For chatbots and beyond: Improving lives with data starts with improving machine learning

Grant and Award Announcement

VIRGINIA TECH

 

IMAGE: RUOXI JIA. view more 

CREDIT: PHOTO BY CHELSEA SEEBER FOR VIRGINIA TECH.

You’d be hard pressed to find an industry today that doesn’t use data in some capacity. Whether it's health care workers using data to report the rate of flu infections in a certain state, manufacturers using data to better understand average production times, or even a small coffee shop owner flipping through sales data to learn about the previous month’s bestselling latte, data can reveal patterns and offer insights into our everyday behavior.

All of this data plays a critical role in artificial intelligence (AI) decision-making. Further, it creates a serious need for people to understand the value of data in the first place. By understanding how individual data sources contribute to technology-based decision-making processes, we can create a more effective and improved experience for all AI users. 

For instance, studies have shown that prevalent facial recognition software performs less reliably in identifying women and people of color compared with white men, reflecting imbalances in facial data representing diverse populations. Measuring the value of data enables us to eliminate inputs that might contribute to biased models. Furthermore, understanding the value of data allows us to assign appropriate pricing to data sources, thereby facilitating data sharing. This is particularly important to industries where certain data is difficult to obtain or for small businesses grappling with limited data access.

Assistant Professor Ruoxi Jia in the Bradley Department of Electrical and Computer Engineering at Virginia Tech has received an National Science Foundation (NSF) Faculty Early Career Development (CAREER) award to investigate fundamental theories and computational tools needed to measure the value of data. 

The five-year $500,000 grant will allow Jia and her team to develop scalable and reliable data valuation techniques that support strategic data acquisition and improve machine learning based data analytics.

“Right now, there is a lot of excitement about machine learning and AI, especially after the emergence of ChatGPT,” said Jia. “But what’s under the hood is a lot of data. That’s what enables this kind of machine, and that’s what we’re aiming to improve.”

ChatGPT, an AI chatbot launched this fall, allows users to ask for help with things such as writing essays, drafting business plans, generating code, and even composing music. As of Dec. 4, ChatGPT already had over 1 million users.

Open AI built its auto-generative system on a model called GPT 3, which is trained on billions of tokens. These tokens, used for natural language processing, are similar to words in a paragraph. For comparison’s sake, the novel “Harry Potter and the Order of the Phoenix” has about 250,000 words and 185,000 tokens. Essentially, ChatGPT has been trained on billions of data points, making this kind of intelligent machine possible. 

Ruoxi Jia works with Ph.D. student Feiyang Kang (at left) on data valuation techniques for their research.

CREDIT

Photo by Chelsea Seeber for Virginia Tech.

Jia noted the importance of data quality and how it can impact machine learning results. 

“If you have bad data feeding into machine learning, you will get bad results,” said Jia. “We call that 'garbage in, garbage out.' We want to get an understanding, especially a quantitative understanding, of which data is more valuable and which is less valuable for the purpose of data selection.” 

The importance of more quality-based data has been noticed by ChatGPT developers as they just announced the release of GPT-4. The latest technology is “multimodal,” meaning images as well as text prompts can spur it to generate content.

A large amount of data is required to develop this type of machine intelligence, but not all data is open sourced or public. Some data sets are owned by private entities and there is privacy involved. Jia hopes that in the future, monetary incentives can be introduced to help acquire these types of data sets and improve the machine learning algorithms that are needed in all industries. 

The University of California-Berkeley grad has had conversations with Google Research and Sony AI Research, among others, who are interested in the research benefits. Jia hopes these companies will adopt the technology developed and serve as advocates for data sharing. Sharing data and adopting improved machine learning algorithms will greatly benefit not only industries but individual consumers as well. For instance, if you’ve ever had a bad experience with a customer service chatbot, you’ve experienced low-quality data and poor machine learning algorithm design. 

Jia hopes to use her background and area expertise to improve these web-based interactions for all. As a school-aged child, Jia always enjoyed math and science, but her decision to enter the electrical and computer engineering field stemmed from her desire to help people.

“Both of my parents are doctors. It was amazing to grow up seeing them help patients with some kind of medical formula,” said Jia. “That’s why I chose to study math and science. You can have a concrete impact. I’m using a different kind of formula to help, but I like that pursuing this career has made me feel like I can make a difference in someone’s life.”

