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.

Much like other learning-based models, recommender systems can be affected by biases in the training data. While typical evaluation metrics (e.g. hit rate) are not concerned with them, some categories of final users are heavily affected by these biases. In this work, we propose using multiple triplet losses terms to extract meaningful and robust representations of users and items. We empirically evaluate the soundness of such representations through several "bias-aware" evaluation metrics, as well as in terms of stability to changes in the training set and agreement of the predictions variance w.r.t. that of each user.

The internet had a collective feel-good moment with the introduction of DALL-E, an artificial intelligence-based image generator inspired by artist Salvador Dali and the lovable robot WALL-E that uses natural language to produce whatever mysterious and beautiful image your heart desires. Seeing typed-out inputs like "smiling gopher holding an ice cream cone" instantly spring to life clearly resonated with the world. Getting said smiling gopher and attributes to pop up on your screen is not a small task. DALL-E 2 uses something called a diffusion model, where it tries to encode the entire text into one description to generate an image. But once the text has a lot of more details, it's hard for a single description to capture it all.

HSO are a leading Microsoft Dynamics Gold partner who were founded in 1987 specialising in sectors such as Retail, Rental, Manufacturing, Professional Services and Local Government. With a head count of over 280 employees in the UK, winning multiple awards such as 2020 Microsoft Partner of the Year, Best Tech company 2021 and Top 3 Large Companies to work for 2021 our reputation in the Dynamics Market is higher than ever. Prioritising customer satisfaction, our expertise and pragmatic approach to each customer's business needs enable us to provide a 100% reference-able solution, supported by award winning 24-hour support. Our recruitment moto has always been – 'We don't want good people to just join us, we want them to stay with us'. Ensuring our employees are challenged, supported and engaged in our wider family is key to our continued success – we have a designated Learning and Development Team who are continuously offering the best training on the market, combined with an Engagement Team who are creative in ways we can have social activities virtually.

To solve the conundrum of how to get timely medical care to people with memory loss or other impaired cognitive functioning, a new study suggests that artificial intelligence may be as accurate as clinicians in taking the first step: diagnosis. Findings from the study, which was conducted by researchers at Boston University School of Medicine, were published online Monday in the journal Nature Communications. "We're trying to leverage AI to create frameworks to mimic neurology experts," for dementia diagnosis, Vijaya B. Kolachalama, the study's principal investigator and assistant professor of medicine and computer science at Boston University, told UPI. He said his lab aims to use computer models to assist clinical practice. Kolachalama stressed that the aim of his team's work is to help reduce the workload of the busy neurology practice, not replace the expert clinician.

Upon looking at photographs and drawing on their past experiences, humans can often perceive depth in pictures that are, themselves, perfectly flat. However, getting computers to do the same thing has proved quite challenging. The problem is difficult for several reasons, one being that information is inevitably lost when a scene that takes place in three dimensions is reduced to a two-dimensional (2D) representation. There are some well-established strategies for recovering 3D information from multiple 2D images, but they each have some limitations. A new approach called "virtual correspondence," which was developed by researchers at MIT and other institutions, can get around some of these shortcomings and succeed in cases where conventional methodology falters.