In physical systems where errors carry tangible consequences, AI creates value through reliability and first-time-right performance. The impact of artificial intelligence extends far beyond the digital world and into our everyday lives, across the cars we drive, the appliances in our homes, and medical devices that keep people alive. More and more, product engineers are turning to AI to enhance, validate, and streamline the design of the items that furnish our worlds. The use of AI in product engineering follows a disciplined and pragmatic trajectory. A significant majority of engineering organizations are increasing their AI investment, according to our survey, but they are doing so in a measured way. This approach reflects the priorities typical of product engineers.

Niantic's AI spinout is training a new world model using 30 billion images of urban landmarks crowdsourced from players. Pokémon Go was the world's first augmented-reality megahit. Released in 2016 by the Google spinout Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. From Chicago to Oslo to Enoshima, players hit the streets in the urgent hope of catching a Jigglypuff or a Squirtle or (with a huge amount of luck) an ultra-rare Galarian Zapdos hovering just out of reach, superimposed on the everyday world. "Five hundred million people installed that app in 60 days," says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out in May last year. According to the video-game firm Scopely, which bought Pokémon Go from Niantic at the same time, the game still drew more than 100 million players in 2024, eight years after it launched.

Plus: GPS jamming has become an invisible battle in the Middle East. Much of the spotlight on AI in the Iran conflict has focused on models like Claude helping the US military decide where to strike. But a wave of "vibe-coded" intelligence dashboards--and the ecosystem surrounding them--reflect a new role that AI is playing in wartime: mediating information, often for the worse. These sorts of intelligence tools have much promise. Yet there are real reasons to be suspicious of their data feeds. The AI firm wants to stop the Pentagon from blacklisting it.

Everyone is panicking because AI is very bad; everyone is panicking because AI is very good. It's just that you never know which one you're going to get. Grok is a pornography machine. Claude Code can do anything from building websites to reading your MRI. So of course Gen Z is spooked by what this means for jobs. Unnerving new research says AI is going to have a seismic impact on the labor market this year.

Plus: Trump has climbed down from his plan for the US to take Greenland. Yann LeCun's new venture is a contrarian bet against large language models Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry's current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. Instead, he thinks we should be betting on world models--a different type of AI that accurately reflects the dynamics of the real world. Perhaps it's no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. LeCun sat down with MIT Technology Review in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas.

Why an AI'godfather' is quitting Meta after 12 years Just a couple of weeks ago, one of the godfathers of artificial intelligence was in St James's Palace being handed an award from King Charles for his work in artificial intelligence (AI). Professor Yann LeCun was being honoured along with six other recipients for his contributions to the field, which have been credited as advancing deep learning. But Mr LeCun is at odds with some of the AI world over the future of the generation-defining technology. And now he is going all-in on his idea of advanced machine intelligence after announcing he is leaving his role as Meta's chief AI scientist to start a new firm. During his 12 years at the company, Prof LeCun won the prestigious Turing Award and witnessed several flurries of excitement around AI - not least the most recent boom in generative AI accelerated by rival OpenAI's launch of ChatGPT in late 2022.

AI'godmother' Fei-Fei Li says she is'proud to be different' The'godmother' of AI, Professor Fei-Fei Li has told the BBC that being the only woman amongst seven pioneers of artificial Intelligence being presented with a top engineering prize by the King today makes her proud to be different. The King will present the 2025 Queen Elizabeth Prize for Engineering to Prof Li and six others during a ceremony at St James's Palace. Those honoured alongside her are Prof Yoshua Bengio, Dr Bill Dally, Dr Geoffrey Hinton, Prof John Hopfield, Nvidia founder Jensen Huang and Meta's Chief AI Scientist Dr Yann LeCun. They are being recognised for their contributions to the development of modern machine learning, a field that underpins the rapid advancement of AI. Who are the Godparents of AI? Dr Hinton, Prof Bengio and Yann LeCun, currently Chief AI Scientist at Meta have widely been recognised as the Godfathers of AI since they were jointly awarded the 2018 Turing Award.

Video representation learning is an increasingly important topic in machine learning research. We present Video JEPA with Variance-Covariance Regularization (VJ-VCR): a joint-embedding predictive architecture for self-supervised video representation learning that employs variance and covariance regularization to avoid representation collapse. We show that hidden representations from our VJ-VCR contain abstract, high-level information about the input data. Specifically, they outperform representations obtained from a generative baseline on downstream tasks that require understanding of the underlying dynamics of moving objects in the videos. Additionally, we explore different ways to incorporate latent variables into the VJ-VCR framework that capture information about uncertainty in the future in non-deterministic settings.

On August 7, a group of renowned professors co-authored a letter urging key lawmakers to support a California AI bill as it enters the final stages of the state's legislative process. In a letter shared exclusively with TIME, Yoshua Bengio, Geoffrey Hinton, Lawrence Lessig, and Stuart Russell argue that the next generation of AI systems pose "severe risks" if "developed without sufficient care and oversight," and describe the bill as the "bare minimum for effective regulation of this technology." The bill, titled the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, was introduced by Senator Scott Wiener in February of this year. It requires AI companies training large-scale models to conduct rigorous safety testing for potentially dangerous capabilities and implement comprehensive safety measures to mitigate risks. "There are fewer regulations on AI systems that could pose catastrophic risks than on sandwich shops or hairdressers," the four experts write.

Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal, however it is difficult to acquire sufficient annotated data to train these systems from scratch. To address this limitation, the Detection and Classification of Acoustic Scenes and Events (DCASE) community has recasted the problem within the framework of few-shot learning and organize an annual challenge for learning to detect animal sounds from only five annotated examples. In this work, we regularize supervised contrastive pre-training to learn features that can transfer well on new target tasks with animal sounds unseen during training, achieving a high F-score of 61.52%(0.48) when no feature adaptation is applied, and an F-score of 68.19%(0.75) when we further adapt the learned features for each new target task. This work aims to lower the entry bar to few-shot bioacoustic sound event detection by proposing a simple and yet effective framework for this task, by also providing open-source code.