Mathematical Formula Tackles Complex Moral Decision-Making in AI

Photo credit: Andy Kelly.

An interdisciplinary team of researchers has developed a blueprint for creating algorithms that more effectively incorporate ethical guidelines into artificial intelligence (AI) decision-making programs. The project was focused specifically on technologies in which humans interact with AI programs, such as virtual assistants or “carebots” used in healthcare settings.

“Technologies like carebots are supposed to help ensure the safety and comfort of hospital patients, older adults and other people who require health monitoring or physical assistance,” says Veljko Dubljević, corresponding author of a paper on the work and an associate professor in the Science, Technology & Society program at North Carolina State University. “In practical terms, this means these technologies will be placed in situations where they need to make ethical judgments.

“For example, let’s say that a carebot is in a setting where two people require medical assistance. One patient is unconscious but requires urgent care, while the second patient is in less urgent need but demands that the carebot treat him first. How does the carebot decide which patient is assisted first? Should the carebot even treat a patient who is unconscious and therefore unable to consent to receiving the treatment?

“Previous efforts to incorporate ethical decision-making into AI programs have been limited in scope and focused on utilitarian reasoning, which neglects the complexity of human moral decision-making,” Dubljević says. “Our work addresses this and, while I used carebots as an example, is applicable to a wide range of human-AI teaming technologies.”

As ransomware attacks increase, new algorithm may help prevent power blackouts

Saurabh Bagchi, a Purdue professor of electrical and computer engineering, develops ways to improve the cybersecurity of power grids and other critical infrastructure.
Credit: Purdue University photo/Vincent Walter

Millions of people could suddenly lose electricity if a ransomware attack just slightly tweaked energy flow onto the U.S. power grid.

No single power utility company has enough resources to protect the entire grid, but maybe all 3,000 of the grid’s utilities could fill in the most crucial security gaps if there were a map showing where to prioritize their security investments.

Purdue University researchers have developed an algorithm to create that map. Using this tool, regulatory authorities or cyber insurance companies could establish a framework that guides the security investments of power utility companies to parts of the grid at greatest risk of causing a blackout if hacked.

Power grids are a type of critical infrastructure, which is any network – whether physical like water systems or virtual like health care record keeping – considered essential to a country’s function and safety. The biggest ransomware attacks in history have happened in the past year, affecting most sectors of critical infrastructure in the U.S. such as grain distribution systems in the food and agriculture sector and the Colonial Pipeline, which carries fuel throughout the East Coast.

Repurposing existing drugs to fight new COVID-19 variants

Photo Credit: Myriam Zilles

MSU researchers are using big data and AI to identify current drugs that could be applied to treat new COVID-19 variants

Finding new ways to treat the novel coronavirus and its ever-changing variants has been a challenge for researchers, especially when the traditional drug development and discovery process can take years. A Michigan State University researcher and his team are taking a hi-tech approach to determine whether drugs already on the market can pull double duty in treating new COVID variants.

“The COVID-19 virus is a challenge because it continues to evolve,” said Bin Chen, an associate professor in the College of Human Medicine. “By using artificial intelligence and really large data sets, we can repurpose old drugs for new uses.”

Chen built an international team of researchers with expertise on topics ranging from biology to computer science to tackle this challenge. First, Chen and his team turned to publicly available databases to mine for the unique coronavirus gene expression signatures from 1,700 host transcriptomic profiles that came from patient tissues, cell cultures and mouse models. These signatures revealed the biology shared by COVID-19 and its variants.

Scientists chart how exercise affects the body

MIT and Harvard Medical School researchers mapped out many of the cells, genes, and cellular pathways that are modified by exercise or high-fat diet.
Photo Credit: Gabin Vallet

Exercise is well-known to help people lose weight and avoid gaining it. However, identifying the cellular mechanisms that underlie this process has proven difficult because so many cells and tissues are involved.

