Three books unpack our infatuation with prediction, and what we lose when we outsource this task to machines. To be human is, fundamentally, to be a forecaster. Trying to see the future, whether through the lens of past experience or the logic of cause and effect, has helped us hunt, avoid hunted, plant crops, forge social bonds, and in general survive in a world that does not prioritize our survival. Indeed, as the tools of divination have changed over the centuries, from tea leaves to data sets, our conviction that the future can be known (and therefore controlled) has only grown stronger. Today, we are awash in a sea of predictions so vast and unrelenting that most of us barely even register them. As I write this sentence, algorithms on some remote server are busy trying to guess my next word based on those I have already typed.

Recent memoirs by the retired bookie Art Manteris and the storied gambler Billy Walters provide a glimpse of an industry in its fledgling form--and a preview of the DraftKings era to come. Las Vegas is no longer the seat of the sportsbook gods. In most states, it's now legal, and extremely popular, to place bets using apps or websites such as FanDuel and DraftKings. From your couch, you can wager on everything from the results of snooker championships to the color of the Gatorade poured over the victorious coach after the Super Bowl. The N.F.L., along with the other major-league American sports associations, has officially partnered with sports-betting sites, and their alliance has proved so lucrative that other industries want in on the action; last month, the Golden Globes made a deal with Polymarket, a predictions-market platform, to encourage wagering (or "trading," if you prefer) on the outcomes of its awards race.

It's nowhere near early enough for those of us in the northern hemisphere to start struggling against winter's somnolent spell, so there's no need for excuses as you take to your bed with a pile of good books. And there's plenty to keep you occupied while you eschew the chilly outdoors. This month, we have climate hope from a well-placed environmental reporter, formerly of this parish, an honest memoir from a star scientist and a jaw-dropping account of the commodification of women's bodies. Given the Valentine's Day fun this month, we also have a book that may challenge what we thought we knew about finding love. It's always good to get all the help we can in that department - enjoy! "On clear moonlit nights we sometimes step outside and howl at the moon together. It is cathartic, primal and a really good laugh. I am not sure what our neighbours think about it, though."

Four new books grapple with a global mental-health crisis and the dawn of algorithmic therapy. A technician adjusts the wiring inside the Mark I Perceptron. This early AI system was designed not by a mathematician but by a psychologist. More than a billion people worldwide suffer from a mental-health condition, according to the World Health Organization. The prevalence of anxiety and depression is growing in many demographics, particularly young people, and suicide is claiming hundreds of thousands of lives globally each year. Given the clear demand for accessible and affordable mental-health services, it's no wonder that people have looked to artificial intelligence for possible relief.

We only included a single environment (Pusher-v2) in the main paper in order to save space. We will include the suggested references into the paper. See also About multi-step rollouts . The reviewer suggests that the paper should first "show that minimizing the TD-error is not Notice, however, that despite being commonly used and thought of as "intuitive", Furthermore, Figure 1 shows indeed that minimizing the TD-error can lead to a critic being far away from the ideal one. We did not write that "model-based RL has no advantage in terms of sample-efficiency than model-free RL".

Whenever I fantasise about a couple of hours of uninterrupted relaxation during the chilly winter months, my mind immediately conjures up images of curling up on the sofa with a deliciously good book. And when summer eventually comes around, just swap the location to a sun lounger in the back garden (or somewhere more exotic). So with 2026 nearly upon us, join me for an eclectic taste of a few literary delights worth feasting upon over the next 12 months. It's the final instalment of Oseman's hit graphic novel series which has followed the lives of Nick and Charlie, two teenage boys who fall for each other at school. Along with their friends, we've followed all the ups and downs of their relationship as they navigated family drama, homophobia and mental health issues, alongside the joy of first love.

The ideas presented in George Lakoff and Srini Narayanan's The Neural Mind are fascinating, but the writing is far less compelling This is a book review in two parts. The first is about the ideas presented in The Neural Mind: How brains think, which are fascinating. The second is about the actual experience of reading it. The book tackles one of the biggest questions in neuroscience: how do neurons perform all the different kinds of human thought possible, from planning motor actions to composing sentences and musing about philosophy? The authors have very different perspectives.

Is the Dictionary Done For? The print edition of Merriam-Webster was once a touchstone of authority and stability. Then the internet brought about a revolution. Wars over words are inevitably culture wars, and debates over the dictionary have raged for as long as it has existed. Once, every middle-class home had a piano and a dictionary. The purpose of the piano was to be able to listen to music before phonographs were available and affordable. Later on, it was to torture young persons by insisting that they learn to do something few people do well. The purpose of the dictionary was to settle intra-family disputes over the spelling of words like "camaraderie" and "sesquipedalian," or over the correct pronunciation of "puttee." This was the state of the world not that long ago. In the late nineteen-eighties, Merriam-Webster's Collegiate Dictionary was on the best-seller list for a hundred and fifty-five consecutive weeks. Fifty-seven million copies were sold, a number believed to be second only, in this country, to sales of the Bible. There was good money in the word business.

This chapter discusses the ethics of generative AI. It provides a technical primer to show how generative AI affords experiencing technology as if it were human, and this affordance provides a fruitful focus for the philosophical ethics of generative AI. It then shows how generative AI can both aggravate and alleviate familiar ethical concerns in AI ethics, including responsibility, privacy, bias and fairness, and forms of alienation and exploitation. Finally, the chapter examines ethical questions that arise specifically from generative AI's mimetic generativity, such as debates about authorship and credit, the emergence of as-if social relationships with machines, and new forms of influence, persuasion, and manipulation.

Transformer models have significantly advanced Natural Language Processing (NLP), demonstrating strong performance in English. However, their effectiveness in Arabic, particularly for Named Entity Recognition (NER), remains limited, even with larger pre-trained models. This performance gap stems from multiple factors, including tokenisation, dataset quality, and annotation inconsistencies. Existing studies often analyze these issues in isolation, failing to capture their joint effect on system behaviour and performance. We introduce DeformAr (Debugging and Evaluation Framework for Transformer-based NER Systems), a novel framework designed to investigate and explain the performance discrepancy between Arabic and English NER systems. DeformAr integrates a data extraction library and an interactive dashboard, supporting two modes of evaluation: cross-component analysis and behavioural analysis. The framework divides each language into dataset and model components to examine their interactions. The analysis proceeds in two stages. First, cross-component analysis provides systematic diagnostic measures across data and model subcomponents, addressing the "what," "how," and "why" behind observed discrepancies. The second stage applies behavioural analysis by combining interpretability techniques with token-level metrics, interactive visualisations, and representation space analysis. These stages enable a component-aware diagnostic process that detects model behaviours and explains them by linking them to underlying representational patterns and data factors. DeformAr is the first Arabic-specific, component-based interpretability tool, offering a crucial resource for advancing model analysis in under-resourced languages.