The CAREER award is the National Science Foundation’s most prestigious award for early-career faculty with the potential to serve as academic role models in research and education and to lead advances in their organization’s mission. Throughout this project, Jia has demonstrated her desire to serve as an academic role model for graduate, undergraduate, and even K-12 students.

She is a core faculty in the Sanghani Center for Artificial Intelligence and Data Analytics, formerly known as the Discovery Analytics Center. The center has more than 20 faculty members and 120 graduate students, two of whom are working directly with Jia to conduct the planned research.

Jia plans to implement an education plan that equips students with the skills to harness data to improve decision-making impacting society. This educational plan will start with new machine learning courses for undergraduate students in the first two years of the project and focus on K-12 engagement in years three through five. 

“There was a famous statistician named John Tukey,” Jia said. “He had a saying that the best thing about being a statistician is that you get to play in everyone's backyard. Machine learning is very much the same. It touches many areas of my colleagues’ work so it is easy for me to build connections and collaborate with other people. I really feel that my research is a privilege. It's a privilege to work in this area that many people care about.”

Kids judge Alexa smarter than Roomba, but say both deserve kindness

Four to 11-year-olds deem it wrong to attack either semi-intelligent robot

Peer-Reviewed Publication

DUKE UNIVERSITY

 

IMAGE: KIDS AGREE THAT IT’S WRONG TO BE ATTACK SMART TECHNOLOGIES LIKE ROOMBA OR AN ALEXA, DESPITE RANKING AMAZON’S VIRTUAL ASSISTANT AS SAVVIER THAN ITS VACUUMING COUNTERPART. view more 

CREDIT: VERONIQUE KOCH, DUKE UNIVERSITY

DURHAM, N.C. –- Most kids know it’s wrong to yell or hit someone, even if they don’t always keep their hands to themselves. But what about if that someone’s name is Alexa?

A new study from Duke developmental psychologists asked kids just that, as well as how smart and sensitive they thought the smart speaker Alexa was compared to its floor-dwelling cousin Roomba, an autonomous vacuum.

Four- to eleven-year-olds judged Alexa to have more human-like thoughts and emotions than Roomba. But despite the perceived difference in intelligence, kids felt neither the Roomba nor the Alexa deserve to be yelled at or harmed. That feeling dwindled as kids advanced towards adolescence, however. The findings appear online April 10 in the journal Developmental Psychology.

The research was inspired in part by lead author Teresa Flanagan seeing how Hollywood depicts human-robot interactions in shows like HBO’s “Westworld.”

“In Westworld and the movie Ex Machina, we see how adults might interact with robots in these very cruel and horrible ways,” said Flanagan, a visiting scholar in the department of psychology & neuroscience at Duke. “But how would kids interact with them?”

To find out, Flanagan recruited 127 children aged four to eleven who were visiting a science museum with their families. The kids watched a 20-second clip of each technology, and then were asked a few questions about each device.

Working under the guidance of Tamar Kushnir, Ph.D., her graduate advisor and a Duke Institute for Brain Sciences faculty member, Flanagan analyzed the survey data and found some mostly reassuring results.

Overall, kids decided that both the Alexa and Roomba probably aren’t ticklish and wouldn’t feel pain if they got pinched, suggesting they can’t feel physical sensations like people do. However, they gave Alexa, but not the Roomba, high marks for mental and emotional capabilities, like being able to think or getting upset after someone is mean to it.

“Even without a body, young children think the Alexa has emotions and a mind,” Flanagan said. “And it’s not that they think every technology has emotions and minds -- they don’t think the Roomba does -- so it’s something special about the Alexa’s ability to communicate verbally.”

Regardless of the different perceived abilities of the two technologies, children across all ages agreed it was wrong to hit or yell at the machines.

“Kids don’t seem to think a Roomba has much mental abilities like thinking or feeling,” Flanagan said. “But kids still think we should treat it well. We shouldn't hit or yell at it even if it can't hear us yelling.”

The older kids got however, the more they reported it would be slightly more acceptable to attack technology.

“Four- and five-year-olds seem to think you don't have the freedom to make a moral violation, like attacking someone," Flanagan said. “But as they get older, they seem to think it's not great, but you do have the freedom to do it.”