In a new study in mice that expands researchers’ understanding of how exercise and diet affect the body, MIT and Harvard Medical School researchers have mapped out many of the cells, genes, and cellular pathways that are modified by exercise or high-fat diet. The findings could offer potential targets for drugs that could help to enhance or mimic the benefits of exercise, the researchers say.

“It is extremely important to understand the molecular mechanisms that are drivers of the beneficial effects of exercise and the detrimental effects of a high-fat diet, so that we can understand how we can intervene, and develop drugs that mimic the impact of exercise across multiple tissues,” says Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard.

The researchers studied mice with high-fat or normal diets, who were either sedentary or given the opportunity to exercise whenever they wanted. Using single-cell RNA sequencing, the researchers cataloged the responses of 53 types of cells found in skeletal muscle and two types of fatty tissue.

Mouse-human comparison shows unimagined functions of the Thalamus

With mathematical models, Bochum and US researchers have simulated processes in the brain of mice and humans.
Credit: RUB, Marquard

Researchers have reproduced the brain functions of the mouse and human in the computer. Artificial intelligence could learn from this.

For a long time, the thalamus was considered a brain region that is primarily responsible for processing sensory stimuli. Current studies have increased the evidence that it is a central switch in cognitive processes. Researchers of neuroscience around Prof. Dr. Burkhard Pleger in Collaborative Research Center 874 of the Ruhr University Bochum and a team from the Massachusetts Institute of Technology (MIT, USA) observed learning processes in the brains of mice and humans and reproduced them in mathematical models. They were able to show that the region of the mediodoral nucleus in the thalamus has a decisive share in cognitive flexibility. They report in the journal PLOS Computational Biology.

AI boosts usability of paper-making waste products

Photo and graphic with birch tree by J. Löfgren

In a new and exciting collaboration with the Department of Bioproducts and Biosystems, researchers in the CEST group have published a study demonstrating how artificial intelligence (AI) can boost the production of renewable biomaterials. Their publication focuses on the extraction of lignin, an organic polymer that together with cellulose makes up the cell walls of plants. As a side-product of papermaking, lignin is produced in large quantities around the world but seldom used as anything other than cheap fuel. Developing valuable materials and chemicals from lignin would consequently be a big step towards a sustainable society.

A key challenge for the valorization of lignin is to find the right experimental extraction conditions. These include things like the temperature in the hot-water reactor where the wood is processed, the reaction time and the ratio of wood to water. These conditions not only affect the amount of lignin that can be extracted, but also the physical and chemical properties of the extracted lignin itself. Therefore, knowing how to choose the right experimental conditions is important since the more lignin can be extracted the better, and different lignin-based products may require lignin with different properties.

Machine learning may enable bioengineering of the most abundant enzyme

Photo Credit: Melissa Askew

A Newcastle University study has for the first time shown that machine learning can predict the biological properties of the most abundant enzyme on Earth - Rubisco.

Rubisco (Ribulose-1,5-bisphosphate carboxylase/oxygenase) is responsible for providing carbon for almost all life on Earth. Rubisco functions by converting atmospheric CO2 from the Earth’s atmosphere to organic carbon matter, which is essential to sustain most life on Earth.

For some time now, natural variation has been observed among Rubisco proteins of land plants and modelling studies have shown that transplanting Rubisco proteins with certain functional properties can increase the amount of atmospheric CO2 crop plants can uptake and store.

Study lead author, Wasim Iqbal, a PhD researcher at Newcastle University’s School of Natural and Environmental Sciences, part of Dr Maxim Kapralov’s group, developed a machine learning tool which can predict the performance properties of numerous land plant Rubisco proteins with surprisingly good accuracy. The hope is that this tool will enable the hunt for a ‘supercharged’ Rubisco protein that can be bioengineered into major crops such as wheat.

