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That’s Funny – but AI Models Don’t Get the Joke

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Newswise — ITHACA, N.Y. — Large neural networks, a form of artificial intelligence, can generate thousands of jokes along the lines of “Why did the chicken cross the road?” But do they understand why they’re funny?

Using hundreds of entries from the New Yorker magazine’s Cartoon Caption Contest as a testbed, researchers challenged AI models and humans with three tasks: matching a joke to a cartoon; identifying a winning caption; and explaining why a winning caption is funny.

In all tasks, humans performed demonstrably better than machines, even as AI advances such as ChatGPT have closed the performance gap. So are machines beginning to “understand” humor? In short, they’re making some progress, but aren’t quite there yet.

“The way people challenge AI models for understanding is to build tests for them – multiple choice tests or other evaluations with an accuracy score,” said Jack Hessel, Ph.D. ’20, research scientist at the Allen Institute for AI (AI2). “And if a model eventually surpasses whatever humans get at this test, you think, ‘OK, does this mean it truly understands?’ It’s a defensible position to say that no machine can truly `understand’ because understanding is a human thing. But, whether the machine understands or not, it’s still impressive how well they do on these tasks.”

Hessel is lead author of “Do Androids Laugh at Electric Sheep? Humor ‘Understanding’ Benchmarks from The New Yorker Caption Contest,” which won a best-paper award at the 61st annual meeting of the Association for Computational Linguistics, held July 9-14 in Toronto.

Lillian Lee ’93, the Charles Roy Davis Professor in the Cornell Ann S. Bowers College of Computing and Information Science, and Yejin Choi, Ph.D. ’10, professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and the senior director of common-sense intelligence research at AI2, are also co-authors on the paper.

For their study, the researchers compiled 14 years’ worth of New Yorker caption contests – more than 700 in all. Each contest included: a captionless cartoon; that week’s entries; the three finalists selected by New Yorker editors; and, for some contests, crowd quality estimates for each submission.  

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For each contest, the researchers tested two kinds of AI – “from pixels” (computer vision) and “from description” (analysis of human summaries of cartoons) – for the three tasks.

“There are datasets of photos from Flickr with captions like, ‘This is my dog,’” Hessel said. “The interesting thing about the New Yorker case is that the relationships between the images and the captions are indirect, playful, and reference lots of real-world entities and norms. And so the task of ‘understanding’ the relationship between these things requires a bit more sophistication.”

In the experiment, matching required AI models to select the finalist caption for the given cartoon from among “distractors” that were finalists but for other contests; quality ranking required models to differentiate a finalist caption from a nonfinalist; and explanation required models to generate free text saying how a high-quality caption relates to the cartoon.

Hessel penned the majority of human-generated explanations himself, after crowdsourcing the task proved unsatisfactory. He generated 60-word explanations for more than 650 cartoons.

“A number like 650 doesn’t seem very big in a machine-learning context, where you often have thousands or millions of data points,” Hessel said, “until you start writing them out.”

This study revealed a significant gap between AI- and human-level “understanding” of why a cartoon is funny. The best AI performance in a multiple choice test of matching cartoon to caption was only 62% accuracy, far behind humans’ 94% in the same setting. And when it came to comparing human- vs. AI-generated explanations, humans’ were preferred roughly 2-to-1.

While AI might not be able to “understand” humor yet, the authors wrote, it could be a collaborative tool humorists could use to brainstorm ideas.

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Other contributors include Ana Marasovic, assistant professor at the University of Utah School of Computing; Jena D. Hwang, research scientist at AI2; Jeff Da, research assistant at the University of Washington Rowan Zellers, researcher at OpenAI; and humorist Robert Mankoff, president of Cartoon Collections and long-time cartoon editor at the New Yorker.

The authors wrote this paper in the spirit of the subject matter, with playful comments and footnotes throughout.

“This three or four years of research wasn’t always super fun,” Lee said, “but something we try to do in our work, or at least in our writing, is to encourage more of a spirit of fun.”

This work was funded in part by the Defense Advanced Research Projects Agency; AI2; and a Google Focused Research Award.

