Unless you've been holidaying on Mars, or perhaps in Spain (alongside the transport secretary), you may have noticed some fuss on social media about something called GPT-3. The GPT bit stands for the "generative pre-training" of a language model that acquires knowledge of the world by "reading" enormous quantities of written text. The "3" indicates that this is the third generation of the system. GPT-3 is a product of OpenAI, an artificial intelligence research lab based in San Francisco. In essence, it's a machine-learning system that has been fed (trained on) 45 terabytes of text data. Given that a terabyte (TB) is a trillion bytes, that's quite a lot.

The most exciting new arrival in the world of AI looks, on the surface, disarmingly simple. It's not some subtle game-playing program that can outthink humanity's finest or a mechanically advanced robot that backflips like an Olympian. You start typing and it predicts what comes next. But while this sounds simple, it's an invention that could end up defining the decade to come. The program itself is called GPT-3 and it's the work of San Francisco-based AI lab OpenAI, an outfit that was founded with the ambitious (some say delusional) goal of steering the development of artificial general intelligence or AGI: computer programs that possess all the depth, variety, and flexibility of the human mind. For some observers, GPT-3 -- while very definitely not AGI -- could well be the first step toward creating this sort of intelligence.

After clearing the institute's security, I was told to wait in a lobby monitored by cameras. On its walls were posters of China's most consequential postwar leaders. He looked serene, as though satisfied with having freed China from the Western yoke. Next to him was a fuzzy black-and-white shot of Deng Xiaoping visiting the institute in his later years, after his economic reforms had set China on a course to reclaim its traditional global role as a great power. The lobby's most prominent poster depicted Xi Jinping in a crisp black suit.

Facebook researchers have developed a general AI framework called Recursive Belief-based Learning (ReBeL) that they say achieves better-than-human performance in heads-up, no-limit Texas hold'em poker while using less domain knowledge than any prior poker AI. They assert that ReBeL is a step toward developing universal techniques for multi-agent interactions -- in other words, general algorithms that can be deployed in large-scale, multi-agent settings. Potential applications run the gamut from auctions, negotiations, and cybersecurity to self-driving cars and trucks. Combining reinforcement learning with search at AI model training and test time has led to a number of advances. Reinforcement learning is where agents learn to achieve goals by maximizing rewards, while search is the process of navigating from a start to a goal state.

The first question many people ask about artificial intelligence (AI) is, "Will it be good or bad?" The answer is … yes. Canadian company BlueDot used AI technology to detect the novel coronavirus outbreak in Wuhan, China, just hours after the first cases were diagnosed. Compiling data from local news reports, social media accounts and government documents, the infectious disease data analytics firm warned of the emerging crisis a week before the World Health Organization made any official announcement. While predictive algorithms could help us stave off pandemics or other global threats as well as manage many of our day-to-day challenges, AI's ultimate impact is impossible to predict.

"This decision was in part based on a larger industry conversation," the company told Reuters in a statement, adding that "other large technology companies seem to be scaling back or rethinking their efforts around facial recognition given increasing uncertainty around the technology's utility." Reuters pieced together how the company's initiative evolved, how the software has been used and how a recent vendor was linked to China, drawing on thousands of pages of internal documents from Rite Aid and its suppliers, as well as direct observations during store visits by Reuters journalists and interviews with more than 40 people familiar with the systems' deployment. Most current and former employees spoke on condition of anonymity, saying they feared jeopardizing their careers. While Rite Aid declined to disclose which locations used the technology, Reuters found facial recognition cameras at 33 of the 75 Rite Aid shops in Manhattan and the central Los Angeles metropolitan area during one or more visits from October through July. The cameras were easily recognizable, hanging from the ceiling on poles near store entrances and in cosmetics aisles.

When a young girl came to New York University (NYU) Langone Health for a routine follow-up, tests seemed to show that the medulloblastoma for which she had been treated a few years earlier had returned. The girl's recurrent cancer was found in the same part of brain as before, and the biopsy seemed to confirm medulloblastoma. With this diagnosis, the girl would begin a specific course of radiotherapy and chemotherapy. But just as neuropathologist Matija Snuderl was about to sign off on the diagnosis and set her on that treatment path, he hesitated. The biopsy was slightly unusual, he thought, and he remembered a previous case in which what was thought to be medulloblastoma turned out to be something else. So, to help him make up his mind, Snuderl turned to a computer.

Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well‐established, but often make data collection and analyses time‐consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large‐scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds.

An ultra-fast camera has captured a video of light as it bounces between mirrors. Although light isn't normally visible in flight, some photons from a laser pulse will scatter off particles in the air and can be picked up by a camera. Using these photons to recreate the pulse's trajectory is difficult, because by the time they reach the camera, the pulse has moved to a new location. Edoardo Charbon at the Swiss Federal Institute of Technology in Lausanne and his colleagues used a camera with a shutter speed of about a trillionth of a second to take pictures and video of a laser beam following a 3D path. Knowing exactly how long the pulse took to get to the camera, along with the pulse's trajectory in a flat plane, allowed a machine learning algorithm to reconstruct the entire 3D path of the burst of light.

Elon Musk has warned that humans risk being overtaken by artificial intelligence within the next five years. The prediction marks a significant revision of previous estimations of the so-called technological singularity, when machine intelligence surpasses human intelligence and accelerates at an incomprehensible rate. Noted futurist Ray Kurzweil previously pegged this superintelligence tipping point at around 2045, citing exponential advances in technologies like robotics, computers and AI. Mr Musk, whose ventures include electric car maker Tesla and space firm SpaceX, said in an interview with The New York Times that current trends suggest AI could overtake humans by 2025. The billionaire engineer, who also helped found the artificial intelligence research lab OpenAI in 2015, has consistently warned of the existential threat posed by advanced artificial intelligence in recent years.