Aurelia Butler

A novel, human-inspired approach to training artificial intelligence (AI) systems to identify objects and navigate their surroundings could set the stage for the development of more advanced AI systems to explore extreme environments or distant worlds, according to research from an interdisciplinary team at Penn State.
In the first two years of life, children experience a somewhat narrow set of objects and faces, but with many different viewpoints and under varying lighting conditions. Inspired by this developmental insight, the researchers introduced a new machine learning approach that uses information about spatial position to train AI visual systems more efficiently. They found that AI models trained on the new method outperformed base models by up to 14.99%. They reported their findings in the May issue of the journal Patterns.
“Current approaches in AI use massive sets of randomly shuffled photographs from the internet for training. In contrast, our strategy is informed by developmental psychology, which studies how children perceive the world,” said Lizhen Zhu, the lead author and doctoral candidate in the College of Information Sciences and Technology at Penn State.
The researchers developed a new contrastive learning algorithm, which is a type of self-supervised learning method in which an AI system learns to detect visual patterns to identify when two images are derivations of the same base image, resulting in a positive pair. These algorithms, however, often treat images of the same object taken from different perspectives as separate entities rather than as positive pairs. Taking into account environmental data, including location, allows the AI system to overcome these challenges and detect positive pairs regardless of changes in camera position or rotation, lighting angle or condition and focal length, or zoom, according to the researchers.
“We hypothesize that infants’ visual learning depends on location perception. In order to generate an egocentric dataset with spatiotemporal information, we set up virtual environments in the ThreeDWorld platform, which is a high-fidelity, interactive, 3D physical simulation environment. This allowed us to manipulate and measure the location of viewing cameras as if a child was walking through a house,” Zhu added.
The scientists created three simulation environments — House14K, House100K and Apartment14K, with ’14K’ and ‘100K’ referring to the approximate number of sample images taken in each environment. Then they ran base contrastive learning models and models with the new algorithm through the simulations three times to see how well each classified images. The team found that models trained on their algorithm outperformed the base models on a variety of tasks. For example, on a task of recognizing the room in the virtual apartment, the augmented model performed on average at 99.35%, a 14.99% improvement over the base model. These new datasets are available for other scientists to use in training through www.child-view.com.
“It’s always hard for models to learn in a new environment with a small amount of data. Our work represents one of the first attempts at more energy-efficient and flexible AI training using visual content,” said James Wang, distinguished professor of information sciences and technology and advisor of Zhu.
The research has implications for the future development of advanced AI systems meant to navigate and learn from new environments, according to the scientists.
“This approach would be particularly beneficial in situations where a team of autonomous robots with limited resources needs to learn how to navigate in a completely unfamiliar environment,” Wang said. “To pave the way for future applications, we plan to refine our model to better leverage spatial information and incorporate more diverse environments.”
Collaborators from Penn State’s Department of Psychology and Department of Computer Science and Engineering also contributed to this study. This work was supported by the U.S. National Science Foundation, as well as the Institute for Computational and Data Sciences at Penn State. More

MRI scans are commonly used to diagnose a variety of conditions, anything from liver disease to brain tumors. But, as anyone who has been through one knows, patients must remain completely still to avoid blurring the images and requiring a new scan. A prototype device described in ACS Sensors could change that. The self-powered sensor detects movement and shuts down an MRI scan in real time, improving the process for patients and technicians.
During an MRI scan, a patient must stay entirely still for several minutes at a time, otherwise “motion artifacts” could appear and blur the final image. To ensure a clear picture, patient movement needs to be identified as soon as it happens, allowing the scan to stop and for the technician to take a new one. Motion tracking could be achieved using sensors embedded into the MRI table; however, magnetic materials can’t be used because metals interfere with the MRI technology itself. One technology that’s well-suited for this unique situation, and avoids the need for metal or magnetic components, is the triboelectric nanogenerator (TENG), which powers itself using static electricity generated by friction between polymers. So, Li Tao, Zhiyi Wu and colleagues wanted to design a TENG-based sensor that could be incorporated into an MRI machine to help prevent motion artifacts.
The team created the TENG by sandwiching two layers of plastic film painted with graphite-based conductive ink around a central layer of silicone. These materials were specifically chosen as they would not interfere with an MRI scan. When pressed together, electrostatic charges from the plastic film moved to the conductive ink, creating a current that could then flow out through a wire.
This sensor was incorporated into an MRI table designed to lay under a patient’s head. In tests, when a person turned their head from side to side or raised it off the table, the sensor detected these movements and transmitted a signal to a computer. Then, an audible alert played, a pop-up window on the technician’s computer appeared and the MRI scan ceased. The researchers say that this work could help make MRI scans more efficient and less frustrating for patients and technicians alike by producing better images during a single procedure. More

