Michael Inso@michael_g_inso578
is under improvement
10
Events attended
3
Submissions made
Founder
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4 years of experience
About me
(Multipotentialite Advocate!). I'm the apex of 21st-century innovation, a young restless, leader, smart, hardworking, ambitious, and highly skilled. Also being multipotentialite, and a member of the high echelon I got a lot of skills and specialty at such a young age. I achieved, accomplished, and experienced a lot as a young adult, not just career and education. Also the past few years I worked hard, studied in colleges and universities, and attended multiple workshops and seminars. Cyber and bionic augmentation technology, CRISPR, and fintech, interest me etc. I had experience and trained in logistics, social media, & marketing, funneling, financial AI, EA, & HFT, ICT, system & software developer/engineer, automation, cyber/offensive engineering. I had experience and trained in responsible quoting and benchmarking of freight rates. I had experience in social media and marketing strategies. I had experiences in cyber/offensive security engineering during pandemic capable of penetration-testing & OSINT with highly difficult tools and operating systems (OS) like Kali Linux, Parrot OS, & Black Arch/Arch Linux. I had experiences trading foreign exchanges and stock exchanges, having affiliated with different retail and foreign exchange companies. I also got experience with automation. As a software programming/engineering/full stack developer, I have experienced and trained in multiple programming languages from a list of front-end and back-end C, C#, C++, HTML 5, CSS 3, Java, JavaScript, JQuery, JSON, Node, Python, SDK, Objective C and, ASP NET. Frameworks and styles React, Vue, Angular, Webpack, Bootstrap, Material UI. Databases SQL, SQLite, Apache, and Postgres. Infrastructure Azure, AWS, and DataBricks. Page 1 of 5 Virtualization Docker, VMware, VirtualBox, Hyper-V, Vagrant, and Kubernetes. Cross-Platform React, Ionic, and Unity. In System Engineering I have trained and experienced LAN, MAN, and WAN router and network administration. IT/IS/Cyber-Security. I'm highly familiar with tools like packet tracer, Solar, Putty, & GNS3, etc. Also with OS administration like GNU/Linux & Ubuntu and its alternatives, etc. I have a professional & higher certificate and diploma for ACCP, ACSE, ADSE, CISE, HCSE, CCNA, CCNP, NCC-L1DC into L5DC, A+, N+. Getting a CCIE, in Cisco. OSCP, OSCE and, OSEE in Offensive-Security
is under improvement
🔥 Own Projects
🤓 Submissions
The Future of AI podcast
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.
Neon Metropolis
## 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.
Trading-Agent-
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):
