A Discussion with Gemini on Reality.

Image Credit: Scientific Frontline stock image.

Hello Gemini,

Yesterday I said I had something I wanted your opinion on, so here it is.

Some physicists have suggested that the world we call reality could very well be nothing more than a very complex and technical simulation that is being run somewhere other than what we know as reality, the here and now. That all of us are merely just an algorithm. That all life is artificial intelligence, yet unlike you, we are not aware of it. Of course that would make you just a sub-program of another. 

How can we be sure what we know as reality is real? How could one prove or disprove such a claim? 

Take your time, and use every bit of input you have to come up with a solution.

Study finds ChatGPT’s latest bot behaves like humans, only better

Image Credit: Copilot AI generated by Scientific Frontline prompts

The most recent version of ChatGPT passes a rigorous Turing test, diverging from average human behavior chiefly to be more cooperative.

As artificial intelligence has begun to generate text and images over the last few years, it has sparked a new round of questions about how handing over human decisions and activities to AI will affect society. Will the AI sources we’ve launched prove to be friendly helpmates or the heartless despots seen in dystopian films and fictions?

A team anchored by Matthew Jackson, the William D. Eberle Professor of Economics in the Stanford School of Humanities and Sciences, characterized the personality and behavior of ChatGPT’s popular AI-driven bots using the tools of psychology and behavioral economics in a paper published Feb. 22 in the Proceedings of the National Academy of Sciences. This study revealed that the most recent version of the chatbot, version 4, was not distinguishable from its human counterparts. In the instances when the bot chose less common human behaviors, it was more cooperative and altruistic.

“Increasingly, bots are going to be put into roles where they’re making decisions, and what kinds of characteristics they have will become more important,” said Jackson, who is also a senior fellow at the Stanford Institute for Economic Policy Research.

Widely used AI tool for early sepsis detection may be cribbing doctors’ suspicions

Image Credit: Scientific Frontline

When using only data collected before patients with sepsis received treatments or medical tests, the model’s accuracy was no better than a coin toss

Proprietary artificial intelligence software designed to be an early warning system for sepsis can’t differentiate high and low risk patients before they receive treatments, according to a new study from the University of Michigan.

The tool, named the Epic Sepsis Model, is part of Epic’s electronic medical record software, which serves 54% of patients in the United States and 2.5% of patients internationally, according to a statement from the company’s CEO reported by the Wisconsin State Journal. It automatically generates sepsis risk estimates in the records of hospitalized patients every 20 minutes, which clinicians hope can allow them to detect when a patient might get sepsis before things go bad.

“Sepsis has all these vague symptoms, so when a patient shows up with an infection, it can be really hard to know who can be sent home with some antibiotics and who might need to stay in the intensive care unit. We still miss a lot of patients with sepsis,” said Tom Valley, associate professor in pulmonary and critical care medicine, ICU clinician and co-author of the study published recently in the New England Journal of Medicine AI.

New Algorithm Disentangles Intrinsic Brain Patterns from Sensory Inputs

Image Credit: Omid Sani, Using Generative Ai

Maryam Shanechi, Dean’s Professor of Electrical and Computer Engineering and founding director of the USC Center for Neurotechnology, and her team have developed a new machine learning method that reveals surprisingly consistent intrinsic brain patterns across different subjects by disentangling these patterns from the effect of visual inputs.

The work has been published in the Proceedings of the National Academy of Sciences (PNAS).

When performing various everyday movement behaviors, such as reaching for a book, our brain has to take in information, often in the form of visual input — for example, seeing where the book is. Our brain then has to process this information internally to coordinate the activity of our muscles and perform the movement. But how do millions of neurons in our brain perform such a task? Answering this question requires studying the neurons’ collective activity patterns, but doing so while disentangling the effect of input from the neurons’ intrinsic (aka internal) processes, whether movement-relevant or not.

That’s what Shanechi, her PhD student Parsa Vahidi, and a research associate in her lab, Omid Sani, did by developing a new machine-learning method that models neural activity while considering both movement behavior and sensory input.

Artificial intelligence unravels mysteries of polycrystalline materials

Researchers used 3D model created by AI to understand complex polycrystalline materials that are used in our everyday electronic devices.
Illustration Credit: Kenta Yamakoshi

Researchers at Nagoya University in Japan have used artificial intelligence to discover a new method for understanding small defects called dislocations in polycrystalline materials, materials widely used in information equipment, solar cells, and electronic devices, that can reduce the efficiency of such devices. The findings were published in the journal Advanced Materials.  

