Lego's next release is a digital brick loaded with sensors that add new layers of interactivity to its play sets. WIRED got exclusive access to the Lego labs where the Smart Brick was born. The secretive division of 237 staff based here and in London, Boston, and Singapore is dedicated to thinking up what comes next for the world's largest toy brand. In front of me, on a plain white table, is a batch of prototypes of Lego's new Smart Brick, the final version of which is a small, sensor-laden 2-by-4 black brick with a big brain. No outsider has seen these prototypes, all of which represent stages of a journey Lego has been charting over the past eight years. Lego hopes this innovation, which lands in stores March 1, will safeguard the future of its plastic empire. The diminutive proportions of the finished Smart Brick belie the fact that the thing is exceedingly clever. Inside is a tiny custom chip running bespoke software that can communicate with onboard sensors to monitor and react to motion, orientation, and magnetic fields. It's also likely no exaggeration that the Smart Brick could represent the most radical product Lego has produced since Jens Nygaard Knudsen, the company's former longtime chief designer, created the minifigure nearly 50 years ago.

Our obsession with deadly game shows--from "The Running Man" and "Squid Game" to MrBeast's real-life reënactments--reflects a shift in the national mood to something increasingly zero-sum. It seems we can't get enough of game shows in which the losers die. "The Hunger Games" became a multibillion-dollar media franchise over the past decade, with audiences returning to the theatre, time and time again, to watch adolescents try to kill one another in an enormous arena--a contest devised by the leaders of a society rife with inequality. Netflix's " Squid Game " followed four hundred and fifty-six desperate individuals into an underworld where they play lethal versions of children's games in the hope of winning a life-changing amount of money. Four weeks after its release, the show had become Netflix's most-watched series ever; to date, the first season has been viewed more than two hundred and sixty-five million times.

Yau's theorem guarantees the existence of a unique Ricci-flat K ahler metric with a given K ahler class on a Calabi-Yau (CY) manifold. Such Ricci-flat metrics are of mathematical interest and they play an important role in compactifications of string theory. Unfortunately, for compact Calabi-Yau manifolds of complex dimension three or higher, analytic expressions for Ricci-flat metrics are not known. Over the past few years substantial progress has nevertheless been made in numerically computing Ricci-flat metrics on Calabi-Yau three-folds, starting with Donaldson's algorithm [1] and its applications [2-9] and, more recently, using machine learning methods [10-16]. The Ricci-flat metric in numerical form is already useful, enabling us to compute the spectrum of the Laplacian on a CY manifold [5,17] or the masses of quarks in a CY string compactification [18], to name just two applications. However, it would be all the more helpful and exciting to deal with the metric analytically.

Motivated by recent progress in the problem of numerical K\"ahler metrics, we survey machine learning techniques in this area, discussing both advantages and drawbacks. We then revisit the algebraic ansatz pioneered by Donaldson. Inspired by his work, we present a novel approach to obtaining Ricci-flat approximations to K\"ahler metrics, applying machine learning within a `principled' framework. In particular, we use gradient descent on the Grassmannian manifold to identify an efficient subspace of sections for calculation of the metric. We combine this approach with both Donaldson's algorithm and learning on the $h$-matrix itself (the latter method being equivalent to gradient descent on the fibre bundle of Hermitian metrics on the tautological bundle over the Grassmannian). We implement our methods on the Dwork family of threefolds, commenting on the behaviour at different points in moduli space. In particular, we observe the emergence of nontrivial local minima as the moduli parameter is increased.

In dynamic epistemic logic (Van Ditmarsch, Van Der Hoek, & Kooi, 2008) it is customary to use an action frame (Baltag & Moss, 2004; Baltag, Moss, & Solecki, 1998) to describe different views of a single action. In this article, action frames are extended to add or remove agents, we call these agent-update frames. This can be done selectively so that only some specified agents get information of the update, which can be used to model several interesting examples such as private update and deception, studied earlier by Baltag and Moss (2004); Sakama (2015); Van Ditmarsch, Van Eijck, Sietsma, and Wang (2012). The product update of a Kripke model by an action frame is an abbreviated way of describing the transformed Kripke model which is the result of performing the action. This is substantially extended to a sum-product update of a Kripke model by an agent-update frame in the new setting. These ideas are applied to an AI problem of modelling a story. We show that dynamic epistemic logics, with update modalities now based on agent-update frames, continue to have sound and complete proof systems. Decision procedures for model checking and satisfiability have expected complexity. For a sublanguage, there are polynomial space algorithms.

