Two armed drones were shot down as they approached an Iraqi military base hosting US forces near Baghdad's international airport, Iraqi security sources said, adding that nobody was hurt in the incident. An official of the US-led international military coalition stationed there said the base's defence system engaged "two fixed-wing suicide drones… they were shot down without incident". "This was a dangerous attack on a civilian airport," the coalition official said in a brief statement on Monday. There was no immediate claim of responsibility for the attack. Footage provided by the coalition showed what the official said was debris of two fixed-wing drones destroyed in the attack, with writing clearly visible on the wing of one drone reading "Soleimani's revenge".

Lets imagine for a moment that the horse you see in the image below is sick and dying.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Two drones were intercepted Monday as they neared an Iraqi base that had been housing American forces on the anniversary of the U.S. assassination of a top Iranian commander, a report said. Reuters, citing Iraqi security officials, reported that the base was located near Baghdad's international airport. The airport was the site of the Jan. 2, 2020, U.S. drone strike that resulted in the death of Qassem Soleimani, the former commander of the elite Quds Force.

In 2022, the adoption of AI will continue to accelerate across markets and economies. The combination of AI, Internet of Things, Automation and 5G will offer significant business opportunities in several sectors such as healthcare, supply chain and retail. The business focus will shift from pilot projects to deployment and scaling up AI applications. The combination of AI and Robotic Process Automation enables rapid end-to-end business process automation and accelerate digital transformation. Across a wide range of industries intelligent automation will be a key business driver.

Southmead hospital in Bristol is using robots to help improve services.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Jerusalem Post said Monday that its website and Twitter account were targeted by "pro-Iranian hackers" who posted an ominous image of a model of the Israeli Dimona nuclear facility under a ballistic missile attack. "We are close to you where you do not think about it," the text in the image read. The paper pointed out that the missile seems to be fired from a ring known to be found on Qassem Soleimani's hand after a Jan. 2, 2020 U.S. drone strike at Baghdad International Airport that resulted in his death.

Common AI uses include intelligent assistants such as Siri, Alexa, or Google Assistant, but one Huntsville contractor is looking into its uses on the battlefield.

As Mr Gref said, we have schoolchildren in the hall. But representatives of the adult audience, professionals are also watching and listening to us and will take part in our meeting. So, I would like to say to our boys and girls: if something seems a bit boring, please forgive me in advance but I must talk to all the participants in our meeting. I will start with general things. Artificial intelligence technology has truly become part of our lives.

We examine the ability of machine learning (ML) and deep learning (DL) algorithms to infer surface/ground exchange flux based on subsurface temperature observations. The observations and fluxes are produced from a high-resolution numerical model representing conditions in the Columbia River near the Department of Energy Hanford site located in southeastern Washington State. Random measurement error, of varying magnitude, is added to the synthetic temperature observations. The results indicate that both ML and DL methods can be used to infer the surface/ground exchange flux. DL methods, especially convolutional neural networks, outperform the ML methods when used to interpret noisy temperature data with a smoothing filter applied. However, the ML methods also performed well and they are can better identify a reduced number of important observations, which could be useful for measurement network optimization. Surprisingly, the ML and DL methods better inferred upward flux than downward flux. This is in direct contrast to previous findings using numerical models to infer flux from temperature observations and it may suggest that combined use of ML or DL inference with numerical inference could improve flux estimation beneath river systems.

Real economies can be seen as a sequential imperfect-information game with many heterogeneous, interacting strategic agents of various agent types, such as consumers, firms, and governments. Dynamic general equilibrium models are common economic tools to model the economic activity, interactions, and outcomes in such systems. However, existing analytical and computational methods struggle to find explicit equilibria when all agents are strategic and interact, while joint learning is unstable and challenging. Amongst others, a key reason is that the actions of one economic agent may change the reward function of another agent, e.g., a consumer's expendable income changes when firms change prices or governments change taxes. We show that multi-agent deep reinforcement learning (RL) can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types, in economic simulations with many agents, through the use of structured learning curricula and efficient GPU-only simulation and training. Conceptually, our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing, that are commonly used for analytical tractability. Our GPU implementation enables training and analyzing economies with a large number of agents within reasonable time frames, e.g., training completes within a day. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes. We validate the learned meta-game epsilon-Nash equilibria through approximate best-response analyses, show that RL policies align with economic intuitions, and that our approach is constructive, e.g., by explicitly learning a spectrum of meta-game epsilon-Nash equilibria in open RBC models.