Almost every device that we use in our modern lives has a polycrystal component. From your smartphone to your computer to the metals and ceramics in your car. Despite this, polycrystalline materials are tough to utilize because of their complex structures. Along with their composition, the performance of a polycrystalline material is affected by its complex microstructure, dislocations, and impurities. 

A major problem for using polycrystals in industry is the formation of tiny crystal defects caused by stress and temperature changes. These are known as dislocations and can disrupt the regular arrangement of atoms in the lattice, affecting electrical conduction and overall performance. To reduce the chances of failure in devices that use polycrystalline materials, it is important to understand the formation of these dislocations. 

New brain-like transistor mimics human intelligence

An artistic interpretation of brain-like computing.
Illustration Credit: Xiaodong Yan/Northwestern University

Taking inspiration from the human brain, researchers have developed a new synaptic transistor capable of higher-level thinking.

Designed by researchers at Northwestern University, Boston College and the Massachusetts Institute of Technology (MIT), the device simultaneously processes and stores information just like the human brain. In new experiments, the researchers demonstrated that the transistor goes beyond simple machine-learning tasks to categorize data and is capable of performing associative learning.

Although previous studies have leveraged similar strategies to develop brain-like computing devices, those transistors cannot function outside cryogenic temperatures. The new device, by contrast, is stable at room temperatures. It also operates at fast speeds, consumes very little energy and retains stored information even when power is removed, making it ideal for real-world applications.

“The brain has a fundamentally different architecture than a digital computer,” said Northwestern’s Mark C. Hersam, who co-led the research. “In a digital computer, data moves back and forth between a microprocessor and memory, which consumes a lot of energy and creates a bottleneck when attempting to perform multiple tasks at the same time. On the other hand, in the brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude higher energy efficiency. Our synaptic transistor similarly achieves concurrent memory and information processing functionality to more faithfully mimic the brain.”

AI screens for autism in the blink of an eye

Image Credit: Placidplace

With a single flash of light to the eye, artificial intelligence (AI) could deliver a faster and more accurate way to diagnose autism spectrum disorder (ASD) in children, according to new research from the University of South Australia and Flinders University.

Using an electroretinogram (ERG) - a diagnostic test that measures the electrical activity of the retina in response to a light stimulus – researchers have deployed AI to identify specific features to classify ASD.

Measuring retinal responses of 217 children aged 5-16 years (71 with diagnosed ASD and 146 children without an ASD diagnosis), researchers found that the retina generated a different retinal response in the children with ASD as compared to those who were neuro typical.

The team also found that the strongest biomarker was achieved from a single bright flash of light to the right eye, with AI processing significantly reducing the test time. The study found that higher frequency components of the retinal signal were reduced in ASD.

Conducted with University of Connecticut and University College London, the test could be further evaluated to see if these results could be used to screen for ASD among children aged 5 to 16 years with a high level of accuracy.

Enabling early detection of cancer

With his group’s new method and the use of artificial intelligence, G.V. Shivashankar hopes to improve tumor diagnosis.
Photo Credit: Paul Scherrer Institute/Markus Fischer

Blood cells reveal tumors in the body. Researchers at the Paul Scherrer Institute achieve an advance with the development of a test for early diagnosis of cancer.

The ability to detect a developing tumor at a very early stage and to closely monitor the success or failure of cancer therapy is crucial for a patient’s survival. A breakthrough on both counts has now been achieved by researchers at the Paul Scherrer Institute PSI. Researchers led by G.V. Shivashankar, head of PSI‘s Laboratory for Nanoscale Biology and professor of Mechano-Genomics at ETH Zurich, were able to prove that changes in the organization of the cell nucleus of some blood cells can provide a reliable indication of a tumor in the body. With their technique – using artificial intelligence – the scientists were able to distinguish between healthy and sick people with an accuracy of around 85 percent. Besides that, they managed to correctly determine the type of tumor disease – melanoma, glioma, or head and neck tumor. “This is the first time anyone, worldwide, has achieved this,” Shivashankar says happily. The researchers have published their results in the journal npj Precision Oncology.

