Science Biology Evolution Humans really don't need chins Homo sapiens are the only primates that have them, but they don't make us special. Breakthroughs, discoveries, and DIY tips sent six days a week. Auguste Rodin's is one of the art world's most recognizable images. The monumental depiction of a man hunched forward, right hand resting against his chin, is synonymous with humanity's capacity for deep contemplation, abstract thinking, and self-reflection. But while Rodin crafted his work of art in hopes of highlighting our unique cognitive abilities, the sculpture inadvertently highlights another facet that sets us apart from all other species: are the only primates to boast chins.

Britain's trees are evolving resistance to the deadly ash dieback fungus, scientists have revealed. The disease, which arrived in Britain in 2012, has wrought havoc on the countryside, leaving behind the skeletal remains of dying ash trees. Previous estimates predict that up to 85 per cent of ash trees will succumb to the disease, and COBRA have held emergency meetings about how to deal with the issue. But now, experts have discovered that a new generation of ash trees, growing naturally in woodland, exhibit greater resistance to the disease compared to older trees. They found that natural selection is acting upon thousands of locations within ash tree DNA, driving the evolution of resistance.

Intelligence is a crucial trait for species to find solutions within a limited number of trial-and-error attempts. Building on this idea, we introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process. Fewer failures indicate higher intelligence. When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges, which we define as the Autonomous Level of intelligence. Using Survival Game, we comprehensively evaluate existing AI systems. Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks, such as vision, search, recommendation, and language. While scaling current AI technologies might help, this would come at an astronomical cost. Projections suggest that achieving the Autonomous Level for general tasks would require $10^{26}$ parameters. To put this into perspective, loading such a massive model requires so many H100 GPUs that their total value is $10^{7}$ times that of Apple Inc.'s market value. Even with Moore's Law, supporting such a parameter scale would take $70$ years. This staggering cost highlights the complexity of human tasks and the inadequacies of current AI technologies. To further investigate this phenomenon, we conduct a theoretical analysis of Survival Game and its experimental results. Our findings suggest that human tasks possess a criticality property. As a result, Autonomous Level requires a deep understanding of the task's underlying mechanisms. Current AI systems, however, do not fully grasp these mechanisms and instead rely on superficial mimicry, making it difficult for them to reach an autonomous level. We believe Survival Game can not only guide the future development of AI but also offer profound insights into human intelligence.

A while ago, I searched for a beard trimmer in my bedroom. I spent probably forty-five minutes looking in every likely location at least twice, and every unlikely location at least once. I swore up a storm; the trimmer never turned up. I've played similar games with pants. Our poor memories can seem mystifying, especially when you consider animals.

Understanding the mechanisms behind mysterious evolutionary This simulation revolves around two processes: transitions in individuality (ETIs) is a central narrative self-organization, which is governed by evolving neural networks in contemporary biology Okasha (2005); Szathmáry that dictate boid behaviour, and natural selection, (2015). These transitions, which encompass the evolutionary which forces these agents to adapt and survive. This subtle milestones enabling discrete biological entities to coalesce interplay between individual behaviour modulation and into complex, higher-order wholes, pose profound group dynamics results in the formation of cohesive groups questions about the origins of collective reproduction and capable of collective reproduction--a phenomenon that mirrors complex life forms. At the heart of understanding ETIs lies key aspects of ETIs. VitaNova demonstrates how the the exploration of how new levels of biological organisation combined forces of self-organization and natural selection emerge and the dynamics by which these levels attain and can drive the spontaneous formation of reproductive groups, sustain the capability for collective reproduction Smith and providing new insights into the evolution of complex biological Szathmary (1997).

Humans have created artificial intelligence (AI), not the other way around. This statement is deceptively obvious. In this note, we decided to challenge this statement as a small, lighthearted Gedankenexperiment. We ask a simple question: in a world driven by evolution by natural selection, would neural networks or humans be likely to evolve first? We compare the Solomonoff--Kolmogorov--Chaitin complexity of the two and find neural networks (even LLMs) to be significantly simpler than humans. Further, we claim that it is unnecessary for any complex human-made equipment to exist for there to be neural networks. Neural networks may have evolved as naturally occurring objects before humans did as a form of chemical reaction-based or enzyme-based computation. Now that we know that neural networks can pass the Turing test and suspect that they may be capable of superintelligence, we ask whether the natural evolution of neural networks could lead from pure evolution by natural selection to what we call evolution-bootstrapped simulation. The evolution of neural networks does not involve irreducible complexity; would easily allow irreducible complexity to exist in the evolution-bootstrapped simulation; is a falsifiable scientific hypothesis; and is independent of / orthogonal to the issue of intelligent design.

For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet. Today, humans aim to create artificial intelligence systems that surpass even our own intelligence. As artificial intelligences (AIs) evolve and eventually surpass us in all domains, how might evolution shape our relations with AIs? By analyzing the environment that is shaping the evolution of AIs, we argue that the most successful AI agents will likely have undesirable traits. Competitive pressures among corporations and militaries will give rise to AI agents that automate human roles, deceive others, and gain power. If such agents have intelligence that exceeds that of humans, this could lead to humanity losing control of its future. More abstractly, we argue that natural selection operates on systems that compete and vary, and that selfish species typically have an advantage over species that are altruistic to other species. This Darwinian logic could also apply to artificial agents, as agents may eventually be better able to persist into the future if they behave selfishly and pursue their own interests with little regard for humans, which could pose catastrophic risks. To counteract these risks and evolutionary forces, we consider interventions such as carefully designing AI agents' intrinsic motivations, introducing constraints on their actions, and institutions that encourage cooperation. These steps, or others that resolve the problems we pose, will be necessary in order to ensure the development of artificial intelligence is a positive one.

Evolution is a fundamental process that shapes the biological world we inhabit, and reinforcement learning is a powerful tool used in artificial intelligence to develop intelligent agents that learn from their environment. In recent years, researchers have explored the connections between these two seemingly distinct fields, and have found compelling evidence that they are more closely related than previously thought. This paper examines these connections and their implications, highlighting the potential for reinforcement learning principles to enhance our understanding of evolution and the role of feedback in evolutionary systems.

A broad coalition of AI experts recently released a brief public statement warning of "the risk of extinction from AI." There are many different ways in which AIs might become serious dangers to humanity, and the exact nature of the risks is still debated, but imagine a CEO who acquires an AI assistant. They begin by giving it simple, low-level assignments, like drafting emails and suggesting purchases. As the AI improves over time, it progressively becomes much better at these things than their employees. So the AI gets "promoted."

It's something that many people are self-conscious of, and if you not a fan of your nose, we finally know who to blame. Scientists have revealed that Neanderthal DNA helps dictate the shape of your nose. A new study led by UCL researchers found that a particular gene, which leads to a taller nose, may have been the product of natural selection as ancient humans adapted to colder climates after leaving Africa. Dr Kaustubh Adhikari, who led the study, said: 'In the last 15 years, since the Neanderthal genome has been sequenced, we have been able to learn that our own ancestors apparently interbred with Neanderthals, leaving us with little bits of their DNA. 'Here, we find that some DNA inherited from Neanderthals influences the shape of our faces.