The sixth generation of Fairphone arrived this week, featuring a modular design built to last from ethically sourced components in a climate-conscious way. It has been a couple of years since its predecessor, the Fairphone 5, and the Fairphone 6 is refreshingly smaller and lighter. It boasts a 6.3-inch OLED screen with a 120-Hz adaptive refresh rate, a Qualcomm Snapdragon 7s Gen 3 processor, and a 4,415 mAh battery that Fairphone says is good for up to two days. You also get a 50-megapixel main camera with a 13-MP ultrawide lens and a 32-MP selfie camera. Fairphone says the new device is made with more than 50 percent fair and recycled materials, including cobalt sourced through the Fair Cobalt Alliance, fair gold, silver, and tungsten, and recycled aluminum and rare earth metals.

It might only feel like yesterday that the new iPhone 15 was released, but experts have already begun to image the phones of the future. Using AI imaging, Mobiles.co.uk has predicted five different ways that smartphones might develop in the future From flexible phones to self-repairing devices, your iPhone may one day look and feel a lot different. While some of the ideas might sound like Star Trek props, the experts say these technologies could be just around the corner. So, do you think these ideas are the future of phones, or are they doomed to die on the drawing board? Your phone will be able to bend into new shapes to avoid damage.

Kurt Knutsson talks about an innovative robot that can explore the depths of the ocean and capture stunning photos and videos. You've seen drones that can fly, but how about a drone that can explore under the ocean? This new powerful underwater robot uses AI to dive into the depths of the sea. It's called FiFish E-GO, and thanks to its unique modular design, the innovative drone is easily customizable and upgradeable. So, whether you are a professional, a hobbyist or an adventurer, the E-GO has got you covered.

We introduce Lumos, a novel framework for training language agents that employs a unified data format and a modular architecture based on open-source large language models (LLMs). Lumos consists of three distinct modules: planning, grounding, and execution. The planning module breaks down a task into a series of high-level, tool-agnostic subgoals, which are then made specific by the grounding module through a set of low-level actions. These actions are subsequently executed by the execution module, utilizing a range of off-the-shelf tools and APIs. In order to train these modules effectively, high-quality annotations of subgoals and actions were collected and are made available for fine-tuning open-source LLMs for various tasks such as complex question answering, web tasks, and math problems. Leveraging this unified data and modular design, Lumos not only achieves comparable or superior performance to current, state-of-the-art agents, but also exhibits several key advantages: (1) Lumos surpasses GPT-4/3.5-based agents in complex question answering and web tasks, while equalling the performance of significantly larger LLM agents on math tasks; (2) Lumos outperforms open-source agents created through conventional training methods and those using chain-of-thoughts training; and (3) Lumos is capable of effectively generalizing to unseen interactive tasks, outperforming larger LLM-based agents and even exceeding performance of specialized agents.

Clustering techniques have been the key drivers of data mining, machine learning and pattern recognition for decades. One of the most popular clustering algorithms is DBSCAN due to its high accuracy and noise tolerance. Many superior algorithms such as DBSCAN have input parameters that are hard to estimate. Therefore, finding those parameters is a time consuming process. In this paper, we propose a novel clustering algorithm Bacteria-Farm, which balances the performance and ease of finding the optimal parameters for clustering. Bacteria- Farm algorithm is inspired by the growth of bacteria in closed experimental farms - their ability to consume food and grow - which closely represents the ideal cluster growth desired in clustering algorithms. In addition, the algorithm features a modular design to allow the creation of versions of the algorithm for specific tasks / distributions of data. In contrast with other clustering algorithms, our algorithm also has a provision to specify the amount of noise to be excluded during clustering.

Opera, which has established itself as a boutique browser vendor, has announced Opera One, a complete redesign and eventual replacement of its existing browser. Opera One features optimizations for modular elements and new AI-powered tab grouping features, called "Tab Islands." All told, Opera One has a slick new look, but with technical underpinnings that leave it open to adding new AI-powered capabilities in the future. Opera is also promising that a new "multithreaded compositor" will improve performance, so that Opera One takes advantage of the multicore capabilities of modern PCs. The browser is in early developer mode, according to Opera, and will be released later this year for Windows, MacOS and Linux.

The control for aggressive driving of autonomous cars is challenging due to the presence of significant tyre slip. Data-driven and mechanism-based methods for the modeling and control of autonomous cars under aggressive driving conditions are limited in data efficiency and adaptability respectively. This paper is an attempt toward the fusion of the two classes of methods. By means of a modular design that is consisted of mechanism-based and data-driven components, and aware of the two-timescale phenomenon in the car model, our approach effectively improves over previous methods in terms of data efficiency, ability of transfer and final performance. The hybrid mechanism-and-data-driven approach is verified on TORCS (The Open Racing Car Simulator). Experiment results demonstrate the benefit of our approach over purely mechanism-based and purely data-driven methods.

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions—into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple’s performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.