It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Saturday, June 01, 2024
Four advances that curd change your next cheese tasting
AMERICAN CHEMICAL SOCIETY
To become cultured throughout National Dairy Month in June, or specifically on National Cheese Day (June 4), food lovers can embrace their passion and pick up something gouda to eat. These udderly tasty products come from cows, buffalo, goats, sheep — and even plants. Despite thousands of years spent maturing this culinary staple, researchers continue to moove forward with cheese advancements. Some can be found in these four papers published in ACS journals. Reporters can request free access to these papers by emailing newsroom@acs.org.
Adding whey protein into a semihard cheese. Traditional semihard cheeses don’t incorporate whey protein, a potentially functional ingredient for cheesemaking. So, a pilot study published in the Journal of Agricultural and Food Chemistry tested adding whey protein to a semihard Edam-type cheese by mixing high heat-treated milk (208 degrees F), which contained denatured whey proteins, into the pasteurized milk (treated at 162 F) used for cheesemaking. The team found that the new cheeses ripened slower and were firmer, though they also tasted slightly more bitter and had a sandier consistency than cheeses made at the same time without the high-heat milk.
A probiotic cottage cheese. Scientists encapsulated probiotic microbes in edible microcapsules and added them to cottage cheese, creating a more healthful product. The cottage cheese with microbes encapsulated in a 1% sodium alginate and 1% carrageenan gum coating produced a pleasant-tasting semisoft dairy product, which panelists preferred to versions containing higher proportions of sodium alginate.The researchers report additional nutritional analyses and sensory test results for the new functional cheese in the open access journal ACS Omega.
Mimicking cheesy scents with plants. To produce a natural cheese-like aroma, researchers evaluated how two fungi-fermented soy or sunflower proteins with coconut oil. The odor compounds produced by both fungi were similar to multiple animal milk-derived cheese samples. Therefore, fungal fermentation could be a sustainable way to produce natural cheese-like aromas for plant-based cheese alternatives, the researchers say in the Journal of Agricultural and Food Chemistry.
Leftover goat milk fats from buttermaking. Milk fats in the whey left over from making goat cheese or butter could be a functional ingredient in infant formula. Of three methods to collect these fats, a study in the Journal of Agricultural and Food Chemistrysuggests that adding rennet to the liquid left over from making goat butter creates a product with the best composition, including the greatest abundance of phospholipids, gangliosides and omega-3 fatty acids such as DHA. Using this material to enrich other foods could have positive effects on a consumer’s health, the authors conclude.
The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, eBooks and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.
To automatically receive news releases from the American Chemical Society, contact newsroom@acs.org.
Note: ACS does not conduct research, but publishes and publicizes peer-reviewed scientific studies.
New Jersey’s cash bail reform reduced incarceration without increasing gun violence, Drexel study says
DREXEL UNIVERSITY
New Jersey’s 2017 cash bail reform law — which eliminated financial barriers to avoiding pretrial detention — successfully reduced the state’s jail population without increasing gun violence, according to a study published this month in JAMA Network Open from researchers at Drexel University’s Dornsife School of Public Health and Boston University.
“We know that removing financial barriers to pretrial release can reduce mass incarceration and related health inequities without sacrificing community safety,” said Jaquelyn Jahn, PhD, an assistant professor in the Dornsife School of Public Health. “This paper offers another metric to discredit the argument against meaningful cash bail reform by showing that there were no increases to gun violence in the three years after New Jersey’s bail reform policy was implemented.”
The authors looked at data on rates of gun deaths from the Centers for Disease Control and Prevention’s National Center for Health Statistics and on fatal and nonfatal shooting numbers from the nonprofit Gun Violence Archive from 2014 to 2019. The research team used 36 states that did not pass bail reform as a control group, and controlled for other factors that may influence violence, such as gun law restrictions, rates of gun ownership, and state senate majority partisanship.
Before New Jersey’s 2017 cash bail law, about 38% of the state’s pretrial population were in jail — while legally innocent and awaiting a trial — solely because they could not afford bail. The law was successful at substantially decreasing the pretrial population in the years since it was passed: 8,899 people were detained before their court date in 2015, but that number dropped to 4,976 people in 2019.