Source: Cornell University

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AI-generated images can exploit how your mind works − here’s why they fool you and how to spot them

Arryn Robbins discusses the challenges of recognizing AI-generated images due to human cognitive limitations and inattentional blindness, emphasizing the importance of critical thinking in a visually fast-paced online environment.

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Arryn Robbins, University of Richmond

I’m more of a scroller than a poster on social media. Like many people, I wind down at the end of the day with a scroll binge, taking in videos of Italian grandmothers making pasta or baby pygmy hippos frolicking.

For a while, my feed was filled with immaculately designed tiny homes, fueling my desire for a minimalist paradise. Then, I started seeing AI-generated images; many contained obvious errors, such as staircases to nowhere or sinks within sinks. Yet, commenters rarely pointed them out, instead admiring the aesthetic.

These images were clearly AI-generated and didn’t depict reality. Did people just not notice? Not care?

As a cognitive psychologist, I’d guess “yes” and “yes.” My expertise is in how people process and use visual information. I primarily investigate how people look for objects and information visually, from the mundane searches of daily life, such as trying to find a dropped earring, to more critical searches, like those conducted by radiologists or search-and-rescue teams.

With my understanding of how people process images and notice − or don’t notice − detail, it’s not surprising to me that people aren’t tuning in to the fact that many images are AI-generated.

We’ve been here before

The struggle to detect AI-generated images mirrors past detection challenges such as spotting photoshopped images or computer-generated images in movies.

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But there’s a key difference: Photo editing and CGI require intentional design by artists, while AI images are generated by algorithms trained on datasets, often without human oversight. The lack of oversight can lead to imperfections or inconsistencies that can feel unnatural, such as the unrealistic physics or lack of consistency between frames that characterize what’s sometimes called “AI slop.”

Despite these differences, studies show people struggle to distinguish real images from synthetic ones, regardless of origin. Even when explicitly asked to identify images as real, synthetic or AI-generated, accuracy hovers near the level of chance, meaning people did only a little better than if they’d just guessed.

In everyday interactions, where you aren’t actively scrutinizing images, your ability to detect synthetic content might even be weaker.

Attention shapes what you see, what you miss

Spotting errors in AI images requires noticing small details, but the human visual system isn’t wired for that when you’re casually scrolling. Instead, while online, people take in the gist of what they’re viewing and can overlook subtle inconsistencies.

Visual attention operates like a zoom lens: You scan broadly to get an overview of your environment or phone screen, but fine details require focused effort. Human perceptual systems evolved to quickly assess environments for any threats to survival, with sensitivity to sudden changes − such as a quick-moving predator − sacrificing precision for speed of detection.

This speed-accuracy trade-off allows for rapid, efficient processing, which helped early humans survive in natural settings. But it’s a mismatch with modern tasks such as scrolling through devices, where small mistakes or unusual details in AI-generated images can easily go unnoticed.

People also miss things they aren’t actively paying attention to or looking for. Psychologists call this inattentional blindness: Focusing on one task causes you to overlook other details, even obvious ones. In the famous invisible gorilla study, participants asked to count basketball passes in a video failed to notice someone in a gorilla suit walking through the middle of the scene.

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If you’re counting how many passes the people in white make, do you even notice someone walk through in a gorilla suit?

Similarly, when your focus is on the broader content of an AI image, such as a cozy tiny home, you’re less likely to notice subtle distortions. In a way, the sixth finger in an AI image is today’s invisible gorilla − hiding in plain sight because you’re not looking for it.

Efficiency over accuracy in thinking

Our cognitive limitations go beyond visual perception. Human thinking uses two types of processing: fast, intuitive thinking based on mental shortcuts, and slower, analytical thinking that requires effort. When scrolling, our fast system likely dominates, leading us to accept images at face value.

Adding to this issue is the tendency to seek information that confirms your beliefs or reject information that goes against them. This means AI-generated images are more likely to slip by you when they align with your expectations or worldviews. If an AI-generated image of a basketball player making an impossible shot jibes with a fan’s excitement, they might accept it, even if something feels exaggerated.

