We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed is a text-adventure game in which players attempt to solve a mystery under a fixed narrative premise, but can freely interact with non-player characters generated by GPT-4, a large language model. We recruit 28 gamers to play the game and use GPT-4 to automatically convert the game logs into a node-graph representing the narrative in the player's gameplay. We find that through their interactions with the non-deterministic behavior of the LLM, players are able to discover interesting new emergent nodes that were not a part of the original narrative but have potential for being fun and engaging. Players that created the most emergent nodes tended to be those that often enjoy games that facilitate discovery, exploration and experimentation.

Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text. Notably, while contemporary encoder-decoder models excel based on the ROUGE metric, they often falter when it comes to the precise generation of numerals in headlines. We identify the lack of datasets providing fine-grained annotations for accurate numeral generation as a major roadblock. To address this, we introduce a new dataset, the NumHG, and provide over 27,000 annotated numeral-rich news articles for detailed investigation. Further, we evaluate five well-performing models from previous headline generation tasks using human evaluation in terms of numerical accuracy, reasonableness, and readability. Our study reveals a need for improvement in numerical accuracy, demonstrating the potential of the NumHG dataset to drive progress in number-focused headline generation and stimulate further discussions in numeral-focused text generation.

Harvey Castro talks about how AI could be used in cold cases and the symbiotic relationship between AI and a detective. A Minnesota man was sentenced to 30 years in prison for raping a woman at gunpoint in Minneapolis 16 years ago. Robert DeLong, 63, will spend the next three decades in prison after he pleaded guilty to assaulting the victim who was jogging on Boom Island in Minneapolis in March 2007. Hennepin County Attorney Mary Moriarty's office told Fox News Digital that 30 years is the "longest possible sentence" for DeLong's crimes. "The victim's courage in the moments after this attack are a significant reason we were able to prosecute this case and hold Mr. DeLong accountable," Moriarty said in a statement.

NEW YORK, NY - APRIL 09: A working Enigma cipher machine that along with the 1942 56-page notebook ... [ ] belonging to codebreaker Alan Turing is to be auctioned Bonham's auction house on April 9, 2015 in New York City. The notebook is to be auctioned in New York on Monday. The notebook alone is expected to go for $1 million. Turing's life and work were recently brought to life in the 2014 blockbuster "The Imitation Game", which drew eight Oscar nominations. The Swiss government's Spiez Laboratory, one of whose specialisations is the study of deadly toxins and infectious diseases, is located right in the heart of Switzerland, incidentally not too far away from the Reichenbach Falls, where Sherlock Holmes vanquished Professor Moriarty (more about him later) in'The Final Problem'.

From Skynet to The culture, science fiction is littered with utopian and dystopian views on what AI will bring. But there is a more fundamental question when it comes to AI, is it there to solve problems or exploit information? Sir Arthur Conan Doyle created two competing personalities that characterize this challenge. Sherlock – the problem solver, the analytical mind (accompanied by an often-startling lack of social skills), and Moriarty the criminal genius who exploited information to get power. These two distinct personalities sum up two different mentalities when we look at how to use AI, namely the use of data to solve a known problem, or the use of data to create new markets and rise above the competition.

"With autonomy we declare that no sphere is off limits. We will send our spacecraft to search beyond the horizon, accepting that we cannot directly control them, and relying on them to tell the tale." A new generation of sensor-rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These systems include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biospherelike life-support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions.

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.

A new generation of sensor-rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These systems include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biospherelike life-support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. Thus, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves to survive decades of autonomous operation. Achieving these large-scale modeling and configuration tasks will require a tight coupling between the higher-level coordination function provided by symbolic reasoning and the lower-level autonomic processes of adaptive estimation and control. To be economically viable, they will need to be programmable purely through high-level compositional models. Self-modeling and self-configuration, autonomic functions coordinated through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous system architecture that is taking us into the new millennium.