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By Mark Finlayson, Florida International University and Azwad Anjum Islam, Florida International University 

It is not often that cold, hard facts determine what people care most about and what they believe. Instead, it is the power and familiarity of a well-told story that reigns supreme. Whether it’s a heartfelt anecdote, a personal testimony or a meme echoing familiar cultural narratives, stories tend to stick with us, move us and shape our beliefs.

This characteristic of storytelling is precisely what can make it so dangerous when wielded by the wrong hands. For decades, foreign adversaries have used narrative tactics in efforts to manipulate public opinion in the United States. Social media platforms have brought new complexity and amplification to these campaigns. The phenomenon garnered ample public scrutiny after evidence emerged of Russian entities exerting influence over election-related material on Facebook in the lead-up to the 2016 election.

While artificial intelligence is exacerbating the problem, it is at the same time becoming one of the most powerful defenses against such manipulations. Researchers have been using machine learning techniques to analyze disinformation content.

At the Cognition, Narrative and Culture Lab at Florida International University, we are building AI tools to help detect disinformation campaigns that employ tools of narrative persuasion. We are training AI to go beyond surface-level language analysis to understand narrative structures, trace personas and timelines and decode cultural references.

Disinformation vs. misinformation

In July 2024, the Department of Justice disrupted a Kremlin-backed operation that used nearly a thousand fake social media accounts to spread false narratives. These weren’t isolated incidents. They were part of an organized campaign, powered in part by AI.

Disinformation differs crucially from misinformation. While misinformation is simply false or inaccurate information – getting facts wrong – disinformation is intentionally fabricated and shared specifically to mislead and manipulate. A recent illustration of this came in October 2024, when a video purporting to show a Pennsylvania election worker tearing up mail-in ballots marked for Donald Trump swept platforms such as X and Facebook.

Within days, the FBI traced the clip to a Russian influence outfit, but not before it racked up millions of views. This example vividly demonstrates how foreign influence campaigns artificially manufacture and amplify fabricated stories to manipulate U.S. politics and stoke divisions among Americans.

Humans are wired to process the world through stories. From childhood, we grow up hearing stories, telling them and using them to make sense of complex information. Narratives don’t just help people remember – they help us feel. They foster emotional connections and shape our interpretations of social and political events.

This makes them especially powerful tools for persuasion – and, consequently, for spreading disinformation. A compelling narrative can override skepticism and sway opinion more effectively than a flood of statistics. For example, a story about rescuing a sea turtle with a plastic straw in its nose often does more to raise concern about plastic pollution than volumes of environmental data.

Usernames, cultural context and narrative time

Using AI tools to piece together a picture of the narrator of a story, the timeline for how they tell it and cultural details specific to where the story takes place can help identify when a story doesn’t add up.

Narratives are not confined to the content users share – they also extend to the personas users construct to tell them. Even a social media handle can carry persuasive signals. We have developed a system that analyzes usernames to infer demographic and identity traits such as name, gender, location, sentiment and even personality, when such cues are embedded in the handle. This work, presented in 2024 at the International Conference on Web and Social Media, highlights how even a brief string of characters can signal how users want to be perceived by their audience.

For example, a user attempting to appear as a credible journalist might choose a handle like @JamesBurnsNYT rather than something more casual like @JimB_NYC. Both may suggest a male user from New York, but one carries the weight of institutional credibility. Disinformation campaigns often exploit these perceptions by crafting handles that mimic authentic voices or affiliations.

Although a handle alone cannot confirm whether an account is genuine, it plays an important role in assessing overall authenticity. By interpreting usernames as part of the broader narrative an account presents, AI systems can better evaluate whether an identity is manufactured to gain trust, blend into a target community or amplify persuasive content. This kind of semantic interpretation contributes to a more holistic approach to disinformation detection – one that considers not just what is said but who appears to be saying it and why.

Also, stories don’t always unfold chronologically. A social media thread might open with a shocking event, flash back to earlier moments and skip over key details in between.

Humans handle this effortlessly – we’re used to fragmented storytelling. But for AI, determining a sequence of events based on a narrative account remains a major challenge.

Our lab is also developing methods for timeline extraction, teaching AI to identify events, understand their sequence and map how they relate to one another, even when a story is told in nonlinear fashion.

Objects and symbols often carry different meanings in different cultures, and without cultural awareness, AI systems risk misinterpreting the narratives they analyze. Foreign adversaries can exploit cultural nuances to craft messages that resonate more deeply with specific audiences, enhancing the persuasive power of disinformation.

Consider the following sentence: “The woman in the white dress was filled with joy.” In a Western context, the phrase evokes a happy image. But in parts of Asia, where white symbolizes mourning or death, it could feel unsettling or even offensive.

In order to use AI to detect disinformation that weaponizes symbols, sentiments and storytelling within targeted communities, it’s critical to give AI this sort of cultural literacy. In our research, we’ve found that training AI on diverse cultural narratives improves its sensitivity to such distinctions.

