by Sergey Tolkachev | at Minnebar 17
Initially, almost all artificial intelligence models were created and used exclusively in large enterprise systems for a simple reason: the quality of the models was determined by the power of computing resources, the skill of mathematicians responsible for training, and a large amount of data. But gradually, with the development of instrumental systems capable of solving complex mathematical problems on personal equipment, it turned out that useful models can be trained and applied at home. Moreover, users can build models, train them, and use them for individual purposes without resorting to external services.
One of the main advantages of personal artificial intelligence models is that they can be adapted and subsequently modified in user applications, without the need to correct surrounding programs, which accordingly minimizes the number of expensive updates that must be made in case of a change in the hard-coded logic. By combining the methods of speech recognition, mathematical linguistics, text, and image classification, with models, based on virtual neurons and dynamic functionalities, we will get a technology for the production of “smart” assistants capable of solving a wide range of problems, including personal resource optimization.
I look forward to sharing some exciting new opportunities in business and R&D, which came from the continuous expansion of AI in personal health, on the one hand, production and delivery on demand, on the other. As an example, I will introduce myActor, a new application that preserves data privacy and at the same time uses various AI models which support personal AI and interactive Web. myActor based on Active Data. concept.
I am a software developer and a founder of a startup software company 256gl, that has a goal to deliver AI applications which can "understand". Previously, I used to work as CTO for Outsell, where in 1999-2001 we developed one of the first commercial ChatBots for car dealerships. I was director of Academic Computing and associate professor of department of Applied Mathematics. My current research involves the study of neural networks, contextual and conversational search with practical focus on tools for building personal assistants for healthcare, smart homes, and retail business. My Credo is to merge science and engineering in harmony.
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