the AI Content Fly dashboard, and for this demonstration, I've chosen to use a niche that I know nothing about to prove that you really can let AI Content Fly do the work to generate amazing content, even if you don't know anything about the topic. So, the niche that I've chosen is aquascaping. It happens to be a popular hobby these days, and those involved in it can spend a lot of money on it.

We present Aquarium, a differentiable fluid-structure interaction solver for robotics that offers stable simulation, accurately coupled fluid-robot physics in two dimensions, and full differentiability with respect to fluid and robot states and parameters. Aquarium achieves stable simulation with accurate flow physics by directly integrating over the incompressible Navier-Stokes equations using a fully implicit Crank-Nicolson scheme with a second-order finite-volume spatial discretization. The fluid and robot physics are coupled using the immersed-boundary method by formulating the no-slip condition as an equality constraint applied directly to the Navier-Stokes system. This choice of coupling allows the fluid-structure interaction to be posed and solved as a nonlinear optimization problem. This optimization-based formulation is then exploited using the implicit-function theorem to compute derivatives. Derivatives can then be passed to downstream gradient-based optimization or learning algorithms. We demonstrate Aquarium's ability to accurately simulate coupled fluid-robot physics with numerous 2D examples, including a cylinder in free stream and a soft robotic fish tail with hardware validation. We also demonstrate Aquarium's ability to provide analytical gradients by performing gradient-based shape-and-gait optimization of an oscillating diamond foil to maximize its generated thrust.

Robotic fish might help solve an ecological problem by scaring an invasive fish species so profoundly that it is put off breeding. Eastern mosquitofish (Gambusia holbrooki) were introduced in many parts of the world to eat mosquito larvae and keep the disease-spreading insects under control. But they have had a negative and unintended consequence on local fauna: they chew the tails of native freshwater fish and tadpoles, then leave them to die. Reducing numbers of eastern mosquitofish without harming other wildlife is a difficult prospect, but Giovanni Polverino at the University of Western Australia and his colleagues have come up with a potential solution. They designed a robotic version of the largemouth bass (Micropterus salmoides), which naturally preys on mosquitofish.

Object detection tasks can be particularly tedious to debug. If you've worked with large object detection datasets in the past, chances are you've run into incorrectly labelled data or data that's missing labels that end up killing your evaluation metrics. Identifying these issues usually involves manually inspecting the individual problematic examples in your dataset. The other issue with object detection is that these models usually output multiple detection boxes for a given image, and evaluation metrics have to be calculated based on different threshold parameters that control the strictness of our detection criteria. The most important parameter in this case, is the Intersection over Union (IoU) threshold.

A machine learning (ML) model's performance is determined by code and data. When trying to improve a ML model you can write better code, increase testing, or improve the data itself. The ML space is maturing with more companies pushing models to production than ever before. With this shift, teams are less challenged by how to build and deploy a model, but rather on improving a model's precision and recall, which often means iterating on the training data. Data has notoriously been a constraint to building great models and has led to the rise of data labeling providers like Scale.

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan. When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue. "Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it's trained on, which is usually the most important part of making the model work in production," Gao told me.

In what could have been the plot of a Hollywood heist movie, the hackers took great interest in the vast aquarium that a Las Vegas casino had installed in its lobby. The casino's owners thought that the huge fish tank was an impressive sight that helped create a classy ambience as people arrived. What they failed to realise was that the aquarium was a easy way to break into the casino's computer system, and the hackers pounced. For while the casino had protected its IT network with the usual firewalls and anti-virus software, staff forgot that the futuristic fish tank was connected to its system so that the water temperature and quality could be automatically monitored. So criminals trying to get their hands on the bank details of the casino's wealthiest gamblers were able to hack into the network via the aquarium.