While not a big deal for tiny home aesthetics, these issues become concerning when AI-generated images may be used to influence public opinion. For example, research shows that people tend to assume images are relevant to accompanying text. Even when the images provide no actual evidence, they make people more likely to accept the text’s claims as true.

Misleading real or generated images can make false claims seem more believable and even cause people to misremember real events. AI-generated images have the power to shape opinions and spread misinformation in ways that are difficult to counter.

Beating the machine

While AI gets better at detecting AI, humans need tools to do the same. Here’s how:

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  1. Trust your gut. If something feels off, it probably is. Your brain expertly recognizes objects and faces, even under varying conditions. Perhaps you’ve experienced what psychologists call the uncanny valley and felt unease with certain humanoid faces. This experience shows people can detect anomalies, even when they can’t fully explain what’s wrong.
  2. Scan for clues. AI struggles with certain elements: hands, text, reflections, lighting inconsistencies and unnatural textures. If an image seems suspicious, take a closer look.
  3. Think critically. Sometimes, AI generates photorealistic images with impossible scenarios. If you see a political figure casually surprising baristas or a celebrity eating concrete, ask yourself: Does this make sense? If not, it’s probably fake.
  4. Check the source. Is the poster a real person? Reverse image search can help trace a picture’s origin. If the metadata is missing, it might be generated by AI.

AI-generated images are becoming harder to spot. During scrolling, the brain processes visuals quickly, not critically, making it easy to miss details that reveal a fake. As technology advances, slow down, look closer and think critically.The Conversation

Arryn Robbins, Assistant Professor of Psychology, University of Richmond

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


A beautiful kitchen to scroll past – but check out the clock. Tiny Homes via Facebook
AI-generated images

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How close are quantum computers to being really useful? Podcast

Quantum computers could revolutionize science by solving complex problems. However, scaling and error correction remain significant challenges before achieving practical applications.

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Gemma Ware, The Conversation

Quantum computers have the potential to solve big scientific problems that are beyond the reach of today’s most powerful supercomputers, such as discovering new antibiotics or developing new materials.

But to achieve these breakthroughs, quantum computers will need to perform better than today’s best classical computers at solving real-world problems. And they’re not quite there yet. So what is still holding quantum computing back from becoming useful?

In this episode of The Conversation Weekly podcast, we speak to quantum computing expert Daniel Lidar at the University of Southern California in the US about what problems scientists are still wrestling with when it comes to scaling up quantum computing, and how close they are to overcoming them.

https://cdn.theconversation.com/infographics/561/4fbbd099d631750693d02bac632430b71b37cd5f/site/index.html

Quantum computers harness the power of quantum mechanics, the laws that govern subatomic particles. Instead of the classical bits of information used by microchips inside traditional computers, which are either a 0 or a 1, the chips in quantum computers use qubits, which can be both 0 and 1 at the same time or anywhere in between. Daniel Lidar explains:

“Put a lot of these qubits together and all of a sudden you have a computer that can simultaneously represent many, many different possibilities …  and that is the starting point for the speed up that we can get from quantum computing.”

Faulty qubits

One of the biggest problems scientist face is how to scale up quantum computing power. Qubits are notoriously prone to errors – which means that they can quickly revert to being either a 0 or a 1, and so lose their advantage over classical computers.

Scientists have focused on trying to solve these errors through the concept of redundancy – linking strings of physical qubits together into what’s called a “logical qubit” to try and maximise the number of steps in a computation. And, little by little, they’re getting there.

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In December 2024, Google announced that its new quantum chip, Willow, had demonstrated what’s called “beyond breakeven”, when its logical qubits worked better than the constituent parts and even kept on improving as it scaled up.

Lidar says right now the development of this technology is happening very fast:

“For quantum computing to scale and to take off is going to still take some real science breakthroughs, some real engineering breakthroughs, and probably overcoming some yet unforeseen surprises before we get to the point of true quantum utility. With that caution in mind, I think it’s still very fair to say that we are going to see truly functional, practical quantum computers kicking into gear, helping us solve real-life problems, within the next decade or so.”