The study’s findings offer insights into the evolving relationship between children and technology and raise important questions about the ethical treatment of AI and machines in general, and as parents. Should adults, for example, model good behavior for their kids by thanking Siri or its more sophisticated counterpart ChatGPT for their help?

For now, Flanagan and Kushnir are trying to understand why children think it is wrong to assault home technology.

In their study, one 10-year-old said it was not okay to yell at the technology because, “the microphone sensors might break if you yell too loudly,” whereas another 10-year-old said it was not okay because “the robot will actually feel really sad.”

“It’s interesting with these technologies because there's another aspect: it’s a piece of property,” Flanagan said. “Do kids think you shouldn't hit these things because it's morally wrong, or because it's somebody's property and it might break?”

This research was supported by the U.S. National Science Foundation (SL-1955280, BCS-1823658).

CITATION: “The Minds of Machines: Children’s Beliefs About the Experiences, Thoughts, and Morals of Familiar Interactive Technologies,” Teresa M. Flanagan, Gavin Wong, Tamar Kushnir. Developmental Psychology, April 10, 2023. DOI: 10.1037/dev0001524.

 

Alexa, You're my Friend

 

Kids agree that it’s wrong to be attack smart technologies like Roomba or an Alexa, despite ranking Amazon’s virtual assistant as savvier than its vacuuming counterpart.

CREDIT

Veronique Koch, Duke University

JOURNAL

Developmental Psychology

DOI

10.1037/dev0001524 

METHOD OF RESEARCH

Survey

SUBJECT OF RESEARCH

People

ARTICLE TITLE

The Minds of Machines: Children’s Beliefs About the Experiences, Thoughts, and Morals of Familiar Interactive Technologies

ARTICLE PUBLICATION DATE

10-Apr-2023

Penn Medicine study reveals new insights on brain development sequence through adolescence

Brain maturation sequence renders youth sensitive to environmental impacts through adolescence

Peer-Reviewed Publication

UNIVERSITY OF PENNSYLVANIA SCHOOL OF MEDICINE

PHILADELPHIA—Brain development does not occur uniformly across the brain, but follows a newly identified developmental sequence, according to a new Penn Medicine study. Brain regions that support cognitive, social, and emotional functions appear to remain malleable—or capable of changing, adapting, and remodeling—longer than other brain regions, rendering youth sensitive to socioeconomic environments through adolescence. The findings were published recently in Nature Neuroscience.

Researchers charted how developmental processes unfold across the human brain from the ages of 8 to 23 years old through magnetic resonance imaging (MRI). The findings indicate a new approach to understanding the order in which individual brain regions show reductions in plasticity during development.

Brain plasticity refers to the capacity for neural circuits—connections and pathways in the brain for thought, emotion, and movement—to change or reorganize in response to internal biological signals or the external environment. While it is generally understood that children have higher brain plasticity than adults, this study provides new insights into where and when reductions in plasticity occur in the brain throughout childhood and adolescence.

The findings reveal that reductions in brain plasticity occur earliest in “sensory-motor” regions, such as visual and auditory regions, and occur later in “associative” regions, such as those involved in higher-order thinking (problem solving and social learning). As a result, brain regions that support executive, social, and emotional functions appear to be particularly malleable and responsive to the environment during early adolescence, as plasticity occurs later in development.

“Studying brain development in the living human brain is challenging. A lot of neuroscientists’ understanding about brain plasticity during development actually comes from studies conducted with rodents. But rodent brains do not have many of what we refer to as the association regions of the human brain, so we know less about how these important areas develop,” said corresponding author Theodore D. Satterthwaite, MD, the McLure Associate Professor of Psychiatry in the Perelman School of Medicine at the University of Pennsylvania, and director of the Penn Lifespan Informatics and Neuroimaging Center (PennLINC).

To address this challenge, the researchers focused on comparing insights from previous rodent studies to youth MRI imaging insights. Prior research examining how neural circuits behave when they are plastic uncovered that brain plasticity is linked to a unique pattern of “intrinsic” brain activity. Intrinsic activity is the neural activity occurring in a part of the brain when it is at rest, or not being engaged by external stimuli or a mental task. When a brain region is less developed and more plastic, there tends to be more intrinsic activity within the region, and that activity also tends to be more synchronized. This is because more neurons in the region are active, and they tend to be active at the same time. As a result, measurements of brain activity waves show an increase in amplitude (or height).

