Bringing back in the specialists and generalists idea - could you have several specialists on each node, and then a generalist agent overseeing all the nodes? Yes, you can think of it that way. We use the term "orchestrator" - an orchestrator is trying to manage everything, but the complexity gets balanced with a hierarchical structure. You tested this policy on some leading LLMs, and they performed differently to each other. Why do you think Gemini did better than, say, Claude or ChatGPT?

Decisions outsourced, chatbots for friends, the natural world an afterthought: Silicon Valley is giving us life void of connection. There is a way out - but it's going to take collective effort Summer after summer, I used to descend into a creek that had carved a deep bed shaded by trees and lined with blackberry bushes whose long thorny canes arced down from the banks, dripping with sprays of fruit. Down in that creek, I'd spend hours picking until I had a few gallons of berries, until my hands and wrists were covered in scratches from the thorns and stained purple from the juice, until the tranquillity of that place had soaked into me. The berries on a single spray might range from green through shades of red to the darkness that gives the fruit its name. Partly by sight and partly by touch, I determined which berries were too hard and which too soft, picking only the ones in between, while listening to birds and the hum of bees, to the music of water flowing, noticing small jewel-like insects among the berries, dragonflies in the open air, water striders in the creek's calm stretches. I went there for berries, but I also went there for the quiet, the calm, the feeling of cool water on my feet and sometimes up to my knees as I waded in where the picking was good. At home I made jars of jam. When I gave them away I was trying to give not just my jam - which was admittedly runny and seedy - but something of the peace of that creek, of summer itself.

'It brings you closer to the natural world': the rise of the Merlin birdsong identifying app W hen Natasha Walter first became curious about the birds around her, she recorded their songs on her phone and arduously tried to match each song with online recordings. After a friend recommended Merlin Bird ID, a free app, she tried it in her London garden and was delighted to discover the birds she assumed were female blackbirds - "this is how bad a birder I was" - were actually song thrushes and mistle thrushes. "I'm obsessed with Merlin - it's wonderful and it's been a joy to me," says Walter, a writer and human rights activist. "This is what AI and machine-learning have been invented for. Merlin is having a moment. The app, developed by the Cornell Lab of Ornithology in New York, which listens for birdsong and identifies the species singing, has been downloaded 33m times, in 240 countries and territories around the world. Britain has the second highest total number of users - more than 1.5 million in 2024, an 88% increase from 2023. Every month, there has been a 30% increase in new users of the app, whose sound identification function was launched in 2021. Merlin has been trained to identify the songs of more than 1,300 species around the world, with more birds added twice a year. Different songs make distinct patterns on spectrograms and Merlin is trained to recognise these different shapes and attribute them to a species. For latecomers to birding, or those lacking a knowledgeable friend, the app has become their teacher. "My fear at first was I wouldn't actually learn because I'm outsourcing my understanding of birds to this app," says Walter. "But that hasn't come to pass.

Should We Look on New Technologies with Awe and Dread? The technological sublime helps us grasp the power of what we're creating--but at a cost. One of the most famous cuts in cinema history, from "2001: A Space Odyssey," perfectly captures a concept known as the technological sublime. First, we see an angry ape bludgeoning one of his fellows to death with a scavenged bone; he's only just discovered that bones can be used this way, and he hurls his weapon into the air in celebration. We follow the bone upward as it tumbles against the unpolluted blue sky.

If the pickled bodies, partial skeletons and stuffed carcasses that fill museums seem a little, well, quiet, fear not. In the latest coup for artificial intelligence, dead animals are to receive a new lease of life to share their stories – and even their experiences of the afterlife. More than a dozen exhibits, ranging from an American cockroach and the remnants of a dodo, to a stuffed red panda and a fin whale skeleton, will be granted the gift of conversation on Tuesday for a month-long project at Cambridge University's Museum of Zoology. Equipped with personalities and accents, the dead creatures and models can converse by voice or text through visitors' mobile phones. The technology allows the animals to describe their time on Earth and the challenges they faced, in the hope of reversing apathy towards the biodiversity crisis.

