Unlike static architectures, it enables agents to evolve via an entropy-guided, memory-aware online learning mechanism, retrieving high-value prior cases from an episodic memory bank and exploring diverse historical contexts.

Ensemble methods for stream mining necessitate managing multiple models and updating them as data distributions evolve. Considering the calls for more sustainability, established methods are however not sufficiently considerate of ensemble members' computational expenses and instead overly focus on predictive capabilities. To address these challenges and enable green online learning, we propose heterogeneous online ensembles (HEROS). For every training step, HEROS chooses a subset of models from a pool of models initialized with diverse hyperparameter choices under resource constraints to train. We introduce a Markov decision process to theoretically capture the trade-offs between predictive performance and sustainability constraints. Based on this framework, we present different policies for choosing which models to train on incoming data. Most notably, we propose the novel $ζ$-policy, which focuses on training near-optimal models at reduced costs. Using a stochastic model, we theoretically prove that our $ζ$-policy achieves near optimal performance while using fewer resources compared to the best performing policy. In our experiments across 11 benchmark datasets, we find empiric evidence that our $ζ$-policy is a strong contribution to the state-of-the-art, demonstrating highly accurate performance, in some cases even outperforming competitors, and simultaneously being much more resource-friendly.

Prompt engineering is crucial for unlocking the potential of Large Language Models (LLMs). Still, since manual prompt design is often complex, non-intuitive, and time-consuming, automatic prompt optimization has emerged as a research area. However, a significant challenge in prompt optimization is managing the inherent trade-off between task performance, such as accuracy, and context size. Most existing automated methods focus on a single objective, typically performance, thereby failing to explore the critical spectrum of efficiency and effectiveness. This paper introduces the MOPrompt, a novel Multi-objective Evolutionary Optimization (EMO) framework designed to optimize prompts for both accuracy and context size (measured in tokens) simultaneously. Our framework maps the Pareto front of prompt solutions, presenting practitioners with a set of trade-offs between context size and performance, a crucial tool for deploying Large Language Models (LLMs) in real-world applications. We evaluate MOPrompt on a sentiment analysis task in Portuguese, using Gemma-2B and Sabiazinho-3 as evaluation models. Our findings show that MOPrompt substantially outperforms the baseline framework. For the Sabiazinho model, MOPrompt identifies a prompt that achieves the same peak accuracy (0.97) as the best baseline solution, but with a 31% reduction in token length.

Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? Trump-Xi meeting: What's at stake and who has the upper hand? United States President Donald Trump expects "a lot of problems" will be solved between Washington and Beijing when he meets China's President Xi Jinping in South Korea for a high-stakes meeting on Thursday, amid growing trade tensions between the two. Relations between the two world powers have been strained in recent years, with Washington and Beijing imposing tit-for-tat trade tariffs topping 100 percent against each other this year, the US restricting its exports of semiconductors vital for artificial intelligence (AI) development and Beijing restricting exports of critical rare-earth metals which are vital for the defence industry and also the development of AI, among other issues. On the sidelines of the Asia-Pacific Economic Cooperation (APEC) summit in Gyeongju, South Korea, on Wednesday, Trump said an expected trade deal between China and the US would be good for both countries and "something very exciting for everybody".

Heathrow, NatWest and Minecraft are among some of the sites and services experiencing problems amid a global Microsoft outage. Outage tracker Downdetector showed thousands of reports of issues with a number of websites globally on Wednesday. Microsoft said some users of Microsoft 365, which includes Outlook and Teams, might see delays. The company's Azure cloud computing platform, which underpins large parts of the internet, reported a degradation of some services at 1600 GMT. It said this was due to DNS issues - the same root cause of the huge Amazon Web Services (AWS) outage last week.

Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? Nvidia has become the first company to reach $5 trillion in market value amid a global artificial intelligence arms race. The chipmaker surge on Wednesday came only three months after the company topped the $4 trillion mark . Since the launch of ChatGPT in 2022, Nvidia's shares have climbed 12-fold as the AI frenzy propelled the S&P 500 to record highs, igniting a debate on whether frothy tech valuations could lead to the next big bubble.

Nvidia has hit a new milestone, becoming the first company in the world to reach a market value of $5tn (£3.8tn). The US chip-maker has rapidly climbed from a niche graphics-chip manufacturer to an AI titan, as euphoria about the potential of artificial intelligence keeps driving demand for its chips and propelling its stock to record highs. The company reached a market value of $1tn for the first time in June 2023 and hit the $4tn valuation mark just three months ago . Shares in the chip-maker rose as much as 5.6% to more than $212 on Wednesday morning, boosted by investor optimism about Nvidia's sales in China, which has been a geopolitical flashpoint. The world's most valuable company - the biggest winner in the AI spending spree - has soared past its rivals in the technology sector.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russia strikes children's hospital in Ukraine as Kyiv hits energy sites A Russian strike on a children's hospital in southern Ukraine has wounded at least nine people, authorities have said, shortly after Kyiv targeted Russian energy sites with drones. Four children were injured in Russia's strike on the medical facility in Kherson on Wednesday, which Ukrainian President Volodymyr Zelenskyy described as a "deliberate" attack that shows Moscow does not want peace.

Character.ai to ban teens from talking to its AI chatbots The platform, founded in 2021, is used by millions to talk to chatbots powered by artificial intelligence (AI). But it is facing several lawsuits in the US from parents, including one over the death of a teenager, with some branding it a clear and present danger to young people. Online safety campaigners have welcomed the move but said the feature should never have been available to children in the first place. Character.ai said it was making the changes after reports and feedback from regulators, safety experts, and parents, which have highlighted concerns about its chatbots' interactions with teens. Experts have previously warned the potential for AI chatbots to make things up, be overly-encouraging, and feign empathy can pose risks to young and vulnerable people.

DeepSeek may have found a new way to improve AI's ability to remember An AI model released by Chinese AI company DeepSeek uses new techniques that could significantly improve AI's ability to "remember." The optical character recognition model works by extracting text from an image and turning it into machine-readable words. This is the same technology that powers scanner apps, translation of text in photos, and many accessibility tools. Researchers say the model's main innovation lies in how it processes information--specifically, how it stores and retrieves data. Improving how AI models "remember" could reduce how much computing power they need to run, thus mitigating AI's large (and growing) carbon footprint. The AI Hype Index: Data centers' neighbors are pivoting to power blackouts That's why we've created the AI Hype Index--a simple, at-a-glance summary of everything you need to know about the state of the industry.