Unlike previous studies that looked at rearrest and reincarceration rates, the current study measured how violence and health outcomes changed at the community level since bail reform was enacted. A 2023 study in American Economic Journal: Applied Economicsfound no evidence that cash bail has an effect on a defendants likelihood of re-arrest or show up for their trial.
“Our research shows that reducing pretrial detention has no measurable impact on firearm violence, suggesting we can significantly reduce the criminal legal system's footprint without harming community safety,” said co-principal investigator Jessica T. Simes, PhD, an associate professor in Boston University’s School of Arts and Sciences.
The U.S. Constitution’s Eighth Amendment states that “excessive bail shall not be required, nor excessive fines imposed, nor cruel and unusual punishments inflicted.” Despite this, a 2022 report from the U.S. Commission on Civil Rights noted that from 1970 to 2015, there was a 433% increase in the number of people who had been detained pretrial, and more than six out of 10 defendants were detained before their trial due to an inability to afford bail.
Cash bail policies also fuel racial and socioeconomic disparities in pretrial detention rates and higher bail costs for men and for Black and LatinX defendants. The same 2022 report also noted study findings that Black defendants had bail amounts that were set at 35% higher than white men and Latino men faced bail amounts that were 19% higher than those of white men.
Proponents of ending cash bail say the policy may help close the gap in racial disparities in incarceration. Of the 1.2 million people incarcerated in the United States, 32% are Black, while Black Americans make up 12.1% of the overall U.S. population, according to the Bureau of Justice Statistics.
“Reducing pretrial detention helps keep families and communities intact, and potentially avoids many of the inequitable health consequences of jail for incarcerated individuals and their loved ones,” said Jahn. “And, programs that reduce gun violence by investing in communities help address racist histories of disinvestment. These and other measures must be prioritized to stem pervasive gun violence in communities across the United States.”
The authors noted that New Jersey has a uniquely comprehensive gun law environment, in addition to cash bail reform, but said the findings can help inform policy debates about bail reform nationwide. However, they also noted that New Jersey’s policy is not without important critique, especially related to racial inequities in outcomes of the state’s pretrial risk assessment tool, which considers factors like other charges or past convictions.
“The public conversation often assumes a link between gun violence levels and what’s happening in the criminal legal system, whether it’s policing, prosecution, or incarceration. This study is important because it directly contradicts that assumption,” said senior author Jonathan Jay, DrPH, JD, an assistant professor at Boston University School of Public Health.
Research for this paper was funded by the Robert Wood Johnson Foundation Evidence4Action program and the National Institutes of Health, National Institute on Minority Health and Health Disparities (grant K01MD016956).
Humans and robots are increasingly interacting within built environments such as cities, buildings, walkways, and parks. Offering adaptability, cost-effectiveness, and scalability, robots are gradually being integrated into various aspects of everyday life, from manufacturing to healthcare to hospitality.
“Ensuring that robots can navigate and operate effectively within built environments is crucial for their widespread adoption and acceptance,” said Associate Professor Mohan Rajesh Elara from the Singapore University of Technology and Design (SUTD).
To have fully autonomous service robots operate in human environments, however, is still a distant goal. Spatial limitations in the built environment restrict a robot’s performance capability. In designing robot-inclusive environments, robot interaction within a built environment must be examined. The current methods used for this involve real-life testing and physical experiments that are costly, time-consuming, and labour-intensive.
To address these limitations, Assoc Prof Mohan and his SUTD team explored an innovative approach in their paper ‘Enhancing robot inclusivity in the built environment: A digital twin-assisted assessment of design guideline compliance’. Here, they demonstrate a novel methodology utilising digital twins to establish the usefulness of built environment design guidelines for robots. They also model some robot archetypes and environments as digital twins to examine robot behaviour within the environments.
A digital twin is a virtual replica of a physical object in a virtual version of its environment. “The digital twin approach offers several key advantages, including the ability to simulate real-world scenarios, enable virtual testing of robot interactions, and provide insights into compliance with design guidelines before physical implementation,” said Assoc Prof Mohan. Moreover, using digital twins allows real-time monitoring, hazard identification, and training a robot’s algorithm before deployment.
In the study, Assoc Prof Mohan uses digital twins to analyse the robot-friendliness of the built environment and prepare for robot deployment. The methodology used is divided into three phases: documentation, digitisation, and design analysis.
