Much has been said about the effective running of machine learning projects. However, the topic keeps coming up. It is vital to remember that the purpose of ML projects is not modeling itself, but achieving defined business goals.
As predicted by McKinsey experts, the technological revolution in the insurance industry has already started, and its progressive development will lead to a massive technological shift over the next decade.
Cost of risk is one of the biggest components in banks’ cost structure. Thus, even a slight improvement in credit risk modelling can translate in huge savings. That’s why machine learning is often implemented in this area.
Computer vision is a foundational element of smart factory solutions. At deepsense.ai we have created diverse AI-driven, automated computer vision-based solutions that undertake the most demanding challenges in many different production lines.
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The concept of machine learning is derived from advanced data analysis of pattern and dependency recognition. ML assumes that when models analyze new data, they can adapt independently and use this new knowledge to develop by learning from previous experiences. Machine learning models can enhance nearly every aspect of a business, from marketing and sales to maintenance.
Interview with Michał Iwanowski, VP of Engineering, about AI development in 2021.
According to research done by Business Insider Intelligence, banks can benefit by $447 billion by 2023 applying Artificial Intelligence. No wonder artificial intelligence is playing an increasingly important role in financial institutions’ roadmap for the coming years – 75% of banks with over $100 billion in assets already have AI strategies in place.
AI has become a powerful force in Computer Vision and it has unleashed tangible business opportunities for 2D visual data such as images and videos. Applying AI can bring tremendous results in a number of fields. To learn more about this exciting area, read our overview of 2D computer vision algorithms and applications.
Modern science is facing a completely new challenge: overload. According to research from the University of Ottawa, the total number of research papers published since 1665 passed the 50 million mark in 2009 and approximately 2.5 million new papers are published every year.