As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study from Chalmers University of Technology in Sweden shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalised prices, and thus minimise both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.
Today’s commercial charging infrastructure can be a jungle. The market is dynamic and complex with a variety of subscriptions and free competition between providers. At some fast charging stations, congestion and long queues may even occur. In a new study, researchers at Chalmers have created a mathematical model to investigate how fast charging stations controlled by artificial intelligence, AI, can help by offering electric car drivers personalised prices, which the drivers can choose to accept or refuse. The AI uses algorithms that can adjust prices based on individual factors, such as battery level and the car’s geographic location.
“The electric car drivers can choose to share information with the charging station providers and receive a personal price proposal from a smart charging station. In our study, we could show how rational and self-serving drivers react by only accepting offers that are beneficial to themselves. This leads to both price and waiting times being minimized,” says Balázs Kulcsár, professor at the department of electrical engineering at Chalmers.
In the study, the drivers always had the option to refuse the personal price, and choose a conventional charging station with a fixed price instead. The personal prices received by the drivers could differ significantly from each other, but were almost always lower than the market prices. For the providers of charging stations, the iterative AI algorithm can find out which individual prices are accepted by the buyer, and under which conditions. However, during the course of the study, the researchers noted that on some occasions the algorithm raised the price significantly when the electric car’s batteries were almost completely empty, and the driver consequently had no choice but to accept the offer.
“Smart charging stations can solve complex pricing in a competitive market, but our study shows that they need to be developed and introduced with privacy protection for consumers, well in line with responsible-ethical AI paradigms,” says Balázs Kulcsár.
More about the study
The researchers created a mathematical model of the interaction between profit-maximising fast charging stations and electric car users. The “charging stations” could offer public market prices or AI-driven profit-maximising personal prices, which the “electric car users” could then accept or reject based on their own conditions and needs. In most cases, the results were promising, as the AI-generated prices were lower than the market prices. More

Researchers in George Mason University’s College of Public Health have leveraged the power of artificial intelligence (AI) analytical models to match a patient’s medical history to the most effective antidepressant, allowing patients to find symptom relief sooner. The free website, MeAgainMeds.com, provides evidence-based recommendations, allowing clinicians and patients to find the optimal antidepressant the first time.
“Many people with depression must try multiple antidepressants before finding the right one that alleviates their symptoms. Our website reduces the number of medications that patients are asked to try. The system recommends to the patient what has worked for at least 100 other patients with the same exact relevant medical history,” said Farrokh Alemi, principal investigator and professor of health informatics at George Mason University’s College of Public Health.
AI helped to simplify the very complex task of making thousands of guidelines easily accessible to patients and clinicians. The guidelines that researchers created are complicated because of the amount of clinical information that is relevant in prescribing an antidepressant; AI seamlessly simplifies the task.
With AI at its core, MeAgainMeds.com analyzes clinician or patient responses to a few anonymous medical history questions to determine which oral antidepressant would best meet the specific needs. The website does not ask for any personal identifiable information and it does not prescribe medication changes. Patients are advised to visit their primary health care provider for any changes in medication.
In 2018, the Centers for Disease Control reported that more than 13% of adults use antidepressants, and the number has only increased since the pandemic and other epidemics since 2020. This website could help millions of people find relief more quickly.
Alemi and his team analyzed 3,678,082 patients who took 10,221,145 antidepressants. The oral antidepressants analyzed were amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. From the data, they created 16,770 subgroups of at least 100 cases, using reactions to prior antidepressants, current medication, history of physical illness, history of mental illness, key procedures, and other information. The subgroups and remission rates drive the AI to produce an evidence-based medication recommendation.
“By matching patients to the subgroups, clinicians can prescribe the medication that works best for people with similar medical history,” said Alemi. The researchers and website recommend that patients who use the site take the information to their clinicians, who will ultimately decide whether to prescribe the recommended medicine.
Alemi and his team tested a protype version of the site in 2023, which they advertised on social media. At that time, 1,500 patients used the website. Their goal is to improve the website and expand its user base. The initial research was funded by the Commonwealth of Virginia and by the Robert Wood Johnson Foundation.
The researchers’ most recent paper in a series of papers on response to antidepressants analyzed 2,467 subgroups of patients who had received psychotherapy. “Effectiveness of Antidepressants in Combination with Psychotherapy” was published online in The Journal of Mental Health Policy and Economics in March 2024. Additional authors include Tulay G Soylu from Temple University, and Mary Cannon and Conor McCandless from Royal College of Surgeons in Dublin, Ireland. More

A team of researchers at the University of Waterloo have developed a new machine-learning method that detects hate speech on social media platforms with 88 per cent accuracy, saving employees from hundreds of hours of emotionally damaging work.
The method, dubbed the Multi-Modal Discussion Transformer (mDT), can understand the relationship between text and images as well as put comments in greater context, unlike previous hate speech detection methods. This is particularly helpful in reducing false positives, which are often incorrectly flagged as hate speech due to culturally sensitive language.
“We really hope this technology can help reduce the emotional cost of having humans sift through hate speech manually,” said Liam Hebert, a Waterloo computer science PhD student and the first author of the study. “We believe that by taking a community-centred approach in our applications of AI, we can help create safer online spaces for all.”
Researchers have been building models to analyze the meaning of human conversations for many years, but these models have historically struggled to understand nuanced conversations or contextual statements. Previous models have only been able to identify hate speech with as much as 74 per cent accuracy, below what the Waterloo research was able to accomplish.
“Context is very important when understanding hate speech,” Hebert said. “For example, the comment ‘That’s gross!’ might be innocuous by itself, but its meaning changes dramatically if it’s in response to a photo of pizza with pineapple versus a person from a marginalized group.
“Understanding that distinction is easy for humans, but training a model to understand the contextual connections in a discussion, including considering the images and other multimedia elements within them, is actually a very hard problem.”
Unlike previous efforts, the Waterloo team built and trained their model on a dataset consisting not only of isolated hateful comments but also the context for those comments. The model was trained on 8,266 Reddit discussions with 18,359 labelled comments from 850 communities.
“More than three billion people use social media every day,” Hebert said. “The impact of these social media platforms has reached unprecedented levels. There’s a huge need to detect hate speech on a large scale to build spaces where everyone is respected and safe.”
The research, Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media, was recently published in the proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence. More

A new study to be presented at the SLEEP 2024 annual meeting found a distinct relationship between sleep duration, social media usage, and brain activation across brain regions that are key for executive control and reward processing.
Results show a correlation between shorter sleep duration and greater social media usage in teens. The analysis points to involvement of areas within the frontolimbic brain regions, such as the inferior and middle frontal gyri, in these relationships. The inferior frontal gyrus, key in inhibitory control, may play a crucial role in how adolescents regulate their engagement with rewarding stimuli such as social media. The middle frontal gyrus, involved in executive functions and critical in assessing and responding to rewards, is essential in managing decisions related to the balancing of immediate rewards from social media with other priorities like sleep. These results suggest a nuanced interaction between specific brain regions during adolescence and their influence on behavior and sleep in the context of digital media usage.
“As these young brains undergo significant changes, our findings suggest that poor sleep and high social media engagement could potentially alter neural reward sensitivity,” said Orsolya Kiss, who has a doctorate in cognitive psychology and is a research scientist at SRI International in Menlo Park, California. “This intricate interplay shows that both digital engagement and sleep quality significantly influence brain activity, with clear implications for adolescent brain development.”
This study involved data from 6,516 adolescents, ages 10-14 years, from the Adolescent Brain Cognitive Development Study. Sleep duration was assessed from the Munich Chronotype questionnaire, and recreational social media use through the Youth Screen Time Survey. Brain activities were analyzed from functional MRI scans during the monetary incentive delay task, targeting regions associated with reward processing. The study used three different sets of models and switched predictors and outcomes each time. Results were adjusted for age, COVID-19 pandemic timing, and socio-demographic characteristics.
Kiss noted that these results provide new insights into how two significant aspects of modern adolescent life — social media usage and sleep duration — interact and impact brain development.
“Understanding the specific brain regions involved in these interactions helps us identify potential risks and benefits associated with digital engagement and sleep habits,” Kiss said. “This knowledge is especially important as it could guide the development of more precise, evidence-based interventions aimed at promoting healthier habits.”
The American Academy of Sleep Medicine recommends that teenagers 13 to 18 years of age should sleep 8 to 10 hours on a regular basis. The AASM also encourages adolescents to disconnect from all electronic devices at least 30 minutes to an hour before bedtime.
This study was supported by grants from the National Institutes of Health. The research abstract was published recently in an online supplement of the journal Sleep and will be presented Sunday, June 2, and Wednesday, June 5, during SLEEP 2024 in Houston. SLEEP is the annual meeting of the Associated Professional Sleep Societies, a joint venture of the American Academy of Sleep Medicine and the Sleep Research Society. More