First, on-site documentation of the environment is necessary for the simulation. It can be done via direct data collection, laser scanning, or photogrammetry techniques. Ideally performed during the building’s design process, direct data collection uses Building Information Modelling (BIM)—a process of generating and managing digital representations of the building. When a building has already been constructed, laser scanning or photogrammetry techniques can be used to generate point cloud data for processing.
Second, digitisation focuses on making the built environment’s digital model suitable for the robot simulation software. In this step, point cloud data will be reconstructed into a digital space and used to generate three-dimensional (3D) models of the built environment.
Finally, the digital model is designed and analysed. Using the digitised model of the environment in the robot simulation software, the behaviours and interactions of various robots are tested within the environment. Virtual scenarios are made based on existing design guidelines of built environments, and the robots are assessed on their navigation, path planning, and interaction with the surrounding.
In one case study, Assoc Prof Mohan used digital twins to test four different cleaning robots in six different environments that adhered to Accessibility Design Guidelines. Of the four robots, one completed the most goals and performed the best in the simulated environments. It is important to note that robot inclusiveness does not always translate to robot performance efficiency. However, an inclusive environment does promote better accessibility for robots, allowing them to complete their tasks properly.
With robots increasingly being used in urban applications such as cleaning, logistics, and building maintenance, this study’s findings will help improve design guidelines for built environments to accommodate robots. Better design guidelines will allow the seamless integration of robots into human-centric spaces and their enhanced efficiency in various applications.
“The findings could shape future space design by emphasising flexibility, adaptability, and accessibility to accommodate robot interactions,” Assoc Prof Mohan adds.
In the future, the research team aims to extend the current methods and autonomously generate the infrastructure modifications required to improve the accessibility of mobile robots through the use of design, AI and technology. Assoc Prof Mohan also hopes to develop a set of design guidelines and recommendations for building robot-friendly infrastructure.
Enhancing robot inclusivity in the built environment: A digital twin-assisted assessment of design guideline compliance
Navigating new horizons: Pioneering AI
framework enhances robot efficiency
and planning
BEIJING INSTITUTE OF TECHNOLOGY PRESS CO., LTD
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SCIENTISTS AT THE INSTITUTE OF FUTURE TECHNOLOGY AT SHANGHAI UNIVERSITY HAVE USED LARGE LANGUAGE MODELS (LLMS) TO IMPROVE THE EFFICIENCY AND EFFECTIVENESS OF ROBOTS PERFORMING COMPLEX, INSTRUCTION-BASED TASKS.
CREDIT: YUAN ZHANG, SCHOOL OF FUTURE TECHNOLOGY, SHANGHAI UNIVERSITY
In a groundbreaking study published in Cyborg Bionic Systems, researchers from Shanghai University have unveiled a new artificial intelligence framework that revolutionizes the way robots interpret and execute tasks. The "Correction and Planning with Memory Integration" (CPMI) framework leverages large language models (LLMs) to improve the efficiency and effectiveness of robots performing complex, instruction-based tasks.
Traditionally, robots required explicit programming and extensive data to navigate and interact with their environment, often struggling with unexpected challenges or changes in their tasks. However, the team, led by Yuan Zhang and Chao Wang, has introduced a dynamic new approach that integrates memory and planning capabilities within LLMs, enabling robots to adapt and learn from their experiences in real-time.
A Leap Forward in Robotic Task Management
The CPMI framework marks a significant departure from conventional methods by using LLMs not just as tools for processing language but as central decision-making elements in robotic tasks. This innovative use of AI allows robots to break down complex instructions into actionable steps, plan their actions more effectively, and correct their course in response to obstacles or errors.
One of the most striking features of the CPMI framework is its memory module, which gives robots the ability to remember and learn from previous tasks. This capability mimics human memory and experience, enabling robots to perform more efficiently over time and adapt to new situations with unprecedented speed.
Demonstrating Superior Performance
The research team tested their framework using the ALFRED simulation environment, where it outperformed existing models in "few-shot" scenarios—situations where robots have limited examples to learn from. The CPMI framework not only achieved higher success rates but also demonstrated significant improvements in task efficiency and adaptability.
"By integrating memory and planning within a single AI-driven framework, we have enabled robots to learn from each interaction and improve their decision-making processes continuously," explained Chao Wang, the corresponding author of the study. "This not only enhances their performance but also reduces the need for extensive pre-programming and data collection."
