Delving deep into artificial intelligence: from mere number-crunching to structured, logical problem-solving.

Artificial Intelligence (AI) is constantly evolving, pushing the boundaries of what’s possible. From its origins in statistical prediction, AI is moving towards a broader understanding that includes vast capabilities for structured problem-solving.

Historically, AI has been used principally for numerical predictions based on vast data sets. These statistical models have been the backbone of various applications, allowing machines to make educated predictions based on previous trends, patterns, and data relationships. But the horizon of AI extends far from statistical models. It is gradually crossing the frontier into the realm of structured problem-solving - an area that requires a blend of deep, stringently conducted research and the ability to reason logically.

Reasoning is a complex cognitive process which consists of making sense of things, establishing and verifying facts, and altering or justifying practices by considering existing pieces of information. By instilling such cognitive abilities into AI models, we can augment them to uncover truths, predict unobserved phenomena, and solve complex problems without human intervention.

Deep research, on the other hand, allows an AI system to understand and recall vast amounts of detailed, contextual information. With extensive data analysis and a deeper understanding of the information, AI systems can generate more precise and accurate solutions.

In conclusion, the fusion of reasoning and deep research is steering AI’s journey from statistical prediction towards structured problem-solving. The implications of this on AI’s growth and its potential applications are nothing short of profound. The future of AI promises exciting, uncharted territories, and this is just the beginning of an epoch-making journey.