Computers Math

A team led by Northwestern University researchers has developed the first artificial intelligence (AI) to date that can intelligently design robots from scratch.
To test the new AI, the researchers gave the system a simple prompt: Design a robot that can walk across a flat surface. While it took nature billions of years to evolve the first walking species, the new algorithm compressed evolution to lightning speed — designing a successfully walking robot in mere seconds.
But the AI program is not just fast. It also runs on a lightweight personal computer and designs wholly novel structures from scratch. This stands in sharp contrast to other AI systems, which often require energy-hungry supercomputers and colossally large datasets. And even after crunching all that data, those systems are tethered to the constraints of human creativity — only mimicking humans’ past works without an ability to generate new ideas.
The study will be published on Oct. 3 in the Proceedings of the National Academy of Sciences.
“We discovered a very fast AI-driven design algorithm that bypasses the traffic jams of evolution, without falling back on the bias of human designers,” said Northwestern’s Sam Kriegman, who led the work. “We told the AI that we wanted a robot that could walk across land. Then we simply pressed a button and presto! It generated a blueprint for a robot in the blink of an eye that looks nothing like any animal that has ever walked the earth. I call this process ‘instant evolution.'”
Kriegman is an assistant professor of computer science, mechanical engineering and chemical and biological engineering at Northwestern’s McCormick School of Engineering, where he is a member of the Center for Robotics and Biosystems. David Matthews, a scientist in Kriegman’s laboratory, is the paper’s first author. Kriegman and Matthews worked closely with co-authors Andrew Spielberg and Daniela Rus (Massachusetts Institute of Technology) and Josh Bongard (University of Vermont) for several years before their breakthrough discovery.
From xenobots to new organisms
In early 2020, Kriegman garnered widespread media attention for developing xenobots, the first living robots made entirely from biological cells. Now, Kriegman and his team view their new AI as the next advance in their quest to explore the potential of artificial life. The robot itself is unassuming — small, squishy and misshapen. And, for now, it is made of inorganic materials. But Kriegman says it represents the first step in a new era of AI-designed tools that, like animals, can act directly on the world. More

Australian researchers have developed a molecular-sized, more efficient version of a widely used electronic sensor, in a breakthrough that could bring widespread benefits.
Piezoresistors are commonly used to detect vibrations in electronics and automobiles, such as in smart phones for counting steps, and for airbag deployment in cars. They are also used in medical devices such as implantable pressure sensors, as well as in aviation and space travel.
In a nationwide initiative, researchers led by Dr Nadim Darwish from Curtin University, Professor Jeffrey Reimers from the University of Technology Sydney, Associate Professor Daniel Kosov from James Cook University, and Dr Thomas Fallon from the University of Newcastle, have developed a piezoresistor that is about 500,000 times smaller than the width of a human hair.
Dr Darwish said they had developed a more sensitive, miniaturised type of this key electronic component, which transforms force or pressure to an electrical signal and is used in many everyday applications.
“Because of its size and chemical nature, this new type of piezoresistor will open up a whole new realm of opportunities for chemical and biosensors, human-machine interfaces, and health monitoring devices,” Dr Darwish said.
“As they are molecular-based, our new sensors can be used to detect other chemicals or biomolecules like proteins and enzymes, which could be game-changing for detecting diseases.”
Dr Fallon said the new piezoresistor was made from a single bullvalene molecule that when mechanically strained reacts to form a new molecule of different shape, altering electricity flow by changing resistance. More

A collaboration of FLEET researchers from the University of Wollongong and Monash University have reviewed the superconducting diode effect, one of the most fascinating phenomena recently discovered in quantum condensed-matter physics.
A superconducting diode enables dissipationless supercurrent to flow in only one direction, and provides new functionalities for superconducting circuits.
This non-dissipative circuit element is key to future ultra-low energy superconducting and semiconducting-superconducting hybrid quantum devices, with potential for quantum technologies in both classical and quantum computing.
SUPERCONDUCTORS AND DIODE EFFECTS
A superconductor is characterized by zero resistivity and perfect diamagnetic behavior, which leads to dissipationless transport and magnetic levitation.
‘Conventional’ superconductors and the underlying phenomenon of low-temperature superconductivity are explained well by microscopic Bardeen-Cooper-Schrieffer (BCS) theory proposed in 1957.
The prediction of Fulde-Ferrell-Larkin-Ovchinnikov ferromagnetic superconducting phase in 1964-65 and the discovery of ‘high-temperature’ superconductivity in antiferromagnetic structures in 1986-87, has set the stage for the field of unconventional superconductivity wherein superconducting order can be stabilized in functional materials such as magnetic superconductors, ferroelectric superconductors, and helical or chiral topological superconductors. More

