The evergreen trees give kangaroos, bison, lions, and more extra shelter and fun. Capybaras use donated Christmas trees as wind breaks to protect their habitats. Breakthroughs, discoveries, and DIY tips sent every weekday. The presents are unwrapped, the cookies are crumbs, and that real Christmas tree will become a fire hazard soon enough. Most of us haul it out to the curb for our local sanitation departments to take care of, but some lucky trees make it into the paws of animals living in zoos.

Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its heuristic improvement of convergence, a rigorous mathematical justification for the benefits of Anderson mixing in RL has not yet been put forward. In this paper, we provide deeper insights into a class of acceleration schemes built on Anderson mixing that improve the convergence of deep RL algorithms. Our main results establish a connection between Anderson mixing and quasi-Newton methods and prove that Anderson mixing increases the convergence radius of policy iteration schemes by an extra contraction factor.

Drone detection technology has led to an increase in airborne smuggling operations over U.S. prisons, putting authorities in a tough spot as federal regulations prevent drones from being brought down.

Environment Animals Wildlife Birds Female Galápagos birds flaunt their sexual partners. The males don't seem to mind. 'Many of these female boobies are really freewheeling it when it comes to sexual behavior.' Breakthroughs, discoveries, and DIY tips sent every weekday. A Galápagos bird species is stunning behaviorists with their "freewheeling" lifestyles.

Players walk clockwise in a circle. When the music stops, everyone sits in a chair. Big Tech is setting in motion its plans for the next gen of lead designers, engineers, AI chiefs, and even CEOs. In Cupertino, Apple execs with familiar faces are retiring or reducing responsibilities. Well, chief operating officer Jeff Williams retired in November, and the speculation is that CEO Tim Cook could follow in the near term. Lisa Jackson, who has led Apple's sustainability efforts since 2013, is now set to retire in January too.

Elon Musk, already the world's richest man, is now on the path to becoming its first trillionaire. Tesla's shareholders recently approved a massive pay package for the CEO, including some $1 trillion in stock options. But the payout will happen only if certain targets are met--including Musk's successful deployment of 1 million Optimus robots. Named after a character, because of course it is, Optimus is a humanoid machine that's supposed to be able to complete boring and dangerous work in place of humans. The robot was unveiled in 2021, when Tesla held an "AI Day" event detailing its future plans.

General Motors' 'Eyes-Off' System Begs the Question: What Happens When Cars Go AI? General Motors' new self-driving system will let the driver speed down the highway without looking at the road. It's one of several features enabled by the adoption of machine intelligence in cars. A new self-driving system coming to Cadillac Escalades will handle the driving on approved highways, enabling the driver to do basically anything they want behind the wheel. General Motors is launching another salvo in the self-driving wars. In 2028, the automaker announced today, it will roll out what it's calling an "eyes-off" driving system on the electric Cadillac Escalade IQ.

It's often more'smash and grab' than'Mission: Impossible.' French police officers stand next to a furniture elevator used by robbers to enter the Louvre Museum, on Quai Francois Mitterrand, in Paris on October 19, 2025. Robbers broke in to the Louvre and fled with jewellery on October 19, 2025 morning, a source close to the case said, adding that its value was still being evaluated. A police source said an unknown number of thieves arrived on a scooter armed with small chainsaws and used a goods lift to reach the room they were targeting. Breakthroughs, discoveries, and DIY tips sent every weekday. A heist at a world famous museum likely evokes images of stealthy cat burglars skulking at night armed with state-of-the-art gadgets, possibly even soundtracked with a cool, jazzy instrumental.

James Anderson says he had not seen signs of an investment bubble in AI until recently. James Anderson says he had not seen signs of an investment bubble in AI until recently. Leading UK tech investor warns of'disconcerting' signs of AI stock bubble Wed 1 Oct 2025 07.07 EDTFirst published on Wed 1 Oct 2025 06.22 EDT A leading British tech investor has described soaring valuations of artificial intelligence companies as "disconcerting", amid concerns of an AI stock market bubble. James Anderson was an early backer of Tesla, Amazon and China's Tencent and Alibaba, generating vast returns for Baillie Gifford's flagship fund. Now at the Italian investment company Lingotto, Anderson said he had not seen signs of an investment bubble until recently, when the ChatGPT developer, OpenAI, and its rival Anthropic announced hefty valuation increases.

Large language models (LLMs) can adapt to new tasks via in-context learning (ICL) without parameter updates, making them powerful learning engines for fast adaptation. While extensive research has examined ICL as a few-shot learner, whether it can achieve long-term retention and cross-task knowledge accumulation when multitasks arrive sequentially remains underexplored. Motivated by human memory studies, we investigate the retention characteristics of ICL in multitask settings and extend it to in-context continual learning (ICCL), where continual learning ability emerges through task scheduling and prompt rearrangement. Experiments on Markov-Chain benchmarks demonstrate that, for specific large-language models, ICCL benefits from distributed practice (DP) in a manner analogous to humans, consistently revealing a spacing "sweet spot" for retention. Beyond retention performance, we propose a human-retention similarity metric to quantify how closely a continual-learning (CL) method aligns with human retention dynamics. Using this metric, we show that linear-attention models such as MAMBA and RWKV exhibit particularly human-like retention patterns, despite their retention performance lagging behind that of Transformer-based LLMs. Overall, our results establish ICCL as both cognitively plausible and practically effective, providing an inference-only CL paradigm that mitigates catastrophic forgetting and addresses the stability-plasticity dilemma in conventional CL methods.