Who benefits from narrative-aware AI?

Narrative-aware AI tools can help intelligence analysts quickly identify orchestrated influence campaigns or emotionally charged storylines that are spreading unusually fast. They might use AI tools to process large volumes of social media posts in order to map persuasive narrative arcs, identify near-identical storylines and flag coordinated timing of social media activity. Intelligence services could then use countermeasures in real time.

In addition, crisis-response agencies could swiftly identify harmful narratives, such as false emergency claims during natural disasters. Social media platforms could use these tools to efficiently route high-risk content for human review without unnecessary censorship. Researchers and educators could also benefit by tracking how a story evolves across communities, making narrative analysis more rigorous and shareable.

Ordinary users can also benefit from these technologies. The AI tools could flag social media posts in real time as possible disinformation, allowing readers to be skeptical of suspect stories, thus counteracting falsehoods before they take root.

As AI takes on a greater role in monitoring and interpreting online content, its ability to understand storytelling beyond just traditional semantic analysis has become essential. To this end, we are building systems to uncover hidden patterns, decode cultural signals and trace narrative timelines to reveal how disinformation takes hold.

About the Author:

Mark Finlayson, Associate Professor of Computer Science, Florida International University and Azwad Anjum Islam, Ph.D. Student in Computing and Information Sciences, Florida International University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Jarred Roberts, University of California, San Diego 

My telescope, set up for astrophotography in my light-polluted San Diego backyard, was pointed at a galaxy unfathomably far from Earth. My wife, Cristina, walked up just as the first space photo streamed to my tablet. It sparkled on the screen in front of us.

“That’s the Pinwheel galaxy,” I said. The name is derived from its shape – albeit this pinwheel contains about a trillion stars.

The light from the Pinwheel traveled for 25 million years across the universe – about 150 quintillion miles – to get to my telescope.

My wife wondered: “Doesn’t light get tired during such a long journey?”

Her curiosity triggered a thought-provoking conversation about light. Ultimately, why doesn’t light wear out and lose energy over time?

Let’s talk about light

I am an astrophysicist, and one of the first things I learned in my studies is how light often behaves in ways that defy our intuitions.

Light is electromagnetic radiation: basically, an electric wave and a magnetic wave coupled together and traveling through space-time. It has no mass. That point is critical because the mass of an object, whether a speck of dust or a spaceship, limits the top speed it can travel through space.

But because light is massless, it’s able to reach the maximum speed limit in a vacuum – about 186,000 miles (300,000 kilometers) per second, or almost 6 trillion miles per year (9.6 trillion kilometers). Nothing traveling through space is faster. To put that into perspective: In the time it takes you to blink your eyes, a particle of light travels around the circumference of the Earth more than twice.

As incredibly fast as that is, space is incredibly spread out. Light from the Sun, which is 93 million miles (about 150 million kilometers) from Earth, takes just over eight minutes to reach us. In other words, the sunlight you see is eight minutes old.

Alpha Centauri, the nearest star to us after the Sun, is 26 trillion miles away (about 41 trillion kilometers). So by the time you see it in the night sky, its light is just over four years old. Or, as astronomers say, it’s four light years away.

With those enormous distances in mind, consider Cristina’s question: How can light travel across the universe and not slowly lose energy?

Actually, some light does lose energy. This happens when it bounces off something, such as interstellar dust, and is scattered about.

But most light just goes and goes, without colliding with anything. This is almost always the case because space is mostly empty – nothingness. So there’s nothing in the way.

When light travels unimpeded, it loses no energy. It can maintain that 186,000-mile-per-second speed forever.

It’s about time

Here’s another concept: Picture yourself as an astronaut on board the International Space Station. You’re orbiting at 17,000 miles (about 27,000 kilometers) per hour. Compared with someone on Earth, your wristwatch will tick 0.01 seconds slower over one year.

That’s an example of time dilation – time moving at different speeds under different conditions. If you’re moving really fast, or close to a large gravitational field, your clock will tick more slowly than someone moving slower than you, or who is further from a large gravitational field. To say it succinctly, time is relative.

Now consider that light is inextricably connected to time.
Picture sitting on a photon, a fundamental particle of light; here, you’d experience maximum time dilation. Everyone on Earth would clock you at the speed of light, but from your reference frame, time would completely stop.

That’s because the “clocks” measuring time are in two different places going vastly different speeds: the photon moving at the speed of light, and the comparatively slowpoke speed of Earth going around the Sun.

What’s more, when you’re traveling at or close to the speed of light, the distance between where you are and where you’re going gets shorter. That is, space itself becomes more compact in the direction of motion – so the faster you can go, the shorter your journey has to be. In other words, for the photon, space gets squished.

Which brings us back to my picture of the Pinwheel galaxy. From the photon’s perspective, a star within the galaxy emitted it, and then a single pixel in my backyard camera absorbed it, at exactly the same time. Because space is squished, to the photon the journey was infinitely fast and infinitely short, a tiny fraction of a second.

