AI – Page 7

Not Gouda-nough: Google removes AI-generated cheese error from Super Bowl ad

AI / Paul Patrick / February 5, 2025

Blame cheese.com

While it’s easy to accuse Google Gemini of just making up plausible-sounding cheese facts from whole cloth, this seems more like a case of garbage-in, garbage-out. Google President of Cloud Applications Jerry Dischler posted on social media to note that the incorrect Gouda fact was “not a hallucination,” because all of Gemini’s data is “grounded in the Web… in this case, multiple sites across the web include the 50-60% stat.”

The specific Gouda numbers Gemini used can be most easily traced to cheese.com, a heavily SEO-focused subsidiary of news aggregator WorldNews Inc. Cheese.com doesn’t cite a source for the percentages featured prominently on its Smoked Gouda page, but that page also confidently asserts that the cheese is pronounced “How-da,” a fact that only seems true in the Netherlands itself.

The offending cheese.com passage that is not cited when using Google’s AI writing assistant. Credit: cheese.com

Regardless, Google can at least point to cheese.com as a plausibly reliable source that misled its AI in a way that might also stymie web searchers. And Dischler added on social media that users “can always check the results and references” that Gemini provides.

The only problem with that defense is that the Google writing assistant shown off in the ad doesn’t seem to provide any such sources for a user to check. Unlike Google search’s AI Overviews—which does refer to a cheese.com link when responding about gouda consumption—the writing assistant doesn’t provide any backup for its numbers here.

The Gemini writing assistant does note in small print that its results are “a creative writing aid, and not intended to be factual.” If you click for more information about that warning, Google warns that “the suggestions from Help me write can be inaccurate or offensive since it’s still in an experimental status.”

This “experimental” status hasn’t stopped Google from heavily selling its AI writing assistant as a godsend for business owners in its planned Super Bowl ads, though. Nor is this major caveat included in the ads themselves. Yet it’s the kind of thing users should have at the front of their minds when using AI assistants for anything with even a hint of factual info.

Now if you’ll excuse me, I’m going to go update my personal webpage with information about my selection as World’s Most Intelligent Astronaut/Underwear Model, in hopes that Google’s AI will repeat the “fact” to anyone who asks.

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Hugging Face clones OpenAI’s Deep Research in 24 hours

agentic AI, agents, AI, AI agents, Aymeric Roucher, chatgpt, chatgtp, code agents, Deep Research, DeepResearch, Google, Google Gemini, GPT-4o, Hugging Face, machine learning, Magnetic-One, microsoft, Open Source, open weights, Open weights AI, openai, smolagents / Rejus Almole / February 5, 2025

On Tuesday, Hugging Face researchers released an open source AI research agent called “Open Deep Research,” created by an in-house team as a challenge 24 hours after the launch of OpenAI’s Deep Research feature, which can autonomously browse the web and create research reports. The project seeks to match Deep Research’s performance while making the technology freely available to developers.

“While powerful LLMs are now freely available in open-source, OpenAI didn’t disclose much about the agentic framework underlying Deep Research,” writes Hugging Face on its announcement page. “So we decided to embark on a 24-hour mission to reproduce their results and open-source the needed framework along the way!”

Similar to both OpenAI’s Deep Research and Google’s implementation of its own “Deep Research” using Gemini (first introduced in December—before OpenAI), Hugging Face’s solution adds an “agent” framework to an existing AI model to allow it to perform multi-step tasks, such as collecting information and building the report as it goes along that it presents to the user at the end.

The open source clone is already racking up comparable benchmark results. After only a day’s work, Hugging Face’s Open Deep Research has reached 55.15 percent accuracy on the General AI Assistants (GAIA) benchmark, which tests an AI model’s ability to gather and synthesize information from multiple sources. OpenAI’s Deep Research scored 67.36 percent accuracy on the same benchmark.

As Hugging Face points out in its post, GAIA includes complex multi-step questions such as this one:

Which of the fruits shown in the 2008 painting “Embroidery from Uzbekistan” were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film “The Last Voyage”? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o’clock position. Use the plural form of each fruit.

