Estonia has said a Nato fighter jet shot down a drone, which it suspects was a Ukrainian projectile knocked off course by Russian electronic jamming, over its territory. Defence Minister Hanno Pevkur said a Romanian F-16 fired a missile and drone debris fell in a marshy area in central Estonia on Tuesday. Ukraine reacted by accusing Russia of deliberately redirecting Ukrainian drones launched at legitimate military targets in Russia, apologising to Estonia and all of our Baltic friends for such unintended incidents. Russia has not commented on the latest in a series of recent drone incursions over Nato members Estonia, Latvia and Lithuania. Last week, Latvian Prime Minister Evika Silina resigned following a political crisis over Russia-bound Ukrainian drones straying into Latvian territory.

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.

To exploit, UCT often selects the same action on successive trials, which can result in it getting stuck in local optima.

Reinforcement learning with the help of neural-guided search consumes huge computational resources to achieve remarkable performance.

Recent methodologies in LLM self-training mostly rely on LLM generating responses and filtering those with correct output answers as training data. This approach often yields a low-quality fine-tuning training set (e.g., incorrect plans or intermediate reasoning).

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.