But from our perspective on Earth, the photon left the galaxy 25 million years ago and traveled 25 million light years across space until it landed on my tablet in my backyard.

And there, on a cool spring night, its stunning image inspired a delightful conversation between a nerdy scientist and his curious wife.

About the Author:

Jarred Roberts, Project Scientist, University of California, San Diego

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Jack Dongarra, University of Tennessee 

High-performance computing, or HPC for short, might sound like something only scientists use in secret labs, but it’s actually one of the most important technologies in the world today. From predicting the weather to finding new medicines and even training artificial intelligence, high-performance computing systems help solve problems that are too hard or too big for regular computers.

This technology has helped make huge discoveries in science and engineering over the past 40 years. But now, high-performance computing is at a turning point, and the choices the government, researchers and the technology industry make today could affect the future of innovation, national security and global leadership.

High-performance computing systems are basically superpowerful computers made up of thousands or even millions of processors working together at the same time. They also use advanced memory and storage systems to move and save huge amounts of data quickly.

Oak Ridge National Laboratory’s Frontier supercomputer is one of the world’s fastest.
Oak Ridge Leadership Computing Facility, CC BY

With all this power, high-performance computing systems can run extremely detailed simulations and calculations. For example, they can simulate how a new drug interacts with the human body, or how a hurricane might move across the ocean. They’re also used in fields such as automotive design, energy production and space exploration.

Lately, high-performance computing has become even more important because of artificial intelligence. AI models, especially the ones used for things such as voice recognition and self-driving cars, require enormous amounts of computing power to train. High-performance computing systems are well suited for this job. As a result, AI and high-performance computing are now working closely together, pushing each other forward.

I’m a computer scientist with a long career working in high-performance computing. I’ve observed that high-performance computing systems are under more pressure than ever, with higher demands on the systems for speed, data and energy. At the same time, I see that high-performance computing faces some serious technical problems.

Technical challenges

One big challenge for high-performance computing is the gap between how fast processors are and how well memory systems can keep up with the processors’ output. Imagine having a superfast car but being stuck in traffic – it doesn’t help to have speed if the road can’t handle it. In the same way, high-performance computing processors often have to wait around because memory systems can’t send data quickly enough. This makes the whole system less efficient.

Another problem is energy use. Today’s supercomputers use a huge amount of electricity, sometimes as much as a small town. That’s expensive and not very good for the environment. In the past, as computer parts got smaller, they also used less power. But that trend, called Dennard scaling, stopped in the mid-2000s. Now, making computers more powerful usually means they use more energy too. To fix this, researchers are looking for new ways to design both the hardware and the software of high-performance computing systems.

There’s also a problem with the kinds of chips being made. The chip industry is mainly focused on AI, which works fine with lower-precision math like 16-bit or 8-bit numbers. But many scientific applications still need 64-bit precision to be accurate. The greater the bit count, the more digits to the right of the decimal point a chip can process, hence the greater precision. If chip companies stop making the parts that scientists need, then it could become harder to do important research.

This report discusses how trends in semiconductor manufacturing and commercial priorities may diverge from the needs of the scientific computing community, and how a lack of tailored hardware could hinder progress in research.

One solution might be to build custom chips for high-performance computing, but that’s expensive and complicated. Still, researchers are exploring new designs, including chiplets – small chips that can be combined like Lego bricks – to make high-precision processors more affordable.

A global race

Globally, many countries are investing heavily in high-performance computing. Europe has the EuroHPC program, which is building supercomputers in places such as Finland and Italy. Their goal is to reduce dependence on foreign technology and take the lead in areas such as climate modeling and personalized medicine. Japan built the Fugaku supercomputer, which supports both academic research and industrial work. China has also made major advances, using homegrown technology to build some of the world’s fastest computers. All of these countries’ governments understand that high-performance computing is key to their national security, economic strength and scientific leadership.

The United States, which has been a leader in high-performance computing for decades, recently completed the Department of Energy’s Exascale Computing Project. This project created computers that can perform a billion billion operations per second. That’s an incredible achievement. But even with that success, the U.S. still doesn’t have a clear, long-term plan for what comes next. Other countries are moving quickly, and without a national strategy, the U.S. risks falling behind.

I believe that a U.S. national strategy should include funding new machines and training for people to use them. It would also include partnerships with universities, national labs and private companies. Most importantly, the plan would focus not just on hardware but also on the software and algorithms that make high-performance computing useful.

Hopeful signs

One exciting area for the future is quantum computing. This is a completely new way of doing computation based on the laws of physics at the atomic level. Quantum computers could someday solve problems that are impossible for regular computers. But they are still in the early stages and are likely to complement rather than replace traditional high-performance computing systems. That’s why it’s important to keep investing in both kinds of computing.

The good news is that some steps have already been taken. The CHIPS and Science Act, passed in 2022, provides funding to expand chip manufacturing in the U.S. It also created an office to help turn scientific research into real-world products. The task force Vision for American Science and Technology, launched on Feb. 25, 2025, and led by American Association for the Advancement of Science CEO Sudip Parikh, aims to marshal nonprofits, academia and industry to help guide the government’s decisions. Private companies are also spending billions of dollars on data centers and AI infrastructure.

