It's not a typical hide-and-seek game, though, but rather one for the digital age: both the seekers and the hiders chase and evade each other by following their real-time locations on a map on their phones.Our reporter Zeyi Yang played a game with 40 strangers in a seven-acre park built on the site of the infamous Kowloon Walled City. Inside a co-working space in the Rosebank neighborhood of Johannesburg, Jade Abbott popped open a tab on her computer and prompted ChatGPT to count from 1 to 10 in isiZulu, a language spoken by more than 10 million people in her native South Africa. The results were "mixed and hilarious," says Abbott, a computer scientist and researcher. Then she typed in a few sentences in isiZulu and asked the chatbot to translate them into English. Abbott's experience mirrors the situation faced by Africans who don't speak English.

In South Africa, there are drones monitoring weeds; in Mauritius, there are computers crunching health data for better outcomes for patients; and in Nairobi, surveillance systems impose a modicum of order on the chaotic traffic. The bright new future of artificial intelligence in Africa is part of the bright new future of the continent as a whole, advocates say. "One thing is clear: Africans have a goldmine at our fingertips. A rapidly growing population of 1.4 billion people, 70% under the age of 30, combined with huge growth in AI investments, creates a potent recipe … We will not sit back and wait for the rest of the world to reap our rewards," wrote Mahamudu Bawumia, the vice-president of Ghana and head of the government's economic management team, in the Guardian earlier this year. Growing alarm about the threats posed by uncontrolled innovation in artificial intelligence has prompted global leaders to hold the first ever safety summit.

In 2021, Elon Musk became the world's richest man (no woman came close), and Time named him Person of the Year: "This is the man who aspires to save our planet and get us a new one to inhabit: clown, genius, edgelord, visionary, industrialist, showman, cad; a madcap hybrid of Thomas Edison, P. T. Barnum, Andrew Carnegie and Watchmen's Doctor Manhattan, the brooding, blue-skinned man-god who invents electric cars and moves to Mars." Right about when Time was preparing that giddy announcement, three women whose ovaries and uteruses were involved in passing down the madcap man-god's genes were in the maternity ward of a hospital in Austin. Musk believes a declining birth rate is a threat to civilization and, with his trademark tirelessness, is doing his visionary edgelord best to ward off that threat. Shivon Zilis, a thirty-five-year-old venture capitalist and executive at Musk's company Neuralink, was pregnant with twins, conceived with Musk by in-vitro fertilization, and was experiencing complications. "He really wants smart people to have kids, so he encouraged me to," Zilis said.

Cuckoos infiltrate the nests of other birds with similar-looking eggs, but drongos have evolved a highly effective way to snuff out the imposters. Their ability to recognise the uniquely patterned marks of their own eggs, like a signature, means they may reject up to 94 per cent of cuckoo eggs. Instead of caring for their own offspring, African cuckoos (Cuculus gularis) lay a single egg in the nests of fork-tailed drongos (Dicrurus adsimilis), tossing out a drongo egg to match the original clutch count. If the young cuckoo is adopted and hatches, it immediately pushes out the remaining drongo eggs to become its hosts' only charge. Jess Lund at the University of Cape Town, South Africa, and her colleagues gathered 192 eggs – including 26 that had been laid by cuckoos – from fork-tailed drongo nests in the forests of southern Zambia.

Next to insects, birds sadly seem to get short shrift from humans. We remain powerfully drawn to scenes of lions hunting in the Kalahari desert or rhinos jousting in eastern India, but remain mostly oblivious to vibrant scenes of life and love enacted under our very noses. Also: AI might enable us to talk to animals soon. The cacophony of evening traffic as birds stream back into their nests, the diligence of solitary hunters of garden worms, the urgent shrieks of young parents, the furious battles among rival suitors -- these are all everyday dramas that are enacted everywhere around you, and you don't have to fork out a small fortune to observe. However, not knowing what you're looking at or listening to can be frustrating.

JOHANNESBURG/LONDON – Mental health counselor Nicole Doyle was stunned when the head of the U.S. National Eating Disorders Association showed up at a staff meeting to announce the group would be replacing its helpline with a chatbot. A few days after the helpline was taken down, the bot -- named Tessa -- would also be discontinued for providing harmful advice to people in the throes of mental illness. "People … found it was giving out weight loss advice to people who told it they were struggling with an eating disorder," said Doyle, 33, one of five workers who were let go in March, about a year after the chatbot was launched. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional objectoriented framework that has provably efficient learning bounds with respect to sample complexity. However, this framework has limitations in terms of the classes of tasks it can efficiently learn. In this paper we introduce a novel deictic objectoriented framework that has provably efficient learning bounds and can solve a broader range of tasks. Additionally, we show that this framework is capable of zero-shot transfer of transition dynamics across tasks and demonstrate this empirically for the Taxi and Sokoban domains.

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management. Crucial common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society's greatest challenges such as food security, inequality and climate change. Here we take inspiration from a recent research program investigating the game-theoretic incentives of humans in social dilemma situations such as the well-known tragedy of the commons. However, instead of focusing on biologically evolved human-like agents, our concern is rather to better understand the learning and operating behaviour of engineered networked systems comprising general-purpose reinforcement learning agents, subject only to nonbiological constraints such as memory, computation and communication bandwidth. Harnessing tools from empirical game-theoretic analysis, we analyse the differences in resulting solution concepts that stem from employing different information structures in the design of networked multi-agent systems. These information structures pertain to the type of information shared between agents as well as the employed communication protocol and network topology. Our analysis contributes new insights into the consequences associated with certain design choices and provides an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.

User background We only use a single criterion for filtering users: Users must be native English speakers. We think this is required for our study (and any study that uses English) since the training, instructions, and ImageNet labels are in English. We used this filter to avoid the cases where users make arbitrary decisions without understanding some words. Prolific shows that our users are diverse, aging from 18-77 (median=31) and coming from a diverse set of countries (US, UK, Poland, India, Korea, Canada, Australia, South Africa, etc.). Please see Prolific for more description of their online userbase, which, according to a study, is more reliable than AMT Turkers [59]. Payment Our rate is higher than the Prolific recommended rate wage of $9.60/hr. In fact, during the study, we had increased our rate to attract more participants (up to $13.68/hr). As participants come from various countries in the world, we did not consider minimum wage per region because this recommended rate is suggested by Prolific and accepted by all participants.

A modification is described to the use of mean field approxima(cid:173) tions in the E step of EM algorithms for analysing data from latent structure models, as described by Ghahramani (1995), among oth(cid:173) ers. The modification involves second-order Taylor approximations to expectations computed in the E step. The potential benefits of the method are illustrated using very simple latent profile models.