Graphical User Interface (GUI) agents, driven by Multi-modal Large Language Models (MLLMs), have emerged as a promising paradigm for enabling intelligent interaction with digital systems. This paper provides a structured summary of recent advances in GUI agents, focusing on architectures enhanced by Reinforcement Learning (RL). We first formalize GUI agent tasks as Markov Decision Processes and discuss typical execution environments and evaluation metrics. We then review the modular architecture of (M)LLM-based GUI agents, covering Perception, Planning, and Acting modules, and trace their evolution through representative works. Furthermore, we categorize GUI agent training methodologies into Prompt-based, Supervised Fine-Tuning (SFT)-based, and RL-based approaches, highlighting the progression from simple prompt engineering to dynamic policy learning via RL. Our summary illustrates how recent innovations in multimodal perception, decision reasoning, and adaptive action generation have significantly improved the generalization and robustness of GUI agents in complex real-world environments. We conclude by identifying key challenges and future directions for building more capable and reliable GUI agents.

In the past two decades, technology has revolutionized nearly every aspect of our lives. From healthcare to communication, the digital age has reshaped how we work, interact, and learn. But as we integrate these technological advancements into our children's classrooms, we must ask: are we doing more harm than good? As a practicing physician, I've watched the benefits, but also the consequences of overexposure to technology unfold, not just in my patients, but also in my own children. The classroom, once a place of dynamic, face-to-face learning and interaction, has become a virtual world where screens dominate.

In this paper, we have gone through different AI-Based frameworks used for various educational tasks like digital customized assignment allotment and performance monitoring, identifying slow-learners and fast-learners, etc. application describes a novel invention, digital screen-integrated tables, designed specifically for educational settings. The tables feature integrated digital screens controlled by a central processing unit (CPU), enabling synchronized display of educational content such as textbooks, presentations, exam questions, and interactive learning materials. Additionally, the invention facilitates the collection of student performance data during classroom activities and assessments. The gathered data is utilized for analysis using machine learning models to identify patterns and trends in student learning behaviours. By leveraging machine learning algorithms, educators can ascertain whether a student is a fast learner or a slow learner, based on which, the teacher can allocate more resources to the slow learners. This innovative approach aims to address the evolving needs of modern classrooms by providing a dynamic and data-driven learning environment. The unique integration of digital screens into traditional classroom furniture represents a significant advancement in educational technology. This patent filing encompasses the design, functionality, and method of operation of the digital screen-integrated tables, emphasizing their innovative features and applications in educational institutions.

Human intelligence scores are dropping across the US for the first time, research suggests, and experts warn technology could be to blame. IQ exams were first introduced in 1905, and throughout the century, there was a 30-point increase based on scores in logic, vocabulary, spatial reasoning and visual and mathematical problem-solving skills. The average IQ score in the US currently stands at 98 but varies by a gap of about eight points between states, with New Hampshire ranking first with an average IQ of 103.2. More research needs to be conducted to determine why IQ scores are falling across the country, but one expert speculated that a drop in reading and an increase in media entertainment, like YouTube, is at fault. There are also concerns that phones degrade our memory and recall because there is less need to store information with Google at our finger tips.

Over-the-air (OTA) computation has recently emerged as a communication-efficient Federated Learning (FL) paradigm to train machine learning models over wireless networks. However, its performance is limited by the device with the worst SNR, resulting in fast yet noisy updates. On the other hand, allocating orthogonal resource blocks (RB) to individual devices via digital channels mitigates the noise problem, at the cost of increased communication latency. In this paper, we address this discrepancy and present ADFL, a novel Analog-Digital FL scheme: in each round, the parameter server (PS) schedules each device to either upload its gradient via the analog OTA scheme or transmit its quantized gradient over an orthogonal RB using the ``digital" scheme. Focusing on a single FL round, we cast the optimal scheduling problem as the minimization of the mean squared error (MSE) on the estimated global gradient at the PS, subject to a delay constraint, yielding the optimal device scheduling configuration and quantization bits for the digital devices. Our simulation results show that ADFL, by scheduling most of the devices in the OTA scheme while also occasionally employing the digital scheme for a few devices, consistently outperforms OTA-only and digital-only schemes, in both i.i.d. and non-i.i.d. settings.

The acquisition and use of digital skills are essential for the digital transformation of European economies. Using digital devices to find, evaluate, use, share, curate and create content has become the norm in many if not most professional and social environments. While digital natives -- Millennials and especially Gen Z -- grew up with these new technologies and have gained early exposure and experience, many older workers -- Gen X and beyond -- face higher learning barriers. Moreover, as digital technologies evolve rapidly, with new platforms emerging and ever more complex tools being introduced, regular updating of essential knowledge about these technologies becomes a necessity. Lot of attention, therefore, focuses on the type of skills needed to be able to properly use digital tools and devices and how to introduce this into updated curricula.

Tech and start-up companies providing'artificial intelligence' (or AI) products can be divided by the degree to which their devices either replicate or distinguish themselves from humans. Some interactive AI products deliberately adopt the persona of a children's book robot. Other tech companies are on a quest to make robots more lifelike and interactions with virtual bots more naturalistic or'seamless'. But should they really be doing this? Most modern robot ethics state that machines shouldn't mislead humans about their identity.

It's 1:50 p.m., just five minutes before the fourth period is set to start at Tanashi Daini Junior High in western Tokyo. From one of its classrooms reverberates the sound of frustrated teachers, who surround and stare anxiously at a large screen set up to replace a green chalkboard that, under normal circumstances, would be commanding the attention of students in the room. At the center of the scene is Megumi Kurihara, a veteran Japanese-language teacher who is supposed to begin her class in just a few minutes. But this isn't like any class she has ever taught in her decadeslong career. It's going to be fully remote, with only that big screen and a tablet connecting her to about 70 students logging in from home.

Scientists affiliated with the University of Melbourne and Synchron, Inc. published earlier this week in the Journal of NeuroInterventional Surgery the first-in-human study of Stentrode, a wireless neuroprosthesis that uses machine learning and a stent. What makes the Stentrode technology unique is that it is a stent that records brain activity inside a blood vessel in the brain. It is implanted through the jugular vein so there is no need for open brain surgery. The technology platform originated from the University of Melbourne, in a collaborative effort with the Royal Melbourne Hospital, the Florey Institute of Neuroscience and Mental Health, Monash University, and Synchron, Inc.. A brain-computer interface (BCI) enables two-way communications between the biological brain and a machine.

Medical treatment today primarily takes the form of drugs and therapy. But a third option is slowly emerging: on-body, digital devices that can treat both mental and physical conditions. Such "wearable" therapy offers unique advantages in that it is often more targeted, cheaper, personalised and has fewer negative side effects. Mobile and wearable devices such as phones or fitness trackers are now routinely used for preventive health. They monitor physiological data and behaviour, increase self-awareness and encourage behaviour change.