Adam Roberts' Lake of Darkness opens as two space ships investigate a black hole The starting point for this novel was that I wanted to write utopian fiction. I hadn't done this before: all my previous novels have been straight science fiction. But utopia, the genre that imagines a better, or a perfect, world, is older than science fiction: the first utopian novel, the work that coined the term, was written by Thomas More all the way back in 1516. I was interested in what happened to the mode: More's Utopia generated lots of imitators. Through the 17th and 18th centuries, a great many utopian books, novels, tracts and treatises were written.

Oscar Wilde thought hard work "the refuge" of those with nothing better to do while he envisaged a society of "cultivated leisure" as machines performed the necessary and unpleasant tasks. Karl Marx's dream was of state-regulated general production that allowed liberated workers to "hunt in the morning, fish in the afternoon, rear cattle in the evening, criticise after dinner" without the drudgery of being tied to one job. The 19th-century socialist activist William Morris advocated for more pleasurable work, believing that once the profit motive of the factory had been abolished, less necessary labour would led to a four-hour day. So Elon Musk's suggestion to Rishi Sunak that society could reach a point where "no job is needed" and "you can do a job if you want a job … but the AI will do everything" revives a debate on the issue of how we work that has long been discussed. Yet a world without work, experts question, may be more dystopian than utopian.

Uncertainty quantification for prediction is an intriguing problem with significant applications in various fields, such as biomedical science, economic studies, and weather forecasts. Numerous methods are available for constructing prediction intervals, such as quantile regression and conformal predictions, among others. Nevertheless, model misspecification (especially in high-dimension) or sub-optimal constructions can frequently result in biased or unnecessarily-wide prediction intervals. In this paper, we propose a novel and widely applicable technique for aggregating multiple prediction intervals to minimize the average width of the prediction band along with coverage guarantee, called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA). The method also allows us to directly construct predictive bands based on elementary basis functions. Our approach is based on linear or convex programming which is easy to implement. All of our proposed methodologies are supported by theoretical guarantees on the coverage probability and optimal average length, which are detailed in this paper. The effectiveness of our approach is convincingly demonstrated by applying it to synthetic data and two real datasets on finance and macroeconomics.

Who is behind it?: Two leading AI labs cited building AGI in their mission statements: OpenAI, founded in 2015, and DeepMind, a research lab founded in 2010 and acquired by Google in 2014. Still, the concept might have stayed on the margins if not for the same wealthy tech investors interested in the outer limits of AI. Musk invested in DeepMind and introduced the company to Google co-founder Larry Page. Musk brought the concept of AGI to OpenAI's other co-founders, like CEO Sam Altman.

Whether you're hip to it or not, conversational AI--which is really the sequencing of technologies like NLU/NLP, code-free programming, RPA, and machine learning inside of organizational ecosystems--has already begun reshaping the world at large. Lemonade, a tech- and user-centric insurance company is upending its industry by providing customers with a rewarding experience buying insurance that's facilitated by Maya, an intelligent digital worker described as "utterly charming" that can quickly connect dots and get customers insured. Maya is essentially an infinitely replicable agent that is always learning and doesn't make the same mistake twice. Compare that with whatever it costs Allstate to retain more than 12,000 agents in the US and Canada who are likely using outdated legacy systems and it's clear to see which way ROI is trending. Even bigger successes have been enjoyed by Ant Group (formerly Ant Financial) a nimble, Chinese financial giant that had surpassed the number of customers served by today's largest US banks by more than 10 times back at the start of 2020. Their IPO--which would have been the world's largest to date--collapsed after Chinese Communist Party leader Xi Jinping allegedly intervened. Subsequently, the company has broadened its scope past fintech to include sustainability and inclusive services (whatever those might be). Still, its core operations were built around a streamlined business structure that uses conversational AI to deliver meaningful experiences. While this kind of adoption of conversational AI in business settings is roundly expected to boom in the coming years, it will quickly seep into our daily lives as well, going beyond how we interact with the many companies in our lives and taking root in our interactions with all of the different technologies we regularly touch. I've always taken an interest in these topics, but like many cutting-edge things, they're hard to approach. Especially if you have no idea where to start. Especially if you don't have the expertise; the lexicon, the mindset, the lived experience. I'm a bit of an accidental Luddite, someone perpetually late to the party when it comes to the latest and greatest. Not to say that I'm completely unfamiliar with these things, just that integrating them into my work, and my life, is hard.

I'm sure you've heard of or seen the movie "The Matrix''. The film follows our protagonist, Neo, as he tries to save humanity from the matrix: a fabricated world created by artificial intelligence to trap humans in. Artificial Intelligence (or "AI" for short) is becoming increasingly more prevalent in our lives and will play an instrumental part in humanity's future. While many view it as something that can be beneficial, it can also be devastatingly destructive. However, the reason we should fear AI has nothing to do with a machine's consciousness or the risk of its rebellion, but something much simpler.

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.

I had not yet received my double lung transplant when I first played Mass Effect, BioWare's sprawling space opera shooter, in 2007. I had not yet started taking pills to quiet my own immune system when Tali'Zorah nar Rayya first graced my screen. Back then, I didn't think twice about the Quarian engineer in the purple bio-suit, immunocompromised from a lifetime adrift in the stars, making her way through a galaxy teeming with alien bacteria and openly antagonistic to her continued health. Over a decade later, though, things have changed. I am, quite literally, a different person.

Picture this: Colossal, gas-powered autonomous robots bulldoze across acres of homogeneous farmland under a blackened sky that reeks of pollution. The trees have all been chopped down and there are no animals in sight. Pesticides are sprayed in excess because humans no longer tend to the fields. The machines do their jobs--producing massive amounts of food to feed our growing population--but it's not without ecological cost. Or, envision another future: Smaller robots cultivate mosaic plots of many different crops, working around the trees, streams, and wildlife of the natural landscape.

But before delving into'behind-the-scenes' of US banking industry meeting ATM, let's turn back time for a second -- on March 27th, 1998, in the New Tech 1998 conference in Denver, Colorado. Here, Neil Postman, a prominent American cultural critic and professor at New York University, gave a keynote lecture. Professor Postman has been a long-time scholar of how new technologies relate to human society, and the book'Amusing Ourselves to Death', a 1985 book that rose to stardom, shows how television technology is destroying public discourse and turning everything into entertainment. I think it has something to do with how we feel about the impact of today's media and how our lives exposed to it are deteriorating. Since this book, Professor Postman has strongly criticized the tendency to respond to all social problems through technical solutions.