Neurodegenerative disease can progress in newly identified patterns

Neurodegenerative diseases — like amyotrophic lateral sclerosis (ALS, or Lou Gehrig's disease), Alzheimer’s, and Parkinson’s — are complicated, chronic ailments that can present with a variety of symptoms, worsen at different rates, and have many underlying genetic and environmental causes, some of which are unknown. ALS, in particular, affects voluntary muscle movement and is always fatal, but while most people survive for only a few years after diagnosis, others live with the disease for decades. Manifestations of ALS can also vary significantly; often slower disease development correlates with onset in the limbs and affecting fine motor skills, while the more serious, bulbar ALS impacts swallowing, speaking, breathing, and mobility. Therefore, understanding the progression of diseases like ALS is critical to enrollment in clinical trials, analysis of potential interventions, and discovery of root causes.

However, assessing disease evolution is far from straightforward. Current clinical studies typically assume that health declines on a downward linear trajectory on a symptom rating scale, and use these linear models to evaluate whether drugs are slowing disease progression. However, data indicate that ALS often follows nonlinear trajectories, with periods where symptoms are stable alternating with periods when they are rapidly changing. Since data can be sparse, and health assessments often rely on subjective rating metrics measured at uneven time intervals, comparisons across patient populations are difficult. These heterogenous data and progression, in turn, complicate analyses of invention effectiveness and potentially mask disease origin.

Conventional Computers Can Learn to Solve Tricky Quantum Problems

Hsin-Yuan (Robert) Huang
Credit: Caltech

There has been a lot of buzz about quantum computers and for good reason. The futuristic computers are designed to mimic what happens in nature at microscopic scales, which means they have the power to better understand the quantum realm and speed up the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals, and more. However, experts say viable quantum computers are still a decade away or more. What are researchers to do in the meantime?

A new Caltech-led study in the journal Science describes how machine learning tools, run on classical computers, can be used to make predictions about quantum systems and thus help researchers solve some of the trickiest physics and chemistry problems. While this notion has been proposed before, the new report is the first to mathematically prove that the method works in problems that no traditional algorithms could solve.

"Quantum computers are ideal for many types of physics and materials science problems," says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, the Richard P. Feynman Professor of Theoretical Physics and the Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Institute for Quantum Science and Technology (IQIM). "But we aren't quite there yet and have been surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this paper is about showing what humans can learn about the physical world."

Wildfire smoke is unraveling decades of air quality gains

Over the last decade, PM2.5 from wildfire smoke has increased in much of the U.S., particularly in Western states, but some areas in the South and East have seen modest declines. This map shows the decadal change in smoke PM2.5, meaning the difference in daily average smoke PM2.5 during 2006−2010 compared to 2016−2020.
Image credit: Childs et al. 2022, Environmental Science & Technology

Stanford researchers have developed an AI model for predicting dangerous particle pollution to help track the American West’s rapidly worsening wildfire smoke. The detailed results show millions of Americans are routinely exposed to pollution at levels rarely seen just a decade ago.

Wildfire smoke now exposes millions of Americans each year to dangerous levels of fine particulate matter, lofting enough soot across parts of the West in recent years to erase much of the air quality gains made over the last two decades.

Those are among the findings of a new Stanford University study published Sept. 22 in Environmental Science & Technology that focuses on a type of particle pollution known as PM2.5, which can lodge deep in our lungs and even get into our bloodstream.

Using statistical modeling and artificial intelligence techniques, the researchers estimated concentrations of PM2.5 specifically from wildfire smoke in sharp enough detail to reveal variations within individual counties and individual smoke events from coast to coast from 2006 to 2020.

“We found that people are being exposed to more days with wildfire smoke and more extreme days with high levels of fine particulate matter from smoke,” said lead study author Marissa Childs, who worked on the research as a PhD student in Stanford’s Emmett Interdisciplinary Program in Environment and Resources (E-IPER). Unlike other major pollutant sources, wildfire smoke is considered an “exceptional event” under the Clean Air Act, she explained, “which means an increasing portion of the particulate matter that people are exposed to is unregulated.”

