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Nearly all state-of-the-art deep learning algorithms rely on error backpropagation, which is generally regarded as biologically implausible. An alternative way of training an artificial neural network is through treating each unit in the network as a reinforcement learning agent, and thus the network is considered as a team of agents. As such, all units can be trained by REINFORCE, a local learning rule modulated by a global signal that is more consistent with biologically observed forms of synaptic plasticity. Although this learning rule follows the gradient of return in expectation, it suffers from high variance and thus the low speed of learning, rendering it impractical to train deep networks. We therefore propose a novel algorithm called MAP propagation to reduce this variance significantly while retaining the local property of the learning rule. Experiments demonstrated that MAP propagation could solve common reinforcement learning tasks at a similar speed to backpropagation when applied to an actor-critic network. Our work thus allows for the broader application of teams of agents in deep reinforcement learning.

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.

There, top hackers from around the globe will rack up points for inducing AI models to err in various ways, with categories of challenges that include political misinformation, defamatory claims, and "algorithmic discrimination," or systemic bias. Leading AI firms such as Google, OpenAI, Anthropic and Stability have volunteered their latest chatbots and image generators to be put to the test. The competition's results will be sealed for several months afterward, organizers said, to give the companies time to address the flaws exposed in the contest before they are revealed to the world.

G42 and Microsoft announced their intention to collaborate on the development of public sector and industry focused solutions that leverage Microsoft's partner ecosystem and cloud capabilities. These solutions will benefit UAE organisations to address citizen and customer needs. Formalised in a memorandum of understanding (MoU) that was signed at G42's premises, the agreement will allow both organizations to further explore joint business development and marketing opportunities across a variety of areas G42 cover including healthcare, energy, public sector digital transformation, financial services, climate action, and beyond. G42 Cloud will work closely with Microsoft to enable joint solutions to be developed and deployed securely and in compliance with regulatory requirements. Naim Yazbeck, General Manager of Microsoft UAE commented: "Organisations globally, especially in the public sector, are increasingly looking for customised cloud solutions that offer additional choice and flexibility. I am very excited about the potential that a collaboration with G42 could bring to the UAE, and I look forward to combining Microsoft's focus on resiliency, agility and security with G42s unique capabilities and vision."

FIFA is using new artificial intelligence to help referees call offsides in this year's World Cup. The system is called semi-automated offside technology (SAOT) and uses 12 cameras attached to the roof of the stadium to track the ball and each player's movements. SAOT uses artificial intelligence to recognize and track players and the ball, calculating their positions 50 times per second. A sensor is attached to the official Qatar 2022 World Cup ball, called Al Rihla -- Arabic for "the journey" -- allowing SAOT to compare the exact moment it was kicked with the position of the team's last defender and the opposing team's striker. This level of precision is key for very tight situations in which it's difficult for referees to quickly call offsides.