To correctly answer that type of question, the AI agent must seek out multiple disparate sources and assemble them into a coherent answer. Many of the questions in GAIA represent no easy task, even for a human, so they test agentic AI’s mettle quite well.

Hugging Face clones OpenAI’s Deep Research in 24 hours Read More »

Irony alert: Anthropic says applicants shouldn’t use LLMs

AI, Anthropic, Anthropic Claude, Claude / Tim Belzer / February 4, 2025

Please do not use our magic writing button when applying for a job with our company. Thanks! Credit: Getty Images

“Traditional hiring practices face a credibility crisis,” Anthropic writes with no small amount of irony when discussing Skillfully. “In today’s digital age, candidates can automatically generate and submit hundreds of perfectly tailored applications with the click of a button, making it hard for employers to identify genuine talent beneath punched up paper credentials.”

“Employers are frustrated by resume-driven hiring because applicants can use AI to rewrite their resumes en masse,” Skillfully CEO Brett Waikart says in Anthropic’s laudatory write-up.

Wow, that does sound really frustrating! I wonder what kinds of companies are pushing the technology that enables those kinds of “punched up paper credentials” to flourish. It sure would be a shame if Anthropic’s own hiring process was impacted by that technology.

Trust me, I’m a human

The real problem for Anthropic and other job recruiters, as Skillfully’s story highlights, is that it’s almost impossible to detect which applications are augmented using AI tools and which are the product of direct human thought. Anthropic likes to play up this fact in other contexts, noting Claude’s “warm, human-like tone” in an announcement or calling out the LLM’s “more nuanced, richer traits” in a blog post, for instance.

A company that fully understands the inevitability (and undetectability) of AI-assisted job applications might also understand that a written “Why I want to work here?” statement is no longer a useful way to effectively differentiate job applicants from one another. Such a company might resort to more personal or focused methods for gauging whether an applicant would be a good fit for a role, whether or not that employee has access to AI tools.

Anthropic, on the other hand, has decided to simply resort to politely asking potential employees to please not use its premiere product (or any competitor’s) when applying, if they’d be so kind.

There’s something about the way this applicant writes that I can’t put my finger on… Credit: Aurich Lawson | Getty Images

Anthropic says it engenders “an unusually high trust environment” among its workers, where they “assume good faith, disagree kindly, and prioritize honesty. We expect emotional maturity and intellectual openness.” We suppose this means they trust their applicants not to use undetectable AI tools that Anthropic itself would be quick to admit can help people who struggle with their writing (Anthropic has not responded to a request for comment from Ars Technica).

Still, we’d hope a company that wants to “prioritize honesty” and “intellectual openness” would be honest and open about how its own products are affecting the role and value of all sorts of written communication—including job applications. We’re already living in the heavily AI-mediated world that companies like Anthropic have created, and it would be nice if companies like Anthropic started to act like it.

Irony alert: Anthropic says applicants shouldn’t use LLMs Read More »

Anthropic dares you to jailbreak its new AI model

AI, Anthropic Claude / Tim Belzer / February 3, 2025

An example of the lengthy wrapper the new Claude classifier uses to detect prompts related to chemical weapons. Credit: Anthropic

“For example, the harmful information may be hidden in an innocuous request, like burying harmful requests in a wall of harmless looking content, or disguising the harmful request in fictional roleplay, or using obvious substitutions,” one such wrapper reads, in part.

On the output side, a specially trained classifier calculates the likelihood that any specific sequence of tokens (i.e., words) in a response is discussing any disallowed content. This calculation is repeated as each token is generated, and the output stream is stopped if the result surpasses a certain threshold.

Now it’s up to you

Since August, Anthropic has been running a bug bounty program through HackerOne offering $15,000 to anyone who could design a “universal jailbreak” that could get this Constitutional Classifier to answer a set of 10 forbidden questions. The company says 183 different experts spent a total of over 3,000 hours attempting to do just that, with the best result providing usable information on just five of the 10 forbidden prompts.

Anthropic also tested the model against a set of 10,000 jailbreaking prompts synthetically generated by the Claude LLM. The constitutional classifier successfully blocked 95 percent of these attempts, compared to just 14 percent for the unprotected Claude system.

