Tue. Jun 25th, 2024

Real-time systems and technologies have become more mainstream in 2023, with businesses leveraging them to automate processes, enable autonomy in devices, and enhance advanced analytics and AI. Real-time systems have delivered quantifiable business benefits in various application areas, including autonomous vehicles, recommender systems, IoT data analysis, and digital twins. However, there are potential obstacles such as legal challenges, liability risks, and concerns about energy consumption that could limit the use of real-time technologies in the future.

Key Points:

  • Real-time systems and technologies have become more mainstream in 2023.
  • Real-time has delivered quantifiable business benefits in various application areas.

Real-time systems and technologies have become increasingly prevalent in 2023, with businesses incorporating them into their operations to automate processes, enable autonomy in devices, and enhance advanced analytics and AI. One notable example of the value of real-time systems is seen in the safety records of Waymo’s driverless cars, which have had significantly fewer accidents with injuries compared to human-driven vehicles during their operations in Arizona and California. This success can be attributed to the real-time capabilities of the vehicles, which allow for quick decision-making and response to potential hazards on the road.

Another area where real-time has made a significant impact is in recommender systems. These systems traditionally rely on existing data to provide recommendations, but leading businesses are now incorporating Large Language Models (LLMs) to enhance their recommendations. LLMs analyze vast amounts of data in real-time to provide more relevant next-best-action recommendations to users. This real-time analysis allows businesses to tailor their recommendations to individual users’ needs and preferences, leading to improved customer satisfaction and engagement.

The increasing prevalence of Internet of Things (IoT) devices has also contributed to the growth of real-time systems. Businesses are now leveraging new technologies to capture, share, analyze, and use the vast volumes of real-time data produced by IoT devices to drive informed decision-making. This real-time data analysis allows businesses to identify trends and patterns, detect anomalies, and make data-driven decisions in real-time. The insights gained from real-time data analysis can optimize various aspects of business operations, such as supply chain management, production optimization, and customer experience management.

Real-time technologies are also revolutionizing the concept of digital twins, which are virtual replicas of physical assets or systems. Digital twins that are constantly fed real-time data about the state of their physical counterparts provide valuable insights for making evidence-based decisions. These “phygital” systems enable businesses to monitor and optimize the performance of their assets in real-time, resulting in improved efficiency, reduced downtime, and cost savings.

While real-time systems have delivered significant benefits in various application areas, there are potential obstacles that could limit their widespread adoption. One major concern is legal challenges related to copyright infringement and content usage. For example, OpenAI has faced copyright violation suits for training its AI models on books and other copyrighted content without permission or acknowledgment. Content sites are also troubled by Google and Microsoft’s practices of presenting answers to search queries in the form of text answers, which can drive up zero-click searches and reduce traffic to original content. These legal challenges could impact the use of real-time technologies in AI applications and content generation.

Another obstacle to the widespread adoption of real-time technologies is the potential liability and business risks associated with AI and automation. Faulty AI models trained on biased data can lead to incorrect decisions and actions that can have serious consequences. For example, an insurance company’s misuse of AI to deny necessary coverage to elderly patients has resulted in a lawsuit, highlighting the need for responsible and ethical AI practices. Businesses must address these liability and risk concerns to ensure the safe and effective use of real-time technologies.

Finally, concerns about energy consumption pose a challenge to the widespread use of real-time technologies. Training AI models requires significant energy resources, with some estimates equating the carbon footprint of training a single large language model to 125 round-trip flights between New York and Beijing. As AI becomes an integral part of business operations, the energy consumption required to run AI models in real-time is also a concern. Researchers are examining the energy usage of running AI models to ensure sustainable and environmentally responsible practices in the use of real-time technologies.

In conclusion, real-time systems and technologies have become increasingly prevalent in 2023, delivering quantifiable business benefits in various application areas. However, potential obstacles such as legal challenges, liability risks, and concerns about energy consumption need to be carefully addressed to ensure the continued growth and adoption of real-time technologies in the future.