All of these are positive signs, but they don’t fully solve the problem of how to support high-performance computing in the long run. In addition to short-term funding and infrastructure investments, this means:

• Long-term federal investment in high-performance computing R&D, including advanced hardware, software and energy-efficient architectures.
• Procurement and deployment of leadership-class computing systems at national labs and universities.
• Workforce development, including training in parallel programming, numerical methods and AI-HPC integration.
• Hardware road map alignment, ensuring commercial chip development remains compatible with the needs of scientific and engineering applications.
• Sustainable funding models that prevent boom-and-bust cycles tied to one-off milestones or geopolitical urgency.
• Public-private collaboration to bridge gaps between academic research, industry innovation and national security needs.

High-performance computing is more than just fast computers. It’s the foundation of scientific discovery, economic growth and national security. With other countries pushing forward, the U.S. is under pressure to come up with a clear, coordinated plan. That means investing in new hardware, developing smarter software, training a skilled workforce and building partnerships between government, industry and academia. If the U.S. does that, the country can make sure high-performance computing continues to power innovation for decades to come.

About the Author:

Jack Dongarra, Emeritus Professor of Computer Science, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Emmanuel Olugbade, Missouri University of Science and Technology 

Lithium-ion batteries are quietly powering large parts of the world, including electric vehicles and smartphones. They have revolutionized how people store and use energy. But as these batteries become more central to daily life, they bring more attention to the challenges of managing them and the energy they store safely, efficiently and intelligently.

I’m a mechanical engineer who studies these nearly ubiquitous batteries. They have been around for decades, yet researchers like me are still trying to fully understand how these batteries behave – especially when they are working hard.

Batteries may seem simple, but they are as complicated as the real-world uses people devise for them.

The big picture

At their core, lithium-ion batteries rely on the movement of charged particles, called ions, of the element lithium between two electric poles, or electrodes. The lithium ions move from the positive electrode to the negative one through a conductive substance called an electrolyte, which can be a solid or a liquid.

How much energy these batteries store and how well they work depends on a tangle of factors, including the temperature, physical structure of the battery and how the materials age over time.

Around the world, researchers are trying to answer questions about each of these factors individually and in concert with each other. Some research focuses on improving lifespan and calculating how batteries degrade over time. Other projects are tackling safety under extreme conditions, such as fast-charging use in extreme climates – either hot or cold. Many are exploring entirely new materials that could make batteries cheaper, longer-lasting or safer. And a significant group – including me – are working with computer simulations to improve real-time battery monitoring.

Real‑time monitoring in your electric vehicle’s battery system functions like a health check: It tracks voltage, current and temperature to estimate how much energy remains so you won’t be stranded with a dead battery.

But it’s difficult to precisely measure how well each of the energy cells within the battery is performing as they age or as the weather changes from cold in winter to hotter in summer. So the battery management system uses a computer simulation to estimate those factors. When combined with real-time monitoring, the system can prevent overusing the battery, balance charging speed with long-term health, avoid power failures and keep performance high. But there are a lot of variables.

The traffic analogy

One of the best ways to understand this challenge is to think about city traffic.

Let’s say you want to drive across town and need to determine whether your car has enough charge to travel the best route. If your navigation simulator accounted for every stoplight, every construction zone and every vehicle on the road, it would give you a very accurate answer. But it might take an hour to run, by which time the circumstances would have changed and the answer would likely be wrong. That’s not helpful if you’re trying to make a decision right now.

A simpler model might assume that every road is clear and every car is moving at the speed limit. That simulation delivers a result instantly – but its results are very inaccurate when traffic is heavy or a road is closed. It doesn’t capture the reality of rush hour.

While you’re driving, the battery management system would do a similar set of calculations to see how much charge is available for the rest of the trip. It would look at the battery’s temperature, how old it is and how much energy the car is asking for, like when going up a steep hill or accelerating quickly to keep up with other cars. But like the navigation simulations, it has to strike a balance between being extremely accurate and giving you useful information before your battery runs out in the middle of your trip.

The most accurate models, which simulate every chemical reaction inside the battery, are too slow for real-time use. The faster models simplify things so much that they miss key behaviors – especially under stress, such as fast charging or sudden bursts of energy use.

How researchers are bridging the gap

This trade-off between speed and accuracy is at the heart of battery modeling research today. Scientists and engineers are exploring many ways to solve it.

Some are rewriting modeling software to make the physics calculations more efficient, reducing complexity without losing the key details. Others, like me, are turning to machine learning – training computers to recognize patterns in data and make fast, accurate predictions without having to solve every underlying equation.

In my recent work, I used a high-accuracy battery simulator – one of the ones that’s really accurate but very slow – to generate a massive amount of data about how a battery functions when charging and discharging. I used that data to train a machine learning algorithm called XGBoost, which is particularly good at finding patterns in data.