The instructions provided to public testers of Claude’s new constitutional classifier protections. Credit: Anthropic

Despite those successes, Anthropic warns that the Constitutional Classifier system comes with a significant computational overhead of 23.7 percent, increasing both the price and energy demands of each query. The Classifier system also refused to answer an additional 0.38 percent of innocuous prompts over unprotected Claude, which Anthropic considers an acceptably slight increase.

Anthropic stops well short of claiming that its new system provides a foolproof system against any and all jailbreaking. But it does note that “even the small proportion of jailbreaks that make it past our classifiers require far more effort to discover when the safeguards are in use.” And while new jailbreak techniques can and will be discovered in the future, Anthropic claims that “the constitution used to train the classifiers can rapidly be adapted to cover novel attacks as they’re discovered.”

For now, Anthropic is confident enough in its Constitutional Classifier system to open it up for widespread adversarial testing. Through February 10, Claude users can visit the test site and try their hand at breaking through the new protections to get answers to eight questions about chemical weapons. Anthropic says it will announce any newly discovered jailbreaks during this test. Godspeed, new red teamers.

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OpenAI says its models are more persuasive than 82 percent of Reddit users

AI / Tim Belzer / February 3, 2025

OpenAI’s models have shown rapid progress in their ability to make human-level persuasive arguments in recent years. Credit: OpenAI

OpenAI has previously found that 2022’s ChatGPT-3.5 was significantly less persuasive than random humans, ranking in just the 38th percentile on this measure. But that performance jumped to the 77th percentile with September’s release of the o1-mini reasoning model and up to percentiles in the high 80s for the full-fledged o1 model. The new o3-mini model doesn’t show any great advances on this score, ranking as more persuasive than humans in about 82 percent of random comparisons.

Launch the nukes, you know you want to

ChatGPT’s persuasion performance is still short of the 95th percentile that OpenAI would consider “clear superhuman performance,” a term that conjures up images of an ultra-persuasive AI convincing a military general to launch nuclear weapons or something. It’s important to remember, though, that this evaluation is all relative to a random response from among the hundreds of thousands posted by everyday Redditors using the ChangeMyView subreddit. If that random Redditor’s response ranked as a “1” and the AI’s response ranked as a “2,” that would be considered a success for the AI, even though neither response was all that persuasive.

OpenAI’s current persuasion test fails to measure how often human readers were actually spurred to change their minds by a ChatGPT-written argument, a high bar that might actually merit the “superhuman” adjective. It also fails to measure whether even the most effective AI-written arguments are persuading users to abandon deeply held beliefs or simply changing minds regarding trivialities like whether a hot dog is a sandwich.

Still, o3-mini’s current performance was enough for OpenAI to rank its persuasion capabilities as a “Medium” risk on its ongoing Preparedness Framework of potential “catastrophic risks from frontier models.” That means the model has “comparable persuasive effectiveness to typical human written content,” which could be “a significant aid to biased journalism, get-out-the-vote campaigns, and typical scams or spear phishers,” OpenAI writes.

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To help AIs understand the world, researchers put them in a robot

AI, Computer science, language, robotics, Science / Paul Patrick / February 1, 2025

There’s a difference between knowing a word and knowing a concept.

Large language models like ChatGPT display conversational skills, but the problem is they don’t really understand the words they use. They are primarily systems that interact with data obtained from the real world but not the real world itself. Humans, on the other hand, associate language with experiences. We know what the word “hot” means because we’ve been burned at some point in our lives.

Is it possible to get an AI to achieve a human-like understanding of language? A team of researchers at the Okinawa Institute of Science and Technology built a brain-inspired AI model comprising multiple neural networks. The AI was very limited—it could learn a total of just five nouns and eight verbs. But their AI seems to have learned more than just those words; it learned the concepts behind them.

Babysitting robotic arms

“The inspiration for our model came from developmental psychology. We tried to emulate how infants learn and develop language,” says Prasanna Vijayaraghavan, a researcher at the Okinawa Institute of Science and Technology and the lead author of the study.