Shutting down backup genes leads to cancer remission in mice

Abhinav Achreja, PhD, Research Fellow at the University of Michigan Biomedical Engineering and Deepak Nagrath, Ph.D. Associate Professor of Biomedical Engineering works on ovarian cancer cell research in the bio-engineering lab at the North Campus Research Center (NCRC).
Image credit: Marcin Szczepanski, Michigan Engineering

The way that tumor cells enable their uncontrolled growth is also a weakness that can be harnessed to treat cancer, researchers at the University of Michigan and Indiana University have shown.

Their machine-learning algorithm can identify backup genes that only tumor cells are using so that drugs can target cancer precisely.

“Most cancer drugs affect normal tissues and cells. However, our strategy allows specific targeting of cancer cells.”

Deepak Nagrath

The team demonstrated this new precision medicine approach for treating ovarian cancer in mice. Moreover, the cellular behavior that exposes these vulnerabilities is common across most forms of cancer, meaning the algorithms could provide better treatment plans for a host of malignancies.

“This could revolutionize the precision medicine field because the drug targeting will only affect and kill cancer cells and spare the normal cells,” said Deepak Nagrath, a U-M associate professor of biomedical engineering and senior author of the study in Nature Metabolism. “Most cancer drugs affect normal tissues and cells. However, our strategy allows specific targeting of cancer cells.”

Even the smartest AI models don’t match human visual processing

The study employed novel visual stimuli called “Frankensteins
Source/Credit: York University

Deep convolutional neural networks (DCNNs) don’t see objects the way humans do – using configural shape perception – and that could be dangerous in real-world AI applications, says Professor James Elder, co-author of a York University study.

Published in the Cell Press journal iScience, Deep learning models fail to capture the configural nature of human shape perception is a collaborative study by Elder, who holds the York Research Chair in Human and Computer Vision and is Co-Director of York’s Centre for AI & Society, and Assistant Psychology Professor Nicholas Baker at Loyola College in Chicago, a former VISTA postdoctoral fellow at York.

The study employed novel visual stimuli called “Frankensteins” to explore how the human brain and DCNNs process holistic, configural object properties.

“Frankensteins are simply objects that have been taken apart and put back together the wrong way around,” says Elder. “As a result, they have all the right local features, but in the wrong places.”

The investigators found that while the human visual system is confused by Frankensteins, DCNNs are not – revealing an insensitivity to configural object properties.

New method for comparing neural networks exposes how artificial intelligence works

Researchers at Los Alamos are looking at new ways to compare neural networks. This image was created with an artificial intelligence software called Stable Diffusion, using the prompt “Peeking into the black box of neural networks.”
Source: Los Alamos National Laboratory

A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.

“The artificial intelligence research community doesn’t necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don’t know how or why,” said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. “Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI.”

Jones is the lead author of the paper “If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness,” which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks.

New AI system predicts how to prevent wildfires

Satellite image of Borneo in 2006 covered by smoke from fires (marked by red dots).
Image Credit: Jeff Schmaltz, MODIS Rapid Response Team / NASA

A machine learning model can evaluate the effectiveness of different management strategies

Wildfires are a growing threat in a world shaped by climate change. Now, researchers at Aalto University have developed a neural network model that can accurately predict the occurrence of fires in peatlands. They used the new model to assess the effect of different strategies for managing fire risk and identified a suite of interventions that would reduce fire incidence by 50-76%.

The study focused on the Central Kalimantan province of Borneo in Indonesia, which has the highest density of peatland fires in Southeast Asia. Drainage to support agriculture or residential expansion has made peatlands increasingly vulnerable to recurring fires. In addition to threatening lives and livelihoods, peatland fires release significant amounts of carbon dioxide. However, prevention strategies have faced difficulties because of the lack of clear, quantified links between proposed interventions and fire risk.