Future Applications and Developments
The potential applications for the CPMI framework are vast, ranging from domestic robots that can better assist in household tasks to industrial robots that can navigate complex manufacturing processes. As LLMs continue to evolve, the capabilities of CPMI-equipped robots are expected to grow, leading to more autonomous and intelligent machines.
The Shanghai University team is optimistic about the future of robotic technology and plans to continue refining their framework. "Our next steps involve enhancing the memory capabilities of the CPMI framework and testing it in more diverse and challenging environments," said Yuan Zhang. "We believe that this technology has the potential to transform not just robotics but any field that relies on complex, real-time decision-making."
This research not only sets a new standard for AI in robotics but also opens up new pathways for the integration of advanced AI technologies in everyday life. With the continued development of frameworks like CPMI, the dream of having intelligent, adaptable robots that can perform a wide range of tasks effectively and independently is becoming a tangible reality.
The paper, "Leave It to Large Language Models! Correction and Planning with Memory Integration," was published in the journal Cyborg and Bionic Systems on Mar 27,2024, at DOI: https://spj.science.org/doi/10.34133/cbsystems.0087
Leave It to Large Language Models! Correction and Planning with Memory Integration
R.U.R.
R.U.R. is a 1920 Science fiction play by the Czech writer Karel Čapek. "R.U.R." stands for Rossumovi Univerzální Roboti. The play had its world premiere on 2 January 1921 in Hradec Králové; it introduced the word "robot" to the English language and to science fiction as a whole. R.U.R. became influe...Wikipedia
Mar 22, 2019 ... There will be no poverty. All work will be done by living machines. Everybody will be free from worry and liberated from the degradation of ...
R.U.R.: Directed by Alex Proyas. With Mallory Jansen, Lindsay Farris. A young woman visits the island factory of Rossum's Universal Robots to emancipate the ...
A.I.
Tennessee institutions partner to develop dependable AI for national security applications
DOE/OAK RIDGE NATIONAL LABORATORY
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AT THE TENNESSEE VALLEY CORRIDOR SUMMIT 2024 IN NASHVILLE, TENN., ON WEDNESDAY, VANDERBILT UNIVERSITY AND OAK RIDGE NATIONAL LABORATORY ANNOUNCED A PARTNERSHIP TO DEVELOP TRAINING, TESTING AND EVALUATION METHODS THAT WILL ACCELERATE THE DEPARTMENT OF DEFENSE’S ADOPTION OF AI-BASED SYSTEMS IN OPERATIONAL ENVIRONMENTS.
Artificial intelligence is rapidly becoming one of the most important assets in global competition, including AI-assisted autonomy and decision-making in battlefield applications. However, today’s AI models are vulnerable to novel cyberattacks and could be exploited by adversaries. Moreover, the models are not sufficiently robust and dependable to orchestrate and execute inherently human-centric, mission-critical decisions.
“AI and autonomous vehicles have great potential to let our military operate in contested environments without having to needlessly put our brave men and women in harm’s way — as long as we can trust the AI,” said U.S. Rep. Chuck Fleischmann. “ORNL and Vanderbilt University have the infrastructure and expertise to develop solutions that will give national security leaders the confidence that these AI systems are secure, reliable and dependable.”
Under a new partnership announced during the Tennessee Valley Corridor 2024 National Summit in Nashville this week, Vanderbilt and ORNL will build on complementary research and development capabilities and create science-based AI assurance methods to:
Ensure AI-enabled systems deployed for national security missions are able to function in the most challenging and contested environments.
Test and evaluate the resilience and performance of AI tools at large scales in mission-relevant environments.
Provide decision-makers with the confidence to rapidly adopt and deploy AI-enabled technologies to maintain U.S. competitive advantage.
Vanderbilt’s basic and applied research in the science and engineering of learning-enabled cyber-physical systems, particularly through the renowned Vanderbilt Institute for Software Integrated Systems, provides a foundation for AI assurance research.
“We are excited to partner with Oak Ridge National Laboratory to ensure AI-enabled programs are safe, accurate and reliable at a time when it has never been more imperative to do so,” said Vanderbilt Chancellor Daniel Diermeier. “This radical collaboration among our best researchers and one of the nation’s premier national laboratories will address these crucial challenges head-on. We look forward to the great work we will do together.”
