Researchers have developed a process using Machine Learning (ML) methods to find rock art in remote, hard-to-access areas of Australia. The study, co-led by Dr. Andrea Jalandoni, a digital archaeologist from Griffith University's Center for Social and Cultural Research, was published in the Aug. 2022 issue of the Journal of Archaeological Science. In the study, university researchers trained a ML model to detect whether painted rock art was present in an image by feeding it hundreds of images of rock art found in Kakadu National Park. The model achieved an impressive 89% success rate. Dr. Jalandoni told the Australian Associated Press, "Our machine learning model picks up whether an area photographed potentially contains previously undiscovered rock art, scientists can then go in and verify if there is rock art present and do more research."

Multiple choice tests provide test-takers the ability to compare answers to eliminate choices (or guess the correct one). Each choice can be compared with the question to infer patterns that might have been missed; it's arguably the ability to narrow down the right answer from sets of answers that's the test of true comprehension. Inspired by this, researchers at Tel Aviv University and Facebook developed a machine learning model that generates answers to the Raven Progressive Matrix (RPM), a type of intelligence test where the goal is to complete the location in a grid of abstract images. The coauthors claim that their algorithm is not only able to generate a plausible set of answers competitive with state-of-the-art methods, but that it could be used to build an automatic tutoring system that adjusts to the proficiencies of individual students. RPM is a nonverbal test typically used in educational settings like schools.

To help keep first responders safe, University of Central Florida researchers have developed an artificial intelligence method that not only rapidly and remotely detects the powerful drug fentanyl, but also teaches itself to detect any previously unknown derivatives made in clandestine batches. The method, published recently in the journal Scientific Reports, uses infrared light spectroscopy and can be used in a portable, tabletop device. "Fentanyl is a leading cause of drug overdose death in the U.S.," said Mengyu Xu, an assistant professor in UCF's Department of Statistics and Data Science and the study's lead author. "It and its derivatives have a low lethal dose and may lead to death of the user, could pose hazards for first responders and even be weaponized in an aerosol." Fentanyl, which is 50 to 100 times more potent than morphine according to the U.S. Centers for Disease Control and Prevention, can be prescribed legally to treat patients who have severe pain, but it also is sometimes made and used illegally.

But even state-of-the-art systems struggle to overcome ambiguities in lip movements, preventing their performance from surpassing that of audio-based speech recognition. In pursuit of a more performant system, researchers at Alibaba, Zhejiang University, and the Stevens Institute of Technology devised a method dubbed Lip by Speech (LIBS), which uses features extracted from speech recognizers to serve as complementary clues. They say it manages industry-leading accuracy on two benchmarks, besting the baseline by a margin of 7.66% and 2.75% in character error rate. LIBS and other solutions like it could help those hard of hearing to follow videos that lack subtitles. It's estimated that 466 million people in the world suffer from disabling hearing loss, or about 5% of the world's population.

How can you distinguish between satire and fake news? It usually comes down to semantic and linguistic differences, but the nuances can be tough to spot. That's why researchers at George Washington University, Amazon AWS AI, and startup AdVerifai investigated a machine learning approach to classifying misleading speech. They say the AI model they developed, which outperformed the baseline, lays the groundwork for the study of additional linguistic features. Their work follows that of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), which earlier this year architected an AI model that could determine whether a source is accurate or politically prejudiced.

Previously, research groups were able to analyze brain activity using electroencephalogram (EEG) tests from which they could use the data to develop predictive models. I was with a friend who had a seizure, and it was incredibly scary. We were sitting at a bar in Brooklyn watching a Mets game, nothing out of the ordinary, when suddenly he just stood up and fell backwards, knocked his head into a chair and went into convulsions. I had no idea he suffered from periodic epileptic seizures. And it's much, much more horrible if you're the one who suffers from seizures.

Epilepsy affects millions of people in the U.S. (approximately three million in 2015, according to Healthline). It's commonly diagnosed by interpretation of electroencephalograms, or EEGs -- measurements of the brain's electrical activity taken from the scalp. But the signals tend to be quite long. This makes them challenging to interpret. Researchers at Edith Cowan University in Australia and Pabna University of Science and Technology in Bangladesh propose a solution in a newly published preprint paper on Arxiv.org

Scientists have developed an algorithm that can spot dating scams. A team of researchers trained AI software to'think like humans' when looking for fake dating profiles. While the algorithm has only been deployed in a research setting, it could one day be used to protect users on popular dating services like Tinder and Match.com. Scientists have developed an algorithm that can spot dating scams. A team of researchers trained AI software to'think like humans' when looking for fake dating profiles Romance scams, where criminals create phony profiles to trick love-lusting victims into sending them money, are on the rise.

Researchers at Auburn University, Harvard, Oxford, the University of Minnesota, and the University of Wyoming have developed a machine learning algorithm that can identify, describe, and count wildlife with 96.6 percent accuracy. The paper, which was written in November 2017, was accepted in the Proceedings of the National Academy of Sciences (PNAS) this week. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into'big data' sciences," Jeff Clune -- associate professor at the the University of Wyoming, senior research manager at Uber's Artificial Intelligence Labs, and senior author of the paper -- said in a statement. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems." The researchers trained the computer vision algorithm on 3.2 million images from Snapshot Serengeti, a citizen science project on Zooniverse.org that recruits volunteers to collect images of elephants, giraffes, gazelles, lions, cheetahs, and other animals in their natural habitats.

A team of engineering researchers from the University of Toronto have created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview with Eureka Alert.