Listen to Lidar explain more about how quantum computers and quantum error correction works on The Conversation Weekly podcast.


This episode of The Conversation Weekly was written and produced by Gemma Ware with assistance from Katie Flood and Mend Mariwany. Sound design was by Michelle Macklem, and theme music by Neeta Sarl.

Clips in this episode from Google Quantum AI and 10 Hours Channel.

You can find us on Instagram at theconversationdotcom or via e-mail. You can also subscribe to The Conversation’s free daily e-mail here.

Listen to The Conversation Weekly via any of the apps listed above, download it directly via our RSS feed or find out how else to listen here.

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Gemma Ware, Host, The Conversation Weekly Podcast, The Conversation

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

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AI gives nonprogrammers a boost in writing computer code

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AI coding handles the hard parts for nonprogrammers. Andriy/Moment via Getty Images

Leo Porter, University of California, San Diego and Daniel Zingaro, University of Toronto

What do you think there are more of: professional computer programmers or computer users who do a little programming?

It’s the second group. There are millions of so-called end-user programmers. They’re not going into a career as a professional programmer or computer scientist. They’re going into business, teaching, law, or any number of professions – and they just need a little programming to be more efficient. The days of programmers being confined to software development companies are long gone.

If you’ve written formulas in Excel, filtered your email based on rules, modded a game, written a script in Photoshop, used R to analyze some data, or automated a repetitive work process, you’re an end-user programmer.

As educators who teach programming, we want to help students in fields other than computer science achieve their goals. But learning how to program well enough to write finished programs can be hard to accomplish in a single course because there is so much to learn about the programming language itself. Artificial intelligence can help.

Lost in the weeds

Learning the syntax of a programming language – for example, where to place colons and where indentation is required – takes a lot of time for many students. Spending time at the level of syntax is a waste for students who simply want to use coding to help solve problems rather than learn the skill of programming.

As a result, we feel our existing classes haven’t served these students well. Indeed, many students end up barely able to write small functions – short, discrete pieces of code – let alone write a full program that can help make their lives better.

a teacher speaks to students in a classroom with a large screen displaying computer code
Learning a programming language can be difficult for those who are not computer science students. LordHenriVoton/E+ via Getty Images

Tools built on large language models such as GitHub Copilot may allow us to change these outcomes. These tools have already changed how professionals program, and we believe we can use them to help future end-user programmers write software that is meaningful to them.

These AIs almost always write syntactically correct code and can often write small functions based on prompts in plain English. Because students can use these tools to handle some of the lower-level details of programming, it frees them to focus on bigger-picture questions that are at the heart of writing software programs. Numerous universities now offer programming courses that use Copilot.

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At the University of California, San Diego, we’ve created an introductory programming course primarily for those who are not computer science students that incorporates Copilot. In this course, students learn how to program with Copilot as their AI assistant, following the curriculum from our book. In our course, students learn high-level skills such as decomposing large tasks into smaller tasks, testing code to ensure its correctness, and reading and fixing buggy code.

Freed to solve problems

In this course, we’ve been giving students large, open-ended projects and couldn’t be happier with what they have created.

For example, in a project where students had to find and analyze online datasets, we had a neuroscience major create a data visualization tool that illustrated how age and other factors affected stroke risk. Or, for example, in another project, students were able to integrate their personal art into a collage, after applying filters that they had created using the programming language Python. These projects were well beyond the scope of what we could ask students to do before the advent of large language model AIs.

Given the rhetoric about how AI is ruining education by writing papers for students and doing their homework, you might be surprised to hear educators like us talking about its benefits. AI, like any other tool people have created, can be helpful in some circumstances and unhelpful in others.

In our introductory programming course with a majority of students who are not computer science majors, we see firsthand how AI can empower students in specific ways – and promises to expand the ranks of end-user programmers.

Leo Porter, Teaching Professor of Computer Science and Engineering, University of California, San Diego and Daniel Zingaro, Associate Professor of Mathematical and Computational Sciences, University of Toronto

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

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