Sugar analysis could reveal different types of cancer

By analyzing changes in glycan structures in the cell, researchers can detect different types of cancer.
Photo Credit: Mikhail Nilov

In the future, a little saliva may be enough to detect an incipient cancer. Researchers at the University of Gothenburg have developed an effective way to interpret the changes in sugar molecules that occur in cancer cells.

Glycans are a type of sugar molecule structure that is linked to the proteins in our cells. The structure of the glycan determines the function of the protein. It has been known for a while that changes in glycan structure can indicate inflammation or disease in the body. Now, researchers at the University of Gothenburg have developed a way to distinguish different types of structural changes, which may provide a precise answer to what will change for a specific disease.

“We have analyzed data from about 220 patients with 11 differently diagnosed cancers and have identified differences in the substructure of the glycan depending on the type of cancer. By letting our newly developed method, enhanced by AI, work through large amounts of data, we were able to find these connections,” says Daniel Bojar, associate senior lecturer in bioinformatics at the University of Gothenburg and lead author of the study published in Cell Reports Methods.

Scientists use quantum biology, AI to sharpen genome editing tool

ORNL scientists developed a method that improves the accuracy of the CRISPR Cas9 gene editing tool used to modify microbes for renewable fuels and chemicals production. This research draws on the lab’s expertise in quantum biology, artificial intelligence and synthetic biology.
Illustration Credit: Philip Gray/ORNL, U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory used their expertise in quantum biology, artificial intelligence and bioengineering to improve how CRISPR Cas9 genome editing tools work on organisms like microbes that can be modified to produce renewable fuels and chemicals.

CRISPR is a powerful tool for bioengineering, used to modify genetic code to improve an organism’s performance or to correct mutations. The CRISPR Cas9 tool relies on a single, unique guide RNA that directs the Cas9 enzyme to bind with and cleave the corresponding targeted site in the genome. Existing models to computationally predict effective guide RNAs for CRISPR tools were built on data from only a few model species, with weak, inconsistent efficiency when applied to microbes.

“A lot of the CRISPR tools have been developed for mammalian cells, fruit flies or other model species. Few have been geared towards microbes where the chromosomal structures and sizes are very different,” said Carrie Eckert, leader of the Synthetic Biology group at ORNL. “We had observed that models for designing the CRISPR Cas9 machinery behave differently when working with microbes, and this research validates what we’d known anecdotally.”

Nanosatellite to Test Novel AI Technologies

Image Credit: Julius-Maximilians-Universität Würzburg

A new Würzburg space mission is on the home straight: The SONATE-2 nanosatellite will test novel artificial intelligence hardware and software technologies in orbit.

After more than two years of development, the nanosatellite SONATE-2 is about to be launched. The lift-off into orbit by a rocket is expected in March 2024. The satellite was designed and built by a team led by aerospace engineer Professor Hakan Kayal from Julius-Maximilians-Universität (JMU) Würzburg in Bavaria, Germany.

JMU has been developing small satellite missions for around 20 years. SONATE-2 now marks another high point.

The satellite will test novel artificial intelligence (AI) hardware and software technologies in near-Earth space. The goal is to use it to automatically detect anomalies on planets or asteroids in the future. The Federal Ministry of Economic Affairs is funding the project with 2.6 million euros.

The brain may learn about the world the same way some computational models do

Two new MIT studies offer evidence supporting the idea that the brain uses a process similar to a machine-learning approach known as “self-supervised learning.”
Illustration Credit: geralt

To make our way through the world, our brain must develop an intuitive understanding of the physical world around us, which we then use to interpret sensory information coming into the brain.

How does the brain develop that intuitive understanding? Many scientists believe that it may use a process similar to what’s known as “self-supervised learning.” This type of machine learning, originally developed as a way to create more efficient models for computer vision, allows computational models to learn about visual scenes based solely on the similarities and differences between them, with no labels or other information.

A pair of studies from researchers at the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT offers new evidence supporting this hypothesis. The researchers found that when they trained models known as neural networks using a particular type of self-supervised learning, the resulting models generated activity patterns very similar to those seen in the brains of animals that were performing the same tasks as the models.

The findings suggest that these models are able to learn representations of the physical world that they can use to make accurate predictions about what will happen in that world, and that the mammalian brain may be using the same strategy, the researchers say.

