Like a hack writer or a friend rehashing their glory days over and over, artificial intelligence tends to tell the same old story. For AI agents instructed to write fiction, that tale usually involves lighthouses and a keeper named Elias, according to new Cornell research.
Minecraft is one of the world's most popular video games, where players explore, gather resources, craft items and try to survive in a virtual universe constructed of 3D cubes that represent different materials.
AI agents tend to do things when we give them specific commands, such as analyzing data and answering questions. But what would they do if left to their own devices?
Generative artificial intelligence models are rooted in ideas of mid-20th-century engineers who thought some amount of irrationality was essential for a machine that could think like a human, contends Benjamin Lindquist, an assistant professor in the MIT History Section, who holds an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS) and is a principal investigator in the Laboratory for Information and Decision Systems.
Inside the human brain, billions of individual neurons connect to form a vast network capable of rapidly processing information and electrical signals. A team of researchers and engineers at Penn State is taking inspiration from these natural computers to build soft networks of water-based droplets, connected by membranes like those found in the walls of living cells.
It's no secret that an ever-increasing number of scientists use AI to help with their papers. While tasks such as polishing text are generally permissible if declared, using the technology to write papers, or parts of papers, from scratch and passing them off as original work is not. But it's happening.
Artificial intelligence can do remarkable things, but how much does it actually understand about the world? Researchers have found that surprisingly specific information can be extracted from the inner workings of large language models (LLMs) like ChatGPT. Using mathematical tools to probe a model's internal activity, they can identify where a city is located on a map, when a historical figure lived or what emotion a character feels, even detecting descriptions of pain.
Researchers from the VUB's Artificial Intelligence Lab and the University of Namur (UNamur) demonstrate in a new large-scale study how autonomous AI systems can spontaneously develop a functional, shared language without human intervention.
Large language models (LLMs), the artificial intelligence (AI) systems underpinning the functioning of ChatGPT and other conversational agents, are now widely used to source information, summarize texts and generate content for specific purposes. These models essentially generate their responses by predicting sequences of words based on the patterns they learned during previous training.
Before large language models (LLMs) can process text, they split it into chunks such as words or word fragments. The LLM behind ChatGPT, for example, splits "LMU München" into the three chunks "LM," "U" and "München," so it never directly sees the individual letters in "München." This step, which is known as subword tokenization, makes LLMs efficient but causes a range of problems, such as limited character-level understanding.
When humans write sentences or solve math problems, they are thinking symbolically—each number, variable or word is expressed by a symbol. While AI chatbots do not "think" that way, if prompted, they can offer cogent advice about writing, math and many other topics as if a person were offering it.
Researchers at the Institute of Information Security at Graz University of Technology (TU Graz) have uncovered security risks in the file notification systems of the widely used operating systems Windows, Linux, Android and macOS. Through side-channel attacks, attackers can track system-wide changes to files, keystrokes and visited websites, and spoof password prompts to intercept entries.
Before a new AI model reaches the public, its developers run it through a battery of tests known as "benchmarks," which score it on everything from reasoning ability to how safe it is for people to use. Billions of investment dollars ride on these benchmark scores, and policymakers increasingly cite them to shape regulations and government procurement decisions that will guide the development of AI.
A new AI system that excels at challenging games with hidden information could someday help human decision-makers select strategies to outfox opponents in complicated situations like military maneuvers.
In the production of video games and animated movies, directors and animators are constantly fine-tuning how a character's movements should appear. In many cases, directors require subtle changes, such as making a character appear a little more restrained or a bit more expressive, and communicate subjectively how much change is needed.
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