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.

Deploying large scale language models on edge devices faces inherent challenges such as high computational demands, energy consumption, and potential data privacy risks. This paper introduces the Shakti Small Language Models (SLMs) Shakti-100M, Shakti-250M, and Shakti-500M which target these constraints headon. By combining efficient architectures, quantization techniques, and responsible AI principles, the Shakti series enables on-device intelligence for smartphones, smart appliances, IoT systems, and beyond. We provide comprehensive insights into their design philosophy, training pipelines, and benchmark performance on both general tasks (e.g., MMLU, Hellaswag) and specialized domains (healthcare, finance, and legal). Our findings illustrate that compact models, when carefully engineered and fine-tuned, can meet and often exceed expectations in real-world edge-AI scenarios.

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."

We are a growing, dynamic computer software company that helps businesses achieve greater levels of financial intelligence across their organization with our world-class financial reporting solutions. At insightsoftware, you will learn and grow in a fast-paced, supportive environment that will take your career to the next level. We are looking for future insighters who can demonstrate teamwork, results orientation, a growth mindset, disciplined execution, and a winning attitude to join our growing team! Insightsoftware celebrates diversity and is proud to have an open and inclusive environment where our rapidly expanding family of 2400 associates feel they belong, and all voices are heard. Account Executive to focus on new business for a fast-growth global software provider ($1bn PE funding & 20 companies acquired since 2018).

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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.

Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. At Weights & Biases, our mission is to build the best developer tools for machine learning. Weights & Biases is a series C company with $200 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more. Reporting to the Head of Data Science, the Machine Learning Engineer (MLE) will own the interface between our Data Science Team and our Data Platform Team, while making the results of Data Science into ML Applications for the business.

At Microsoft, we believe that energized, empowered employees are the key to a durable, competitive advantage for every organization. The Microsoft Work Trend Index shows that leaders today need to end productivity paranoia, embrace the fact that people come into the office for each other, and re-recruit everyone.1 Empowering today's digitally connected, distributed workforce requires the right culture and the right technology. At Microsoft Ignite, we're sharing new innovations across Microsoft 365, Microsoft Teams, and Microsoft Viva to help everyone thrive. Global experiences, localized content, in-person opportunities, and more--let's get ready for a new kind of Microsoft Ignite. Microsoft 365 is the cloud-first platform for all the ways that people work today--wherever, whenever, however.