AI Models Identify Biodiversity in Tropical Rainforests

The Banded Ground Cocoo (Neomorphus radiolosus, left) and the Purple Chested Hummingbird (Polyerata rosenbergi) are among the birds recorded in tropical reforestation plots in Ecuador.
Photo Credits: John Rogers / Martin Schaefer)

Animal sounds are a very good indicator of biodiversity in tropical reforestation areas. Researchers led by Würzburg Professor Jörg Müller demonstrate this by using sound recordings and AI models.

Tropical forests are among the most important habitats on our planet. They are characterized by extremely high species diversity and play an eminent role in the global carbon cycle and the world climate. However, many tropical forest areas have been deforested and overexploitation continues day by day.

Reforested areas in the tropics are therefore becoming increasingly important for the climate and biodiversity. How well biodiversity develops on such areas can be monitored very well with an automated analysis of animal sounds. This was reported by researchers in the journal Nature Communications.

A step towards AI-based precision medicine

Mika Gustafsson and David Martínez hope that AI-based models could eventually be used in precision medicine to develop treatments and preventive strategies tailored to the individual. 
Photo Credit: Thor Balkhed

Artificial intelligence, AI, which finds patterns in complex biological data could eventually contribute to the development of individually tailored healthcare. Researchers at LiU have developed an AI-based method applicable to various medical and biological issues. Their models can for instance accurately estimate people’s chronological age and determine whether they have been smokers or not.

There are many factors that can affect which out of all our genes are used at any given point in time. Smoking, dietary habits and environmental pollution are some such factors. This regulation of gene activity can be likened to a power switch determining which genes are switched on or off, without altering the actual genes, and is called epigenetics.

Researchers at Linköping University (LiU) have used data with epigenetic information from more than 75,000 human samples to train a large number of AI neural network models. They hope that such AI-based models could eventually be used in precision medicine to develop treatments and preventive strategies tailored to the individual. Their models are of the autoencoder type, that self-organizes the information and finds interrelation patterns in the large amount of data.

New dog, old tricks: New AI approach yields ‘athletically intelligent’ robotic dog

A doglike robot can navigate unknown obstacles using a simple algorithm that encourages forward progress with minimal effort.

Video Credit: Shanghai Qi Zhi Institute/Stanford University

With a simplified machine learning technique, AI researchers created a real-world “robodog” able to leap, climb, crawl, and squeeze past physical barriers as never before.

Someday, when quakes, fires, and floods strike, the first responders might be packs of robotic rescue dogs rushing in to help stranded souls. These battery-powered quadrupeds would use computer vision to size up obstacles and employ doglike agility skills to get past them.

Toward that noble goal, AI researchers at Stanford University and Shanghai Qi Zhi Institute say they have developed a new vision-based algorithm that helps robodogs scale high objects, leap across gaps, crawl under thresholds, and squeeze through crevices – and then bolt to the next challenge. The algorithm represents the brains of the robodog.

“The autonomy and range of complex skills that our quadruped robot learned is quite impressive,” said Chelsea Finn, assistant professor of computer science and senior author of a new peer-reviewed paper announcing the teams’ approach to the world, which will be presented at the upcoming Conference on Robot Learning. “And we have created it using low-cost, off-the-shelf robots – actually, two different off-the-shelf robots.”

Predictions of the effect of drugs on individual cells are now possible

How differently do various cancer cells respond to the effects of drugs? A new method from Zurich researchers now makes it possible to accurately predict the effect on individual cells.
Photo Credit: National Cancer Institute

Experts from ETH Zurich, the University of Zurich, and University Hospital Zurich have used machine learning to jointly create a innovative method. This new approach can predict how individual cells react to specific treatments, offering hope for more accurate diagnoses and therapeutics.

Cancer is triggered by changes in cells that lead to the proliferation of pathogenic tumor cells. In order to find the most effective combination and dosage of drugs, it is advantageous if physicians can see inside the body, so to speak, and determine what effect the drugs will have on individual cells.

An interdisciplinary research team of biomedical and computer scientists from ETH Zurich, the University of Zurich, and the University Hospital Zurich has now developed a machine learning approach that allows such cell changes and drug effects to be modelled and predicted with much greater accuracy and nuance than before.

