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With the increase in legislative documents at the EU, the number of new terms and their definitions is increasing as well. As per the Joint Practical Guide of the European Parliament, the Council and the Commission, terms used in legal documents shall be consistent, and identical concepts shall be expressed without departing from their meaning in ordinary, legal, or technical language. Thus, while drafting a new legislative document, having a framework that provides insights about existing definitions and helps define new terms based on a document's context will support such harmonized legal definitions across different regulations and thus avoid ambiguities. In this paper, we present LexDrafter, a framework that assists in drafting Definitions articles for legislative documents using retrieval augmented generation (RAG) and existing term definitions present in different legislative documents. For this, definition elements are built by extracting definitions from existing documents. Using definition elements and RAG, a Definitions article can be suggested on demand for a legislative document that is being drafted. We demonstrate and evaluate the functionality of LexDrafter using a collection of EU documents from the energy domain.

In 2013, Jeff Bezos announced Amazon was developing a drone delivery service. He estimated at the time that air-dropped packages were "four, five years" away. Nearly a decade later, the service is promised to begin by the end of this year – albeit in only two locations in the US. According to David Carbon, an Australian expat and vice-president of the firm's drone delivery division, Amazon wants to deliver 500m packages annually by drone from 2030. Carbon told AAP earlier this month that the firm was planning a wider rollout for air deliveries in the US and potentially Australia.

In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies. The chance-constrained alternating current (AC) optimal power flow (OPF) framework finds the minimum cost generation dispatch maintaining the power grid operations within security limits with a prescribed probability. Unfortunately, the AC-OPF problem's chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and additional assumptions on the behavior of renewable distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach based on Gaussian process (GP) regression to close this gap. The GP approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertainty inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is illustrated over numerous IEEE test cases.

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.

Today, nearly everyone accepts that in order to slow the damage we are doing to our planet and environment, humans must transition away from the use of fossil fuels. This has led to many science and business innovations as we search for new sustainable or renewable alternatives to coal, oil, and gas. Although it would be nice to think everyone wants to do their part in order to save the world, there are strong financial incentives too. The value of the renewable energy market is set to grow from $880 billion to nearly $2 trillion by 2030. And the growing awareness of the importance of environmental and social governance (ESG) issues means there are tremendous political incentives, too.

If you started annotating today, your new year's resolutions will have comfortably been made and broken by the time you finish. And that's just for something novel, like training a system to pick out a lost child in a busy shopping mall. It takes even more images to help a delivery robot service safely navigate spaces where children are playing. The data scientists working on these systems can spend up to 80% of their time gathering, cleaning, and manually annotating real-world images to be digested by AI systems. It doesn't leave any time for network development or gleaning insights from the data.

Offshore wind power has fast become one of the most promising renewable sources of energy. Its growth is expected to continue, with generation capacity predicted to soar from 35GW to 234GW over the next 10 years, according to the Global Wind Energy Council (GWEC), which ranks the UK, Germany, and China as the largest national markets. The sector is a particular focus in governments' energy strategies, given the plummeting costs and the fact turbines can now be placed ever further from coastlines. Boris Johnson has even stated that he wants the UK to become the'Saudi Arabia of wind power'. The GWEC predicts significant growth over the next five years, with an estimated compound annual growth rate of nearly 32 percent, compared to just 0.3 percent with land-based turbines.

Human brains are extremely energy-efficient. When a person thinks in a concentrated manner, his or her brain consumes a mere 21 watts of electricity. But AI doing the same degree of intensive thinking requires over 10,000 times more electricity. If that is the case, the international competitiveness of businesses will depend on factors concerning the supply and cost of electricity in their home country. How, then, does Japan stand with regard to power supply and cost?

Advances in technology can allow you to order food by voice or unlock your phone with your face, but those new capabilities could take a toll on the environment. Enhanced tech capabilities are being developed through the use of artificial-intelligence approaches like neural networks, which detect patterns in speech and images by training programs across countless data points. That process constantly crunches reams of information on power-hungry servers in data centers that use a substantial amount of energy to power, cool and monitor the servers. The result: Training a neural network can emit 17 times more carbon dioxide than an average American does in a year, and five times the lifetime emissions of an average car. Those are the findings of a recent paper by researchers at the University of Massachusetts, Amherst, which highlighted the substantial power generated by AI technologies.