Young people (24 years and younger) spend an average of six hours a day online, primarily using their smartphones, according to research from the University of Surrey. Older people (those 24 years and older) spend 4.6 hours online.
Surrey’s study, which involved 796 participants, introduces a new internet addiction spectrum, categorising internet users into five groups: Casual Users (14.86%): This group mainly goes online for specific tasks and logs off without lingering. They show no signs of addiction and are generally older, with an average age of 33.4 years. They are the least interested in exploring new apps. Initial Users (22.86%): These individuals often find themselves online longer than they initially planned and are somewhat neglectful of household chores but don’t consider themselves addicted. They are moderately interested in apps and have an average age of 26.1 years. Experimenters (21.98%): This group feels uneasy or anxious when not connected to the internet. Once they go online, they feel better. Experimenters are more willing to try out new apps and technology, and their average age is between 22.8 and 24.3 years. Addicts-in-Denial (17.96%): These users display addictive behaviours like forming new relationships online and neglecting real-world responsibilities to be online. However, they won’t admit to feeling uneasy when they’re not connected. They are also quite confident in using mobile technology. Addicts (22.36%): This group openly acknowledges their internet addiction and recognises its negative impact on their lives. They are the most confident in using new apps and technology. Their time online is significantly greater than that of the Casual Users. More

Identifying the underlying cause of extreme events such as floods, heavy downpours or tornados is immensely difficult and can take a concerted effort by scientists over several decades to arrive at feasible physical explanations.
Extreme events cause significant deviation from expected behavior and can dictate the overall outcome for a number of scientific problems and practical situations. For example, practical scenarios where a fundamental understanding of extreme events can be of vital importance include rogue waves in the ocean that could endanger ships and offshore structures or increasingly frequent “1,000-year rains,” such as the life-threatening deluge in April that deposited 20 inches of rainfall within a seven-hour period in the Fort Lauderdale area.
At the core of uncovering such extreme events is the physics of fluids — specifically turbulent flows, which exhibit a wide range of interesting behavior in time and space. In fluid dynamics, a turbulent flow refers to an irregular flow whereby eddies, swirls and flow instabilities occur. Because of the random nature and irregularity of turbulent streams, they are notoriously difficult to understand or to apply order through equations.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science leveraged a computer-vision deep learning technique and adapted it for nonlinear analysis of extreme events in wall-bounded turbulent flows, which are pervasive in numerous physics and engineering applications and impact wind and hydrokinetic energy, among others.
The study focused on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the non-linear nature of this phenomenon.
Results, published in the journal Physical Review Fluids, demonstrate that the technique the researchers employed can be invaluable for accurately identifying the sources of extreme events in a completely data-driven manner. The framework they formulated is sufficiently general to be extendable to other scientific domains, where the underlying spatial dynamics governing the evolution of critical phenomena may not be known beforehand.
Using a neural network architecture called Convolutional Neural Network (CNN) that specializes in uncovering spatial relationships, researchers trained a network to estimate the relative intensity of ejection structures within turbulent flow simulation without any a-priori knowledge of the underlying flow dynamics. More

Someone’s prior beliefs about an artificial intelligence agent, like a chatbot, have a significant effect on their interactions with that agent and their perception of its trustworthiness, empathy, and effectiveness, according to a new study.
Researchers from MIT and Arizona State University found that priming users — by telling them that a conversational AI agent for mental health support was either empathetic, neutral, or manipulative — influenced their perception of the chatbot and shaped how they communicated with it, even though they were speaking to the exact same chatbot.
Most users who were told the AI agent was caring believed that it was, and they also gave it higher performance ratings than those who believed it was manipulative. At the same time, less than half of the users who were told the agent had manipulative motives thought the chatbot was actually malicious, indicating that people may try to “see the good” in AI the same way they do in their fellow humans.
The study revealed a feedback loop between users’ mental models, or their perception of an AI agent, and that agent’s responses. The sentiment of user-AI conversations became more positive over time if the user believed the AI was empathetic, while the opposite was true for users who thought it was nefarious.
“From this study, we see that to some extent, the AI is the AI of the beholder,” says Pat Pataranutaporn, a graduate student in the Fluid Interfaces group of the MIT Media Lab and co-lead author of a paper describing this study. “When we describe to users what an AI agent is, it does not just change their mental model, it also changes their behavior. And since the AI responds to the user, when the person changes their behavior, that changes the AI, as well.”
Pataranutaporn is joined by co-lead author and fellow MIT graduate student Ruby Liu; Ed Finn, associate professor in the Center for Science and Imagination at Arizona State University; and senior author Pattie Maes, professor of media technology and head of the Fluid Interfaces group at MIT.
The study, published in Nature Machine Intelligence, highlights the importance of studying how AI is presented to society, since the media and popular culture strongly influence our mental models. The authors also raise a cautionary flag, since the same types of priming statements in this study could be used to deceive people about an AI’s motives or capabilities. More

