The convergence of robotics and virtual reality (VR) has enabled safer and more efficient workflows in high-risk laboratory settings, particularly virology labs. As biohazard complexity increases, minimizing direct human exposure while maintaining precision becomes essential. We propose GAMORA (Gesture Articulated Meta Operative Robotic Arm), a novel VR-guided robotic system that enables remote execution of hazardous tasks using natural hand gestures. Unlike existing scripted automation or traditional teleoperation, GAMORA integrates the Oculus Quest 2, NVIDIA Jetson Nano, and Robot Operating System (ROS) to provide real-time immersive control, digital twin simulation, and inverse kinematics-based articulation. The system supports VR-based training and simulation while executing precision tasks in physical environments via a 3D-printed robotic arm. Inverse kinematics ensure accurate manipulation for delicate operations such as specimen handling and pipetting. The pipeline includes Unity-based 3D environment construction, real-time motion planning, and hardware-in-the-loop testing. GAMORA achieved a mean positional discrepancy of 2.2 mm (improved from 4 mm), pipetting accuracy within 0.2 mL, and repeatability of 1.2 mm across 50 trials. Integrated object detection via YOLOv8 enhances spatial awareness, while energy-efficient operation (50% reduced power output) ensures sustainable deployment. The system's digital-physical feedback loop enables safe, precise, and repeatable automation of high-risk lab tasks. GAMORA offers a scalable, immersive solution for robotic control and biosafety in biomedical research environments.

In this paper a new dataset for robot grasping task is proposed. Compared to grasping data collected in a lab environment, the authors propose to collect the data from real world environments (homes). To collect data in the wild, the authors propose to use cheap robots (measured by the cost) with low DoF. In order to compensate the noisy behavior of the less calibrated robots, the authors model the noise as a latent variable and jointly learn it with the grasping task. Results show that the combination of the aforementioned ideas result in a robot grasping model that can work well on both lab environments, and new real world environment.

In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc+ Neuroheadset to identify six emotional parameters including engagement, excitement, focus, stress, relaxation, and interest using brain signals by experimenting the neuroheadset among three human subjects where we utilize two supervised learning classifiers, Naive Bayes and Linear Regression to show the accuracy and competency of the Epoc+ device and its associated applications in neurotechnological research. We present experimental studies and the demonstration indicates 69% and 62% improved accuracy for the aforementioned classifiers respectively in reading the performance matrices of the participants. We envision that non-invasive, insertable, and low-cost BCI approaches shall be the focal point for not only an alternative for patients with physical paralysis but also understanding the brain that would pave us to access and control the memories and brain somewhere very near.

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I'd like to take a step back and give a high-level view of what we're observing with our clients before answering the question, "What is AI model governance?" Financial institutions have data scientists who create models meant to improve specific functions of the business, such as the fraud prevention department. A data scientist working there's focused on fraud detection with the goal of reducing fraud and its impact on the company's bottom line. This can only happen if the data scientist's model uncovers fraudulent transactions and the company can then take the appropriate action. Unfortunately, after models leave the lab environment, there's very little visibility on what's happening to them.

Artificial Intelligence is often defined as the ability of a machine to learn how to solve cognitive problems--a process akin to human intelligence. Within the realm of scientific methodology and laboratory interconnectivity, the most applicable way of thinking about AI is not that it seeks to replicate human reasoning precisely, but rather that it uses human reasoning as a model with the goal of supplementing and augmenting human observation and decision processes. In this context, an AI algorithm must be trained to interpret available data and specified criteria to meet business objectives and drive operational decision making. AI further refines this process through an accumulation of relevant observations over time as both the accumulated data and business objectives are refined, allowing the understanding of the machine to approach that of human cognition. The inherent diversity of equipment and systems involved in science and discovery is particularly conducive to the benefits of AI, where laboratory operations can be continuously optimized by this constant and cumulative refinement ability.

A thousand years from now when someone writes the history of the human race, the emergence of Machine Learning will be hailed as a significant milestone. Machine Learning (ML), a branch of Artificial Intelligence (AI), enables computers to learn from data without being explicitly programmed. Today, ML has established itself as the key to unlocking the value from customer data. Netflix's movie recommendations, Facebook's ability to spot our faces, Google's self-driving cars are all early examples of ML-powered solutions. However, ML and AI are still in a nascent stage with the majority of industry leaders still struggling to cut through the hype and set right priorities for their businesses. Given the largely untapped potential of this innovation, the following steps can serve as a strategic roadmap for building ML capabilities that help create tangible value for your business.