The increasing integration of renewable energy sources has led to greater volatility and unpredictability in electricity generation, posing challenges to grid stability. Ancillary service markets, such as the German control reserve market, allow industrial consumers and producers to offer flexibility in their power consumption or generation, contributing to grid stability while earning additional income. However, many participants use simple bidding strategies that may not maximize their revenues. This paper presents a methodology for forecasting bidding prices in pay-as-bid ancillary service markets, focusing on the German control reserve market. We evaluate various machine learning models, including Support Vector Regression, Decision Trees, and k-Nearest Neighbors, and compare their performance against benchmark models. To address the asymmetry in the revenue function of pay-as-bid markets, we introduce an offset adjustment technique that enhances the practical applicability of the forecasting models. Our analysis demonstrates that the proposed approach improves potential revenues by 27.43 % to 37.31 % compared to baseline models. When analyzing the relationship between the model forecasting errors and the revenue, a negative correlation is measured for three markets; according to the results, a reduction of 1 EUR/MW model price forecasting error (MAE) statistically leads to a yearly revenue increase between 483 EUR/MW and 3,631 EUR/MW. The proposed methodology enables industrial participants to optimize their bidding strategies, leading to increased earnings and contributing to the efficiency and stability of the electrical grid.

Market-based agents refer to reinforcement learning agents which determine their actions based on an internal market of sub-agents. We introduce a new type of market-based algorithm where the state itself is factored into several axes called ``goods'', which allows for greater specialization and parallelism than existing market-based RL algorithms. Furthermore, we argue that market-based algorithms have the potential to address many current challenges in AI, such as search, dynamic scaling and complete feedback, and demonstrate that they may be seen to generalize neural networks; finally, we list some novel ways that market algorithms may be applied in conjunction with Large Language Models for immediate practical applicability.

Back when Munich Re launched the industry's first insurance coverage for artificial intelligence (AI) at the end of 2018, the policy, aiSure, found few takers. In subsequent years, as more companies digitally transformed their business and AI models and machine learning tools went mainstream, business picked up, prompting Munich Re last year to form a dedicated AI insurance team to scale aiSure and develop additional AI insurance policies. Online bonus: Munich Re provides a real-life example of an actual AI risk insured with the aiSure offering. Chief among the reasons is that Munich Re's estimable reputation and clout is sure to inspire other insurers and reinsurers to develop innovative AI-based products, culminating in a new insurance market. Given the rapidly growing use of AI software by companies on a function-by-function basis, viable risk transfer solutions absorbing the risks of AI-generated errors are sorely needed.

Almost every industry in the world of commerce has been invaded by artificial intelligence. It has changed how people and business function, and it is quickly becoming into an essential element of modern warfare. The size and capability of an army is one of the factors that determine how powerful a country is. In some of the most developed countries, investment in this area is the highest when compared to other sectors. A significant portion of these funds are devoted to research and development in modern technologies, including artificial intelligence (AI) for use in the military.

PORTLAND, OR, USA, UNITED STATES, November 9, 2022 / / -- Increase in investment by companies in & machine learning and rise in preference for personalized insurance services boost the growth of the global market. Allied Market Research published a report, titled, 'AI in Insurance Market by Offering (Hardware, Software, Service), by Deployment Model (On-premise, Cloud), by Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), by Enterprise Size (Large Enterprises, SMEs), by End-user (Life and Health Insurance, Property and Casualty Insurance), by Application (Fraud Detection and Credit Analysis, Customer Profiling and Segmentation, Product and Policy Design, Underwriting and Claims Assessment): Global Opportunity Analysis and Industry Forecast, 2021-2031'. According to the report, the global AI in insurance industry generated $2.74 billion in 2021, and is anticipated to generate $45.74 billion by 2031, witnessing a CAGR of 32.5% from 2022 to 2031. Increase in investment by insurance companies in AI & machine learning, surge in collaboration between insurance companies and AI & machine learning solution companies, and rise in preference for personalized insurance services boost the growth of the global AI in insurance market. However, high deployment cost of AI & advanced machine learning and lack of skilled labor hamper the market growth. On the contrary, increase in government initiatives and rise in investments to leverage the AI technology are expected to offer remunerative opportunities for expansion of the market during the forecast period.

LONDON – The Global Artificial Intelligence in Aviation Market size was estimated at USD 508.89 million in 2021, USD 697.59 million in 2022, and is projected to grow at a CAGR of 37.25% to reach USD 3,402.84 million by 2027. Late last month, the "Artificial Intelligence in Aviation Market Research Report by Technology, Offering, Application, Region – Global Forecast to 2027 – Cumulative Impact of COVID-19" Report was published by Research And Markets. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period. The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Aviation Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support). The Matrix also considers Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Reply is one of the leading full-service providers for Digital Experience Services (DXS) in the new study "The Market for Digital Experience Services in Germany 2022" released by the market research company Lünendonk. The assessment includes the revenues generated in 2021 on three main categories – i.e., Digital Consulting Services, Digital Agency Services and Digital Technology Services – as well as the evaluation received by providers and customers of Digital Experience Services. According to the Lünendonk study, digital experience services are gaining increasing importance. In fact, the revenue in the digital experience services segment has considerably expanded in the last two years and is forecast to grow by 17.8 percent in 2023. Next-gen AI-On-Demand Platform: European Commission Pumps in $9.15 Million to Develop Next-gen AI-On-Demand Platform The user companies interviewed by Lünendonk confirmed that the investments in digital experience services of the past years are showing beneficial results and many are planning to invest further on cybersecurity, artificial intelligence (AI), cloud native and the development of data platforms. Even the metaverse, for which not all companies have already identified use cases for, it is also gaining higher consideration.

The Artificial Intelligence In Genomics Market Size is expected to reach USD Billion by 2027, at a CAGR of 53% during the forecast period from 2021 to 2027. This report covers a sub-market in this field the Artificial Intelligence In Genomics Market by offering type in detail, segmenting the market as Software, Services. The scope of the report covers technology segment which includes Machine Learning, Deep Learning, Supervised Learning, Reinforcement Learning, and Unsupervised Learning. The segment Functionality type segregated into Genome Sequencing Gene Editing Clinical Workflows Predictive Genetic Testing & Preventive Medicine. Moreover, it provides in-sights on Application that segregates into Diagnostics Drug Discovery & Development Precision Medicine Agriculture & animal Research Other Applications.

Machine Learning as a Service (MLaaS) Market Innovative Strategy by 2030 | Amazon, Oracle, IBM, Microsoftn, Google. Photo of Mark MarkOctober 4, 2022.

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