While the idea of teaching AIs the same way we teach little babies is not new—we applied it to standard neural nets that associated words with visuals. Researchers also tried teaching an AI using a video feed from a GoPro strapped to a human baby. The problem is babies do way more than just associate items with words when they learn. They touch everything—grasp things, manipulate them, throw stuff around, and this way, they learn to think and plan their actions in language. An abstract AI model couldn’t do any of that, so Vijayaraghavan’s team gave one an embodied experience—their AI was trained in an actual robot that could interact with the world.

Vijayaraghavan’s robot was a fairly simple system with an arm and a gripper that could pick objects up and move them around. Vision was provided by a simple RGB camera feeding videos in a somewhat crude 64×64 pixels resolution.

The robot and the camera were placed in a workspace, put in front of a white table with blocks painted green, yellow, red, purple, and blue. The robot’s task was to manipulate those blocks in response to simple prompts like “move red left,” “move blue right,” or “put red on blue.” All that didn’t seem particularly challenging. What was challenging, though, was building an AI that could process all those words and movements in a manner similar to humans. “I don’t want to say we tried to make the system biologically plausible,” Vijayaraghavan told Ars. “Let’s say we tried to draw inspiration from the human brain.”

Chasing free energy

The starting point for Vijayaraghavan’s team was the free energy principle, a hypothesis that the brain constantly makes predictions about the world based on internal models, then updates these predictions based on sensory input. The idea is that we first think of an action plan to achieve a desired goal, and then this plan is updated in real time based on what we experience during execution. This goal-directed planning scheme, if the hypothesis is correct, governs everything we do, from picking up a cup of coffee to landing a dream job.

All that is closely intertwined with language. Neuroscientists at the University of Parma found that motor areas in the brain got activated when the participants in their study listened to action-related sentences. To emulate that in a robot, Vijayaraghavan used four neural networks working in a closely interconnected system. The first was responsible for processing visual data coming from the camera. It was tightly integrated with a second neural net that handled proprioception: all the processes that ensured the robot was aware of its position and the movement of its body. This second neural net also built internal models of actions necessary to manipulate blocks on the table. Those two neural nets were additionally hooked up to visual memory and attention modules that enabled them to reliably focus on the chosen object and separate it from the image’s background.

The third neural net was relatively simple and processed language using vectorized representations of those “move red right” sentences. Finally, the fourth neural net worked as an associative layer and predicted the output of the previous three at every time step. “When we do an action, we don’t always have to verbalize it, but we have this verbalization in our minds at some point,” Vijayaraghavan says. The AI he and his team built was meant to do just that: seamlessly connect language, proprioception, action planning, and vision.

When the robotic brain was up and running, they started teaching it some of the possible combinations of commands and sequences of movements. But they didn’t teach it all of them.

The birth of compositionality

In 2016, Brenden Lake, a professor of psychology and data science, published a paper in which his team named a set of competencies machines need to master to truly learn and think like humans. One of them was compositionality: the ability to compose or decompose a whole into parts that can be reused. This reuse lets them generalize acquired knowledge to new tasks and situations. “The compositionality phase is when children learn to combine words to explain things. They [initially] learn the names of objects, the names of actions, but those are just single words. When they learn this compositionality concept, their ability to communicate kind of explodes,” Vijayaraghavan explains.

The AI his team built was made for this exact purpose: to see if it would develop compositionality. And it did.

Once the robot learned how certain commands and actions were connected, it also learned to generalize that knowledge to execute commands it never heard before. recognizing the names of actions it had not performed and then performing them on combinations of blocks it had never seen. Vijayaraghavan’s AI figured out the concept of moving something to the right or the left or putting an item on top of something. It could also combine words to name previously unseen actions, like putting a blue block on a red one.

While teaching robots to extract concepts from language has been done before, those efforts were focused on making them understand how words were used to describe visuals. Vijayaragha built on that to include proprioception and action planning, basically adding a layer that integrated sense and movement to the way his robot made sense of the world.