“Imagine that individual neurons within a region of the brain are like instruments in an orchestra. As more instruments begin to play together in synchrony, the sound level of the orchestra increases, and the amplitude of the sound wave gets higher,” said first author Valerie Sydnor, a Neuroscience PhD student. “Just like decibel meters can measure the amplitude of a sound wave, the amplitude of intrinsic brain activity can be measured with functional MRI while kids are simply resting in the scanner. This allowed our team to study a functional marker of brain plasticity safely and non-invasively in youth.”

Analyzing MRI scans from more than 1,000 individuals, the authors found that the functional marker of brain plasticity declined in earlier childhood in sensory-motor regions but did not decline until mid-adolescence in associative regions.

“These slow-developing associative regions are also those that are vital for children’s cognitive attainment, social interactions, and emotional well-being,” Satterthwaite added. “We are really starting to understand the uniqueness of human’s prolonged developmental program.”

“If a brain region remains malleable for longer, it may also remain sensitive to environmental influences for a longer window of development,” Sydnor said. “This study found evidence for just that.”

The authors studied relationships between youths’ socioeconomic environments and the same functional marker of plasticity. They found that the effects of the environment on the brain were not uniform across regions nor static across development. Rather, the effects of the environment on the brain changed as the identified developmental sequence progressed.

Critically, youths’ socioeconomic environments generally had a larger impact on brain development in the late-maturing associative brain regions, and the impact was found to be largest in adolescence.

“This work lays the foundation for understanding how the environment shapes neurodevelopmental trajectories even through the teenage years,” said Bart Larsen, PhD, a PennLINC postdoctoral researcher and co-author.

Sydnor elaborated, “The hope is that studying developmental plasticity will help us to understand when environmental enrichment programs will have a beneficial impact on each child’s neurodevelopmental trajectory. Our findings support that programs designed to alleviate disparities in youths’ socioeconomic environments remain important for brain development throughout the adolescent period.”

This study was supported by the National Institute of Health (R01MH113550, R01MH120482, R01MH112847, R01MH119219, R01MH123563, R01MH119185, R01MH120174, R01NS060910, R01EB022573, RF1MH116920., RF1MH121867, R37MH125829, R34DA050297, K08MH120564, K99MH127293, T32MH014654). The study was also supported by the National Science Foundation Graduate Research Fellowship (DGE-1845298).

Additional support was provided by the Penn-CHOP Lifespan Brain Institute and the Penn Center for Biomedical Image Computing and Analytics.

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JOURNAL

Nature Neuroscience

DOI

10.1038/s41593-023-01282-y 

ARTICLE TITLE

Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth

Scientists create model to predict depression and anxiety using artificial intelligence and social media

A study by a group at the University of São Paulo reported in a scientific journal involved the construction of a database and models. Preliminary results are described in the article.

Peer-Reviewed Publication

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO

Researchers at the University of São Paulo (USP) in Brazil are using artificial intelligence (AI) and Twitter, one of the world’s largest social media platforms, to try to create anxiety and depression prediction models that could in future provide signs of these disorders before clinical diagnosis.

The study is reported in an article published in the journal Language Resources and Evaluation. 

Construction of a database, called SetembroBR, was the first step in the study. The name is a reference to Yellow September, an annual suicide awareness and prevention campaign, and also to the fact that data collection for the study began one day in September.

The second step is still in progress but has provided some preliminary findings, such as the possibility of detecting whether a person is likely to develop depression solely on the basis of their social media friends and followers, without taking their own posts into account.

The database compiled by the group contains information relating to a corpus of texts (in Portuguese) and the network of connections involving 3,900 Twitter users who reported having been diagnosed with or treated for mental health problems before the survey. The corpus includes all public tweets posted by these users individually (without retweets), for a total of some 47 million of these short texts.

“First, we collected timelines manually, analyzing tweets by some 19,000 users, equivalent to the population of a village or small town. We then used two datasets, one for users who reported being diagnosed with a mental health problem and another selected at random for control purposes. We wanted to distinguish between people with depression and the general population,” said Ivandre Paraboni, last author of the article and a professor at USP’s School of Arts, Sciences and Humanities (EACH).

