This New York times article from June 15, 1989 by Julie Lew explores the launch of Maxis Software's *SimCity*, a pioneering simulation game that tasks players with building and managing complex urban environments. Eschewing traditional "win or lose" mechanics, the game encourages experimentation through zoning, taxation, and infrastructure management to create thriving cityscapes. From surviving historical disasters to constructing digital utopias, the software is praised for its sophisticated modeling and potential as a legitimate tool for teaching urban planning.
The Indus River Valley Civilization presents an archaeological enigma through its highly sophisticated yet strangely egalitarian social structure. Unlike other contemporary civilizations such as those in Egypt or Mesopotamia, these settlements lacked monumental palaces, temples, or elite dwellings dedicated to rulers or priests. Instead, the civilization was defined by extreme standardization in urban design, including grid layouts, uniform baked bricks, and advanced sanitation systems that were consistent across many cities.
- The absence of royal burials and monuments suggests a society based on consensus rather than centralized monarchy.
- Highly organized water management and hygiene systems indicate strong civic cooperation.
- Archaeological evidence shows little sign of warfare, with few weapons found beyond those used for hunting or fishing.
- Governance may have functioned through heterarchical structures or collective decision-making by merchants or intellectuals.
- The absence of royal burials and monuments suggests a society based on consensus rather than centralized monarchy.
- Highly organized water management and hygiene systems indicate strong civic cooperation.
- Archaeological evidence shows little sign of warfare, with few weapons found beyond those used for hunting or fishing.
- Governance may have functioned through heterarchical structures or collective decision-making by merchants or intellectuals.
Large language models (LLMs) are rapidly being implemented in a wide range of disciplines, with the promise of unlocking new possibilities for scientific exploration. However, while the development of LLMs brings opportunities to science, it also comes with pressing challenges. This Focus discusses the current state of the art, highlights key obstacles, and examines some of the potential pitfalls and biases of implementing and using LLMs across different domains, including healthcare, urban planning, chemistry, linguistics, humanities, and computer science. In addition, the Focus explores emerging technologies – such as neuromorphic engineering – that show promise in enhancing the energy efficiency of LLM deployment on hardware platforms.