The new model uses measurements taken before each fire season in 2002-2019 to predict the distribution of peatland fires. While the findings can be broadly applied to peatlands elsewhere, a new analysis would have to be done for other contexts. ‘Our methodology could be used for other contexts, but this specific model would have to be re-trained on the new data,’ says Alexander Horton, the postdoctoral researcher who carried out study.

Artificial intelligence against child cancer

Stefan Posch
Photo Credit: Uni Halle / Markus Scholz

"Artificial Professor" is the nickname for a new research project at the University Hospital Leipzig and at the Martin Luther University Halle-Wittenberg (MLU). A team of doctors and bioinformatics wants to use self-learning software to significantly improve the therapy of lymphatic cancer (Hodgkin's lymphoma) in children. The second phase of the multi-year project recently began with 40,000 euros in funding from the Mitteldeutsche Kinderkrebsforschung Foundation.

Children affected by lymph gland cancer can now be cured with modern treatment methods such as chemotherapy and radiation in 95 percent of cases. However, intensive treatment in childhood often leads to late damage. Irradiation in particular increases the risk of developing a second cancer later. Long-term studies show massive over-mortality due to second diseases, such as cancer or heart diseases in adulthood.

Therefore, the primary goal of the medical profession is: only as little treatment as necessary. The data analysis developed in the project, based on artificial intelligence, is intended to help and optimize therapy for each individual patient. In the first phase, the researchers first prepared and prepared a unique data set for the big data analysis: a network of 270 child cancer clinics from 21 countries sent the data from the imaging PET examinations anonymously to Leipzig for years. The three-dimensional image series shows how well individual therapies work and how the tumor tissue develops over time.

A Novel Approach to Creating Tailored Odors and Fragrances Using Machine Learning

Can we use machine learning methods to predict the sensing data of odor mixtures and design new smells? A new study by researchers from Tokyo Tech does just that. The novel method is bound to have applications in the food, health, beauty, and wellness industries, where odors and fragrances are of keen interest.

The sense of smell is one of the basic senses of animal species. It is critical to finding food, realizing attraction, and sensing danger. Humans detect smells, or odorants, with olfactory receptors expressed in olfactory nerve cells. These olfactory impressions of odorants on nerve cells are associated with their molecular features and physicochemical properties. This makes it possible to tailor odors to create an intended odor impression. Current methods only predict olfactory impressions from the physicochemical features of odorants. But that method cannot predict the sensing data, which is indispensable for creating smells.

To tackle this issue, scientists from Tokyo Institute of Technology (Tokyo Tech) have employed the innovative strategy of solving the inverse problem. Instead of predicting the smell from molecular data, this method predicts molecular features based on the odor impression. This is achieved using standard mass spectrum data and machine learning (ML) models. "We used a machine-learning-based odor predictive model that we had previously developed to obtain the odor impression. Then we predicted the mass spectrum from odor impression inversely based on the previously developed forward model," explains Professor Takamichi Nakamoto, the leader of the research effort by Tokyo Tech. The findings have been published in PLoS One.

How Artificial Intelligence can explain its decisions

They have brought together the seemingly incompatible inductive approach of machine learning with deductive logic: Stephanie Schörner, Axel Mosig and David Schuhmacher (from left).
Credit: RUB, Marquard

If an algorithm in a tissue sample makes up a tumor, it does not yet reveal how it came to this result. It is not very trustworthy. Bochum researchers are therefore taking a new approach.

Artificial intelligence (AI) can be trained to recognize whether a tissue image contains tumor. How she makes her decision has so far remained hidden. A team from the Research Center for Protein Diagnostics, or PRODI for short, at the Ruhr University Bochum is developing a new approach: with it, the decision of an AI can be explained and thus trustworthy. The researchers around Prof. Dr. Axel Mosig in the journal "Medical Image Analysis".

