Will Storr: 'I was shocked to find my daily average was over four hours.' Will Storr: 'I was shocked to find my daily average was over four hours.' We have lost so much of ourselves to smartphones: can we get it back? My use of mobile phones has been compulsive - has it been for better or for worse? From a priest to a pensioner, a teenager to a tech CEO: can you guess our screen time? I n 2003, the Stanford social scientist BJ Fogg published an extraordinarily prescient book.

"Designed to be deleted" is the tagline of one of the UK's most popular dating apps. Hinge promises that it is "the dating app for people who want to get off dating apps" – the place to find lasting love. But critics say modern dating is in crisis. They claim that dating apps, which have been downloaded hundreds of millions of times worldwide, are "exploitative" and are designed not to be deleted but to be addictive, to retain users in order to create revenue. An Observer investigation has found that dating apps are increasingly pushing users to buy extras that have been likened to "gambling products" and can cost hundreds of pounds a year.

Multi-agent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. We propose a photonic-based decision-making algorithm to address one of the most fundamental problems in MARL, called the competitive multi-armed bandit (CMAB) problem. Our numerical simulations demonstrate that chaotic oscillations and cluster synchronization of optically coupled lasers, along with our proposed decentralized coupling adjustment, efficiently balance exploration and exploitation while facilitating cooperative decision-making without explicitly sharing information among agents. Our study demonstrates how decentralized reinforcement learning can be achieved by exploiting complex physical processes controlled by simple algorithms.

A class-action lawsuit filed in a US federal court last Valentine's Day accuses Match Group – the owners of Tinder, Hinge and OkCupid dating apps, among others – of using a "predatory business model" and of doing everything in its power to keep users hooked, in flagrant opposition to Hinge's claim that it is "designed to be deleted". The lawsuit crystallised an ocean of dissatisfaction with the apps, and stimulated a new round of debate over their potential to harm mental health, but for scientists who study romantic relationships it sidestepped the central issue: do they work? Does using the apps increase your chances of finding your soulmate, or not? The answer is, nobody knows. "The science isn't there," says sociologist Elizabeth Bruch of the University of Michigan, who has studied online dating for a decade.

We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their beliefs in an asymmetric manner and learn more from better-than-expected outcomes than from worse-than-expected ones. Furthermore, we show that this effect reverses when learning about counterfactual feedback and disappears when no agency is implied. We corroborate these findings by investigating idealized in-context learning agents derived through meta-reinforcement learning, where we observe similar patterns. Taken together, our results contribute to our understanding of how in-context learning works by highlighting that the framing of a problem significantly influences how learning occurs, a phenomenon also observed in human cognition.

Photonic accelerators [1] have been gaining attention in recent years, and a variety of implementations and applications have now been explored [2-9]. These advancements can be attributed to a growing awareness of the saturating speed of performance improvements in conventional computational systems [10], despite the soaring demands for information processing in an extensive range of applications, especially in machine learning. Reinforcement learning [11] is a subfield of machine learning that involves optimizing computer outputs or actions to maximize the reward function. Its applications are now essential to our daily lives, ranging from self-driving vehicles [12] and targeted advertising [13] to wireless networking [14], and there is now a strong demand for computational acceleration. Specifically, what we focus on here is decision-making.

After years spent pursuing a career in game development, Italian coder Luca Galante had given up. Uprooting himself from a comfortable life in Rome, he flew to England in the hope of finally making his childhood dream a reality. Yet after countless rejected job applications, Galante found himself flipping Big Macs in Thornton Heath McDonald's. Dejected, he gave up on his digital dream, leaving what he says might be "the worst McDonald's in the UK" to code slot machines for a gambling company. Now, 10 years and one bedroom-made game later, Galante is the proud owner of two Baftas.

A random walk(RW) is one of the most ubiquitous stochastic processes and is employed for both mathematical analyses and applications, such as describing real-world phenomena and constructing various algorithms. Meanwhile, along with the increasing interest in quantum mechanics from both theoretical and applied perspectives, the quantum counterpart of a RW, known as a quantum walk (QW), is also attracting attention [1-4]. A QW includes the effects of quantum superposition or time evolution. In classical RWs, a random walker(RWer) selects in which direction to go probabilistically at each time step, and thus one can track where the RWer is at any time step. On the other hand, in QWs, one cannot tell where a quantum walker (QWer) exists during the time evolution, and the location is determined only after conducting the measurement. QWs have a property that classical RWs do not possess: the coexistence of linear spreading and localization [5, 6]. As a result, QWs show probability distributions that are totally different from those of random walks, which weakly converge to normal distributions. The former behavior, linear spreading, means that the standard deviation of the probability distribution of measurement of quantum walkers (QWers) grows in proportion to the run time t.

Charles Fey was the original inventor of the slot machine. However, if he had been cryogenically frozen in the early 1900s and then thawed out now and presented with a modern-day internet slot game, he probably wouldn't have a clue how to use it. That's how far the games have come since the San Francisco mechanic came up with the concept for the Liberty Bell, the first ever hand operated slot machine. Slots have evolved with every major technological innovation throughout their rich history, and they look set to take the next step with artificial intelligence. This revolutionary platform that's currently sweeping the world could enhance the games greatly.

A multi-armed bandit (MAB) is a machine learning framework that uses complex algorithms to dynamically allocate resources when presented with multiple choices. In other words, it's an advanced form of A/B testing that's most commonly used by data analysts, medicine researchers, and marketing specialists. Before we delve deeper into the concept of multi-armed bandits, we need to discuss reinforcement learning, as well as the exploration vs. exploitation dilemma. Then, we can focus on various bandit solutions and practical applications. Alongside supervised and unsupervised learning, reinforcement learning is one of the basic three paradigms of machine learning. Unlike the first two archetypes we mentioned, reinforcement learning focuses on rewards and punishments for the agent whenever it interacts with the environment.