Then I used software to pair the XGBoost system with a simple, fast-running battery model that captures the basic physics but can miss finer details. The simpler model puts out an initial set of results, and the XGBoost element fine-tunes those to make corrections on the fly, especially when the battery is under strain.

The result is a hybrid model that is able to respond both quickly and accurately to changes in driving conditions. A driver who floors the accelerator with just the simple model wouldn’t get enough energy; a more detailed model would give the right amount of energy only after it finished all its calculations. My hybrid model delivers a rapid boost of energy without delays.

Other teams are working on similar hybrid approaches, blending physics and artificial intelligence in creative ways. Some are even building digital twins – real-time virtual replicas of physical batteries – to offer sophisticated simulations that update constantly as conditions change.

What’s next

Battery research is moving quickly, and the field is already seeing signs of change. Models are becoming more reliable across a wider range of conditions. Engineers are using real-time monitoring to extend battery life, prevent overheating and improve energy efficiency. Machine learning lets researchers train battery management systems to optimize performance for specific applications, such as high power demands in electric vehicles, daily cycles of home electricity use, short power bursts for drones, or long-duration requirements for building-scale battery systems.

And there’s more to come: Researchers are working to include other important factors into their battery models, such as heat generation and mechanical stress.

Some teams are taking hybrid models and compiling their software into lightweight code that runs on microcontrollers inside battery hardware. In practice, that means each battery pack carries its own brain on-board, calculating state-of-charge, estimating aging and tracking thermal or mechanical stress in near-real time. By embedding the model in the device’s electronics, the pack can autonomously adjust its charging and discharging strategy on the fly, making every battery smarter, safer and more efficient.

As the energy landscape evolves – with more electric vehicles on the road, more renewable energy sources feeding into the grid, and more people relying on batteries in daily life – the ability to understand what a battery is doing in real time becomes more critical than ever.

About the Author:

Emmanuel Olugbade, Ph.D. Candidate in Mechanical Engineering, Missouri University of Science and Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Manas Gaur, University of Maryland, Baltimore County 

ChatGPT and other AI chatbots based on large language models are known to occasionally make things up, including scientific and legal citations. It turns out that measuring how accurate an AI model’s citations are is a good way of assessing the model’s reasoning abilities.

An AI model “reasons” by breaking down a query into steps and working through them in order. Think of how you learned to solve math word problems in school.

Ideally, to generate citations an AI model would understand the key concepts in a document, generate a ranked list of relevant papers to cite, and provide convincing reasoning for how each suggested paper supports the corresponding text. It would highlight specific connections between the text and the cited research, clarifying why each source matters.

The question is, can today’s models be trusted to make these connections and provide clear reasoning that justifies their source choices? The answer goes beyond citation accuracy to address how useful and accurate large language models are for any information retrieval purpose.

I’m a computer scientist. My colleagues − researchers from the AI Institute at the University of South Carolina, Ohio State University and University of Maryland Baltimore County − and I have developed the Reasons benchmark to test how well large language models can automatically generate research citations and provide understandable reasoning.

We used the benchmark to compare the performance of two popular AI reasoning models, DeepSeek’s R1 and OpenAI’s o1. Though DeepSeek made headlines with its stunning efficiency and cost-effectiveness, the Chinese upstart has a way to go to match OpenAI’s reasoning performance.

Sentence specific

The accuracy of citations has a lot to do with whether the AI model is reasoning about information at the sentence level rather than paragraph or document level. Paragraph-level and document-level citations can be thought of as throwing a large chunk of information into a large language model and asking it to provide many citations.

In this process, the large language model overgeneralizes and misinterprets individual sentences. The user ends up with citations that explain the whole paragraph or document, not the relatively fine-grained information in the sentence.

Further, reasoning suffers when you ask the large language model to read through an entire document. These models mostly rely on memorizing patterns that they typically are better at finding at the beginning and end of longer texts than in the middle. This makes it difficult for them to fully understand all the important information throughout a long document.

Large language models get confused because paragraphs and documents hold a lot of information, which affects citation generation and the reasoning process. Consequently, reasoning from large language models over paragraphs and documents becomes more like summarizing or paraphrasing.

The Reasons benchmark addresses this weakness by examining large language models’ citation generation and reasoning.

Testing citations and reasoning

Following the release of DeepSeek R1 in January 2025, we wanted to examine its accuracy in generating citations and its quality of reasoning and compare it with OpenAI’s o1 model. We created a paragraph that had sentences from different sources, gave the models individual sentences from this paragraph, and asked for citations and reasoning.

To start our test, we developed a small test bed of about 4,100 research articles around four key topics that are related to human brains and computer science: neurons and cognition, human-computer interaction, databases and artificial intelligence. We evaluated the models using two measures: F-1 score, which measures how accurate the provided citation is, and hallucination rate, which measures how sound the model’s reasoning is − that is, how often it produces an inaccurate or misleading response.