Bioinformatician Axel Mosig cooperated with Prof. Dr. Andrea Tannapfel, head of the Institute of Pathology, the oncologist Prof. Dr. Anke Reinacher-Schick from St. Josef Hospital of the Ruhr University as well as the biophysicist and PRODI founding director Prof. Dr. Klaus Gerwert. The group developed a neural network, i.e. an AI that can classify whether a tissue sample contains tumor or not. To do this, they fed the AI with many microscopic tissue images, some of which contained tumors, others were tumor-free.

New methodology predicts coronavirus and other infectious disease threats to wildlife

The rate that emerging wildlife diseases infect humans has steadily increased over the last three decades. Viruses, such as the global coronavirus pandemic and recent monkeypox outbreak, have heightened the urgent need for disease ecology tools to forecast when and where disease outbreaks are likely. A University of South Florida assistant professor helped develop a methodology that will do just that – predict disease transmission from wildlife to humans, from one wildlife species to another and determine who is at risk of infection.

The methodology is a machine-learning approach that identifies the influence of variables, such as location and climate, on known pathogens. Using only small amounts of information, the system is able to identify community hot spots at risk of infection on both global and local scales.

“Our main goal is to develop this tool for preventive measures,” said co-principal investigator Diego Santiago-Alarcon, assistant professor of integrative biology. “It’s difficult to have an all-purpose methodology that can be used to predict infections across all the diverse parasite systems, but with this research, we contribute to achieving that goal.”

With help from researchers at the Universiad Veracruzana and Instituto de Ecologia, located in Mexico, Santiago-Alarcon examined three host-pathogen systems – avian malaria, birds with West Nile virus and bats with coronavirus – to test the reliability and accuracy of the models generated by the methodology.

Soaking up the sun with artificial intelligence

Machine learning methods are being developed at Argonne to advance solar energy research with perovskites.
Credit: Maria Chan/ Argonne National Laboratory

The sun continuously transmits trillions of watts of energy to the Earth. It will be doing so for billions more years. Yet, we have only just begun tapping into that abundant, renewable source of energy at affordable cost.

Solar absorbers are a material used to convert this energy into heat or electricity. Maria Chan, a scientist in the U.S. Department of Energy’s (DOE) Argonne National Laboratory, has developed a machine learning method for screening many thousands of compounds as solar absorbers. Her co-author on this project was Arun Mannodi-Kanakkithodi, a former Argonne postdoc who is now an assistant professor at Purdue University.

“We are truly in a new era of applying AI and high-performance computing to materials discovery.” — Maria Chan, scientist, Center for Nanoscale Materials

“According to a recent DOE study, by 2035, solar energy could power 40% of the nation’s electricity,” said Chan. ​“And it could help with decarbonizing the grid and provide many new jobs.”

Machine learning algorithm predicts how to get the most out of electric vehicle batteries

Credit: (Joenomias) Menno de Jong from Pixabay 

The researchers, from the University of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power electric vehicles by suggesting routes and driving patterns that minimize battery degradation and charging times.

The team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.

"This method could unlock value in so many parts of the supply chain, whether you’re a manufacturer, an end user, or a recycler, because it allows us to capture the health of the battery beyond a single number"

Alpha Lee

If developed commercially, the algorithm could be used to recommend routes that get drivers from point to point in the shortest time without degrading the battery, for example, or recommend the fastest way to charge the battery without causing it to degrade. The results are reported in the journal Nature Communications.

The health of a battery, whether it’s in a smartphone or a car, is far more complex than a single number on a screen. “Battery health, like human health, is a multi-dimensional thing, and it can degrade in lots of different ways,” said first author Penelope Jones, from Cambridge’s Cavendish Laboratory. “Most methods of monitoring battery health assume that a battery is always used in the same way. But that’s not how we use batteries in real life. If I’m streaming a TV show on my phone, it’s going to run down the battery a whole lot faster than if I’m using it for messaging. It’s the same with electric cars – how you drive will affect how the battery degrades.”