The rapid growth of scientific techniques and knowledge is reflected in the exponential increase in new patents filed annually. While these patents drive innovation, they also present significant burden for researchers and engineers, especially newcomers. To avoid the tedious work of navigating a vast and complex landscape to identify trends and breakthroughs, researchers urgently need efficient tools to summarize, evaluate, and contextualize patents, revealing their innovative contributions and underlying scientific principles.To address this need, we present EvoPat, a multi-LLM-based patent agent designed to assist users in analyzing patents through Retrieval-Augmented Generation (RAG) and advanced search strategies. EvoPat leverages multiple Large Language Models (LLMs), each performing specialized roles such as planning, identifying innovations, and conducting comparative evaluations. The system integrates data from local databases, including patents, literature, product catalogous, and company repositories, and online searches to provide up-to-date insights. The ability to collect information not included in original database automatically is also implemented. Through extensive testing in the natural language processing (NLP) domain, we demonstrate that EvoPat outperforms GPT-4 in tasks such as patent summarization, comparative analysis, and technical evaluation. EvoPat represents a significant step toward creating AI-powered tools that empower researchers and engineers to efficiently navigate the complexities of the patent landscape.

Innovations within FinTech are causing major changes to the dynamic between clients and wealth management providers. With technology as a driving force behind industry changes, understanding how client perspectives are shifting is crucial. For the wealth management sector, there are three key innovations that institutions need to be paying close attention to. These are artificial intelligence, open banking, and agile distribution. Understanding these will be the foundation for meeting new customer demands in the coming years.

Since the beginning of the photography, Image colorization may have been reserved for those with artistic talent in the past, but now thanks to Artificial Intelligence, is it possible to colorize black and white images and video with outstanding quality. One interesting example is the paper Fully Automatic Video Colorization with Self-Regularization and Diversity ( you can read it here), which refers to one experiment by the Hong Kong University of Science and Technology, which presents a fully automatic method for colorizing black and white films without any human guidance or references. Typical image colorization methods require some sort of labeled reference. A key innovation of this paper is a novel framework consisting of a colorization network with self-learning techniques. The researchers used the ranked diversity loss function proposed in a CVPR paper to differentiate different solution modes.

IBM has introduced its annual forecast "IBM 5 in 5", highlighting innovations in science and technology, which will have the greatest impact on our lives in the next five years. Among them are AI, quantum computing, blockchain and cybersecurity. Our oceans are polluted, but microscopic robots based on AI can save them, as they believe in IBM. Small autonomous microscopic units using AI and cloud computing technologies will be introduced around the world. Such a network will constantly monitor the state of water, one of the most important and vulnerable resources of the Earth.