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Artificial Intelligence (AI) plays an increasingly important role in the research process. AI-based algorithms are being used to improve the efficiency of research and provide new perspectives on explored topics. They are valuable not only for making connections between different pieces of information but also for proposing and testing new hypotheses. AI Use Cases in Research A major advancement in artificial intelligence research recently came with a machine learning algorithm capable of inventing radical new proteins that can combat diseases. Also, AI researchers are now developing algorithms that can search for scientific research papers and extract information from them to automatically correct scientific papers. Let’s take a look at some more uses cases of AI in research. #1: Automated Data Artificial intelligence is also used to optimize resources in research laboratories, automate the acquisition of data, and facilitate the synthesis and analysis of complex datasets. For example, AI has recently been used to help manage the activities in large-scale, long-term studies by providing real-time guidance. An AI system may be able to monitor the health of each participant in a study and alert a scientist if a participant’s status changes. #2: Equipment Optimization AI is also being used to optimize laboratory techniques and equipment. AI-driven robots can automatically perform several tasks that were previously only carried out by humans, such as organizing and storing scientific equipment, preparing samples for analysis, and carrying out routine diagnostic tests. In addition, automated systems are also able to carry out tasks that are too dangerous or difficult for scientists or technicians to complete themselves. AI and robotics are also being used in the design of experiments—helping researchers determine which parameters should be changed, how the experiment should be designed, and what measurements should be made. #3: Healthcare Many believe AI will soon be used to identify new drugs and drug combinations, diagnose diseases from medical images, and assist in surgeries. AI was used to predict an enzyme better than any other prediction before. A technique called deep learning was used. The system was able to predict the three-dimensional structure of an enzyme. The most important thing is that the 3D structure was more complex than those the algorithm was previously trained to deal with. Artificial intelligence has also been successfully used in cancer research to create better ways to detect, diagnose, and treat cancer patients. It was reported that machine vision was used to analyze human behavior and physical characteristics in videos of people with autism and Asperger’s Syndrome. AI algorithms based on deep learning were used with a dataset of 1,200 videos featuring 12-megapixel cameras, like the ones on iPhone 13, and individuals making facial expressions or engaged in social interactions, such as smiling or nodding. The analysis revealed ten distinct facial states of autism, while deep neural networks also accurately projected the severity of symptoms. #4: Computer Science Researchers use AI-based algorithms to search databases of molecules and find effective molecules with desired properties. Such an algorithm may be able to search databases of millions of molecules in a fraction of the time it would take an expert scientist. Computer scientists also created a system that can be used to generate new educational games based on existing video games. The researchers used AI to develop new algorithms for recombining existing game elements into new types of games. They used machine learning to create the system, which uses a personalized learning algorithm to select elements from a large amount of video game content and then recombines them in an unpredictable way. The researchers suggest that this technique could be useful for exploring different genres of video games or creating new genres based on already existing ones. The Future of Research It has been asserted that AI affects the nature of humans, their intelligence, and the decision-making process. With the advent of AI, there is concern over how its creations could affect human beings, including encouraging biases in human thought processes. A common concern is that machines would become smarter than humans and thus gain control. Regardless, AI is proving a powerful tool for connecting information and drawing new hypotheses.

The Imperative of Efficiency in Architecture In the highly competitive field of architecture, where margins are often slim, efficiency is more than a goal—it’s a necessity. A critical avenue for achieving this efficiency is artificial intelligence (AI). With its ability to analyze massive datasets and automate complex tasks, AI is becoming an indispensable tool in the arsenal of modern architecture firms. One prime example is Mosaic, an AI-powered resource management software that’s revolutionizing project planning in the industry. AI-Powered Optimization of Project Planning Mosaic uses AI to make project planning more effective and less time-consuming. Here are some ways this AI tool is streamlining operations in architecture firms: Intelligent Project Assignment An essential function of management is ensuring that every team member has enough work. Mosaic analyzes various data points—such as current workload, upcoming deadlines, recent work history, and skill sets—to recommend appropriate projects for each team member. This leads to increased utilization, and consequently, enhanced profitability. Smart Team Formation Building the right project team is often a time-consuming process of trial and error. Mosaic simplifies this by analyzing historical project data to suggest suitable staff members. The recommendations, powered by machine learning, continuously improve over time, making team formation more efficient. Automated Scheduling and Rescheduling Keeping project schedules up-to-date is a daunting task, especially when changes occur frequently. Mosaic offers automated scheduling and rescheduling, keeping things current based on the progress of work. It can even suggest staff members who could help meet deadlines, an innovation so profound that it has received two U.S. patents. Hiring Recommendations Understanding when to hire new staff is a critical decision for business leaders. Mosaic assesses the demand for each role and compares it with the capacity of existing staff. It then alerts leaders when demand exceeds capacity, providing timely insights for hiring decisions. Identifying Profitable and Non-Profitable Sectors Mosaic’s AI analyzes historical data to identify profitable and non-profitable sectors within a firm’s projects. This knowledge enables firms to focus more on profitable projects and less on the ones causing losses, thereby enhancing overall profitability. Forecasting Workload and Revenue Accurate forecasting of workload and revenue is crucial in business planning. Mosaic leverages project, planning, and budget data to predict workload and revenue, providing more certainty for business owners. Enabling a More Productive Future in Architecture With AI tools like Mosaic, architecture firms can significantly reduce time spent on planning, allowing for more focus on billable work, business development, and learning. Contrary to fears, AI is not here to replace architects but to empower them to do better, more meaningful work. With AI-enhanced efficiency, firms can either boost their profits or opt for reduced work hours. The choice, ultimately, lies in their hands.