The study also collected tweets from friends and followers, in accordance with the observation that people with mental health problems tend to follow certain accounts, such as discussion forums, influencers and celebrities who publicly acknowledge their depression. “These people are attracted to each other. They have shared interests,” said Paraboni, who is a researcher with the Center for Artificial Intelligence (C4AI), an Engineering Research Center (ERC) established by FAPESP and IBM Brazil at USP.

FAPESP also supported the project study via the project “Social media language analysis for early detection of mental health disorders”, led by Paraboni. 

Mental health disturbances, including depression and anxiety, are a growing global concern. The World Health Organization (WHO) estimated on the basis of 2021 data that 3.8% of the world population, or some 280 million people, were affected by depression.

WHO also estimated an increase of 25% in global prevalence of these mental health problems during the COVID-19 pandemic. The tweets were collected for the study during this period.

In a recent survey by the Brazilian Health Ministry involving 784,000 participants, 11.3% said they had been diagnosed with depression. Most were women.

According to previous research, mental health problems are often reflected by the language used by the sufferers. This finding has led to a considerable number of studies involving natural language processing (NLP), with a focus on depression, anxiety and bipolar disorder, among others. However, most of these studies analyze texts in English and do not always match the profile of most Brazilians.

Models

The researchers pre-processed the corpus to remove hashtags, URLs, emoticons and non-standard characters while maintaining the original texts. They then deployed deep learning, an AI technique that teaches computers to process data in a way inspired by the human brain, to create four text classifiers and word embeddings (context-dependent mathematical representations of relations between words) using models based on bidirectional encoder representations from transformers (BERT), a machine learning algorithm for NLP. These models correspond to a neural network that learns contexts and meanings by monitoring sequential data relationships, such as words in a sentence.

The training input consisted of a sample of 200 tweets selected at random from each user. The parameters were defined by executing cross-validation of the training data five times and calculating the average result.

The conclusion was that BERT performed best in terms of predicting depression and anxiety, with a statistically significant difference between it and LogReg, the next best option. Because the models analyzed sequences of words and complete sentences, it was possible to observe that people with depression, for example, tended to write about subjects connected to themselves, using verbs and phrases in the first person, as well as topics such as death, crisis and psychology.

“The signs of depression that can be detected during a visit to the doctor aren’t necessarily the same as the ones that appear on social media,” Paraboni said. “For example, use of the first-person singular pronouns I and me was very evident, and in psychology this is considered a classic sign of depression. We also observed frequent use of the heart emoji by depressive users. This is widely felt to be a symbol of affection and love, but maybe psychologists haven’t yet characterized it as such.”

All the collected texts were anonymized. “We published neither actual tweets nor users’ names. We took care to ensure that the students involved in the project didn’t have access to user data so as to protect people’s identity,” he said.

The researchers are now extending the database, refining their computational techniques and upgrading the models in order to see if they can produce a tool for future use in screening prospective sufferers from mental health problems and helping families and friends of young people at risk from depression and anxiety. 

Brazil ranks third among the countries that most consume social media in the world, according to a Comscore survey published in early March, behind India and Indonesia but ahead of the United States, Mexico and Argentina. Its 131.5 million users are online for 46 hours a month on average. The most widely used platforms are YouTube, Facebook, Instagram, TikTok, Kwai and Twitter, which recently changed its rules and began charging for certain services.

About São Paulo Research Foundation (FAPESP)

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.

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DOI

10.1007/s10579-022-09633-0 

ARTICLE TITLE

SetembroBR: a social media corpus for depression and anxiety disorder prediction

Table tennis brain teaser: Playing against robots makes our brains work harder

Brain scans taken during table tennis reveal differences in how we respond to human versus machine opponents

Peer-Reviewed Publication

UNIVERSITY OF FLORIDA

 

VIDEO: A PARTICIPANT PLAYS TABLE TENNIS AGAINST GRADUATE STUDENT AMANDA STUDNICKI WHILE HAVING HIS BRAIN IMAGED VIA AN EEG CAP. THE EXPERIMENT REVEALED BIG DIFFERENCES IN HOW OUR BRAINS RESPOND TO HUMAN AND MACHINE OPPONENTS DURING SPORTS. view more 

CREDIT: FRAZIER SPRINGFIELD

Captain of her high school tennis team and a four-year veteran of varsity tennis in college, Amanda Studnicki had been training for this moment for years.

