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The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

The article utilizes the adaptive learning computer system AlphaZero in order to examine how variations of the game of chess would proceed at what is referred to as a superhuman level. According to the article, AlphaZero marks a significant milestone in the field of artificial intelligence (AI) and the use of algorithms in computer chess.

The article focuses on the use of algorithms and machine intelligence to develop new chemical molecules and retrosynthesis. It comments on researchers Marwin Segler and Mark Waller's development of a neural network which learned the rules of organic chemistry and predicted routes to complex structures that would require six synthetic steps to make. It states chemist Elias Corey developed the concept of retrosynthesis and used it on the molecule longifolene which is found in pine resin.

Purpose – When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. Design/methodology/approach – This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. Findings – The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models. Originality/value – This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold. [ABSTRACT FROM AUTHOR]

As the most important property of neural networks (NNs), the universal approximation capability of NNs is widely used in many applications. However, this property is generally proven for continuous systems. Most industrial systems are hybrid systems (e.g., piecewise continuous), which is a significant limitation for real applications. Recently, many identification methods have been proposed for hybrid system approximation; however, these methods only operate in linear hybrid systems. In this paper, the progressive learning machine—a new learning algorithm based on multi-NNs—is proposed for general hybrid nonlinear/linear system approximation. This algorithm classifies hybrid systems into several continuous systems and can approximate any hybrid system with zero output error. The performance of the proposed learning method is demonstrated via numerical examples and with experimental data from real applications. [ABSTRACT FROM AUTHOR]

Algorithms, Cyborgs, Emerging Technologies, Machine Learning, Metaheuristic Algorithm, Networks, Neural Networks, Supervised Learning Keywords: Algorithms; Cyborgs; Emerging Technologies; Machine Learning; Metaheuristic Algorithm; Networks; Neural Networks; Supervised Learning EN Algorithms Cyborgs Emerging Technologies Machine Learning Metaheuristic Algorithm Networks Neural Networks Supervised Learning 1433 1433 1 05/02/23 20230505 NES 230505 2023 MAY 5 (NewsRx) -- By a News Reporter-Staff News Editor at Genomics & Genetics Weekly -- Investigators discuss new findings in artificial intelligence. [Extracted from the article]

The traditional extreme learning machine (ELM) approach is based on a random assignment of the hidden weight values, while the linear coefficients of the output layer are determined analytically. This brief presents an analysis based on geometric properties of the sampling points used to assign the weight values, investigating the replacement of random generation of such values with low-discrepancy sequences (LDSs). Such sequences are a family of sampling methods commonly employed for numerical integration, yielding a more efficient covering of multidimensional sets with respect to random sequences, without the need for any computationally intensive procedure. In particular, we prove that the universal approximation property of the ELM is guaranteed when LDSs are employed, and how an efficient covering affects the convergence positively. Furthermore, since LDSs are generated deterministically, the results do not have a probabilistic nature. Simulation results confirm, in practice, the good theoretical properties given by the combination of ELM with LDSs. [ABSTRACT FROM AUTHOR]

The article reports on the ability of the artificial intelligence (AI) developed in Japan by researcher Ichiro Kobayashi and his colleagues at Ochanomizu University where it learned to associate certain patterns of brain activity with certain features within the images of the person was viewing. It mentions that both components of the AI contain a type of algorithm called a neural network inspired by the way neurons connect in the brain.