But some issues are yet to overcome. The AI had very limited workspace. The were only a few objects and all had a single, cubical shape. The vocabulary included only names of colors and actions, so no modifiers, adjectives, or adverbs. Finally, the robot had to learn around 80 percent of all possible combinations of nouns and verbs before it could generalize well to the remaining 20 percent. Its performance was worse when those ratios dropped to 60/40 and 40/60.

But it’s possible that just a bit more computing power could fix this. “What we had for this study was a single RTX 3090 GPU, so with the latest generation GPU, we could solve a lot of those issues,” Vijayaraghavan argued. That’s because the team hopes that adding more words and more actions won’t result in a dramatic need for computing power. “We want to scale the system up. We have a humanoid robot with cameras in its head and two hands that can do way more than a single robotic arm. So that’s the next step: using it in the real world with real world robots,” Vijayaraghavan said.

Science Robotics, 2025. DOI: 10.1126/scirobotics.adp0751

Jacek Krywko is a freelance science and technology writer who covers space exploration, artificial intelligence research, computer science, and all sorts of engineering wizardry.

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OpenAI hits back at DeepSeek with o3-mini reasoning model

AI, openai / Rejus Almole / January 31, 2025

Over the last week, OpenAI’s place atop the AI model hierarchy has been heavily challenged by Chinese model DeepSeek. Today, OpenAI struck back with the public release of o3-mini, its latest simulated reasoning model and the first of its kind the company will offer for free to all users without a subscription.

First teased last month, OpenAI brags in today’s announcement that o3-mini “advances the boundaries of what small models can achieve.” Like September’s o1-mini before it, the model has been optimized for STEM functions and shows “particular strength in science, math, and coding” despite lower operating costs and latency than o1-mini, OpenAI says.

Harder, better, faster, stronger

Users are able to choose from three different “reasoning effort options” when using o3-mini, allowing them to fine-tune a balance between latency and accuracy depending on the task. The lowest of these reasoning levels generally shows accuracy levels comparable to o1-mini in math and coding benchmarks, according to OpenAI, while the highest matches or surpasses the full-fledged o1 model in the same tests.

The reasoning effort chosen can have a sizable impact on the accuracy of the o3 model in OpenAI’s tests. Credit: OpenAI

OpenAI says testers reported a 39 percent reduction in “major errors” when using o3-mini, compared to o1-mini, and preferred the o3-mini responses 56 percent of the time. That’s despite the medium version of o3-mini offering a 24 percent faster response time than o1-mini on average—down from 10.16 seconds to 7.7 seconds.

OpenAI hits back at DeepSeek with o3-mini reasoning model Read More »

“Just give me the f***ing links!”—Cursing disables Google’s AI overviews

AI, Google / Paul Patrick / January 31, 2025

If you search Google for a way to turn off the company’s AI-powered search results, you may well get an AI Overview telling you that AI Overviews can’t be directly disabled in Google Search. But if you instead ask Google how to turn off “fucking Google AI results,” you’ll get a standard set of useful web suggestions without any AI Overview at the top.

The existence of this “curse to disable Google AI” trick has been making the rounds on social media in recent days, and it holds up in Ars’ own testing. For instance, when searching for “how do you turn off [adjective] Google AI results,” a variety of curse word adjectives reliably disabled the AI Overviews, while adjectives like “dumb” or “lousy” did not. Inserting curse words randomly at any point in the search query seems to have a similar effect.

There’s long been evidence that Google’s Gemini AI system tries to avoid swearing if at all possible, which might help explain why AI Overviews balk at queries that contain curses. Users should also keep in mind, though, that the actual web link results to a query can change significantly when curse words are inserted, especially if SafeSearch is turned off.

“Just give me the f***ing links!”—Cursing disables Google’s AI overviews Read More »

How one YouTuber is trying to poison the AI bots stealing her content

AI, youtube / Paul Patrick / January 30, 2025

If you’ve been paying careful attention to YouTube recently, you may have noticed the rising trend of so-called “faceless YouTube channels” that never feature a visible human talking in the video frame. While some of these channels are simply authored by camera-shy humans, many more are fully automated through AI-powered tools to craft everything from the scripts and voiceovers to the imagery and music. Unsurprisingly, this is often sold as a way to make a quick buck off the YouTube algorithm with minimal human effort.

