Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive. This paper proposes proximal gradient algorithms for feedback controls of finite-time horizon stochastic control problems. The state dynamics are nonlinear diffusions with control-affine drift, and the cost functions are nonconvex in the state and nonsmooth in the control. The system noise can degenerate, which allows for deterministic control problems as special cases. We prove under suitable conditions that the algorithm converges linearly to a stationary point of the control problem, and is stable with respect to policy updates by approximate gradient steps. The convergence result justifies the recent reinforcement learning heuristics that adding entropy regularization or a fictitious discount factor to the optimization objective accelerates the convergence of policy gradient methods.

We are witnessing the emergence of an "AI economy and society" where AI technologies are increasingly impacting health care, business, transportation and many aspects of everyday life. Many successes have been reported where AI systems even surpassed the accuracy of human experts. However, AI systems may produce errors, can exhibit bias, may be sensitive to noise in the data, and often lack technical and judicial transparency resulting in reduction in trust and challenges in their adoption. These recent shortcomings and concerns have been documented in scientific but also in general press such as accidents with self-driving cars, biases in healthcare, hiring and face recognition systems for people of color, seemingly correct medical decisions later found to be made due to wrong reasons etc. This resulted in emergence of many government and regulatory initiatives requiring trustworthy and ethical AI to provide accuracy and robustness, some form of explainability, human control and oversight, elimination of bias, judicial transparency and safety. The challenges in delivery of trustworthy AI systems motivated intense research on explainable AI systems (XAI). Aim of XAI is to provide human understandable information of how AI systems make their decisions. In this paper we first briefly summarize current XAI work and then challenge the recent arguments of "accuracy vs. explainability" for being mutually exclusive and being focused only on deep learning.

The rapid development of remote sensing technologies have gained significant attention due to their ability to accurately localize, classify, and segment objects from aerial images. These technologies are commonly used in unmanned aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to capture data over large areas. This data is useful for various applications, such as monitoring and inspecting cities, towns, and terrains. In this paper, we presented a method for classifying and segmenting city road traffic dashed lines from aerial images using deep learning models such as U-Net and SegNet. The annotated data is used to train these models, which are then used to classify and segment the aerial image into two classes: dashed lines and non-dashed lines. However, the deep learning model may not be able to identify all dashed lines due to poor painting or occlusion by trees or shadows. To address this issue, we proposed a method to add missed lines to the segmentation output. We also extracted the x and y coordinates of each dashed line from the segmentation output, which can be used by city planners to construct a CAD file for digital visualization of the roads.

The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of QCD and QED processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.

When you think of artificial intelligence, you might imagine a world where robots and computers run the show. But looking at how AI is used in today's world, from voice-activated assistants to virtual assistants answering phones, you will recognize how this technology is improving our lives. Here is a deeper dive into artificial intelligence and the companies leading the way right now. Artificial intelligence is the concept of teaching a computer to think and act independently. That includes rational speech, complex problem-solving, speech recognition, decision making, and initiating actions.

The U.S. is solidifying a defense package to Ukraine, which would help assist Ukraine with shooting down Russian drone strikes on civilian targets. North Korea is supplying arms to a Russian mercenary group and could continue to deliver military equipment to support the Kremlin's war against Ukraine, the Biden administration said Thursday. The White House said the weapons "will not change battlefield dynamics," however, the private entity receiving the equipment, Wagner Group, is committing atrocities and human rights abuses across Ukraine. "Because the Russian military is struggling in Ukraine, President [Vladimir] Putin has increasingly been turning to Wagner, which is owned by Yevgeny Prigozhin, for military support," White House National Security Council spokesman John Kirby said Thursday. Kirby said Prigozhin has been spending more than $100 million per month to fund Wagner's efforts inside Ukraine.

National security analyst Dr. Rebecca Grant joined'Fox & Friends First' to discuss Zelenskyy's visit to the White House and his request for additional aid in the war against Russia. Iran on Thursday took a swing at Ukrainian President Volodymyr Zelenskyy over comments he made to Congress this week and denied accusations that Tehran has supplied Russia with drones. Zelenskyy had better know that Iran's strategic patience over such unfounded accusations is not endless," Iranian Foreign Ministry spokesman Nasser Kanaani said in a threatening message posted to the ministry's website. Kanaani also advised Zelenskyy "to draw a lesson from the fate of some other political leaders who contented themselves with the US support." Volodymyr Zelenskyy, Ukraine's president, arrives to speak during a joint meeting of Congress at the U.S. Capitol on Wednesday, Dec. 21, 2022. The spokesman's comments came one day after Zelenskky addressed the U.S. Congress in an appeal for additional aid – a plea aimed at GOP lawmakers who are divided on whether providing support to Kyiv is a matter of national security. "When Russia cannot reach our cities by its artillery, it tries to destroy them with missile attacks," he said. "More than that, Russia found an ally in its genocidal policy – Iran.

This process is painful, time-consuming, and expensive. Today, we already use ML to perform image segmentation to not only isolate the location of lesions, but to also reconstruct a high fidelity 3D model of a patient's arteries. In other words, ML is already playing an essential role in identifying and building the right geometry. Machine learning can also speed up segmentation for particularly complicated diseases, such as aortic dissection, which can easily take eight or nine hours for a trained person. Using ML, however, segmentation might take just a few minutes.

NASA's Perseverance Rover has finally deposited its first sample of Martian rock to be returned to Earth. The car-sized robot began its mission to find ancient biomarkers in the clay on the Red Planet on April 22, which could indicate if alien life ever existed there. It has been roaming around a delta to look for sampling sites that might contain ancient microbes and organics, before drilling down to extract a specimen. Most of those it has collected so far remain in its belly, however this one is the first to be dropped at the base of the delta, and may be retrieved in a future mission. This titanium tube (pictured) contains a core of igneous rock extracted from a region of Mars' Jezero Crater called'South Séítah' on January 31 NASA's Perseverance rover (pictured) chooses a sample using its suite of onboard instruments to detect whether organic molecules are present in some rock before coring. Mars is the fourth planet from the sun, with a'near-dead' dusty, cold, desert world with a very thin atmosphere.

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