Building on expertise in high-performance computing, data sciences and national security sciences, ORNL recently established the Center for Artificial Intelligence Security Research, or CAISER, to address emerging AI threats. CAISER leads AI security research and AI evaluation at scale, capable of training and testing the largest AI models.
“With ORNL’s unique expertise and capabilities in computing and AI security, we can train, test, analyze and harden AI models using massive datasets,” said ORNL Director Stephen Streiffer. “Working in close cooperation with Vanderbilt, I look forward to advancing the Defense Department’s deployment of AI-based systems for national defense.”
The partnership will initially focus on enabling the U.S. Air Force to fully utilize autonomous vehicles, such as the AI-enabled X-62A VISTA that recently took Air Force Secretary Frank Kendall for a flight featuring simulated threats and combat maneuvers without human intervention. Together, Vanderbilt and ORNL will provide evidence-based assurance that enables Air Force systems to meet DoD’s requirements for Continuous Authorization to Operate in vital national security roles.
“The growth in AI applications is breathtaking, most notably in the commercial marketplace but increasingly in the national defense space as well. While all users of AI are concerned about security and trust of these systems, none is more concerned than the DoD, which is actively developing processes to ensure their appropriate use,” stated Mark Linderman, chief scientist at Air Force Research Laboratory’s Information Directorate. “This partnership will advance the science to enable the U.S. Air Force to confidently field autonomous vehicles, such as the AI-enabled X-62A VISTA, improve situation awareness, and accelerate human decision making.”
Autonomous vehicles operating in a truly independent fashion could be a game-changer for the U.S. military.
“Stewarding our national security and military is one of my greatest responsibilities as a Senator,” said U.S. Sen. Marsha Blackburn. “Tennessee is leading the way in developing the advanced technologies that will ensure our nation’s global leadership and protect the lives of our brave service members.”
The collaborative new research program at Vanderbilt and ORNL continues Tennessee’s tradition of helping the U.S. maintain global leadership.
“Tennessee is once again leading the way to keep Americans safe. This exciting partnership will leverage two world-class institutions and employ their renowned expertise and resources to make our military stronger and more effective,” said U.S. Sen. Bill Hagerty. “Technological dominance is a key pillar of national security, and this partnership will ensure that the Department of Defense can utilize this developing technology in a secure, robust, continuous and dependable fashion.”
About Vanderbilt University
Founded in 1873 as an institution that would “contribute to strengthening the ties that should exist between all sections of our common country,” Vanderbilt University is globally renowned for its transformative education and pathbreaking research. The university’s 10 schools reside on a parklike campus set in the heart of Nashville, Tennessee, contributing to a collaborative culture that empowers leaders of tomorrow and prizes free expression, open inquiry and civil discourse.
Top-ranked in both academics and financial aid, Vanderbilt offers an immersive residential undergraduate experience, with programs in the liberal arts and sciences, engineering, music, education and human development. The university also is home to nationally and internationally recognized graduate schools of law, education, business, medicine, nursing and divinity, and offers robust graduate-degree programs across a range of academic disciplines. Vanderbilt’s prominent alumni base includes Nobel Prize winners, members of Congress, governors, ambassadors, judges, admirals, CEOs, university presidents, physicians, attorneys, and professional sports figures.
Vanderbilt and the affiliated nonprofit Vanderbilt University Medical Center frequently engage in interdisciplinary collaborations to drive positive change across society at large. The two entities recently reached a combined total of more than $1 billion in external research funding in a single year. This landmark achievement reflects the university’s deep commitment to expanding the global impact of its innovation and research as it increases opportunities for faculty, students and staff to pursue bold new ideas and discoveries.
Oak Ridge National Laboratory is managed by UT-Battelle for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Tennessee institutions partner to develop dependable AI for national security applications
Moe Khaleel, center, associate laboratory director for national security sciences at ORNL, joined Padma Raghavan, left, vice provost for research and innovation at Vanderbilt University, and Ryan Luley, right, with the Information Warfare Division of the U.S. Air Force Research Laboratory, on Wednesday for a panel discussion titled, "Dependable AI for National Security" during the Tennessee Valley Corridor National Summit 2024 in Nashville, Tenn.
CREDIT
ORNL, U.S. Dept. of Energy
AI-controlled stations can charge electric cars at a personal price
CHALMERS UNIVERSITY OF TECHNOLOGY
As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study from Chalmers University of Technology in Sweden shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalised prices, and thus minimise both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.