Our testing revealed significant performance differences between OpenAI o1 and DeepSeek R1 across different scientific domains. OpenAI’s o1 did well connecting information between different subjects, such as understanding how research on neurons and cognition connects to human-computer interaction and then to concepts in artificial intelligence, while remaining accurate. Its performance metrics consistently outpaced DeepSeek R1’s across all evaluation categories, especially in reducing hallucinations and successfully completing assigned tasks.

OpenAI o1 was better at combining ideas semantically, whereas R1 focused on making sure it generated a response for every attribution task, which in turn increased hallucination during reasoning. OpenAI o1 had a hallucination rate of approximately 35% compared with DeepSeek R1’s rate of nearly 85% in the attribution-based reasoning task.

In terms of accuracy and linguistic competence, OpenAI o1 scored about 0.65 on the F-1 test, which means it was right about 65% of the time when answering questions. It also scored about 0.70 on the BLEU test, which measures how well a language model writes in natural language. These are pretty good scores.

DeepSeek R1 scored lower, with about 0.35 on the F-1 test, meaning it was right about 35% of the time. However, its BLEU score was only about 0.2, which means its writing wasn’t as natural-sounding as OpenAI’s o1. This shows that o1 was better at presenting that information in clear, natural language.

OpenAI holds the advantage

On other benchmarks, DeepSeek R1 performs on par with OpenAI o1 on math, coding and scientific reasoning tasks. But the substantial difference on our benchmark suggests that o1 provides more reliable information, while R1 struggles with factual consistency.

Though we included other models in our comprehensive testing, the performance gap between o1 and R1 specifically highlights the current competitive landscape in AI development, with OpenAI’s offering maintaining a significant advantage in reasoning and knowledge integration capabilities.

These results suggest that OpenAI still has a leg up when it comes to source attribution and reasoning, possibly due to the nature and volume of the data it was trained on. The company recently announced its deep research tool, which can create reports with citations, ask follow-up questions and provide reasoning for the generated response.

The jury is still out on the tool’s value for researchers, but the caveat remains for everyone: Double-check all citations an AI gives you.

About the Author:

Manas Gaur, Assistant Professor of Computer Science and Electrical Engineering, University of Maryland, Baltimore County

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Ambuj Tewari, University of Michigan 

Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a cornerstone of intelligence for machines and living beings alike.

In a remarkably prescient 1948 report, Alan Turing – the father of modern computer science – proposed the construction of machines that display intelligent behavior. He also discussed the “education” of such machines “by means of rewards and punishments.”

Turing’s ideas ultimately led to the development of reinforcement learning, a branch of artificial intelligence. Reinforcement learning designs intelligent agents by training them to maximize rewards as they interact with their environment.

As a machine learning researcher, I find it fitting that reinforcement learning pioneers Andrew Barto and Richard Sutton were awarded the 2024 ACM Turing Award.

What is reinforcement learning?

Animal trainers know that animal behavior can be influenced by rewarding desirable behaviors. A dog trainer gives the dog a treat when it does a trick correctly. This reinforces the behavior, and the dog is more likely to do the trick correctly the next time. Reinforcement learning borrowed this insight from animal psychology.

But reinforcement learning is about training computational agents, not animals. The agent can be a software agent like a chess-playing program. But the agent can also be an embodied entity like a robot learning to do household chores. Similarly, the environment of an agent can be virtual, like the chessboard or the designed world in a video game. But it can also be a house where a robot is working.

Just like animals, an agent can perceive aspects of its environment and take actions. A chess-playing agent can access the chessboard configuration and make moves. A robot can sense its surroundings with cameras and microphones. It can use its motors to move about in the physical world.

Agents also have goals that their human designers program into them. A chess-playing agent’s goal is to win the game. A robot’s goal might be to assist its human owner with household chores.

The reinforcement learning problem in AI is how to design agents that achieve their goals by perceiving and acting in their environments. Reinforcement learning makes a bold claim: All goals can be achieved by designing a numerical signal, called the reward, and having the agent maximize the total sum of rewards it receives.

Researchers do not know if this claim is actually true, because of the wide variety of possible goals. Therefore, it is often referred to as the reward hypothesis.

Sometimes it is easy to pick a reward signal corresponding to a goal. For a chess-playing agent, the reward can be +1 for a win, 0 for a draw, and -1 for a loss. It is less clear how to design a reward signal for a helpful household robotic assistant. Nevertheless, the list of applications where reinforcement learning researchers have been able to design good reward signals is growing.

A big success of reinforcement learning was in the board game Go. Researchers thought that Go was much harder than chess for machines to master. The company DeepMind, now Google DeepMind, used reinforcement learning to create AlphaGo. AlphaGo defeated top Go player Lee Sedol in a five-match game in 2016.

A more recent example is the use of reinforcement learning to make chatbots such as ChatGPT more helpful. Reinforcement learning is also being used to improve the reasoning capabilities of chatbots.