All she had to do now was think small. Like ping pong small.

For weeks, Studnicki, a graduate student at the University of Florida, served and rallied against dozens of players on a table tennis court. Her opponents sported a science-fiction visage, a cap of electrodes streaming off their heads into a backpack as they played against either Studnicki or a ball-serving machine. That cyborg look was vital to Studnicki’s goal: to understand how our brains react to the intense demands of a high-speed sport like table tennis – and what difference a machine opponent makes.

Studnicki and her advisor, Daniel Ferris, discovered that the brains of table tennis players react very differently to human or machine opponents. Faced with the inscrutability of a ball machine, players’ brains scrambled themselves in anticipation of the next serve. While with the obvious cues that a human opponent was about to serve, their neurons hummed in unison, seemingly confident of their next move.

The findings have implications for sports training, suggesting that human opponents provide a realism that can’t be replaced with machine helpers. And as robots grow more common and sophisticated, understanding our brains’ response could help make our artificial companions more naturalistic.

“Robots are getting more ubiquitous. You have companies like Boston Dynamics that are building robots that can interact with humans and other companies that are building socially assistive robots that help the elderly,” said Ferris, a professor of biomedical engineering at UF. “Humans interacting with robots is going to be different than when they interact with other humans. Our long term goal is to try to understand how the brain reacts to these differences.”

Ferris’s lab has long studied the brain’s response to visual cues and motor tasks, like walking and running. He was looking to upgrade to studying complex, fast-paced action when Studnicki, with her tennis background, joined the research group. So the lab decided tennis was the perfect sport to address these questions with. But the oversized movements – especially high overhand serves – proved an obstacle to the burgeoning tech.

“So we literally scaled things down to table tennis and asked all the same questions we had for tennis before,” Ferris said. The researchers still had to compensate for the smaller movements of table tennis. So Ferris and Studnicki doubled the 120 electrodes in a typical brain-scanning cap, each bonus electrode providing a control for the rapid head movements during a table tennis match.

With all these electrodes scanning the brain activity of players, Studnicki and Ferris were able to tune into the brain region that turns sensory information into movement. This area is known as the parieto-occipital cortex.

“It takes all your senses – visual, vestibular, auditory – and it gives information on creating your motor plan. It’s been studied a lot for simple tasks, like reaching and grasping, but all of them are stationary,” Studnicki said. “We wanted to understand how it worked for complex movements like tracking a ball in space and intercepting it, and table tennis was perfect for this.”

The researchers analyzed dozens of hours of play against both Studnicki and the ball machine. When playing against another human, players’ neurons worked in unison, like they were all speaking the same language. In contrast, when players faced a ball-serving machine, the neurons in their brains were not aligned with one another. In the neuroscience world, this lack of alignment is known as desynchronization.

“If we have 100,000 people in a football stadium and they’re all cheering together, that’s like synchronization in the brain, which is a sign the brain is relaxed" Ferris said. “If we have those same 100,000 people but they’re all talking to their friends, they’re busy but they’re not in sync. In a lot of cases, that desynchronization is an indication that the brain is doing a lot of calculations as opposed to sitting and idling.”

The team suspects that the players’ brains were so active while waiting for robotic serves because the machine provides no cues of what they are going to do next. What’s clear is that our brains process these two experiences very differently, which suggests that training with a machine might not offer the same experience as playing against a real opponent.

“I still see a lot of value in practicing with a machine,” Studnicki said. “But I think machines are going to evolve in the next 10 or 20 years, and we could see more naturalistic behaviors for players to practice against.”

More than 100 electrodes capture fine detail of the brain activity of participants while they play a fast-paced game of table tennis.

To keep participants mobile, the EEG recording took place in a backpack.

A research participant prepared to play table tennis while their brain is imaged with an EEG cap.

A research participant plays against a ball-serving machine while his brain is imaged with an EEG cap. The research revealed that our brains respond differently when playing against human or machine opponents in sports.

