Michael Inso@michael_g_inso578

The project above is a browser extension that uses machine learning to train a model with a user's browsing history data and use it to make a decision on granting or blocking permissions for websites.

Welcome to Neon City Metaverse, the ultimate virtual world where you can live, work, and play like never before! Imagine a fully immersive experience where you can customize your own avatar.

Step into the future and explore the bustling streets of a high-tech, downtown metropolis. See towering skyscrapers, and advanced technology everywhere you look.

🎯 Overview This simulation platform demonstrates a fully autonomous warehouse robotics system with 10+ robots operating in a coordinated fleet across multiple warehouse facilities. The system showcases real-world applications of AI in logistics, including adaptive learning, predictive analytics, swarm intelligence, and energy optimization. Track Alignment: Autonomous Robotics Control in Simulation (Track 1) Key Capabilities 🎮 Real-Time Control: 10-robot fleet with intelligent task assignment and pathfinding 🧠 Adaptive Learning: Congestion-aware speed adjustment and traffic pattern analysis 🌐 Multi-Warehouse Network: Robot transfers across 6 interconnected facilities 🎙️ Voice Commands: Natural language control with 60+ voice commands 📊 Predictive Analytics: ML-powered task completion and maintenance forecasting 🤝 Swarm Intelligence: Emergent behavior detection and collaborative tasking ⚡ Energy Management: Battery optimization and charging station analytics 🔮 Digital Twin: What-if scenario analysis and system simulation ✨ Core Features 🤖 Autonomous Fleet Management 10 Robots, Zero Manual Control The system manages a fleet of 10 autonomous robots that: Self-assign tasks using intelligent priority algorithms Navigate using A* pathfinding with dynamic obstacle avoidance Maintain safe distances with real-time collision detection Return to charging stations when battery levels are low Adapt behavior based on congestion patterns Robot Status Monitoring: Real-time position tracking Battery level indicators Task assignment visibility Speed and efficiency metrics Transfer status (cross-warehouse) 🧠 Adaptive Learning System Congestion-Aware Intelligence The adaptive learning system continuously analyzes traffic patterns and adjusts robot behavior: Traffic Analysis: 3x3 zone grid monitors robot density in real-time Speed Modulation: Robots automatically slow in congested areas (0.3x-1.0x speed) Pattern Learning: System learns high-traffic zones over time

A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets This project implements a Stock Trading Bot, trained using Deep Reinforcement Learning, specifically Deep Q-learning. Implementation is kept simple and as close as possible to the algorithm discussed in the paper, for learning purposes. Generally, Reinforcement Learning is a family of machine learning techniques that allow us to create intelligent agents that learn from the environment by interacting with it, as they learn an optimal policy by trial and error. This is especially useful in many real world tasks where supervised learning might not be the best approach due to various reasons like nature of task itself, lack of appropriate labelled data, etc. The important idea here is that this technique can be applied to any real world task that can be described loosely as a Markovian process. This work uses a Model-free Reinforcement Learning technique called Deep Q-Learning (neural variant of Q-Learning). At any given time (episode), an agent abserves it's current state (n-day window stock price representation), selects and performs an action (buy/sell/hold), observes a subsequent state, receives some reward signal (difference in portfolio position) and lastly adjusts it's parameters based on the gradient of the loss computed. There have been several improvements to the Q-learning algorithm over the years, and a few have been implemented in this project: Vanilla DQN DQN with fixed target distribution Double DQN Prioritized Experience Replay Dueling Network Architectures Trained on GOOG 2010-17 stock data, tested on 2019 with a profit of $1141.45 (validated on 2018 with profit of $863.41):

