Nike's Robotic Shoe Gets Humans One Step Closer to Cyborg Project Amplify is Nike's latest attempt to put some spring in your step with help from a powered mechanism that enhances the natural movement of the human ankle and lower leg. If you want to run faster or farther, you have options. You can put in the work, getting up 40 minutes earlier to train, changing your diet, going harder and longer on each of your runs to build up strength. Or, you can strap on one of Nike's new robot shoes and mechanically boost your speed, your stamina, and your overall performance in a flash. Sounds way easier, and probably more fun too.

Reinforcement Learning (RL) has shown great potential for autonomous decision-making in the cybersecurity domain, enabling agents to learn through direct environment interaction. However, RL agents in Autonomous Cyber Operations (ACO) typically learn from scratch, requiring them to execute undesirable actions to learn their consequences. In this study, we integrate external knowledge in the form of a Large Language Model (LLM) pretrained on cybersecurity data that our RL agent can directly leverage to make informed decisions. By guiding initial training with an LLM, we improve baseline performance and reduce the need for exploratory actions with obviously negative outcomes. We evaluate our LLM-integrated approach in a simulated cybersecurity environment, and demonstrate that our guided agent achieves over 2x higher rewards during early training and converges to a favorable policy approximately 4,500 episodes faster than the baseline.

--Simulated environments have proven invaluable in Autonomous Cyber Operations (ACO) where Reinforcement Learning (RL) agents can be trained without the computational overhead of emulation. These environments must accurately represent cybersecurity scenarios while producing the necessary signals to support RL training. In this study, we present a framework where we first extend CybORG's Cage Challenge 2 environment by implementing three new actions: Patch, Isolate, and Unisolate, to better represent the capabilities available to human operators in real-world settings. We then propose a design for agent development where we modify the reward signals and the agent's feature space to enhance training performance. T o validate these modifications, we train DQN and PPO agents in the updated environment. Our study demonstrates that CybORG can be extended with additional realistic functionality, while maintaining its ability to generate informative training signals for RL agents.

Fast and effective incident response is essential to prevent adversarial cyberattacks. Autonomous Cyber Defense (ACD) aims to automate incident response through Artificial Intelligence (AI) agents that plan and execute actions. Most ACD approaches focus on single-agent scenarios and leverage Reinforcement Learning (RL). However, ACD RL-trained agents depend on costly training, and their reasoning is not always explainable or transferable. Large Language Models (LLMs) can address these concerns by providing explainable actions in general security contexts. Researchers have explored LLM agents for ACD but have not evaluated them on multi-agent scenarios or interacting with other ACD agents. In this paper, we show the first study on how LLMs perform in multi-agent ACD environments by proposing a new integration to the CybORG CAGE 4 environment. We examine how ACD teams of LLM and RL agents can interact by proposing a novel communication protocol. Our results highlight the strengths and weaknesses of LLMs and RL and help us identify promising research directions to create, train, and deploy future teams of ACD agents.

In the advanced field of bio-inspired robotics, the emergence of cyborgs represents the successful integration of engineering and biological systems. Building on previous research that showed how electrical stimuli could initiate and speed up a jellyfish's movement, this study presents a groundbreaking approach that explores how the natural embodied intelligence of the animal can be harnessed to address pivotal challenges such as spontaneous exploration, navigation in various environments, control of whole-body motion, and real-time predictions of behavior. We have developed a comprehensive data acquisition system and a unique setup for stimulating jellyfish, allowing for a detailed study of their movements. Through careful analysis of both spontaneous behaviors and behaviors induced by targeted stimulation, we have identified subtle differences between natural and induced motion patterns. By using a machine learning method called physical reservoir computing, we have successfully shown that future behaviors can be accurately predicted by directly measuring the jellyfish's body shape when the stimuli align with the animal's natural dynamics. Our findings also reveal significant advancements in motion control and real-time prediction capabilities of jellyfish cyborgs. In summary, this research provides a comprehensive roadmap for optimizing the capabilities of jellyfish cyborgs, with potential implications in marine reconnaissance and sustainable ecological interventions.

Social robotics researchers are increasingly interested in multi-party trained conversational agents. With a growing demand for real-world evaluations, our study presents Large Language Models (LLMs) deployed in a month-long live show at the Edinburgh Festival Fringe. This case study investigates human improvisers co-creating with conversational agents in a professional theatre setting. We explore the technical capabilities and constraints of on-the-spot multi-party dialogue, providing comprehensive insights from both audience and performer experiences with AI on stage. Our human-in-the-loop methodology underlines the challenges of these LLMs in generating context-relevant responses, stressing the user interface's crucial role. Audience feedback indicates an evolving interest for AI-driven live entertainment, direct human-AI interaction, and a diverse range of expectations about AI's conversational competence and utility as a creativity support tool. Human performers express immense enthusiasm, varied satisfaction, and the evolving public opinion highlights mixed emotions about AI's role in arts.

Navigating multi-robot systems in complex terrains has always been a challenging task. This is due to the inherent limitations of traditional robots in collision avoidance, adaptation to unknown environments, and sustained energy efficiency. In order to overcome these limitations, this research proposes a solution by integrating living insects with miniature electronic controllers to enable robotic-like programmable control, and proposing a novel control algorithm for swarming. Although these creatures, called cyborg insects, have the ability to instinctively avoid collisions with neighbors and obstacles while adapting to complex terrains, there is a lack of literature on the control of multi-cyborg systems. This research gap is due to the difficulty in coordinating the movements of a cyborg system under the presence of insects' inherent individual variability in their reactions to control input. In response to this issue, we propose a novel swarm navigation algorithm addressing these challenges. The effectiveness of the algorithm is demonstrated through an experimental validation in which a cyborg swarm was successfully navigated through an unknown sandy field with obstacles and hills. This research contributes to the domain of swarm robotics and showcases the potential of integrating biological organisms with robotics and control theory to create more intelligent autonomous systems with real-world applications.

Elon Musk has left even his most ardent fans terrified after he revealed his tech start-up Neuralink has become the first to successfully implant a microchip into a human brain. The world's richest man said the operation took place on Sunday and'initial results show promising neuron spike detection'. The device - called'Telepathy' will'enable control of your phone or computer, and through them almost any device, just by thinking', he said. But many of his 170 million followers on X, formerly Twitter, accused him of'mind control', creating'cyborgs', and even'playing God'. 'The negative potential of this makes me very uneasy,' one person wrote in a reply to his announcement.

In August 1998, Professor Kevin Warwick inadvertently ushered an era of'biohacking' when he had a small cylindrical chip implanted in his arm. Around the length of a 2p coin, it let him open doors and switch on lights with a casual wave while walking around the cybernetics department at the University of Reading. Today, he's referred to as'Captain Cyborg' and is considered the first'biohacker' – someone who makes alterations to the body with technology to make life easier. Now the Vice-Chancellor at Coventry University, the 69-year-old looks back at the experiment a quarter of a century ago as'quite cool' and'good fun'. 'At the time nobody had done anything like that,' he told MailOnline.

"JIZAI Body"2 allows each person to live as they wish by controlling their natural body and altered robotic body parts. The Japanese term "jizai" originates from the Sanskrit word "isvara," which means supreme being free from earthly desires and constrains. JIZAI Body emphasizes the transformability of physical bodies and the effects on spiritual and internal minds fostered in Asian spiritualities such as Buddhism, Hinduism, and others, beyond enhancement or augmentation contextualized in Western cultures. For example, a wearable robotic limb system called JIZAI ARMS4 (see Figure 1) allows social interaction between multiple wearers and explores communication between them. Wearers can exchange the arms and share the body parts.