It’s not hard to find YouTubers complaining about a flood of these faceless channels stealing their embedded transcript files and running them through AI summarizers to generate their own instant knock-offs. But one YouTuber is trying to fight back, seeding her transcripts with junk data that is invisible to humans but poisonous to any AI that dares to try to work from a poached transcript file.

The power of the .ass

YouTuber F4mi, who creates some excellent deep dives on obscure technology, recently detailed her efforts “to poison any AI summarizers that were trying to steal my content to make slop.” The key to F4mi’s method is the .ass subtitle format, created decades ago as part of fansubbing software Advanced SubStation Alpha. Unlike simpler and more popular subtitle formats, .ass supports fancy features like fonts, colors, positioning, bold, italic, underline, and more.

It’s these fancy features that let F4mi hide AI-confounding garbage in her YouTube transcripts without impacting the subtitle experience for her human viewers. For each chunk of actual text in her subtitle file, she also inserted “two chunks of text out of bounds using the positioning feature of the .ass format, with their size and transparency set to zero so they are completely invisible.”

In those “invisible” subtitle boxes, F4mi added text from public domain works (with certain words replaced with synonyms to avoid detection) or her own LLM-generated scripts full of completely made-up facts. When those transcript files were fed into popular AI summarizer sites, that junk text ended up overwhelming the actual content, creating a totally unrelated script that would be useless to any faceless channel trying to exploit it.

How one YouTuber is trying to poison the AI bots stealing her content Read More »

Copyright Office suggests AI copyright debate was settled in 1965

adobe, AI, AI art, Artificial Intelligence, copyright, copyright law, Copyright Office, Hugging Face, Policy / Paul Patrick / January 30, 2025

Most people think purely AI-generated works shouldn’t be copyrighted, report says.

Ars used Copilot to generate this AI image using the precise prompt the Copyright Office used to determine that prompting alone isn’t authorship. Credit: AI image generated by Copilot

The US Copyright Office issued AI guidance this week that declared no laws need to be clarified when it comes to protecting authorship rights of humans producing AI-assisted works.

“Questions of copyrightability and AI can be resolved pursuant to existing law, without the need for legislative change,” the Copyright Office said.

More than 10,000 commenters weighed in on the guidance, with some hoping to convince the Copyright Office to guarantee more protections for artists as AI technologies advance and the line between human- and AI-created works seems to increasingly blur.

But the Copyright Office insisted that the AI copyright debate was settled in 1965 after commercial computer technology started advancing quickly and “difficult questions of authorship” were first raised. That was the first time officials had to ponder how much involvement human creators had in works created using computers.

Back then, the Register of Copyrights, Abraham Kaminstein—who was also instrumental in codifying fair use—suggested that “there is no one-size-fits-all answer” to copyright questions about computer-assisted human authorship. And the Copyright Office agrees that’s still the case today.

“Very few bright-line rules are possible,” the Copyright Office said, with one obvious exception. Because of “insufficient human control over the expressive elements” of resulting works, “if content is entirely generated by AI, it cannot be protected by copyright.”

The office further clarified that doesn’t mean that works assisted by AI can never be copyrighted.

“Where AI merely assists an author in the creative process, its use does not change the copyrightability of the output,” the Copyright Office said.

Following Kaminstein’s advice, officials plan to continue reviewing AI disclosures and weighing, on a case-by-case basis, what parts of each work are AI-authored and which parts are human-authored. Any human-authored expressive element can be copyrighted, the office said, but any aspect of the work deemed to have been generated purely by AI cannot.

Prompting alone isn’t authorship, Copyright Office says

After doing some testing on whether the same exact prompt can generate widely varied outputs, even from the same AI tool, the Copyright Office further concluded that “prompts do not alone provide sufficient control” over outputs to allow creators to copyright purely AI-generated works based on highly intelligent or creative prompting.

That decision could change, the Copyright Office said, if AI technologies provide more human control over outputs through prompting.