Artificial Intelligence (AI) – models that process large and diverse datasets and make predictions from them – can have many uses in nature conservation, such as remote monitoring (like the use of camera traps to study animals or plants) or data analysis. Some of these are controversial because AI can be trained to be biased, but others are valuable research tools. Biologist Daniele Silvestro has developed an AI tool that can help identify conservation and restoration priorities. We asked him to tell us more about how it works and what it offers. Artificial intelligence (AI) is a term indicating a broad family of models used to process large and diverse datasets and make predictions from them. We built a model using biodiversity datasets as well as socioeconomic data.

The 11th International Conference on Learning Representations (ICLR) is taking place this week in Kigali, Rwanda, the first time a major AI conference has taken place in-person in Africa. The program includes workshops, contributed talks, affinity group events, and socials. In addition, a total of six invited talks covered a broad range of topics. In this post we give a flavour of the first two of these presentations. Sofia Crepso is an artist who explores the interaction between biological systems and AI.

Hundreds of Evolutionary Computation approaches have been reported. From an evolutionary perspective they focus on two fundamental mechanisms: cultural inheritance in Swarm Intelligence and genetic inheritance in Evolutionary Algorithms. Contemporary evolutionary biology looks beyond genetic inheritance, proposing a so-called ``Extended Evolutionary Synthesis''. Many concepts from the Extended Evolutionary Synthesis have been left out of Evolutionary Computation as interest has moved toward specific implementations of the same general mechanisms. One such concept is epigenetic inheritance, which is increasingly considered central to evolutionary thinking. Epigenetic mechanisms allow quick non- or partially-genetic adaptations to environmental changes. Dynamic multi-objective optimisation problems represent similar circumstances to the natural world where fitness can be determined by multiple objectives (traits), and the environment is constantly changing. This paper asks if the advantages that epigenetic inheritance provide in the natural world are replicated in dynamic multi-objective optimisation problems. Specifically, an epigenetic blocking mechanism is applied to a state-of-the-art multi-objective genetic algorithm, MOEA/D-DE, and its performance is compared on three sets of dynamic test functions, FDA, JY, and UDF. The mechanism shows improved performance on 12 of the 16 test problems, providing initial evidence that more algorithms should explore the wealth of epigenetic mechanisms seen in the natural world.

Machine learning is a very specific form of artificial intelligence. Through algorithms designed to learn from experience, machine learning -- also known as ML -- adapts and grows in efficiency over time as more data is added. The ML-driven program "learns" from its mistakes, and in doing so can reduce the time it takes to analyze mountains of data from years to minutes. Melissa Guzman and Sam Silva are using machine learning to find insights into patterns underlying the natural world. Two recently hired faculty members, Melissa Guzman, Gabilan Assistant Professor of Biological Sciences, and Sam Silva, assistant professor of Earth sciences, both at at the USC Dornsife College of Letters, Arts and Sciences, are already garnering attention for their usage of machine learning to find insights into the seemingly unknowable -- the patterns underlying the natural world.

Inspired by water-walking insects, scientists have built liquid robots that work autonomously and continuously without the need for electrical inputs, transporting chemicals back and forth while partially submerged in solution. The "liquibot" technology may lead to further developments in automated chemical synthesis or drug delivery systems for pharmaceuticals, say the researchers, including those from Lawrence Berkeley National Laboratory in the US. Earlier studies had demonstrated the working of liquibots that autonomously perform a task, but just once, and some that can perform a task continuously, but need electricity to do so continuously. In the new research, published in the journal Nature Chemistry, scientists demonstrated the first self-powered liquid robot – which look like little open sacks just 2mm in diameter – that can run continuously on energy from the chemicals in its surroundings instead of electricity. "We have broken a barrier in designing a liquid robotic system that can operate autonomously by using chemistry to control an object's buoyancy," study co-author Tom Russell from Berkeley Lab's Materials Sciences Division said in a statement.