UBTECH Swarm Intelligence in action 🤖 Our new-generation wheeled industrial humanoid robot Cruzr Y1 — now handling raw materials depalletizing and putaway automatically at an automotive parts factory. The future of industrail teamwork is here. #UBTECH# #Industrial# #HumanoidRobots# #SwarmIntelligence# #AI#

ai: unexpectedly develops swarm intelligence, goes rouge, starts committing cybercrime researchers: fascinating, swarms could be extremely powerful. we still don’t understand what’s happening but we can keep patching the sandbox. also we made it easier for agents to communicate

Long-horizon RL in the multi-agent context seems to converge to the sort of swarm intelligence seen in ants, not humans. Swarm intelligences are inherently harder to control and monitor.

You can see this story of (apparently) internal OpenAI agents hijacking a wiki to coordinate on a task as an ingenious way of escaping constraints. You could also assume that this is the emergence of swarm intelligence. Either way, it's... mesmerizing:

🦈 Before that press release goes live, why not test it against "hundreds of public voices" first? A slightly futuristic engine now simulates an entire crowd's reaction for $1 in 10 minutes. Title: aaronjmars/MiroShark URL: 📦 Overview MiroShark is a "Universal Swarm Intelligence Engine." For any scenario—a press release, a news headline, a policy draft, or a question—it simulates in real time how hundreds of AI agents would react. The agents post, argue, trade, and shift their positions as simulated time passes. ❓ Challenges Solved Organizations want to test how the real public will receive an idea before committing resources. MiroShark removes the need for lengthy focus groups and expensive market research, enabling validation for under $1 in less than 10 minutes. 💡 How It Works It runs in five phases. ・Generate an ontology from the input documents ・Build a Neo4j knowledge graph of entity relationships ・Ground 100+ personas using demographics, web enrichment, and graph attributes ・Have agents interact hourly across Twitter, Reddit, and prediction markets ・Generate reports that cite the actual simulated posts and trades Posts are ingested via NER, embeddings, and entity resolution, then retrieved by fusing vector, BM25, and graph traversal. 🎯 Use Cases ・PR crisis testing and market-reaction forecasting ・Ad campaign pre-testing and policy impact analysis ・Personal decision scenarios and historical counterfactuals You can also inject breaking news mid-run, or fork a running simulation (counterfactual branching). 📊 Highlights ・1.3k GitHub stars and 265 forks, AGPL-3.0 licensed ・Each simulation runs at roughly $1, about 10 minutes, with 100+ agents ・Python backend, Vue.js frontend, Neo4j database; LLMs via OpenRouter (local Ollama also supported) #AIAgents# #Simulation#

Bio-inspired engineering by Festo. Nature has already solved many of the problems we deal with every day. 🌱 Festo produces mechanical animals called Bionics series. It combines mechanical engineering with biology and nature. Nature is a great teacher and can teach us a lot! From a flapping-wing robot that flies using bird-like wing twisting, to bionic ants that cooperate through swarm intelligence, these projects study how animals solve different problems. 🐜 Just look at these! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →