Hackers could try to take over a military aircraft; can a cyber shuffle stop them?

Sandia National Laboratories cybersecurity expert Chris Jenkins sits in front of a whiteboard with the original sketch of the moving target defense idea for which he is the team lead. When the COVID-19 pandemic hit, Jenkins began working from home, and his office whiteboard remained virtually undisturbed for more than two years.
Photo Credit: Craig Fritz

A cybersecurity technique that shuffles network addresses like a blackjack dealer shuffles playing cards could effectively befuddle hackers gambling for control of a military jet, commercial airliner or spacecraft, according to new research. However, the research also shows these defenses must be designed to counter increasingly sophisticated algorithms used to break them.

Many aircraft, spacecraft and weapons systems have an onboard computer network known as military standard 1553, commonly referred to as MIL-STD-1553, or even just 1553. The network is a tried-and-true protocol for letting systems like radar, flight controls and the heads-up display talk to each other.

Securing these networks against a cyberattack is a national security imperative, said Chris Jenkins, a Sandia National Laboratories cybersecurity scientist. If a hacker were to take over 1553 midflight, he said, the pilot could lose control of critical aircraft systems, and the impact could be devastating.

Jenkins is not alone in his concerns. Many researchers across the country are designing defenses for systems that utilize the MIL-STD-1553 protocol for command and control. Recently, Jenkins and his team at Sandia partnered with researchers at Purdue University in West Lafayette, Indiana, to test an idea that could secure these critical networks.

Let's get wasted and apply some deep thinking to rubbish

Photo Credit: John Cameron

Artificial intelligence has made a giant leap into our rubbish bins thanks to new technology being deployed at the University of South Australia.

Using algorithms to analyze data from smart bin sensors, UniSA PhD student Sabbir Ahmed is designing a deep learning model to predict where waste is accumulating in cities and how often public bins should be cleared.

“Sensors in the public smart bins can give us a lot of information about how busy specific locations are, what type of rubbish is being disposed of and even how much methane gas is being produced from food waste in bins,” Ahmed says.

“All that data can be fed into a neural network model to predict where bins in parks, shopping centers and other public places are likely to fill up quickly and, conversely, which locations are rarely visited.

“This can help councils to optimize their waste management services, schedule bin clearances and even relocate rarely used bins to where they are needed most.”

Infants Outperform AI in “Commonsense Psychology”

New Study Shows How Infants Are More Adept at Spotting Motivations that Drive Human Behavior

Infants outperform artificial intelligence in detecting what motivates other people’s actions, finds a new study by a team of psychology and data science researchers. Its results, which highlight fundamental differences between cognition and computation, point to shortcomings in today’s technologies and where improvements are needed for AI to more fully replicate human behavior. 

“Adults and even infants can easily make reliable inferences about what drives other people’s actions,” explains Moira Dillon, an assistant professor in New York University’s Department of Psychology and the senior author of the paper, which appears in the journal Cognition. “Current AI finds these inferences challenging to make.”

“The novel idea of putting infants and AI head-to-head on the same tasks is allowing researchers to better describe infants’ intuitive knowledge about other people and suggest ways of integrating that knowledge into AI,” she adds.

“If AI aims to build flexible, commonsense thinkers like human adults become, then machines should draw upon the same core abilities infants possess in detecting goals and preferences,” says Brenden Lake, an assistant professor in NYU’s Center for Data Science and Department of Psychology and one of the paper’s authors.

Researchers aim to bring humans back into the loop, as AI use and misuse rises

U-M researchers aim to bring humans back into the loop, as AI use and misuse rises
Image Credit: Gerd Altmann

Artificial intelligence is dominating headlines—enabling new innovations that drive business performance—yet the negative implications for society are an afterthought.

How can humans get back into the loop in the quest toward a better society for all?

A trans-Atlantic team of researchers, including two from the University of Michigan, has reviewed information systems research on what’s known as the “Fourth Industrial Revolution” and found an overwhelming focus on technology-enabled business benefits.

The focus means far less attention is being paid to societal implications—what the researchers refer to as “the increasing risk and damage to humans.”

“We’re talking about AI the wrong way—focusing on technology not people—moving us away from the things we want, such as better medications, elder care and safety regulations, and toward the things we don’t, like harmful deepfakes, job losses and biased decision making,” said Nigel Melville, associate professor of technology and operations at U-M’s Ross School of Business and design science program director.

AI could improve mental health care

Photo Credit: SHVETS production

Patients are often asked to rate their feelings using a rating scale, when talking to psychologists or doctors about their mental health. This is currently how depression and anxiety are diagnosed. However, a new study from Lund University in Sweden shows that allowing patients to describe their experience using their own words - is potentially viewed as more precise and preferred by the patients. The Lund researchers have developed an AI-tool that could help doctors analyze their patients’ answers.

The study, published in PLOS ONE, shows that clinicians and patients differ somewhat, as clinics often prefer rating scales when diagnosing a patient (e.g., little interest in doing things: not at all, sometimes, often, daily) whereas patients prefer free language (e.g., Describe your mental health).

The researchers surveyed a group of 150 patients with self-diagnosed depression or anxiety, posing the same questions to a control group of 150 other participants.

AI with infrared imaging enables precise colon cancer diagnostics

Klaus Gerwert, Stephanie Schörner and Frederik Großerüschkamp (from left) want to improve the diagnosis of colon cancer with the help of artificial intelligence.
Photo Credit: © RUB, Marquard

Artificial intelligence and infrared imaging automatically classify tumors and are faster than previous methods.

The immense progress in the area of therapy options over the past few years has significantly improved the chances of recovery for patients with colon cancer. However, these new approaches, such as immunotherapy, require a precise diagnosis so that they can be tailored to the respective person. Researchers at the Center for Protein Diagnostics PRODI at the Ruhr University Bochum use artificial intelligence in combination with infrared imaging to optimally coordinate the therapy of colon cancer with the individual patient. The label-free and automatable method can complement existing pathological analyzes. The team around Prof. Dr. Klaus Gerwert reports in the journal "European Journal of Cancer" on January 28, 2023.

Deep insights into human tissue within an hour

The PRODI team has been developing a new method of digital imaging for several years: The so-called label-free infrared (IR) imaging measures the genomic and proteomic composition of the tissue examined, i.e. provides molecular information based on the infrared spectra. This information is decoded using artificial intelligence and displayed as false color images. For this purpose, the researchers use image analysis methods from the field of deep learning.

VISTA X-62 Advancing Autonomy and Changing the Face of Air Power

The X-62A VISTA Aircraft flying above Edwards Air Force Base, California.
Photo Credit: Kyle Brasier, U.S. Air Force

The Lockheed Martin VISTA X-62A, a one-of-a-kind training aircraft, was flown by an artificial intelligence agent for more than 17 hours recently, representing the first time AI engaged on a tactical aircraft.

VISTA, short for Variable In-flight Simulation Test Aircraft, is changing the face of air power at the U.S. Air Force Test Pilot School (USAF TPS) at Edwards Air Force Base in California.

VISTA is a one-of-a-kind training airplane developed by Lockheed Martin Skunk Works® in collaboration with Calspan Corporation for the USAF TPS. Built on open systems architecture, VISTA is fitted with software that allows it to mimic the performance characteristics of other aircraft.

"VISTA will allow us to parallelize the development and test of cutting-edge artificial intelligence techniques with new uncrewed vehicle designs," said Dr. M. Christopher Cotting, U.S. Air Force Test Pilot School director of research. "This approach, combined with focused testing on new vehicle systems as they are produced, will rapidly mature autonomy for uncrewed platforms and allow us to deliver tactically relevant capability to our warfighter."

Efficient technique improves machine-learning models’ reliability

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a new technique that can enable a machine-learning model to quantify how confident it is in its predictions, but does not require vast troves of new data and is much less computationally intensive than other techniques.
Image Credit: MIT News, iStock
Creative Commons Attribution Non-Commercial No Derivatives license

Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans know when to trust a model’s predictions.

Uncertainty quantification is one tool that improves a model’s reliability; the model produces a score along with the prediction that expresses a confidence level that the prediction is correct. While uncertainty quantification can be useful, existing methods typically require retraining the entire model to give it that ability. Training involves showing a model millions of examples so it can learn a task. Retraining then requires millions of new data inputs, which can be expensive and difficult to obtain, and also uses huge amounts of computing resources.

Researchers at MIT and the MIT-IBM Watson AI Lab have now developed a technique that enables a model to perform more effective uncertainty quantification, while using far fewer computing resources than other methods, and no additional data. Their technique, which does not require a user to retrain or modify a model, is flexible enough for many applications.

Robots and A.I. team up to discover highly selective catalysts

Close up of the semi-automated synthesis robot used to generate training data
Photo Credit: ICReDD

Researchers used a chemical synthesis robot and computationally cost effective A.I. model to successfully predict and validate highly selective catalysts.

Artificial intelligence (A.I.) has made headlines recently with the advent of ChatGPT’s language processing capabilities. Creating a similarly powerful tool for chemical reaction design remains a significant challenge, especially for complex catalytic reactions. To help address this challenge, researchers at the Institute for Chemical Reaction Design and Discovery and the Max Planck Institut für Kohlenforschung have demonstrated a machine learning method that utilizes advanced yet efficient 2D chemical descriptors to accurately predict highly selective asymmetric catalysts—without the need for quantum chemical computations.  

“There have been several advanced technologies which can “predict” catalyst structures, but those methods often required large investments of calculation resources and time; yet their accuracy was still limited,” said joint first author Nobuya Tsuji. “In this project, we have developed a predictive model which you can run even with an everyday laptop PC.”

Earth likely to cross critical climate thresholds even if emissions decline

Already, the world is 1.1 degrees Celsius hotter on average than it was before fossil fuel combustion took off in the 1800s. More extreme rainfall and flooding are among the litany of impacts from that warming.
Photo Credit: Chris Gallagher

Artificial intelligence provides new evidence our planet will cross the global warming threshold of 1.5 degrees Celsius within 10 to 15 years. Even with low emissions, we could see 2 C of warming. But a future with less warming remains within reach.

A new study has found that emission goals designed to achieve the world’s most ambitious climate target – 1.5 degrees Celsius above pre-industrial levels – may in fact be required to avoid more extreme climate change of 2 degrees Celsius.

The study, published Jan. 30 in Proceedings of the National Academy of Sciences, provides new evidence that global warming is on track to reach 1.5 degrees Celsius (2.7 Fahrenheit) above pre-industrial averages in the early 2030s, regardless of how much greenhouse gas emissions rise or fall in the coming decade.

The new “time to threshold” estimate results from an analysis that employs artificial intelligence to predict climate change using recent temperature observations from around the world.

A.I. used to predict space weather like Coronal Mass Ejections

 Dr Andy Smith of Northumbria University
Photo Credit: Northumbria University/Simon Veit-Wilson.

A physicist from Northumbria University has received over £500,000 to create AI that will safeguard the Earth from destructive space storms.

Coronal Mass Ejections, which are solar eruptions from the Sun, can send plasma hurtling towards Earth at high speeds. These space storms can cause severe disruptions to power grids and communication systems.

With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011.

Researcher and his team analyzed huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

Machine learning identifies drugs that could potentially help smokers quit

Penn State College of Medicine researchers helped identify eight medications that may be repurposed to help people quit smoking. A team of more than 70 researchers contributed to the analysis of genetic and smoking behavior data from more than 1.3 million people.
Image Credit: Scientific Frontline

Medications like dextromethorphan, used to treat coughs caused by cold and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine learning method, where computer programs analyze data sets for patterns and trends, to identify the drugs and said that some of them are already being tested in clinical trials.

Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person’s risk for engaging in those behaviors. The researchers found in a prior study that people with certain genes are more likely to become addicted to tobacco.

Using genetic data from more than 1.3 million people, Dajiang Liu, Ph.D., professor of public health sciences, and of biochemistry and molecular biology and Bibo Jiang, Ph.D., assistant professor of public health sciences, co-led a large multi-institution study that used machine learning to study these large data sets — which include specific data about a person’s genetics and their self-reported smoking behaviors.

MIT researchers develop an AI model that can detect future lung cancer risk

Caption:Researchers from Massachusetts General Hospital and MIT stand in front of a CT scanner at MGH, where some of the validation data was generated. Left to right: Regina Barzilay, Lecia Sequist, Florian Fintelmann, Ignacio Fuentes, Peter Mikhael, Stefan Ringer, and Jeremy Wohlwend
 Photo Credit: Guy Zylberberg.

The name Sybil has its origins in the oracles of Ancient Greece, also known as sibyls: feminine figures who were relied upon to relay divine knowledge of the unseen and the omnipotent past, present, and future. Now, the name has been excavated from antiquity and bestowed on an artificial intelligence tool for lung cancer risk assessment being developed by researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH).

Lung cancer is the No. 1 deadliest cancer in the world, resulting in 1.7 million deaths worldwide in 2020, killing more people than the next three deadliest cancers combined. 

"It’s the biggest cancer killer because it’s relatively common and relatively hard to treat, especially once it has reached an advanced stage,” says Florian Fintelmann, MGCC thoracic interventional radiologist and coauthor on the new work. “In this case, it’s important to know that if you detect lung cancer early, the long-term outcome is significantly better. Your five-year survival rate is closer to 70 percent, whereas if you detect it when it’s advanced, the five-year survival rate is just short of 10 percent.” 

Why faces might not be as attention-grabbing as we think

Data from the study’s 30 participants revealed they looked at the faces of just 16 per cent of the people they walked past.
Photo Credit: John Cameron

Research combining wearable eye-tracking technology and AI body detection software suggests our eyes aren’t drawn to the faces of passers-by as much as previously thought.

Faces are key to everyday social interaction. Just a brief glance can give us important signals about someone’s emotional state, intentions and identity that helps us to navigate our social world.

But researchers studying social attention – how we notice and process the actions and behaviors of others in social contexts – have been mostly limited to lab-based studies where participants view social scenes on computer screens. Now, researchers from the School of Psychology at UNSW Science have developed a new approach that could enable more studies of social attention in natural settings.

The novel method correlates eye-movement data from wearable eye-tracking glasses with analysis from an automatic face and body detection algorithm to record when and where participants looked when fixating on other people. The methodology, detailed in the journal Scientific Reports, could have a range of future applications in settings from clinical research to sports science.

Scientists use machine learning to gain unprecedented view of small molecules

Metabolites are extremely small – the diameter of a human hair is 100,000 nanometers, while that of a glucose molecule is approximately one nanometer.
Illustration Credit: Matti Ahlgren/Aalto University.

A new tool to identify small molecules offers benefits for diagnostics, drug discovery and fundamental research.

A new machine learning model will help scientists identify small molecules, with applications in medicine, drug discovery and environmental chemistry. Developed by researchers at Aalto University and the University of Luxembourg, the model was trained with data from dozens of laboratories to become one of the most accurate tools for identifying small molecules.

Thousands of different small molecules, known as metabolites, transport energy and transmit cellular information throughout the human body. Because they are so small, metabolites are difficult to distinguish from each other in a blood sample analysis – but identifying these molecules is important to understand how exercise, nutrition, alcohol use and metabolic disorders affect wellbeing.

Artificial Intelligence in Veterinary Medicine Raises Ethical Challenges

Chimmi (Chimichanga) a few hours before having his spleen removed due to a mass. Detected by Hi-Def Ultrasound by a radiologist. 7/2021
Photo Credit: Heidi-Ann Fourkiller

Use of artificial intelligence (AI) is increasing in the field of veterinary medicine, but veterinary experts caution that the rush to embrace the technology raises some ethical considerations.

“A major difference between veterinary and human medicine is that veterinarians have the ability to euthanize patients – which could be for a variety of medical and financial reasons – so the stakes of diagnoses provided by AI algorithms are very high,” says Eli Cohen, associate clinical professor of radiology at NC State’s College of Veterinary Medicine. “Human AI products have to be validated prior to coming to market, but currently there is no regulatory oversight for veterinary AI products.”

In a review for Veterinary Radiology and Ultrasound, Cohen discusses the ethical and legal questions raised by veterinary AI products currently in use. He also highlights key differences between veterinary AI and AI used by human medical doctors.

AI model predicts if a covid-19 test might be positive or not

Xingquan “Hill” Zhu, Ph.D., (left) senior author and a professor; and co-author Magdalyn E. Elkin, a Ph.D. student, both in FAU’s Department of Electrical Engineering and Computer Science.
Photo Credit: Florida Atlantic University

COVID-19 and its latest Omicron strains continue to cause infections across the country as well as globally. Serology (blood) and molecular tests are the two most commonly used methods for rapid COVID-19 testing. Because COVID-19 tests use different mechanisms, they vary significantly. Molecular tests measure the presence of viral SARS-CoV-2 RNA while serology tests detect the presence of antibodies triggered by the SARS-CoV-2 virus.

Currently, there is no existing study on the correlation between serology and molecular tests and which COVID-19 symptoms play a key role in producing a positive test result. A study from Florida Atlantic University ’s College of Engineering and Computer Science using machine learning provides important new evidence in understanding how molecular tests versus serology tests are correlated, and what features are the most useful in distinguishing between COVID-19 positive versus test outcomes.

Researchers from the College of Engineering and Computer Science trained five classification algorithms to predict COVID-19 test results. They created an accurate predictive model using easy-to-obtain symptom features, along with demographic features such as number of days post-symptom onset, fever, temperature, age and gender.

Fossil-Sorting Robots Will Help Researchers Study Oceans, Climate

Researchers have developed and demonstrated a robot capable of sorting, manipulating, and identifying microscopic marine fossils. The new technology automates a tedious process that plays a key role in advancing our understanding of the world’s oceans and climate – both today and in the prehistoric past.

“The beauty of this technology is that it is made using relatively inexpensive off-the-shelf components, and we are making both the designs and the artificial intelligence software open source,” says Edgar Lobaton, co-author of a paper on the work and an associate professor of electrical and computer engineering at North Carolina State University. “Our goal is to make this tool widely accessible, so that it can be used by as many researchers as possible to advance our understanding of oceans, biodiversity and climate.”

The technology, called Forabot, uses robotics and artificial intelligence to physically manipulate the remains of organisms called foraminifera, or forams, so that those remains can be isolated, imaged and identified.

Forams are protists, neither plant nor animal, and have been prevalent in our oceans for more than 100 million years. When forams die, they leave behind their tiny shells, mostly less than a millimeter wide. These shells give scientists insights into the characteristics of the oceans as they existed when the forams were alive. For example, different types of foram species thrive in different kinds of ocean environments, and chemical measurements can tell scientists about everything from the ocean’s chemistry to its temperature when the shell was being formed.

Aging is driven by unbalanced genes

Northwestern University researchers have discovered a previously unknown mechanism that drives aging.

In a new study, researchers used artificial intelligence to analyze data from a wide variety of tissues, collected from humans, mice, rats and killifish. They discovered that the length of genes can explain most molecular-level changes that occur during aging.

All cells must balance the activity of long and short genes. The researchers found that longer genes are linked to longer lifespans, and shorter genes are linked to shorter lifespans. They also found that aging genes change their activity according to length. More specifically, aging is accompanied by a shift in activity toward short genes. This causes the gene activity in cells to become unbalanced.

Surprisingly, this finding was near universal. The researchers uncovered this pattern across several animals, including humans, and across many tissues (blood, muscle, bone and organs, including liver, heart, intestines, brain and lungs) analyzed in the study.

The new finding potentially could lead to interventions designed to slow the pace of — or even reverse — aging.

Neural Network Learned to Create a Molecular Dynamics Model of Liquid Gallium

The melt viscosity determines the choice of casting mode, ingot formation conditions and other parameters.
Photo Credit: Ilya Safarov

Scientists at the Institute of Metallurgy, Ural Branch of the Russian Academy of Sciences, and Ural Federal University have developed a method for theoretically high-precision determination of the viscosity of liquid metals using a trained artificial neural network. The method was successfully tested in the process of building the deep learning potential of the neural network on the example of liquid gallium. Scientists were able to significantly increase the spatiotemporal scale of the simulation. The results of molecular dynamics modeling of liquid gallium are particularly accurate. Previous calculations were notoriously inaccurate, especially in the low temperature range. An article describing the research was published in the journal Computational Materials Science.

"First, liquids are in principle difficult to be described theoretically. The reason, in our opinion, lies in the absence of a simple initial approximation for this class of systems (for example, the initial approximation for gases is the ideal gas model). Secondly, the atomistic calculation of viscosity requires processing of a large volume of statistical data and, at the same time, a large accuracy of description of the potential energy surface and forces acting on atoms. Direct calculations cannot achieve such an effect. Thirdly, gallium in the liquid state is difficult to describe theoretically, because, due to certain features, its structure differs from that of most other metals," explains Vladimir Filippov, Senior Researcher at the Department of Rare Metals and Nanomaterials at UrFU, research participant and co-author of the article.