CREDIT

Frazier Springfield

Playing against a machine oppo [VIDEO] | 

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JOURNAL

eNeuro

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Parieto-Occipital Electrocortical Dynamics during Real-World Table Tennis

ARTICLE PUBLICATION DATE

10-Apr-2023

Brain-inspired intelligent robotics: Theoretical analysis and systematic application

Peer-Reviewed Publication

BEIJING ZHONGKE JOURNAL PUBLISING CO. LTD.

 

IMAGE: THE RESEARCHERS CONSTRUCTED A HARDWARE PLATFORM WITH THE SAME MUSCLE DISTRIBUTION AND STRUCTURE. view more 

CREDIT: BEIJING ZHONGKE JOURNAL PUBLISING CO. LTD.

Robots have become a crucial indicator for measuring the competitive strength of a country in science and technology. Robotic systems have made advancements in fields such as mechanical engineering, control and artificial intelligence technologies. However, the performance of current robotic systems still exists limitations and cannot satisfy the demands of an increasing number of applications. In order to deal with these problems, a brain-inspired intelligent robotic system is constructed.

A team of scientists led by Professor Qiao Hong from the State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, have conducted a review on the cutting-edge works along the research chain of brain-inspired robots. Firstly, they introduce the core neural mechanisms in vision, decision-making, control, and body structure and the corresponding brain-inspired algorithm. Secondly, they present the software and hardware system integration. The simulation platform for brain-inspired robots integrates brain-inspired algorithms in vision, decision making, and movement control, providing efficient tools for researchers from different fields. The hardware platform was designed to mimic the human musculoskeletal system, providing a physical system to validate the performance of the brain-inspired algorithm.

“Brain-inspired motion-learning algorithms can use sparse rewards to realize generalized control policy learning. With this method, robotics can accomplish a series of manipulations after simple training.” “System robustness comes from redundancy and anti-interference can improve system reliability.” “The special muscle actuator provides nonlinear dynamics and coupled feedback modulation, which can reduce the effects of disturbances from the control input and environment.” They describe the advantages of the brain-inspired intelligent robotics.

Furthermore, they make assumptions about the future development of next-generation robotics. “Next-generation robotics could be developed with numerous brain-inspired algorithms and novel musculoskeletal structures.” “Organic structural design and hardware construction should be reinforced and emphasized.” “We hope that this generation of robotics can provide inspiration and reference for brain-computer interface control.” More time and efforts are supposed to be devoted to the development of the brain-inspired intelligent robotics.

See the article:

Brained-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

http://doi.org/10.1007/s11633-022-1390-8

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JOURNAL

Machine Intelligence Research

DOI

10.1007/s11633-022-1390-8 

ARTICLE TITLE

Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

Scientists advocate for integration of biogeography and behavioral ecology to rapidly respond to biodiversity loss

Peer-Reviewed Publication

UNIVERSITY OF OKLAHOMA

 

IMAGE: UNIVERSITY OF OKLAHOMA RESEARCHERS (PICTURED FROM LEFT) ASHLEE ROWE, HAYLEY LANIER, KATHARINE MARSKE, LAURA STEIN AND CAMERON SILER AUTHORED A PERSPECTIVE ARTICLE ADVOCATING FOR CONVERGENT RESEARCH THAT INTEGRATES THE FIELDS OF BIOGEOGRAPHY AND BEHAVIORAL ECOLOGY TO MORE RAPIDLY RESPOND TO CHALLENGES ASSOCIATED WITH CLIMATE CHANGE AND BIODIVERSITY LOSS. view more 

CREDIT: IMAGE PROVIDED BY THE UNIVERSITY OF OKLAHOMA

An interdisciplinary team of researchers at the University of Oklahoma has published a perspective article in the journal Proceedings of the National Academy of Sciences advocating for convergent research that integrates the fields of biogeography and behavioral ecology to more rapidly respond to challenges associated with climate change and biodiversity loss.

While news about climate change fills headlines, the crisis of biodiversity loss has gotten less attention. In their article, the authors contend that “identifying solutions that prevent large-scale extinction requires addressing critical questions about biodiversity dynamics that – despite widespread interest – have been challenging to answer thus far.”

From microorganisms that support soil health, fish that we eat, forests that clean water, to pollination, lumber and medicine, protecting ecosystems and the variety of plants and animals within them is vital to the health of the planet and for humanity to thrive.