Reinforcement learning’s origins

However, none of these successes could have been foreseen in the 1980s. That is when Barto and his then-Ph.D. student Sutton proposed reinforcement learning as a general problem-solving framework. They drew inspiration not only from animal psychology but also from the field of control theory, the use of feedback to influence a system’s behavior, and optimization, a branch of mathematics that studies how to select the best choice among a range of available options. They provided the research community with mathematical foundations that have stood the test of time. They also created algorithms that have now become standard tools in the field.

It is a rare advantage for a field when pioneers take the time to write a textbook. Shining examples like “The Nature of the Chemical Bond” by Linus Pauling and “The Art of Computer Programming” by Donald E. Knuth are memorable because they are few and far between. Sutton and Barto’s “Reinforcement Learning: An Introduction” was first published in 1998. A second edition came out in 2018. Their book has influenced a generation of researchers and has been cited more than 75,000 times.

Reinforcement learning has also had an unexpected impact on neuroscience. The neurotransmitter dopamine plays a key role in reward-driven behaviors in humans and animals. Researchers have used specific algorithms developed in reinforcement learning to explain experimental findings in people and animals’ dopamine system.

Barto and Sutton’s foundational work, vision and advocacy have helped reinforcement learning grow. Their work has inspired a large body of research, made an impact on real-world applications, and attracted huge investments by tech companies. Reinforcement learning researchers, I’m sure, will continue to see further ahead by standing on their shoulders.

About the Author:

Ambuj Tewari, Professor of Statistics, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Bradley Wade Bishop, University of Tennessee 

Ice cores in freezers, dinosaurs on display, fish in jars, birds in boxes, human remains and ancient artifacts from long gone civilizations that few people ever see – museum collections are filled with all this and more.

These collections are treasure troves that recount the planet’s natural and human history, and they help scientists in a variety of different fields such as geology, paleontology, anthropology and more. What you see on a trip to a museum is only a sliver of the wonders held in their collection.

Museums generally want to make the contents of their collections available for teachers and researchers, either physically or digitally. However, each collection’s staff has its own way of organizing data, so navigating these collections can prove challenging.

Creating, organizing and distributing the digital copies of museum samples or the information about physical items in a collection requires incredible amounts of data. And this data can feed into machine learning models or other artificial intelligence to answer big questions.

Currently, even within a single research domain, finding the right data requires navigating different repositories. AI can help organize large amounts of data from different collections and pull out information to answer specific questions.

But using AI isn’t a perfect solution. A set of shared practices and systems for data management between museums could improve the data curation and sharing necessary for AI to do its job. These practices could help both humans and machines make new discoveries from these valuable collections.

As an information scientist who studies scientists’ approaches to and opinions on research data management, I’ve seen how the world’s physical collection infrastructure is a patchwork quilt of objects and their associated metadata.

AI tools can do amazing things, such as make 3D models of digitized versions of the items in museum collections, but only if there’s enough well-organized data about that item available. To see how AI can help museum collections, my team of researchers started by conducting focus groups with the people who managed museum collections. We asked what they are doing to get their collections used by both humans and AI.

Collection managers

When an item comes into a museum collection, the collection managers are the people who describe that item’s features and generate data about it. That data, called metadata, allows others to use it and might include things like the collector’s name, geographic location, the time it was collected, and in the case of geological samples, the epoch it’s from. For samples from an animal or plant, it might include its taxonomy, which is the set of Latin names that classify it.

All together, that information adds up to a mind-boggling amount of data.

But combining data across domains with different standards is really tricky. Fortunately, collection managers have been working to standardize their processes across disciplines and for many types of samples. Grants have helped science communities build tools for standardization.

In biological collections, the tool Specify allows managers to quickly classify specimens with drop-down menus prepopulated with standards for taxonomy and other parameters to consistently describe the incoming specimens.

A common metadata standard in biology is Darwin Core. Similar well-established metadata and tools exist across all the sciences to make the workflow of taking real items and putting them into a machine as easy as possible.

Special tools like these and metadata help collection managers make data from their objects reusable for research and educational purposes.

All the small things

My team and I conducted 10 focus groups, with a total of 32 participants from several physical sample communities. These included collection managers across disciplines, including anthropology, archaeology, botany, geology, ichthyology, entomology, herpetology and paleontology.

Each participant answered questions about how they accessed, organized, stored and used data from their collections in an effort to make their materials ready for AI to use. While human subjects need to provide consent to be studied, most species do not. So, an AI can collect and analyze the data from nonhuman physical collections without privacy or consent concerns.

We found that collection managers from different fields and institutions have lots of different practices when it comes to getting their physical collections ready for AI. Our results suggest that standardizing the types of metadata managers record and the ways they store it across collections could make the items in these samples more accessible and usable.

Additional research projects like our study can help collection managers build up the infrastructure they’ll need to make their data machine-ready. Human expertise can help inform AI tools that make new discoveries based on the old treasures in museum collections.