Today's commercial charging infrastructure can be a jungle. The market is dynamic and complex with a variety of subscriptions and free competition between providers. At some fast charging stations, congestion and long queues may even occur. In a new study, researchers at Chalmers have created a mathematical model to investigate how fast charging stations controlled by artificial intelligence, AI, can help by offering electric car drivers personalised prices, which the drivers can choose to accept or refuse. The AI uses algorithms that can adjust prices based on individual factors, such as battery level and the car's geographic location.
“The electric car drivers can choose to share information with the charging station providers and receive a personal price proposal from a smart charging station. In our study, we could show how rational and self-serving drivers react by only accepting offers that are beneficial to themselves. This leads to both price and waiting times being minimized”, says Balázs Kulcsár, professor at the department of electrical engineering at Chalmers.
In the study, the drivers always had the option to refuse the personal price, and choose a conventional charging station with a fixed price instead. The personal prices received by the drivers could differ significantly from each other, but were almost always lower than the market prices. For the providers of charging stations, the iterative AI algorithm can find out which individual prices are accepted by the buyer, and under which conditions. However, during the course of the study, the researchers noted that on some occasions the algorithm raised the price significantly when the electric car's batteries were almost completely empty, and the driver consequently had no choice but to accept the offer.
“Smart charging stations can solve complex pricing in a competitive market, but our study shows that they need to be developed and introduced with privacy protection for consumers, well in line with responsible-ethical AI paradigms”, says Balázs Kulcsár.
More about the study
The researchers created a mathematical model of the interaction between profit-maximising fast charging stations and electric car users. The "charging stations" could offer public market prices or AI-driven profit-maximising personal prices, which the "electric car users" could then accept or reject based on their own conditions and needs. In most cases, the results were promising, as the AI-generated prices were lower than the market prices.
The researchers involved in the study are Balázs Kulcsár, Sangjun Bae and Sebastian Gros, and they are active at Chalmers University of Technology, Sweden; Seyong Cyber University, China, and Norwegian University of Science and Technology.
The research has been financed by the Swedish Electromobility Center and partially by the EU project E-Laas.
For more information, please contact
Balázs Kulcsár, Professor, Department of Electrical Engineering, Chalmers University of Technology, +46 31-772 17 85, kulcsar@chalmers.se
The contact person speaks English and is available for live and pre-recorded interviews. At Chalmers, we have podcast studios and broadcast filming equipment on site and would be able to assist a request for a television, radio or podcast interview.
JOURNAL
Transportation Research Part C Emerging Technologies
Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach
Detecting machine-written content in scientific articles
UNIVERSITY OF CHICAGO MEDICAL CENTER
The recent surge in popularity of AI tools such as ChatGPT is forcing the science community to reckon with its place in scientific literature. Prestigious journals such as Science and Nature have attempted to restrict or prohibit AI use in submissions, but are finding it difficult to enforce because of how challenging it is becoming to detect machine-generated language.
Because AI is getting more advanced at mimicking human language, researchers at the University of Chicago were interested in learning how frequently authors are using AI and how well it can produce convincing scientific articles. In a study published in the Journal of Clinical Oncology Clinical Cancer Informatics, Saturday, June 1, Frederick Howard, MD, and colleagues evaluated text from over 15,000 abstracts from the American Society for Clinical Oncology (ASCO) Annual Meeting from 2021 to 2023 using several commercial AI content detectors. They found that there were approximately twice as many abstracts characterized as containing AI content in 2023 as compared to 2021 and 2022 – indicating a clear signal that researchers are utilizing AI tools in scientific writing. Interestingly, the content detectors were much better at distinguishing text generated by older versions of AI chatbots from human-written text, but were less accurate in identifying text from the newer, more accurate AI models or mixtures of human-written and AI-generated text.
As the use of AI in scientific writing will likely increase with the development of more effective AI language models in the coming years, Howard and colleagues warn that it is important that safeguards are instituted to ensure only factually accurate information is included in scientific work given the propensity of AI models to write plausible but incorrect statements. They also concluded that although AI content detectors will never reach perfect accuracy, they could be used as a screening tool to indicate that the presented content requires additional scrutiny from reviewers, but should not be used as the sole means to assess AI content on scientific writing.
Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts from 2021 to 2023
ARTICLE PUBLICATION DATE
1-Jun-2024
Innovating learning with ChatGPT-based Prompt Tutor
SINGAPORE MANAGEMENT UNIVERSITY
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SMU ASSOCIATE PROFESSOR OUH ENG LIEH IS DEVELOPING SINGAPORE’S FIRST CHATGPT-BASED PROMPT TUTOR TO PROVIDE REAL-TIME FEEDBACK TO ENHANCE STUDENT LEARNING.
SMU Office of Research – “Giving students immediate and frequent feedback makes online learning more effective,” Associate Professor Ouh Eng Lieh told the Office of Research.
However, based on how most online lessons are designed, questions could not be answered nor doubts clarified until students meet their instructor in the following face-to-face class.
The time delay of a few days to a few weeks can impede student learning as it might make it difficult for students to catch up and understand the subsequent topics in the course.
Learning also does not occur until the knowledge gained is stored in long‐term memory.
The Cognitive Load Theory (CLT) states that for short-term knowledge to be committed to long-term memory, there must be effective cognitive load management, repeated reinforcements and clarification of doubts.
“That’s why providing timely feedback is vital for maintaining a seamless and effective student learning experience. It also helps the student to manage the cognitive load and learn,” Professor Ouh explained to the Office of Research.
He added: “It has been found that reflection prompts, which are essentially reminders to get students to reflect and journal their learnings, can significantly improve learning by getting students to retain and apply what they have learned. Additionally, by leveraging ChatGPT, we can evaluate the degree of understanding by comparing what the student has written to the context or the topic of the lesson.”
“With these in mind, we wanted to develop a learning tool that can provide personalised prompts so that students could either have their questions answered or doubts clarified as they go through their online lesson. This tool, which we have fondly named ‘Prompt Tutor’, will also be programmed to assign additional exercises or generate a quiz for students to hone their learning,” Professor Ouh elaborated.
“And to the best of our knowledge, we believe that this will be the very first Prompt Tutor to be developed in Singapore for teaching computing courses in tertiary institutions,” Professor Ouh went on.
The research
Professor Ouh’s project is funded by an MOE Tertiary Education Research Fund (TRF) grant. An expected three years is needed to develop a fully functioning Prompt Tutor and conduct the experimental research.
He is collaborating with Associate Professor Tan Kar Way and Assistant Professor Lo Siaw Ling, both of whom are from Singapore Management University (SMU), as well as Dr. Lin Feng from Singapore University of Social Sciences (SUSS).
The research builds on Professor Ouh’s previous work where he has successfully developed a Doubt Identification Machine Learning Model to single out doubts from written reflections.
The integration of Prompt Tutor to SMU’s ITSS (Interactive Tutorial Software System) makes it possible for the research team to engineer real time feedback by providing the context to the machine learning algorithms.
The initial part of the research project entails developing a sufficiently accurate Prompt Tutor – essentially a Prompt Tutor that can provide at least 90 percent accurate responses to the student queries or detect doubts in the written reflections.
A total of 80 SMU students enrolled in introductory programming courses will be recruited to participate in the research project. 40 students will be exposed to the Prompt Tutor while the remainder 40 will not be.
There are four steps in the research methodology.
Step 1: Students will be required to watch a five-minute video and write a reflection on what they have learnt.
Step 2: With the use of the Doubt Identification Machine Learning Model, the Prompt Tutor will be programmed to detect doubts or inaccuracies in the student reflections.
Step 3: The Prompt Tutor will then evaluate the students’ written work by checking against the context and accuracy of the topic of the lesson. If any doubt or inaccuracy is detected, the Prompt Tutor will notify the student by sending a personalised prompt such as asking the student a follow-up question for him/her to further reflect and learn.
Step 4: If no inaccuracy is detected, the Prompt Tutor will either prompt the student with more exercises or generate a quiz to test understanding.
Implications of the research
This research has wide ranging applications beyond the teaching of Computer Science or Information Systems courses.
Given that the development of the Prompt Tutor is based on CLT, and therefore, discipline agnostic, its use can be easily extended to the teaching in fields such as Social Science, Medicine, and even languages.
Other than benefiting the students, the Prompt Tutor can benefit instructors – by reducing the time spent on student consultation.