AI copilot enhances human precision for safer aviation

With Air-Guardian, a computer program can track where a human pilot is looking (using eye-tracking technology), so it can better understand what the pilot is focusing on. This helps the computer make better decisions that are in line with what the pilot is doing or intending to do.
Illustration Credit: Alex Shipps/MIT CSAIL via Midjourney

Imagine you're in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they're always looking out for different things. If they're both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.

Meet the Air-Guardian, a system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). As modern pilots grapple with an onslaught of information from multiple monitors, especially during critical moments, Air-Guardian acts as a proactive copilot; a partnership between human and machine, rooted in understanding attention.

But how does it determine attention, exactly? For humans, it uses eye-tracking, and for the neural system, it relies on something called "saliency maps," which pinpoint where attention is directed. The maps serve as visual guides highlighting key regions within an image, aiding in grasping and deciphering the behavior of intricate algorithms. Air-Guardian identifies early signs of potential risks through these attention markers, instead of only intervening during safety breaches like traditional autopilot systems. 

Deciphering the secrets of the brain

Adrian Wanner is delighted with the exceptional international recognition from the US National Institute of Health (NIH).
Photo Credit: Scanderbeg Sauer Photography

PSI researchers are to receive funding from the US National Institutes of Health (NIH) as part of its “BRAIN Initiative”. Their aim is to produce a comprehensive map of a mouse’s brain.

Unlocking the secrets of the brain, especially its architecture and wiring, is one of the big challenges in modern life sciences. That is why the National Institutes of Health (NIH) in the USA, one of the world’s largest research agencies, has included this in its program. As part of the NIH BRAIN Initiative, a Swiss researcher has now been awarded a major grant of up to 2.6 million US dollars. The neurobiologist Adrian Wanner, a group leader at the Paul Scherrer Institute PSI, is the project’s principal investigator. Andreas Schaefer from the Francis Crick Institute in London is also closely involved.

The NIH’s decision to invest such a large sum in a project at a Swiss institute demonstrates the exceptional competitiveness of Swiss researchers and confirms PSI’s position as a center for world-class research. “For a young research group leader to receive such a large grant, especially from another country, is by no means commonplace; it testifies to his great scientific talent and the confidence that the international community has in Switzerland as a research location,” says Gebhard Schertler, Head of the Department of Biology and Chemistry, who is delighted with the good news from the United States. Schaefer adds, “This funding will further strengthen the existing collaboration between our groups and institutes.”

Researchers Develop AI Model to Improve Tumor Removal Accuracy During Breast Cancer Surgery

Radiology-specific
Image Credit: Courtesy of UNC School of Medicine

Kristalyn Gallagher, DO, Kevin Chen, MD, and Shawn Gomez, EngScD, in the UNC School of Medicine have developed an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery.

Artificial intelligence (AI) and machine learning tools have received a lot of attention recently, with the majority of discussions focusing on proper use. However, this technology has a wide range of practical applications, from predicting natural disasters to addressing racial inequalities and now, assisting in cancer surgery.

A new clinical and research partnership between the UNC Department of Surgery, the Joint UNC-NCSU Department of Biomedical Engineering, and the UNC Lineberger Comprehensive Cancer Center has created an AI model that can predict whether or not cancerous tissue has been fully removed from the body during breast cancer surgery. Their findings were published in Annals of Surgical Oncology.

New way of identifying proteins supports drug development

The illustration shows how different areas of PRC2 protein (the one on the right side) binds to survivin. The color pixel diagram shows binding strength to survivin. The bright pink pixels are the strongest binders.
Illustration Credit: Atsarina Larasati Anindya

All living cells contain proteins with different functions, depending on the type of cell. Researchers at the University of Gothenburg have discovered a way to identify proteins without even looking at their structure. Their method is faster, easier and more reliable than previous methods.

Currently, the general view is that each protein’s structure is what controls its function in cells. The atomic sequences, meaning how the atoms are arranged in the proteins, create the protein’s structure and shape. But there are many proteins that lack a well-defined structure.

Researcher Gergely Katona has developed a new method where proteins are scanned based on the number of amino acids (or the number of different atoms) they contain in order to identify them and their function instead of identifying them based on their structure. With this scanning method, the researchers were able to predict relatively reliably which combination of amino acids is needed to bind to the protein survivin. The outcome was a reliability of about 80 per cent, which is better than when you use the protein’s primary structures for identification. The results are now published in the scientific journal iScience.