AI-Descartes, an AI scientist developed by researchers at IBM Research, Samsung AI, and the University of Maryland, Baltimore County, has reproduced key parts of Nobel Prize-winning work, including Langmuir's gas behavior equations and Kepler's third law of planetary motion. Supported by the Defense Advanced Research Projects Agency (DARPA), the AI system utilizes symbolic regression to find equations fitting data, and its most distinctive feature is its logical reasoning ability. This enables AI-Descartes to determine which equations best fit with background scientific theory. The system is particularly effective with noisy, real-world data and small data sets. The team is working on creating new datasets and training computers to read scientific papers and construct background theories to refine and expand the system's capabilities.

In 1918, the American chemist Irving Langmuir published a paper examining the behavior of gas molecules sticking to a solid surface. Guided by the results of careful experiments, as well as his theory that solids offer discrete sites for the gas molecules to fill, he worked out a series of equations that describe how much gas will stick, given the pressure. Now, about a hundred years later, an "AI scientist" developed by researchers at IBM Research, Samsung AI, and the University of Maryland, Baltimore County (UMBC) has reproduced a key part of Langmuir's Nobel Prize-winning work. The system--artificial intelligence (AI) functioning as a scientist--also rediscovered Kepler's third law of planetary motion, which can calculate the time it takes one space object to orbit another given the distance separating them, and produced a good approximation of Einstein's relativistic time-dilation law, which shows that time slows down for fast-moving objects. A paper describing the results is published in Nature Communications on April 12.

In recent years, countless computer scientists worldwide have been developing deep neural network-based models that can predict people's emotions based on their facial expressions. Most of the models developed so far, however, merely detect primary emotional states such as anger, happiness and sadness, rather than more subtle aspects of human emotion. Past psychology research, on the other hand, has delineated numerous dimensions of emotion, for instance, introducing measures such as valence (i.e., how positive an emotional display is) and arousal (i.e., how calm or excited someone is while expressing an emotion). While estimating valence and arousal simply by looking at people's faces is easy for most humans, it can be challenging for machines. Researchers at Samsung AI and Imperial College London have recently developed a deep-neural-network-based system that can estimate emotional valence and arousal with high levels of accuracy simply by analyzing images of human faces taken in everyday settings.

There have been huge advancements in recent years in the area of AI "deepfakes", or fake photos or videos of humans created using neural networks. Fake videos of a person usually require a large number of photos of that individual, but Samsung has figured out how to create realistic talking heads from as little as a single portrait photo. In a newly published paper titled, "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models," a team of researchers at the Samsung AI Center in Moscow, Russia, share their new system that has this "few-shot capability." Once it's familiar with human faces, it's able to create talking heads of previously unseen people using one or a few shots of that person. For each photo, the AI is able to detect various "landmarks" on the face -- things like the eyes, nose, mouth, and various lengths and shapes.