AI technologies, including large language models (LLMs), have rapidly become integral to everyday life. However, the computational power required to support these technologies is driven by data centers, which consume massive amounts of energy. In Germany alone, data centers consumed around 16 billion kilowatt-hours (kWh) of electricity in 2020—approximately 1% of the nation’s total energy consumption. By 2025, this figure is expected to rise to 22 billion kWh, highlighting the growing energy demands of AI systems.

As AI applications become increasingly complex, their energy needs are expected to grow, especially in the realm of training neural networks, which demand significant computational resources. In response to this challenge, researchers have pioneered a revolutionary training method that is 100 times faster than traditional approaches, all while maintaining the same level of accuracy. This innovation has the potential to substantially reduce the energy required for AI training, offering a promising solution to the industry’s sustainability concerns.

A team of scientists has unveiled a groundbreaking new AI-assisted digital twin model that can not only replicate real-world machines but also control and adapt to them in real-time. This innovative concept, described in a study published in the journal IEEE Access, has the potential to revolutionize how smart cities and autonomous systems operate in the future.

The new approach, termed Intelligent Acting Digital Twins (IADT), allows digital twins—digital replicas of physical objects or machines—to go beyond mere simulations. Traditionally, digital twins serve as real-time representations of physical entities, constantly updated with data to monitor their state. These models are widely used in industries like manufacturing, healthcare, and defense, where they help engineers visualize, assess, and predict the behavior of physical machines.

A group of Chinese software engineers has developed what they claim to be the “world’s first” fully autonomous artificial intelligence (AI) agent, named Manus. Unlike existing AI systems such as ChatGPT, Google’s Gemini, or Grok— which all require human input—Manus can perform complex tasks independently, making proactive decisions and taking actions without waiting for human guidance. This marks a significant leap forward in AI technology.

Manus operates in a way that sets it apart from conventional AI systems. While traditional AIs rely on user instructions to perform tasks, Manus can autonomously assess situations and carry out multi-step workflows. For example, if asked to “Find me an apartment,” Manus doesn’t just search through listings. It evaluates factors like crime rates, weather patterns, and market trends to offer tailored recommendations, all without any human intervention.

Fujitsu is collaborating with Nvidia to integrate AI-powered radio access networks into video analytics, offering the ability to detect potential criminal activity or identify vulnerable individuals in distress, such as someone who has fallen. Radio access networks, which connect devices to mobile telecommunications systems via radio connections, are being enhanced with AI to reduce latency and improve real-time responsiveness in critical applications.

At the Mobile World Congress in Barcelona, Fujitsu is showcasing a range of innovative applications powered by this advanced technology. One of the standout uses involves CCTV monitoring, where the system not only interprets what is happening in a video feed but also analyzes body movements to predict criminal intent or detect emotional cues.