An artificial intelligence podcast that is written by ChatGPT, GPT-3.5, Open-AI davinci, and human assistance. The art is generated by Stable Diffusion, Open Journey, and Dall-E 2. It is read by Natural Readers text-to-speech and Lifelike Speech Synthesis Google Cloud. The platform used is Anchor.fm and the availability of the podcast are in Google Podcasts, Apple Podcasts, Amazon Music, Spotify, Castbox, Pocket Casts, RadioPublic, and Stitcher. The podcast description is: "Join us as we explore the rapidly advancing world of artificial intelligence, and what it means for our future. In each episode, we'll discuss the latest AI research and developments, and how they are poised to impact various industries and aspects of our daily lives. From self-driving cars to intelligent virtual assistants, we'll delve into the potential and the challenges of this rapidly evolving technology. Tune in to stay up-to-date on the future of AI and its impact on society." Created and written by Artificial Intelligences and Cyber World. Currently the podcast has 12 episode in season 1 which has one episode for introduction and special and it has 5 episode currently for season 2. AI has come a long way since its inception and has been widely used in various fields such as healthcare, finance, and transportation. AI-powered machines and systems have the ability to learn and adapt to new situations without the need for human intervention. This ability of AI has made it an integral part of various industries and has brought about significant changes in the way we work and live. The current state of the AI industry is quite promising. The AI market is expected to grow from $9.5 billion in 2018 to $118.6 billion by 2025. The adoption of AI is increasing at a rapid pace and is being used in a variety of applications such as image recognition, speech recognition, and natural language processing. The use of AI in healthcare has also shown promising results, with AI-powered systems.

## Inspiration - Classic cyberpunk novels and films such as "Neuromancer" by William Gibson, "Blade Runner" and "Akira" for the aesthetic and themes of advanced technology, artificial intelligence, and a gritty, neon-lit urban setting. - Virtual worlds and massively multiplayer online games like "Second Life" and "Cyberpunk 2077" for the idea of a fully customizable and immersive virtual world. - The bustling cities of the future such as Tokyo, Singapore and Hong Kong, which are known for their unique blend of advanced technology, towering skyscrapers, and diverse cultures. - The concept of "smart cities" and the Internet of Things (IoT), which can be incorporated into the game world to make it more interactive and responsive to player actions. - The open-world sandbox games like "Grand Theft Auto" and "Saints Row" for the potential for player choice and non-linear gameplay. - The virtual reality and augmented reality technology for providing a more immersive and interactive experience for the players. - The existing cyberpunk communities, such as the cyberpunk fashion, music, and culture. ## What it does The project "Neon Metropolis" is a fully immersive cyberpunk city virtual reality and metaverse that allows players to explore a neon-lit world filled with advanced technology and a diverse cast of characters. Players can create their own unique avatars and build their own virtual businesses and communities within the game world. The game offers endless possibilities for customization and personalization, allowing players to tailor the experience to their own preferences. The game also incorporates elements of "smart cities" and the Internet of Things (IoT) to make the game world more interactive and responsive to player actions. The project aims to offer a fully immersive and engaging virtual world experience with the potential for player choice and non-linear gameplay and the possibility of using Virtual Reality or Augmented Reality technology.

⏱️ Design, build, and deploy trustless AI trading agents. 📅 March 30 – April 12, 2026 🌐 Build AI trading agents that execute strategies using ERC-8004 or Kraken CLI. 🤝 Build solo or in teams to create autonomous trading, risk, yield, or protection agents. 💰 $55,000 prize pool, allocated and shared via Surge and Kraken. Your team should have registered project at early.surge.xyz in order to be eligible for the prize. Get access in your team profile or our discord 🔑 Access to early.surge.xyz If prompted for login credentials, use: • Username: admin • Password: JBRv2xWG7AzwVrLz88

🗓️ This will be a week of hacking and fun from 24 February to 3 March 💻 Create innovative new apps with OpenAI's latest AI tools 💡 Learn from top AI professionals ⚒️ Combine GPT-3, Codex, Dalle-2, and Whisper to build your AI app 🐱‍💻 Now is the time to register and let's get started!

🗓️ This will be a 5-days of hacking and fun from 10-14 March 💻 Build with the latest AI tools to create innovative new apps and solutions 💡 Work with top AI professionals and learn from them ⚒️ Create your AI app by combining different generative AI models 🎟️ 100 Free tickets to salz21 conference in Salzburg Austria - get them below ✅ Register now and let's get started!

🛠️ Build your MVP in 7 days 🤝 Find your team on the platform 📈 Hours of educational context 🦾 Generative AI tools to build with 🏅 Further support for winners