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I am an Artificial Intelligence researcher. Until recently I worked as a consultant and freelance data scientist, but for the next couple of years I will be pursuing a PhD in artificial intelligence. I have built a startup based on an AI system of my own design to automate many types of software tasks, especially in Data Science. I always look for new and creative ways to solve problems. But at the same time, I care more about practical results than about theory. This allows me to identify opportunities and challenges that other people miss, and to find new ways to drive profit. Other scientists often focus on sharpening their skills with their favorite algorithms. This tends to lead to "when all you have is a hammer, every problem looks like a nail". I instead focus on the ability to always pick the right tool for the job. I achieved some of my biggest successes in data science by inventing my own custom AI algorithms specifically for the problem at hand. I have been working as an independent AI researcher since high school. My research has enabled me to create profitable custom AI algorithms at Volkswagen and Huawei. I received a research grant even though I am not in academia. The idea for my startup Elody was also a result of my research. My programming skills are competitive on the national level. I was a finalist of a national programming competition, and won the award for the most creative ideas, nationwide. I have experience working as a full-stack developer from building a tech startup. All of this is because of my main goal in life: I want to understand how thinking works. I want to know why people make the decisions they do, how data and rigorous techniques can improve on human abilities, and how the full breadth of human intelligence can be replicated in a machine. I am pursuing a PhD in AI. I am trying to figure out how to emulate introspection in an AI. I believe that this is a key component of consciousness, and current AI technologies do not exhibit this ability at all. In more practical terms, my research is related to Continual Learning: Teaching a single AI to be able to do many different tasks, without getting confused by this as contemporary systems do. Companies currently have to throw away existing models and replace them with newly trained ones from scratch, instead of improving an existing model. This is very wasteful, and I want to find a way to fix that.