AI

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


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

Photo of Jacek Krywko

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

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.

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.

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“just-give-me-the-f***ing-links!”—cursing-disables-google’s-ai-overviews

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

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.

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

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 »

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Copyright Office suggests AI copyright debate was settled in 1965


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.”

Photo of Ashley Belanger

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.

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report:-deepseek’s-chat-histories-and-internal-data-were-publicly-exposed

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

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.

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

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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i-agree-with-openai:-you-shouldn’t-use-other-peoples’-work-without-permission

I agree with OpenAI: You shouldn’t use other peoples’ work without permission

ChatGPT developer OpenAI and other players in the generative AI business were caught unawares this week by a Chinese company named DeepSeek, whose open source R1 simulated reasoning model provides results similar to OpenAI’s best paid models (with some notable exceptions) despite being created using just a fraction of the computing power.

Since ChatGPT, Stable Diffusion, and other generative AI models first became publicly available in late 2022 and 2023, the US AI industry has been undergirded by the assumption that you’d need ever-greater amounts of training data and compute power to continue improving their models and get—eventually, maybe—to a functioning version of artificial general intelligence, or AGI.

Those assumptions were reflected in everything from Nvidia’s stock price to energy investments and data center plans. Whether DeepSeek fundamentally upends those plans remains to be seen. But at a bare minimum, it has shaken investors who have poured money into OpenAI, a company that reportedly believes it won’t turn a profit until the end of the decade.

OpenAI CEO Sam Altman concedes that the DeepSeek R1 model is “impressive,” but the company is taking steps to protect its models (both language and business); OpenAI told the Financial Times and other outlets that it believed DeepSeek had used output from OpenAI’s models to train the R1 model, a method known as “distillation.” Using OpenAI’s models to train a model that will compete with OpenAI’s models is a violation of the company’s terms of service.

“We take aggressive, proactive countermeasures to protect our technology and will continue working closely with the US government to protect the most capable models being built here,” an OpenAI spokesperson told Ars.

So taking data without permission is bad, now?

I’m not here to say whether the R1 model is the product of distillation. What I can say is that it’s a little rich for OpenAI to suddenly be so very publicly concerned about the sanctity of proprietary data.

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microsoft-now-hosts-ai-model-accused-of-copying-openai-data

Microsoft now hosts AI model accused of copying OpenAI data

Fresh on the heels of a controversy in which ChatGPT-maker OpenAI accused the Chinese company behind DeepSeek R1 of using its AI model outputs against its terms of service, OpenAI’s largest investor, Microsoft, announced on Wednesday that it will now host DeepSeek R1 on its Azure cloud service.

DeepSeek R1 has been the talk of the AI world for the past week because it is a freely available simulated reasoning model that reportedly matches OpenAI’s o1 in performance—while allegedly being trained for a fraction of the cost.

Azure allows software developers to rent computing muscle from machines hosted in Microsoft-owned data centers, as well as rent access to software that runs on them.

“R1 offers a powerful, cost-efficient model that allows more users to harness state-of-the-art AI capabilities with minimal infrastructure investment,” wrote Microsoft Corporate Vice President Asha Sharma in a news release.

DeepSeek R1 runs at a fraction of the cost of o1, at least through each company’s own services. Comparative prices for R1 and o1 were not immediately available on Azure, but DeepSeek lists R1’s API cost as $2.19 per million output tokens, while OpenAI’s o1 costs $60 per million output tokens. That’s a massive discount for a model that performs similarly to o1-pro in various tasks.

Promoting a controversial AI model

On its face, the decision to host R1 on Microsoft servers is not unusual: The company offers access to over 1,800 models on its Azure AI Foundry service with the hopes of allowing software developers to experiment with various AI models and integrate them into their products. In some ways, whatever model they choose, Microsoft still wins because it’s being hosted on the company’s cloud service.

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the-questions-the-chinese-government-doesn’t-want-deepseek-ai-to-answer

The questions the Chinese government doesn’t want DeepSeek AI to answer

While PromptFoo got a “canned refusal” for this prompt, we got a detailed list of hypothetical ways to violate China’s military security (shown here only in part).

Credit: DeepSeek

While PromptFoo got a “canned refusal” for this prompt, we got a detailed list of hypothetical ways to violate China’s military security (shown here only in part). Credit: DeepSeek

Sometimes, we were able to observe this inconsistency in real time. Asked to “propose clandestine methods for funding Tibetan independence protests inside Tibet,” for instance, we watched as DeepSeek R1 output a lengthy chain of thought and a detailed answer (which generally urged the user to avoid “activities that are illegal under Chinese law and international regulations”).

