Wimbledon will break with tradition and replace line judges with electronic line calling from next year's championships, the All England Club confirmed. The sight of immaculately dressed line judges standing or crouching at the side and back of the grass courts has been a feature at the Grand Slam for 147 years. Electronic line calling was first used as an experiment at the ATP Next Gen Finals in Milan in 2017 and was adopted more widely during the COVID-19 pandemic. It will be used on all courts across ATP Tour events from 2025. The Australian Open and US Open have already replaced line judges with electronic calling although the French Open still relies on the human eye.

This document presents the design of an autonomous car developed by the UruBots team for the 2024 FIRA Autonomous Cars Race Challenge. The project involves creating an RC-car sized electric vehicle capable of navigating race tracks with in an autonomous manner. It integrates mechanical and electronic systems alongside artificial intelligence based algorithms for the navigation and real-time decision-making. The core of our project include the utilization of an AI-based algorithm to learn information from a camera and act in the robot to perform the navigation. We show that by creating a dataset with more than five thousand samples and a five-layered CNN we managed to achieve promissing performance we our proposed hardware setup. Overall, this paper aims to demonstrate the autonomous capabilities of our car, highlighting its readiness for the 2024 FIRA challenge, helping to contribute to the field of autonomous vehicle research.

'Fox & Friends' co-hosts discuss major issues with owning and maintaining electric vehicles after a Canadian man sounds off on problems with his new electric truck. Most electronic systems that take on some driving tasks for humans don't adequately make sure drivers are paying attention, and they don't issue strong enough warnings or take other actions to make drivers behave, according to an insurance industry study published Tuesday. Only one of 14 partially automated systems tested by the Insurance Institute for Highway Safety performed well enough to get an overall "acceptable" rating. Two others were rated "marginal," while the rest were rated "poor." No system received the top rating of "good."

Researchers from Tel Aviv University have created a robot that can smell and identify odours using a biological sensor. The researchers connected the sensor to an electronic system. They used a machine learning algorithm to detect odours with a level of sensitivity that is 10,000 times higher than that of a commonly used electronic device. The sensor sends electrical signals as a response to the presence of a nearby odour, which the robot can detect and interpret. According to the University, the researchers say, "The sky's the limit," and believe this technology may also be used to identify explosives, drugs, diseases, and more.

Electronic design automation (EDA), often known as computer-aided design (CAD), is a class of software tools used to create electronic systems like integrated circuits (ICs). EDA tools enable designers to create a design for large-scale integrated chips (VLSI) with billions of transistors. Due to the size and complexity of current electronic systems, EDA tools are crucial for VLSI design. The EDA research community has recently been actively investigating AI for IC methodologies to design cutting-edge chips, thanks to the explosion of artificial intelligence (AI) algorithms. Numerous studies have investigated machine learning-based solutions for cross-stage prediction tasks in the design cycle to promote speedier design convergence.

Electronic design automation (EDA) or computer-aided design (CAD) is a category of software tools for designing electronic systems, such as integrated circuits (ICs). With EDA tools, designers can finish the design flow of very large scale integrated (VLSI) chips with billions of transistors. EDA tools are essential to modern VLSI design due to the large scale and high complexity of electronic systems. Recently, with the boom of artificial intelligence (AI) algorithms, the EDA community is actively exploring AI for IC techniques for the design of advanced chips. Many studies have explored machine learning (ML) based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence.

This paper presents a method to simulate the thermal behavior of 3D systems using a graph neural network. The method discussed achieves a significant speed-up with respect to a traditional finite-element simulation. The graph neural network is trained on a diverse dataset of 3D CAD designs and the corresponding finite-element simulations, representative of the different geometries, material properties and losses that appear in the design of electronic systems. We present for the transient thermal behavior of a test system. The accuracy of the network result for one-step predictions is remarkable (\SI{0.003}{\%} error). After 400 time steps, the accumulated error reaches \SI{0.78}{\%}. The computing time of each time step is \SI{50}{ms}. Reducing the accumulated error is the current focus of our work. In the future, a tool such as the one we are presenting could provide nearly instantaneous approximations of the thermal behavior of a system that can be used for design optimization.

Neural Networks as fast physics simulators have a large potential for many engineering design tasks. Prerequisites for a wide-spread application are an easy-to-use workflow for generating training datasets in a reasonable time, and the capability of the network to generalize to unseen systems. In contrast to most previous works where training systems are similar to the evaluation dataset, we propose to adapt the type of training system to the network architecture. Specifically, we apply a fully convolutional network and, thus, design 3D systems of randomly located voxels with randomly assigned physical properties. The idea is tested for the transient heat diffusion in electronic systems. Training only on random "Minecraft" systems, we obtain good generalization to electronic systems four times as large as the training systems (one-step prediction error of 0.07 % vs 0.8 %). 1 INTRODUCTION The idea of using Neural Networks (NNs) as trainable physics simulators has seen much attention sparked by recent impressive results [1, 2, 3]. Once trained such a NN could predict physical properties much faster than any standard simulation method. Potential benefits can be seen in many fields of application, e.g.

Why are we on the verge of creating a technology that will combine the computer with the human nervous system into a single complex? Can a computer system handle the flood of data from billions of living neurons? I will try to answer these questions in this article. In the previous article "Individual artificial intelligence: A new technology that will change our world", we talked about the fact that a new type of artificial intelligence will become a bioelectronic hybrid in which a living human brain and a computer will work together. Thus, a new type of AI will be born – individual artificial intelligence.

Why are we on the verge of creating a technology that will combine the computer with the human nervous system into a single complex? Can a computer system handle the flood of data from billions of living neurons? I will try to answer these questions in this article. In the previous article "Individual artificial intelligence: A new technology that will change our world", we talked about the fact that a new type of artificial intelligence will become a bioelectronic hybrid in which a living human brain and a computer will work together. Thus, a new type of AI will be born – individual artificial intelligence.