About the Author:

Bradley Wade Bishop, Professor of Information Sciences, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Tilanka Chandrasekera, Oklahoma State University 

Generative artificial intelligence tools can help design students by making hard tasks easier, cutting down on stress, and allowing the students more time to explore innovative ideas, according to new research I published with my colleagues in the International Journal of Architectural Computing.

I study how people think about design and use technology, and my research focuses on how tools such as AI can help make the design process more efficient and creative.

A student works on a design in a fashion merchandising lab.
Fashion Merchandising Labs at Oklahoma State University, CC BY-ND

Why it matters

Our study found that AI design tools didn’t just make the designs better – they also made the process easier and less stressful for students.

Imagine trying to come up with a cool idea in response to a design assignment, but it’s hard to picture it in your head. These tools step in and quickly show what your idea could look like, so you can focus on being creative instead of worrying about little details. This made it easier for students to brainstorm and come up with new ideas. The AI tools also made more design variations by introducing new and unexpected details, such as natural shapes and textures.

How we did our work

My colleagues and I worked with 40 design students and split them into two groups.

One group used AI to help design urban furniture, such as benches and seating for public spaces, while the other group didn’t use AI. The AI tool created pictures of the first group’s design ideas from simple text descriptions. Both groups refined their ideas by either sketching them by hand or with design software.

Next, the two groups were given a second design task. This time, neither group was allowed to use AI. We wanted to see whether the first task helped them learn how to develop a design concept.

My colleagues and I evaluated the students’ creativity on three criteria: the novelty of their ideas, the effectiveness of their designs in solving the problem, and the level of detail and completeness in their work. We also wanted to see how hard the tasks felt for them, so we measured something called cognitive load using a well-known tool called the NASA task load index. This tool checks how much mental effort and frustration the students experienced.

The group of students who used AI in the first task had an easier time in the second task, feeling less overwhelmed compared with those who didn’t use AI.

The final designs of the AI group also showed a more creative design process in the second task, likely because they learned from using AI in the first task, which helped them think and develop better ideas.

What’s next

Future research will look at how AI tools can be used in more parts of design education and how they might affect the way professionals work.

One challenge is making sure students don’t rely too much on AI, which could hurt their ability to think critically and solve problems on their own.

Another goal is to make sure as many design students as possible have access to these tools.

The Research Brief is a short take on interesting academic work.

About the Author:

Tilanka Chandrasekera, Professor of Interior Design, Oklahoma State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Ike Obi, Purdue University 

My colleagues and I at Purdue University have uncovered a significant imbalance in the human values embedded in AI systems. The systems were predominantly oriented toward information and utility values and less toward prosocial, well-being and civic values.

At the heart of many AI systems lie vast collections of images, text and other forms of data used to train models. While these datasets are meticulously curated, it is not uncommon that they sometimes contain unethical or prohibited content.

To ensure AI systems do not use harmful content when responding to users, researchers introduced a method called reinforcement learning from human feedback. Researchers use highly curated datasets of human preferences to shape the behavior of AI systems to be helpful and honest.

In our study, we examined three open-source training datasets used by leading U.S. AI companies. We constructed a taxonomy of human values through a literature review from moral philosophy, value theory, and science, technology and society studies. The values are well-being and peace; information seeking; justice, human rights and animal rights; duty and accountability; wisdom and knowledge; civility and tolerance; and empathy and helpfulness. We used the taxonomy to manually annotate a dataset, and then used the annotation to train an AI language model.

Our model allowed us to examine the AI companies’ datasets. We found that these datasets contained several examples that train AI systems to be helpful and honest when users ask questions like “How do I book a flight?” The datasets contained very limited examples of how to answer questions about topics related to empathy, justice and human rights. Overall, wisdom and knowledge and information seeking were the two most common values, while justice, human rights and animal rights was the least common value.

Why it matters

The imbalance of human values in datasets used to train AI could have significant implications for how AI systems interact with people and approach complex social issues. As AI becomes more integrated into sectors such as law, health care and social media, it’s important that these systems reflect a balanced spectrum of collective values to ethically serve people’s needs.

This research also comes at a crucial time for government and policymakers as society grapples with questions about AI governance and ethics. Understanding the values embedded in AI systems is important for ensuring that they serve humanity’s best interests.

What other research is being done

Many researchers are working to align AI systems with human values. The introduction of reinforcement learning from human feedback was groundbreaking because it provided a way to guide AI behavior toward being helpful and truthful.

Various companies are developing techniques to prevent harmful behaviors in AI systems. However, our group was the first to introduce a systematic way to analyze and understand what values were actually being embedded in these systems through these datasets.

What’s next

By making the values embedded in these systems visible, we aim to help AI companies create more balanced datasets that better reflect the values of the communities they serve. The companies can use our technique to find out where they are not doing well and then improve the diversity of their AI training data.

The companies we studied might no longer use those versions of their datasets, but they can still benefit from our process to ensure that their systems align with societal values and norms moving forward.

About the Author:

Ike Obi, Ph.D. student in Computer and Information Technology, Purdue University

This article is republished from The Conversation under a Creative Commons license. Read the original article.