We propose a nested weighted Tchebycheff Multi-objective Bayesian optimization framework where we build a regression model selection procedure from an ensemble of models, towards better estimation of the uncertain parameters of the weighted-Tchebycheff expensive black-box multi-objective function. In existing work, a weighted Tchebycheff MOBO approach has been demonstrated which attempts to estimate the unknown utopia in formulating acquisition function, through calibration using a priori selected regression model. However, the existing MOBO model lacks flexibility in selecting the appropriate regression models given the guided sampled data and therefore, can under-fit or over-fit as the iterations of the MOBO progress, reducing the overall MOBO performance. As it is too complex to a priori guarantee a best model in general, this motivates us to consider a portfolio of different families of predictive models fitted with current training data, guided by the WTB MOBO; the best model is selected following a user-defined prediction root mean-square-error-based approach. The proposed approach is implemented in optimizing a multi-modal benchmark problem and a thin tube design under constant loading of temperature-pressure, with minimizing the risk of creep-fatigue failure and design cost. Finally, the nested weighted Tchebycheff MOBO model performance is compared with different MOBO frameworks with respect to accuracy in parameter estimation, Pareto-optimal solutions and function evaluation cost. This method is generalized enough to consider different families of predictive models in the portfolio for best model selection, where the overall design architecture allows for solving any high-dimensional (multiple functions) complex black-box problems and can be extended to any other global criterion multi-objective optimization methods where prior knowledge of utopia is required.

For decades, countless explanations have been proposed for this phenomenon, but the scientific community has yet to reach a consensus on the topic. Recently, Eric Hall, Research Assistant Professor of Neuroscience at Tufts University (USA), added his own theory to the list. Published in the scientific journal Patterns – a drawing, Hoel's hypothesis is inspired by technologies used to train deep AI neural networks and suggests that the weirdness of our dreams helps the brain better adapt to our everyday experiences. "(The theory) assumes that the very experience of dreams is the cause of our dream," says Hoyle. To support this argument, Hoel relies on an AI training process.

An AI trawled 3.5M books and found fundamental differences in the written language we use to describe men and women. An unsupervised machine learning study presented at the 2019 meeting of Association for Computational Linguistics--which examined 3.5M books published between 1900 and 2008--indicates that men are described based on their behavior, where women are described based on appearance. In specific, words like "beautiful" and "sexy" are two of the adjectives most frequently used to describe women, while common descriptors for men were "brave," "rational," and "righteous." The books, which amounted to approximately 11B words in sum, included a mix of fiction and non-fiction. "We are clearly able to see that the words used for women refer much more to their appearances than the words used to describe men," said University of Copenhagen computer scientist and assistant professor Isabelle Augenstein in a statement.

An unsupervised machine learning study presented at the 2019 meeting of Association for Computational Linguistics--which examined 3.5M books published between 1900 and 2008--indicates that men are described based on their behavior, where women are described based on appearance. In specific, words like "beautiful" and "sexy" are two of the adjectives most frequently used to describe women, while common descriptors for men were "brave," "rational," and "righteous." The books, which amounted to approximately 11B words in sum, included a mix of fiction and non-fiction. "We are clearly able to see that the words used for women refer much more to their appearances than the words used to describe men," said University of Copenhagen computer scientist and assistant professor Isabelle Augenstein in a statement. "Thus, we have been able to confirm a widespread perception, only now at a statistical level."

Voiceless in his life so far, a severely disabled 16-year-old is marvelling at being able to speak for the first time after breaking his silence with the words "Hello Mum", using a digital communication aid. James Walker is a rugby fan, likes pop music, lives with his family in Hull and has a girlfriend - Emily. He has a condition which caused hundreds of daily seizures when he was a child. Known as Lennox-Gastaut Syndrome, it left him with a severe learning disability and without the ability to walk or move. He says it's "funny" after being silent for so long that he can now communicate with friends and family and, as he puts it, "learn something exciting".