Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.

Can AI replicate individual humans? An experiment simulating more than 1000 real people using the artificial intelligence model behind ChatGPT has successfully replicated their unique thoughts and personalities with high accuracy, sparking concerns about the ethics of mimicking individuals in this way. Joon Sung Park at Stanford University in California and his colleagues wanted to use generative AI tools to model individuals as a way of forecasting the impact of policy changes. Historically, this has been attempted using more simplistic rule-based statistical models, with limited success. How does ChatGPT work and do AI-powered chatbots "think" like…

It's a Wednesday afternoon and I've just spent the past 15 minutes texting with a 60-year-old, AI-generated version of myself. My AI future self, which was trained on survey questions I filled out moments before, has just finished spamming me with a string of messages advising me to "stay true" to myself and follow my passions. My 60-year-old AI doppleganger described a fulfilled if slightly boring life. But things suddenly took a turn when I probed the AI about its biggest regrets. After a brief pause, the AI spits out another message explaining how my professional ambitions had led me to neglect my mother in favor of completing my first book.

Harriet Tubman didn't give many interviews in her lifetime, and when she did, they were generally conducted by one of her friends, Sarah Hopkins Bradford, a White children's book author in Upstate New York, where Tubman spent the last decades of her life. The result of those interviews were two biographies, published in 1869 and 1886. Though Bradford obviously admired Tubman, the books suffer from her sometimes patronizing attitude toward her subject, her use of racial slurs and her awkward attempts to re-create the speech patterns of a Black woman raised enslaved in Maryland. Some of the long "quotes" from Tubman were completely made up, and it shows. So I was curious to see what would happen recently when I had my own "interview" with Tubman -- using the online educator Khan Academy's new artificial intelligence learning tool Khanmigo, which enables users to have live chats with dozens of simulated historical figures like Abigail Adams, Genghis Khan, Montezuma and Winston Churchill. And if so, would it come off horribly, a 21st-century minstrelsy?

To cope with the seemingly never-ending supply chain crisis, business leaders are turning to artificial intelligence to make strategic business decisions. A recent survey by PwC found that 48% of business leaders use AI to drive supply chain decisions, and 54% of business leaders plan to use AI-driven simulations to enhance supply chain operations. AI allows for simulations of vast amounts of data from suppliers, customers, competitors, and external factors like weather or geopolitical events. In the process, leaders can better predict supply chain dynamics and disruptions, and have the most up-to-date integrated business plans in place to navigate the complexities of a rapidly shifting business environment. Seventy-four percent of tech leaders are using AI for decision-making.

Legislation is an inherently human endeavor. But just as organizations across industries are unlocking new capabilities and efficiencies through artificial intelligence (AI), governments also can aid their legislative processes through the application of AI. For the past five years, we've studied the potential impact of AI on government. We've looked at everything from how much time AI could save workers in each US federal agency to the rate of AI adoption in US federal, state, and local governments.2 While AI can help many different areas of the legislative process--from AI assistants answering members' questions about legislation to natural language processing analyzing the US Code for contradictions--two key applications stand out.

"Will Japan be sustainable in 2050?" Our research group adopted this question as the starting point to conduct simulations of the future of Japanese society, using artificial intelligence to develop policy proposals. Figure 1 shows Japan's long-term population trends. The population rose sharply from the start of the Meiji era (1868–1912), peaking in 2008 before switching into decline. If the birthrate remains around the present rate (1.42% in 2018), Japan's population is expected to fall below 100 million after 2050, and to continue to decline thereafter.