ChatGPT, launched by OpenAI in November, quickly went viral for its often elegant, information-packed responses to various questions, gripping the imaginations of regular people, business leaders and investors including Microsoft Corp., which began backing OpenAI in 2019 and said Monday that it would make a multibillion-dollar investment in the startup. OpenAI last week said it would soon add ChatGPT, which stands for chat generative pre-trained transformer, to its application programming interface, or API, which lets developers embed OpenAI technology into their own products. But customer-experience executives said overreliance on such AI models could lead to companies dishing out incorrect information to customers online without knowing they are doing so. While many chatbots are trained to deliver a version of "I don't know" to requests they cannot compute, ChatGPT, for example, is more likely to spout off a response with complete confidence--even if the information is wrong. CMO Today delivers the most important news of the day for media and marketing professionals.

From traffic control and waste management to biometric surveillance systems and predictive policing models, the potential uses of artificial intelligence (AI) in cities are incredibly diverse, and could impact every aspect of urban life. In response to the increasing deployment of AI in cities – and the general lack of authority that municipal governments have to challenge central government decisions or legislate themselves – London, Barcelona and Amsterdam launched the Global Observatory on Urban AI in June 2021. The initiative aims to monitor AI deployment trends and promote its ethical use, and is part of the wider Cities Coalition for Digital Rights (CC4DR), which was set up in November 2018 by Amsterdam, Barcelona and New York to promote and defend digital rights. It now has more than 50 cities participating worldwide. Apart from city participants, the Observatory is also being run in partnership with UN-Habitat, a United Nations initiative to improve the quality of life in urban areas, and research group CIDOB-Barcelona Centre for International Affairs.

We apply machine learning to the problem of finding numerical Calabi-Yau metrics. Building on Donaldson's algorithm for calculating balanced metrics on K\"ahler manifolds, we combine conventional curve fitting and machine-learning techniques to numerically approximate Ricci-flat metrics. We show that machine learning is able to predict the Calabi-Yau metric and quantities associated with it, such as its determinant, having seen only a small sample of training data. Using this in conjunction with a straightforward curve fitting routine, we demonstrate that it is possible to find highly accurate numerical metrics much more quickly than by using Donaldson's algorithm alone, with our new machine-learning algorithm decreasing the time required by between one and two orders of magnitude.

With the passage of the Chronic Care Act, Medicare Advantage plans have been scrambling to figure out how to offer supplemental benefits to their members. Passed as part of a Bipartisan Budget Act last year, the Chronic Care Act promotes the use of benefits that maintain health or keep a beneficiary's health from deteriorating, and the benefits don't have to be health-related. Instead, they can include help for social determinants of health that include housing, nutrition and transportation. Michael Cantor, MD, chief medical officer at CareCentrix, a company that works with payers and providers to create programs that improve quality of care while lowering costs, says social determinants are "significant barriers" to achieving good health for some beneficiaries and the Chronic Care Act is opening doors to improve outcomes. Under the act, the supplements can also be tailored to the individual, when it comes to qualifications.

Christie Rice, left, of Intel, welcomes panelists Jeff Donaldson of Intriosity; Dawn Dickson of PopCom; and Laura Rea Dickey of Dickey's Barbecue. Artificial intelligence has emerged as one of the most powerful tools for improving the customer experience, but retailers must be willing to accept operational change once they embark on their AI journeys. That was a key theme of a panel on driving innovation with AI during the Interactive Customer Experience Summit at the Omni Frisco Hotel in Frisco, Texas. Retailers will find themselves inundated with more data to manage, and they will likely uncover the need to make organizational changes and reassign some employee responsibilities. "Understanding how to use your data is the biggest thing," said panel moderator Christie Rice, worldwide kiosk and digital signage segment manager at Intel.