“The ways that we respond to climate change also have a big impact on outcomes for biodiversity – which is also a critical part of how the global climate system works,” said article co-author Katharine Marske, Ph.D., assistant professor in the Department of Biology, Dodge Family College of Arts and Sciences.

“Climate change is a major threat to biodiversity, but it’s not the only threat. We also have habitat loss and degradation, direct overharvest of some species and so forth, so it’s also its own unique crisis that needs to be considered on equal footing.”

“Historically in Oklahoma, we can point to cases where we have rapidly removed or changed natural habitats, such as the Dust Bowl,” said co-author Hayler Lanier, Ph.D., assistant curator of mammalogy at the Sam Noble Museum and an assistant professor of biology. “That was a case where we came through and stripped out a lot of the existing natural systems that do things to hold onto the soil and create nutrients, and that was sort of one small example. As we move into the future, we need to think about what sort of world we want to live in, and it is definitely one where we have these sorts of ecosystem services.”

By integrating the fields of biogeography, or the study of how and why biological diversity varies across the Earth, with behavioral ecology, or the study of the evolution of behavior in relation to ecological pressures, the authors argue that scientists will be better able to develop a more comprehensive understanding of how to leverage “existing biodiversity knowledge into predictive frameworks for how biodiversity will respond to environmental change, and where habitat conservation can be most effective.”

“This interdisciplinary connection between behavioral ecologists and scientists who study biogeography has not been linked well to date,” said Laura Stein, Ph.D., article co-author and an assistant professor of biology. “I think in many cases, biogeographers are not thinking about day-to-day activities of animals as much as behavioral ecologists are, and behavioral ecologists are not necessarily considering differences and overlaps in both current and historical ranges and how behaviors have been shaped by past geographic events that might help predict where they will be in the future. And so, by combining these two fields, we can get a much broader picture of what we can do now and what is important for protecting biodiversity into the future.”

The article’s authors have led a pilot of such integrative efforts at the University of Oklahoma, supported by funding from the National Science Foundation.

Co-author Cameron Siler, Ph.D., associate professor of biology and associate curator of herpetology at the Sam Noble Museum, said “We, in the Department of Biology, together with the Sam Noble Museum, carried out a series of cluster hires over the last five years aimed strategically at bringing together integrative researchers with the capacity to think beyond these typically isolated fields, and what’s exciting is this work is a culmination of the success of that early effort to bring scientists like this together at OU.”

Lanier described their work as hopeful. Biodiversity loss and climate change are large, complex and challenging problems to solve. “What we’re trying to do is to harness a lot of information that we already have as scientific and conservation communities and bring it together in new ways to very quickly answer some of these questions.”

Agreeing, Marske added, “The scope of the challenges that society faces require integration, so providing opportunities for this across biology, and amongst all disciplines, increases your chances to bring people together and talk about novel solutions. The more people you can have at that table, the better.”

 

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About the Project

“Integrating biogeography and behavioral ecology to rapidly address biodiversity loss,” published April 5, 2023, in the journal Proceedings of the National Academy of Sciences, DOI 10.1073/pnas.2110866120. Marske is the first author, with co-authors Lanier, Siler, Ashlee H. Rowe, and Stein.

About the University of Oklahoma Office of the Vice President for Research and Partnerships

The University of Oklahoma is a leading research university classified by the Carnegie Foundation in the highest tier of research universities in the nation. Faculty, staff, and students at OU are tackling global challenges and accelerating the delivery of practical solutions that impact society in direct and tangible ways through research and creative activities. OU researchers expand foundational knowledge while moving beyond traditional academic boundaries, collaborating across disciplines and globally with other research institutions as well as decision makers and practitioners from industry, government and civil society to create and apply solutions for a better world. Find out more at ou.edu/research.

About the Sam Noble Museum

Designated as the official Oklahoma Museum of Natural History in 1987 by the Oklahoma Legislature, the Sam Noble Museum today houses more than 10 million objects divided between 12 collections, and maintains laboratories, offices, libraries and exhibit space within a 198,000-square-foot facility. The Sam Noble Museum is located on the University of Oklahoma Norman campus at 2401 Chautauqua Ave.

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JOURNAL

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2110866120 

ARTICLE TITLE

Integrating biogeography and behavioral ecology to rapidly address biodiversity loss