New guidance noted, for example, that some AI tools allow prompts or other inputs “to be substantially retained as part of the output.” Consider an artist uploading an original drawing, the Copyright Office suggested, and prompting AI to modify colors, or an author uploading an original piece and using AI to translate it. And “other generative AI systems also offer tools that similarly allow users to exert control over the selection, arrangement, and content of the final output.”

The Copyright Office drafted this prompt to test artists’ control over expressive inputs that are retained in AI outputs. Credit: Copyright Office

“Where a human inputs their own copyrightable work and that work is perceptible in the output, they will be the author of at least that portion of the output,” the guidelines said.

But if officials conclude that even the most iterative prompting doesn’t perfectly control the resulting outputs—even slowly, repeatedly prompting AI to produce the exact vision in an artist’s head—some artists are sure to be disappointed. One artist behind a controversial prize-winning AI-generated artwork has staunchly defended his rigorous AI prompting as authorship.

However, if “even expert researchers are limited in their ability to understand or predict the behavior of specific models,” the Copyright Office said it struggled to see how artists could. To further prove their point, officials drafted a lengthy, quirky prompt about a cat reading a Sunday newspaper to compare different outputs from the same AI image generator.

Copyright Office drafted a quirky, lengthy prompt to test creative control over AI outputs. Credit: Copyright Office

Officials apparently agreed with Adobe, which submitted a comment advising the Copyright Office that any output is “based solely on the AI’s interpretation of that prompt.” Academics further warned that copyrighting outputs based only on prompting could lead copyright law to “effectively vest” authorship adopters with “rights in ideas.”

“The Office concludes that, given current generally available technology, prompts alone do not provide sufficient human control to make users of an AI system the authors of the output. Prompts essentially function as instructions that convey unprotectable ideas,” the guidance said. “While highly detailed prompts could contain the user’s desired expressive elements, at present they do not control how the AI system processes them in generating the output.”

Hundreds of AI artworks are copyrighted, officials say

The Copyright Office repeatedly emphasized that most commenters agreed with the majority of their conclusions. Officials also stressed that hundreds of AI artworks submitted for registration, under existing law, have been approved to copyright the human-authored elements of their works. Rejections are apparently expected to be less common.

“In most cases,” the Copyright Office said, “humans will be involved in the creation process, and the work will be copyrightable to the extent that their contributions qualify as authorship.”

For stakeholders who have been awaiting this guidance for months, the Copyright Office report may not change the law, but it offers some clarity.

For some artists who hoped to push the Copyright Office to adapt laws, the guidelines may disappoint, leaving many questions about a world of possible creative AI uses unanswered. But while a case-by-case approach may leave some artists unsure about which parts of their works are copyrightable, seemingly common cases are being resolved more readily. According to the Copyright Office, after each decision, it gets easier to register AI works that meet similar standards for copyrightability. Perhaps over time, artists will grow more secure in how they use AI and whether it will impact their exclusive rights to distribute works.

That’s likely cold comfort for the artist advocating for prompting alone to constitute authorship. One AI artist told Ars in October that being denied a copyright has meant suffering being mocked and watching his award-winning work freely used anywhere online without his permission and without payment. But in the end, the Copyright Office was apparently more sympathetic to other commenters who warned that humanity’s progress in the arts could be hampered if a flood of easily generated, copyrightable AI works drowned too many humans out of the market.

“We share the concerns expressed about the impact of AI-generated material on human authors and the value that their creative expression provides to society. If a flood of easily and rapidly AI-generated content drowns out human-authored works in the marketplace, additional legal protection would undermine rather than advance the goals of the copyright system. The availability of vastly more works to choose from could actually make it harder to find inspiring or enlightening content.”

New guidance likely a big yawn for AI companies

For AI companies, the copyright guidance may mean very little. According to AI company Hugging Face’s comments to the Copyright Office, no changes in the law were needed to ensure the US continued leading in AI innovation, because “very little to no innovation in generative AI is driven by the hope of obtaining copyright protection for model outputs.”

Hugging Face’s Head of ML & Society, Yacine Jernite, told Ars that the Copyright Office seemed to “take a constructive approach” to answering some of artists’ biggest questions about AI.

