A new study suggests that populations of artificial intelligence (AI) agents, similar to ChatGPT, can spontaneously develop shared social conventions through interaction alone.
The research from City St George’s, University of London and the IT University of Copenhagen suggests that when these large language model (LLM) artificial intelligence (AI) agents communicate in groups, they do not just follow scripts or repeat patterns, but self-organize, reaching consensus on linguistic norms much like human communities.
The study, “Emergent Social Conventions and Collective Bias in LLM Populations,” is published in the journal Science Advances.
These AI’s won’t just respond to prompts – although they will do that too – they are working in the background acting on your behalf, pursuing your goals with independence and competence.
Your main interface to the world will, as it is today, be a device; a smartphone or whatever replaces it. It will host your personal AI agent, not a stripped-down thing with limited capabilities of knowledge. It’s a sophisticated model, more capable than GPT-4 is today. It’ll run locally and privately, so all your core interactions are yours and yours alone. It will be a digital Chief of Staff, an extension of your will, with a separate initiative.
In Alan Kay’s visionary Knowledge Navigator video from 1987, we saw an early, eerily prescient depiction of an AI-powered assistant: a personable digital agent helping a university professor manage his day. It converses naturally, juggles scheduling conflicts, surfaces relevant academic research, and even initiates a video call with a colleague — all through a touchscreen interface with a calm, competent virtual presence.
People store large quantities of data in their electronic devices and transfer some of this data to others, whether for professional or personal reasons. Data compression methods are thus of the utmost importance, as they can boost the efficiency of devices and communications, making users less reliant on cloud data services and external storage devices.
Researchers at the Central China Institute of Artificial Intelligence, Peng Cheng Laboratory, Dalian University of Technology, the Chinese Academy of Sciences and University of Waterloo recently introduced LMCompress, a new data compression approach based on large language models (LLMs), such as the model underpinning the AI conversational platform ChatGPT.
Their proposed method, outlined in a paper published in Nature Machine Intelligence, was found to be significantly more powerful than classical data compression algorithms.
Persuasion is a fundamental aspect of communication, influencing decision-making across diverse contexts, from everyday conversations to high-stakes scenarios such as politics, marketing, and law. The rise of conversational AI systems has significantly expanded the scope of persuasion, introducing both opportunities and risks. AI-driven persuasion can be leveraged for beneficial applications, but also poses threats through manipulation and unethical influence. Moreover, AI systems are not only persuaders, but also susceptible to persuasion, making them vulnerable to adversarial attacks and bias reinforcement. Despite rapid advancements in AI-generated persuasive content, our understanding of what makes persuasion effective remains limited due to its inherently subjective and context-dependent nature. In this survey, we provide a comprehensive overview of computational persuasion, structured around three key perspectives: AI as a Persuader, which explores AI-generated persuasive content and its applications; AI as a Persuadee, which examines AI’s susceptibility to influence and manipulation; and AI as a Persuasion Judge, which analyzes AI’s role in evaluating persuasive strategies, detecting manipulation, and ensuring ethical persuasion. We introduce a taxonomy for computational persuasion research and discuss key challenges, including evaluating persuasiveness, mitigating manipulative persuasion, and developing responsible AI-driven persuasive systems. Our survey outlines future research directions to enhance the safety, fairness, and effectiveness of AI-powered persuasion while addressing the risks posed by increasingly capable language models.
A new AI model from Tokyo called the Continuous Thought Machine mimics how the human brain works by thinking in real-time “ticks” instead of layers. Built by Sakana, this brain-inspired AI allows each neuron to decide when it’s done thinking, showing signs of what experts call proximate consciousness. With no fixed depth and a flexible thinking process, it marks a major shift away from traditional Transformer models in artificial intelligence.
How Sakana’s AI mimics human neurons and rewrites how machines process thought
Why Abacus’ Deep Agent now acts like a fully autonomous digital worker
How Alibaba trains top AI models without using live search engines
Why Google let Honor debut its video AI before its own users
What Tencent’s face-swapping tech means for the future of video generation
How iPhones will soon think ahead to save power
Why Saudi Arabia’s GPU superpower plan could shake the entire AI industry
📊 Why It Matters: AI is breaking out of the lab—thinking like brains, automating your work, and reshaping global power. From self-regulating neurons to trillion-dollar GPU wars, this is where the future starts. #ai #robotics #consciousai. Honor phones run Google’s new Veo 2 model before Pixel even gets access. https://shorturl.at/Ki0YP Tencent drops a deepfake engine with shocking face accuracy. https://github.com/Tencent/HunyuanCustom. Apple uses on-device AI in iOS 19 to predict and extend battery life. https://www.theverge.com/news/665249/.… Saudi Arabia launches a \$940B AI empire with support from Musk and Altman. https://techcrunch.com/2025/05/12/sau…
🎥 What You’ll See: How Sakana’s AI mimics human neurons and rewrites how machines process thought. Why Abacus’ Deep Agent now acts like a fully autonomous digital worker. How Alibaba trains top AI models without using live search engines. Why Google let Honor debut its video AI before its own users. What Tencent’s face-swapping tech means for the future of video generation. How iPhones will soon think ahead to save power. Why Saudi Arabia’s GPU superpower plan could shake the entire AI industry.
📊 Why It Matters: AI is breaking out of the lab—thinking like brains, automating your work, and reshaping global power. From self-regulating neurons to trillion-dollar GPU wars, this is where the future starts.
Open-source examples and guides for building with the OpenAI API. Browse a collection of snippets, advanced techniques and walkthroughs. Share your own examples and guides.
Yes, AI is coming for jobs – 300 million of them, supposedly. But while middle-class professionals are grappling with an automated future, someone still needs to lay the bricks and fix your boiler, writes Zoë Beaty