A strategy for cellular reprogramming involves using targeted genetic interventions to engineer a cell into a new state. The technique holds great promise in immunotherapy, for instance, where researchers could reprogram a patient’s T-cells so they are more potent cancer killers. Someday, the approach could also help identify life-saving cancer treatments or regenerative therapies that repair disease-ravaged organs.
But the human body has about 20,000 genes, and a genetic perturbation could be on a combination of genes or on any of the over 1,000 transcription factors that regulate the genes. Because the search space is vast and genetic experiments are costly, scientists often struggle to find the ideal perturbation for their particular application.
Researchers from MIT and Harvard University developed a new, computational approach that can efficiently identify optimal genetic perturbations based on a much smaller number of experiments than traditional methods.
Their algorithmic technique leverages the cause-and-effect relationship between factors in a complex system, such as genome regulation, to prioritize the best intervention in each round of sequential experiments.
The researchers conducted a rigorous theoretical analysis to determine that their technique did, indeed, identify optimal interventions. With that theoretical framework in place, they applied the algorithms to real biological data designed to mimic a cellular reprogramming experiment. Their algorithms were the most efficient and effective.
“Too often, large-scale experiments are designed empirically. A careful causal framework for sequential experimentation may allow identifying optimal interventions with fewer trials, thereby reducing experimental costs,” says co-senior author Caroline Uhler, a professor in the Department of Electrical Engineering and Computer Science (EECS) who is also co-director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems and Society (IDSS).
Joining Uhler on the paper, which appears today in Nature Machine Intelligence, are lead author Jiaqi Zhang, a graduate student and Eric and Wendy Schmidt Center Fellow; co-senior author Themistoklis P. Sapsis, professor of mechanical and ocean engineering at MIT and a member of IDSS; and others at Harvard and MIT. More

Increased government investment in climate change mitigation is prompting agricultural sectors to find reliable methods for measuring their contribution to climate change. With that in mind, a team led by scientists at the University of Illinois Urbana-Champaign proposed a supercomputing solution to help measure individual farm field-level greenhouse gas emissions.
Although locally tested in the Midwest, the new approach can be scaled up to national and global levels and help the industry grasp the best practices for reducing emissions.
The new study, directed by natural resources and environmental sciences professor Kaiyu Guan, synthesized more than 25 of the group’s previous studies to quantify greenhouse gas emissions produced by U.S. farmland. The findings — completed in collaboration with partners from the University of Minnesota, Lawrence Berkeley National Laboratory and Project Drawdown, a climate solutions nonprofit organization — are published in the journal Earth Science Reviews.
“There are many farming practices that can go a long way to reduce greenhouse gas emissions, but the scientific community has struggled to find a consistent method for measuring how well these practices work,” Guan said.
Guan’s team built a solution based on “agricultural carbon outcomes,” which it defines as the related changes in greenhouse gas emissions from farmers adopting climate mitigation practices like cover cropping, precision nitrogen fertilizer management and use of controlled drainage techniques.
“We developed what we call a ‘system of systems’ solution, which means we integrated a variety of sensing techniques and combined them with advanced ecosystem models,” said Bin Peng, co-author of the study and a senior research scientist at the U. of I. Institute for Sustainability, Energy and Environment. “For example, we fuse ground-based imaging with satellite imagery and process that data with algorithms to generate information about crop emissions before and after farmers adopt various mitigation practices.”
“Artificial intelligence also plays a critical role in realizing our ambitious goals to quantify every field’s carbon emission,” said Zhenong Jin, a professor at the University of Minnesota who co-led the study. “Unlike traditional model-data fusion approaches, we used knowledge-guided machine learning, which is a new way to bring together the power of sensing data, domain knowledge and artificial intelligence techniques.”
The study also details how emissions and agricultural practices data can be cross-checked against economic, policy and carbon market data to find best-practice and realistic greenhouse gas mitigation solutions locally to globally — especially in economies struggling to farm in an environmentally conscious manner. More