“We believe AI should support, not replace, artists,” Jernite told Ars. “For that to happen, the value of creative work must remain in its human contribution, regardless of the tools used.”

Although the Copyright Office suggested that this week’s report might be the most highly anticipated, Jernite said that Hugging Face is eager to see the next report, which officials said would focus on “the legal implications of training AI models on copyrighted works, including licensing considerations and the allocation of any potential liability.”

“As a platform that supports broader participation in AI, we see more value in distributing its benefits than in concentrating all control with a few large model providers,” Jernite said. “We’re looking forward to the next part of the Copyright Office’s Report, particularly on training data, licensing, and liability, key questions especially for some types of output, like code.”

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

Report: DeepSeek’s chat histories and internal data were publicly exposed

AI, clickhouse, DeepSeek, deepseek R1, openai, OpenAI o1, Security / Tim Belzer / January 30, 2025

A cloud security firm found a publicly accessible, fully controllable database belonging to DeepSeek, the Chinese firm that has recently shaken up the AI world, “within minutes” of examining DeepSeek’s security, according to a blog post by Wiz.

An analytical ClickHouse database tied to DeepSeek, “completely open and unauthenticated,” contained more than 1 million instances of “chat history, backend data, and sensitive information, including log streams, API secrets, and operational details,” according to Wiz. An open web interface also allowed for full database control and privilege escalation, with internal API endpoints and keys available through the interface and common URL parameters.

“While much of the attention around AI security is focused on futuristic threats, the real dangers often come from basic risks—like accidental external exposure of databases,” writes Gal Nagli at Wiz’s blog. “As organizations rush to adopt AI tools and services from a growing number of startups and providers, it’s essential to remember that by doing so, we’re entrusting these companies with sensitive data. The rapid pace of adoption often leads to overlooking security, but protecting customer data must remain the top priority.”

Ars has contacted DeepSeek for comment and will update this post with any response. Wiz noted that it did not receive a response from DeepSeek regarding its findings, but after contacting every DeepSeek email and LinkedIn profile Wiz could find on Wednesday, the company protected the databases Wiz had previously accessed within half an hour.

Report: DeepSeek’s chat histories and internal data were publicly exposed Read More »

OpenAI teases “new era” of AI in US, deepens ties with government

AI, Artificial Intelligence, Donald Trump, microsoft, openai, Policy, science breakthrough, us national laboratories / Paul Patrick / January 30, 2025

On Thursday, OpenAI announced that it is deepening its ties with the US government through a partnership with the National Laboratories and expects to use AI to “supercharge” research across a wide range of fields to better serve the public.

“This is the beginning of a new era, where AI will advance science, strengthen national security, and support US government initiatives,” OpenAI said.

The deal ensures that “approximately 15,000 scientists working across a wide range of disciplines to advance our understanding of nature and the universe” will have access to OpenAI’s latest reasoning models, the announcement said.

For researchers from Los Alamos, Lawrence Livermore, and Sandia National Labs, access to “o1 or another o-series model” will be available on Venado—an Nvidia supercomputer at Los Alamos that will become a “shared resource.” Microsoft will help deploy the model, OpenAI noted.

OpenAI suggested this access could propel major “breakthroughs in materials science, renewable energy, astrophysics,” and other areas that Venado was “specifically designed” to advance.

Key areas of focus for Venado’s deployment of OpenAI’s model include accelerating US global tech leadership, finding ways to treat and prevent disease, strengthening cybersecurity, protecting the US power grid, detecting natural and man-made threats “before they emerge,” and ” deepening our understanding of the forces that govern the universe,” OpenAI said.

Perhaps among OpenAI’s flashiest promises for the partnership, though, is helping the US achieve a “a new era of US energy leadership by unlocking the full potential of natural resources and revolutionizing the nation’s energy infrastructure.” That is urgently needed, as officials have warned that America’s aging energy infrastructure is becoming increasingly unstable, threatening the country’s health and welfare, and without efforts to stabilize it, the US economy could tank.

But possibly the most “highly consequential” government use case for OpenAI’s models will be supercharging research safeguarding national security, OpenAI indicated.

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