Safety has always been the ultimate concern in the field of autonomous vehicles, and aiming to address these concerns, software provider Oxbotica is using advanced artificial intelligence (AI) in the metaverse to accelerate the safe and efficient deployment of autonomous vehicle technology, while also reducing carbon emissions generated by vehicles when driving. The launch comes just weeks after Oxbotica announced that it had completed the first safe and sustainable deployment of a zero-occupancy, fully autonomous, new-type electric vehicle on publicly accessible roads in Europe. The new Oxbotica MetaDriver suite of tools includes virtual world simulation, automated discovery of challenging scenarios, and real-time data expansion. It is designed to help autonomous vehicles answer three core questions: "Do I see it right?", MetaDriver generates what the company says is a "vast" bank of scenarios that are used to test and refine autonomous vehicle operations and behaviours without ever needing to physically drive in them, accelerating commercial readiness.

The 50ft (15m) long solar-powered trimaran is capable of speeds of up to 10 knots (20km/h) and was navigated by on-board artificial intelligence (AI) created by IBM with information from six cameras and 50 sensors.

In the fast-changing world, technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are leading the next wave of productivity gains and tech changes. Artificial intelligence is the main branch of prediction-based technologies which includes domains like machine learning, neural networks and data science. The AI and ML technologies have thrown open a plethora of doors to new applications, solving complex problems and lessening the efforts. In the aame way, AI and ML may endorse the growth of the renewable energy sector in myriad ways. Enabling AI in grid management will also mean shifting from infrastructure-heavy legacy models to a grid that is more resilient and flexible.

An amphibious, 'shape-shifting' drone has been created that's worthy of its own James Bond film. The'dual robot' drone, called MEDUSA (Multi-Environment Dual robot for Underwater Sample Acquisition), is able to fly through the air and land on water in order to quickly collect samples for scientific studies. It has a pod tethered to it that can be deployed underwater remotely at hard-to-reach aquatic environments. Engineers at Imperial College London use the drone to measure lake water for signs of microorganisms and algal blooms, which can pose hazards to human health. In the future, it could be used to monitor climate clues like temperature changes in Arctic seas.

This paper described a method for reconstruction of detailed-resolution depth structure maps, usually obtained after the 3D seismic surveys, using the data from 2D seismic depth maps. The method uses two algorithms based on the generative-adversarial neural network architecture. The first algorithm StyleGAN2-ADA accumulates in the hidden space of the neural network the semantic images of mountainous terrain forms first, and then with help of transfer learning, in the ideal case - the structure geometry of stratigraphic horizons. The second algorithm, the Pixel2Style2Pixel encoder, using the semantic level of generalization of the first algorithm, learns to reconstruct the original high-resolution images from their degraded copies (super-resolution technology). There was demonstrated a methodological approach to transferring knowledge on the structural forms of stratigraphic horizon boundaries from the well-studied areas to the underexplored ones. Using the multimodal synthesis of Pixel2Style2Pixel encoder, it is proposed to create a probabilistic depth space, where each point of the project area is represented by the density of probabilistic depth distribution of equally probable reconstructed geological forms of structural images. Assessment of the reconstruction quality was carried out for two blocks. Using this method, credible detailed depth reconstructions comparable with the quality of 3D seismic maps have been obtained from 2D seismic maps.

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research.

Hot on the heels of forging a partnership with Ericsson to provide commercial 5G private networks for the UK market, BT is to install a new 5G private network and other surveillance and smart technology to enable the Port of Tyne to advance its ambition to become a world-class "smart port". The Port of Tyne is one of the UK's major deep-sea ports – operating in bulk and conventional cargo, car terminals, cruise and ferry, and port-centric logistics and estates. Entirely self-financing, it receives no government funding, is run on a commercial basis and reinvests all profits back into the port for the benefit of all of its stakeholders. During a decade of development, the Port of Tyne has invested more than £130m in diversifying its operations to handle a growing range of commodities. Building on the port's in-house capability – the 2050 Maritime Innovation Hub – the Port of Tyne is now implementing a new hybrid fibre, 4G and 5G private network to build a future-proofed digital technology infrastructure.

Whether it's deployed at the customer interface or on a purely operational level, AI can help firms to extract precious gems of insight from the mountain of data they're sitting on. The race for the data to help organisations reduce their emissions and strive for carbon neutrality also appears to be fuelling an unprecedented acceleration in the uptake of AI. "For example, companies can look to automatically link their energy usage with their physical asset systems to identify predictive maintenance opportunities to improve their environmental performance. Nowhere is that more evident than in the humming global data centres of Google, a company that's responsible for a significant proportion of the world's electricity output, particularly the energy needed to keep its servers cool and thereby reduce the risk of a devastating outage. If the technology can find a way to minimise its own carbon footprint, it will close a virtuous circle, helping its users to hit the net-zero targets that matter so much to all stakeholders in the climate crisis. Whether it's deployed at the customer interface or on a purely operational level, AI can help firms to extract precious gems of insight from the mountain of data they're sitting on. The race for the data to help organisations reduce their emissions and strive for carbon neutrality also appears to be fuelling an unprecedented acceleration in the uptake of AI. "For example, companies can look to automatically link their energy usage with their physical asset systems to identify predictive maintenance opportunities to improve their environmental performance.

This article was published as a part of the Data Science Blogathon. The Hough transform (HT) is a feature extraction approach in image analysis, computer vision, and digital image processing [1]. It uses a voting mechanism to identify bad examples of objects inside a given class of forms. This voting mechanism is carried out in parameter space. Object candidates are produced as local maxima in an accumulator space using the HT algorithm.

New Delhi: Around 2018, the overall cost of generating electricity from Renewable sources (solar, wind) became cheaper than the traditional methods of electricity generation (coal, oil, gas, nuclear). More than half of new electricity generation capacity added in 2021 were Renewables, and at the same time, the amount electricity distribution grids were willing to pay per unit of Renewable energy began to drop significantly. Managing the accelerated growth in capacity, while driving down costs, has become a must for Renewable plants. Just as Renewable energy has grown in the last decade so has the field of Artificial Intelligence (AI). Traditional computing is software programmers creating algorithms, to solve for complex engineering problems.