Can language models reason in a real-world setting? USC researchers explored this question in a recent paper published at AAAI. Your coffee has gone cold. You pick up your cup, place it in the microwave, and zap it. For a robot, however, the task is not easy – even if it has been "taught" by language models (LMs) where the water, cup and microwave are.

Niki Parmar and Ashish Vaswani co-authored a seminal paper that set the groundwork for ChatGPT and other generative AI models. ChatGPT has taken the world by storm, but seeds of the groundbreaking technology were sown at the USC Viterbi School of Engineering. The seminal paper "Attention Is All You Need," which laid the foundation for ChatGPT and other generative AI systems, was co-authored by Ashish Vaswani, a PhD computer science graduate ('14) and Niki Parmar, a master's in computer science graduate ('15). The landmark paper was presented at the 2017 Conference on Neural Information Processing Systems (NeurIPS), one of the top conferences in AI and machine learning. In the paper, the researchers introduced the transformer architecture, a powerful type of neural network that has become widely used for natural language processing tasks, from text classification to language modeling.

Astronaut Danny Olivas joins ISI's Visual Intelligence and Multimedia Analytics Laboratory (VIMAL) to look for ways to use AI in space. John Daniel "Danny" Olivas, former NASA astronaut and current member of the NASA Advisory Council, has joined the staff of the Visual Intelligence and Multimedia Analytics Laboratory (VIMAL) of USC's Information Sciences Institute (ISI) as Co-Director for AI Initiatives in Space. A veteran of space shuttle missions in 2007 and 2009, he is the recipient of two NASA Space Flight Medals and the NASA Exceptional Service and Exceptional Achievement Medals. Olivas completed five space walks totaling over 34 hours outside of the International Space Station. His expertise in space is rivaled only by his passion for it, and he brings both to his new role.

About 6 million people in the US are currently living with Alzheimer's disease, the most common form of dementia, according to the Alzheimer's Association. Despite being the sixth-leading cause of death in the country, there is currently no known cure for the memory-robbing condition. But diagnosing the disease early can help people seek preventative care and slow its progress. That's why a team of students at USC is developing machine learning tools to detect early-onset Alzheimer's disease using speech patterns, and democratize the diagnosis process. The team started working on the system in spring 2021 as a project for CAIS, the student branch of the Center for Artificial Intelligence in Society, in collaboration with students from MEDesign, the biomedical engineering design group.

A demonstration of virtual reality goggles used to access the Virtual Interactive Training Agent platform. From left: Sharon Mozgai and Arno Hartholt. The job interview can be a terrifying ordeal. The anxiety and stress of preparing for it, especially if you have never done one before, stops many people from bringing their best self or even showing up at all. It is also challenging to practice for.

Music is an indispensable element in film: it establishes atmosphere and mood, drives the viewer's emotional reactions, and significantly influences the audience's interpretation of the story. In a recent paper published in PLOS ONE, a research team at the USC Viterbi School of Engineering, led by Professor Shrikanth Narayanan, sought to objectively examine the effect of music on cinematic genres. Their study aimed to determine if AI-based technology could predict the genre of a film based on the soundtrack alone. "By better understanding how music affects the viewer's perception of a film, we gain insights into how film creators can reach their audience in a more compelling way," said Narayanan, University Professor and Niki and Max Nikias Chair in Engineering, professor of electrical and computer engineering and computer science and the director of USC Viterbi's Signal Analysis and Interpretation Laboratory (SAIL). The notion that different film genres are more likely to use certain musical elements in their soundtrack is rather intuitive: a lighthearted romance might include rich string passages and lush, lyrical melodies, while a horror film might instead feature unsettling, piercing frequencies and eerily discordant notes.

Study authors include Professor Shrikanth Narayanan, Timothy Greer, Dillon Knox, and Benjamin Ma. (Images Courtesy of Narayanan, Greer, Knox, and Ma) Music is an indispensable element in film: it establishes atmosphere and mood, drives the viewer's emotional reactions, and significantly influences the audience's interpretation of the story. In a recent paper published in PLOS One, a research team at the USC Viterbi School of Engineering, led by Professor Shrikanth Narayanan, sought to objectively examine the effect of music on cinematic genres. Their study aimed to determine if AI-based technology could predict the genre of a film based on the soundtrack alone. "By better understanding how music affects the viewer's perception of a film, we gain insights into how film creators can reach their audience in a more compelling way," said Narayanan, University Professor and Niki and Max Nikias Chair in Engineering, professor of electrical and computer engineering and computer science and the director of USC Viterbi's Signal Analysis and Interpretation Laboratory (SAIL). The notion that different film genres are more likely to use certain musical elements in their soundtrack is rather intuitive: a lighthearted romance might include rich string passages and lush, lyrical melodies, while a horror film might instead feature unsettling, piercing frequencies and eerily discordant notes.

The new AI system takes its inspiration from humans: when a human sees a color from one object, we can easily apply it to any other object by substituting the original color with the new one. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. In other words, as humans, it's easy to envision an object with different attributes.

Psychologists have long found that people behave differently than when they learn of peers' actions. A new study by computer scientists found that when individuals in an experiment about autonomous vehicles were informed that their peers were more likely to sacrifice their own safety to program their vehicle hit a wall rather than hit pedestrians who were at risk, the percentage of individuals willing to sacrifice their own safety increased by approximately two-thirds. As computer scientists train machines to act as people's agents in all sorts of situations, the study's authors indicate that the social component of decision-making is often overlooked. This could be of great consequence, note the paper's authors who show that the trolly problem –long shown to be the scenario moral psychologists turn to--is problematic. The problem, the authors indicate, fails to show the complexity of how humans make decisions.

Movie ratings are vital to a film's bottom line and determine its impact on audiences. Traditionally, a movie is manually rated by humans watching it, taking into account violence, drug abuse, and sexual content. This dynamic could change soon with the rise of artificial intelligence (AI). Recently, researchers at the USC Viterbi School of Engineering used AI tools to rate a movie within seconds. One of the most impressive aspects of this approach is that the rating could be done based solely on the movie script, without shooting a single shot.