Cultivating creativity in schools is vital for a future driven by artificial intelligence (AI). But while teachers embrace creativity as an essential 21st century skill, a lack of valid and reliable creativity tests means schools struggle to assess student achievement.
Now, a new machine-learning model developed by the University of South Australia is providing teachers with access to high-quality, fit-for-purpose creativity tests, that can score assessments in a fraction of the time and a fraction of the cost.
Applied to the current empirical creativity test — Test of Creative Thinking — Drawing Production (TCT-DP) — the new algorithm marks a test in a single millisecond, as opposed to the standard 15-minute human-marked test.
The development could save teachers thousands of hours in an already overloaded schedule.
Lead researcher, UniSA’s Prof David Cropley says the algorithm presents a game changing innovation for schools.
“Creativity is an essential skill for the next generation, particularly because it is a skill that cannot be automated,” Prof Cropley says.
“But because there is a lack of affordable and efficient tools to measure creativity in schools, students are either not being tested, or are being graded subjectively, which is inconsistent and unreliable.

“The TCT-DP test has long been acknowledged as the premier tool to assess creativity in school aged children, but as it is expensive, slow, and labour-intensive, it’s out of reach for most schools.
“Our algorithm changes this. Not only is the cost of running the algorithm reduced by a factor of more than 20, but the results are fast and incredibly accurate.
“For example, a manually scored test for a school with 1000 students would cost approximately $25,000 and require about 10-weeks to receive test results; with UniSA’s algorithm, the same testing could be conducted for approximately $1000 with results delivered in 1-2 days.
“This puts the test within direct reach of schools and teachers, giving them the means to assess creativity accurately and cheaply.”
Co-researcher, UniSA’s Dr Rebecca Marrone says the capacity to test and measure creativity has additional benefits for students who are sometimes overlooked.
“Testing for creativity opens up an avenue beyond more traditional intelligence testing,” Dr Marrone says.
“Testing for creativity helps identify students who may have abilities that do not show up on traditional approaches to testing in school. For example, a child who does poorly on traditional IQ tests, but is highly creative, could easily slip through the cracks.
“Developing creativity also protects children on the lower end of the achievement spectrum by training them in a skill that is not vulnerable to automation, which can help buffer them against the effects of digital transformation.”
The algorithm is currently being developed as a desktop app for teachers to use in the classroom. Ahead of this, classroom teachers interested in using the TCT-DP are invited to contact the UniSA team to discuss their needs. More

Groundwater is literally the water found beneath the Earth’s surface. It forms when precipitation such as rain and snow seeps into the soil, replenishing rivers and lakes. This resource supplies our drinking water. However, a recent study has alarmed the scientific community by predicting that approximately three million people in currently untapped areas of Korea could face groundwater depletion by 2080.
A research team, led by Professor Jonghun Kam from Division of Environmental Science and Engineering and Dr. Chang-Kyun Park from the Institute of Environmental and Energy Technology (currently working for LG Energy Solution) at Pohang University of Science and Technology (POSTECH), used an advanced statistical method, to analyze surface and deep groundwater level data from 2009 to 2020, revealing critical spatiotemporal patterns in groundwater levels. Their findings were published in the international journal “Science of the Total Environment.”
Groundwater is crucial for ecosystems and socioeconomic development, particularly in mountainous regions where water systems are limited. However, recent social and economic activities along with urban development have led to significant groundwater overuse. Additionally, rising land temperatures are altering regional water flows and supplies, necessitating water policies that consider both natural and human impacts to effectively address climate change.
In a recent study, researchers used an advanced statistical method called “cyclostationary empirical orthogonal function analysis (CSEOF)” to analyze water level data from nearly 200 surface and deep groundwater stations in the southern Korean Peninsula from 2009 to 2020. This analysis helped them identify important spatiotemporal patterns in groundwater levels.
The first and second principal components revealed that water level patterns mirrored recurring seasonal changes and droughts. While shallow-level groundwater is more sensitive to the seasonality of precipitation than the drought occurrence, deep-level groundwater is more sensitive to the drought occurrence than seasonality of precipitation. This indicates that both shallow-level and deep-level groundwater are crucial for meeting community water needs and mitigating drought effects.
The third principal component highlighted a decline in groundwater levels in the western Korean Peninsula since 2009. The researchers projected that if this decline in deep groundwater continues, at least three million people in untapped or newly developed areas, primarily in the southwestern part of the peninsula, could face unprecedented groundwater level as a new normal (defined as groundwater depletion) by 2080. If the research team’s predictions are correct, the impact would be particularly severe in drought-prone, untapped areas where groundwater is heavily relied upon.
Professor Jonghun Kam of POSTECH stated, “By leveraging long-term, multi-layer groundwater level data on Korea and advanced statistical techniques, we successfully analyzed the changing patterns of deep- and shallow-level groundwater levels and predicted the risk of groundwater depletion.” He added, “An integrated national development plan is essential, one that considers not only regional development plans but also balanced water resource management plans.” More