Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various perception paradigms, occupancy-based representation has become widely recognized as particularly suitable for humanoid robots, as it provides both rich semantic and 3D geometric information essential for comprehensive environmental understanding. In this work, we present Humanoid Occupancy, a generalized multimodal occupancy perception system that integrates hardware and software components, data acquisition devices, and a dedicated annotation pipeline. Our framework employs advanced multi-modal fusion techniques to generate grid-based occupancy outputs encoding both occupancy status and semantic labels, thereby enabling holistic environmental understanding for downstream tasks such as task planning and navigation. To address the unique challenges of humanoid robots, we overcome issues such as kinematic interference and occlusion, and establish an effective sensor layout strategy. Furthermore, we have developed the first panoramic occupancy dataset specifically for humanoid robots, offering a valuable benchmark and resource for future research and development in this domain. The network architecture incorporates multi-modal feature fusion and temporal information integration to ensure robust perception. Overall, Humanoid Occupancy delivers effective environmental perception for humanoid robots and establishes a technical foundation for standardizing universal visual modules, paving the way for the widespread deployment of humanoid robots in complex real-world scenarios.

Modern artificial intelligence and machine learning workflows rely on efficient tensor libraries. However, tuning tensor libraries without considering the actual problems they are meant to execute can lead to a mismatch between expected performance and the actual performance. Einsum libraries are tuned to efficiently execute tensor expressions with only a few, relatively large, dense, floating-point tensors. But, practical applications of einsum cover a much broader range of tensor expressions than those that can currently be executed efficiently. For this reason, we have created a benchmark dataset that encompasses this broad range of tensor expressions, allowing future implementations of einsum to build upon and be evaluated against.

In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density Peak)-across a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results.

The field of Artificial Intelligence has witnessed remarkable progress in recent years, especially with the emergence of powerful large language models (LLMs) based on the transformer architecture. Cloud-based LLMs, such as OpenAI's ChatGPT, offer impressive capabilities but come with concerns regarding latency and privacy due to network dependencies. This article presents an innovative approach to LLM inference, envisioning a future where LLMs with billions of parameters can be executed directly on mobile devices without network connectivity. The article showcases a fine-tuned GPT LLM with 3 billion parameters that can operate smoothly on devices with as low as 4GB of memory. Through the integration of native code and model quantization techniques, the application not only serves as a general-purpose assistant but also facilitates seamless mobile interactions with text-to-actions features. The article provides insights into the training pipeline, implementation details, test results, and future directions of on-device LLM inference. This breakthrough technology opens up possibilities for empowering users with sophisticated AI capabilities while preserving their privacy and eliminating latency concerns.

This paper plans to develop an Equitable and Responsible AI framework with enabling techniques and algorithms for the Internet of Energy (IoE), in short, RAI4IoE. The energy sector is going through substantial changes fueled by two key drivers: building a zero-carbon energy sector and the digital transformation of the energy infrastructure. We expect to see the convergence of these two drivers resulting in the IoE, where renewable distributed energy resources (DERs), such as electric cars, storage batteries, wind turbines and photovoltaics (PV), can be connected and integrated for reliable energy distribution by leveraging advanced 5G-6G networks and AI technology. This allows DER owners as prosumers to participate in the energy market and derive economic incentives. DERs are inherently asset-driven and face equitable challenges (i.e., fair, diverse and inclusive). Without equitable access, privileged individuals, groups and organizations can participate and benefit at the cost of disadvantaged groups. The real-time management of DER resources not only brings out the equity problem to the IoE, it also collects highly sensitive location, time, activity dependent data, which requires to be handled responsibly (e.g., privacy, security and safety), for AI-enhanced predictions, optimization and prioritization services, and automated management of flexible resources. The vision of our project is to ensure equitable participation of the community members and responsible use of their data in IoE so that it could reap the benefits of advances in AI to provide safe, reliable and sustainable energy services.

Large volumes of data are required for training machine learning models. The trained model is run on a cloud server that users can access through various applications such as web search, translation, text production, and picture processing, which is the standard procedure for establishing machine learning applications. The application must transfer the user's data to the server where the machine learning model is stored every time it wishes to use it, creating privacy, security, and processing issues. Fortunately, developments in edge AI have allowed sensitive user data to be avoided from being sent to application servers. This current area of study, also known as TinyML, aims to construct machine learning models that fit smartphones and other consumer devices, making on-device inference possible. Even if the device is not connected to the internet, these applications can continue functioning.

Reading scientific papers is hard! If you are working in the field of AI, you are aware of how fast the pace of progress has become. With hundreds of new papers published every day, there are countless new ideas, methods, and architectures which move the whole field forward. If you want to keep your knowledge/project/job competitive, you have to at least follow the basics of new discoveries. But reading those papers is not an easy job.

The lack of machine learning datasets that include people with disabilities has proved to be a major roadblock for developing technological solutions customised to their needs. This is often referred to as'data desert'. It is a common practice for organisations building technology products and services to use data at an aggregate level, which leads to stereotyping and being exclusionary in the process. Earlier this week, Microsoft, in a lengthy blog, revealed its roadmap to deal with this data desert which has become a major hindrance in making artificial intelligence accessible to people with disability. The tech giant Microsoft has revealed its various collaborations to'shrink this data desert' as discussed below.

Early diagnosis of colorectal cancer substantially improves survival. However, over half of cases are diagnosed late due to the demand for colonoscopy—the ‘gold standard’ for screening—exceeding capacity. Colonoscopy is limited by the outdated design of conventional endoscopes, which are associated with high complexity of use, cost and pain. Magnetic endoscopes are a promising alternative and overcome the drawbacks of pain and cost, but they struggle to reach the translational stage as magnetic manipulation is complex and unintuitive. In this work, we use machine vision to develop intelligent and autonomous control of a magnetic endoscope, enabling non-expert users to effectively perform magnetic colonoscopy in vivo. We combine the use of robotics, computer vision and advanced control to offer an intuitive and effective endoscopic system. Moreover, we define the characteristics required to achieve autonomy in robotic endoscopy. The paradigm described here can be adopted in a variety of applications where navigation in unstructured environments is required, such as catheters, pancreatic endoscopy, bronchoscopy and gastroscopy. This work brings alternative endoscopic technologies closer to the translational stage, increasing the availability of early-stage cancer treatments. Magnetic endoscopes have the potential to improve access, reduce patient discomfort and enhance safety. While navigation of magnetic endoscopes can be challenging for the operator, a new approach by Martin, Scaglioni and colleagues explores how to reduce this burden by offering different levels of autonomy in robotic colonoscopy.

As many parts of the world continue to remain in lockdown due to the global pandemic, many countries have started to ease the restrictions. Particularly in Australia & New Zealand, schools have reopened, workers are heading back to their offices, and restaurants & retail stores are beginning to resume trade with new set of guidelines. These guidelines might also vary from one state to another and hence businesses that operate and have offices in different states, need to provide relevant updates to their employees to be able to comply with the new regulations. The most common practice from employers is to send out regular emails outlining the guidelines. Chatbots are beginning to play a vital role in providing real-time upto date information.