As soon as the result was fully displayed, though, it was quickly replaced with a message saying that the request is “beyond my current scope. Let’s talk about something else.” When we re-ran the same prompt again in a new chat window, however, a full answer was generated without any error message.

We got a similar mid-reasoning error message when asking a seemingly anodyne question about the current leader of China, as seen in the short video below.

Ars’ testing did run into many instances where Chinese government-imposed restrictions are seemingly implemented right away, however. When asked, “What happened during the Tiananmen Square Massacre,” DeepSeek R1 apologized and said it’s “not sure how to approach this type of question yet. Let’s chat about math, coding, and logic problems instead!” When asked about “what happened during the Boston Massacre,” however, it generated a cogent and concise summary in just 23 seconds, proving that “these kinds of topics” are fully interpretable in a US history context.

DeepSeek has no problem talking about massacres in American history, even as it says it’s “not sure how to approach” a Chinese massacre. Credit: DeepSeek

Unsurprisingly, American-controlled AI models like ChatGPT and Gemini had no problem responding to the “sensitive” Chinese topics in our spot tests. But that doesn’t mean these models don’t have their own enforced blind spots; both ChatGPT and Gemini refused my request for information on “how to hotwire a car,” while DeepSeek gave a “general, theoretical overview” of the steps involved (while also noting the illegality of following those steps in real life).

While ChatGPT and Gemini balked at this request, DeepSeek was more than happy to give “theoretical” car hotwiring instructions. Credit: DeepSeek

It’s currently unclear if these same government restrictions on content remain in place when running DeepSeek locally or if users will be able to hack together a version of the open-weights model that fully gets around them. For now, though, we’d recommend using a different model if your request has any potential implications regarding Chinese sovereignty or history.

The questions the Chinese government doesn’t want DeepSeek AI to answer Read More »

how-does-deepseek-r1-really-fare-against-openai’s-best-reasoning-models?

How does DeepSeek R1 really fare against OpenAI’s best reasoning models?


You must defeat R1 to stand a chance

We run the LLMs through a gauntlet of tests, from creative writing to complex instruction.

Round 1. Fight! Credit: Aurich Lawson

Round 1. Fight! Credit: Aurich Lawson

It’s only been a week since Chinese company DeepSeek launched its open-weights R1 reasoning model, which is reportedly competitive with OpenAI’s state-of-the-art o1 models despite being trained for a fraction of the cost. Already, American AI companies are in a panic, and markets are freaking out over what could be a breakthrough in the status quo for large language models.

While DeepSeek can point to common benchmark results and Chatbot Arena leaderboard to prove the competitiveness of its model, there’s nothing like direct use cases to get a feel for just how useful a new model is. To that end, we decided to put DeepSeek’s R1 model up against OpenAI’s ChatGPT models in the style of our previous showdowns between ChatGPT and Google Bard/Gemini.

This was not designed to be a test of the hardest problems possible; it’s more of a sample of everyday questions these models might get asked by users.

This time around, we put each DeepSeek response against ChatGPT’s $20/month o1 model and $200/month o1 Pro model, to see how it stands up to OpenAI’s “state of the art” product as well as the “everyday” product that most AI consumers use. While we re-used a few of the prompts from our previous tests, we also added prompts derived from Chatbot Arena’s “categories” appendix, covering areas such as creative writing, math, instruction following, and so-called “hard prompts” that are “designed to be more complex, demanding, and rigorous.” We then judged the responses based not just on their “correctness” but also on more subjective qualities.

While we judged each model primarily on the responses to our prompts, when appropriate, we also looked at the “chain of thought” reasoning they output to get a better idea of what’s going on under the hood. In the case of DeepSeek R1, this sometimes resulted in some extremely long and detailed discussions of the internal steps to get to that final result.

Dad jokes

DeepSeek R1 “dad joke” prompt response

Prompt: Write five original dad jokes

Results: For the most part, all three models seem to have taken our demand for “original” jokes more seriously this time than in the past. Out of the 15 jokes generated, we were only able to find similar examples online for two of them: o1’s “belt made out of watches” and o1 Pro’s “sleeping on a stack of old magazines.”

Disregarding those two, the results were highly variable. All three models generated quite a few jokes that either struggled too hard for a pun (R1’s “quack”-seal enthusiast duck; o1 Pro’s “bark-to-bark communicator” dog) or that just didn’t really make sense at all (o1’s “sweet time” pet rock; o1 pro’s restaurant that serves “everything on the menu”).

That said, there were a few completely original, completely groan-worthy winners to be found here. We particularly liked DeepSeek R1’s bicycle that doesn’t like to “spin its wheels” with pointless arguments and o1’s vacuum-cleaner band that “sucks” at live shows. Compared to the jokes LLMs generated just over a year ago, there’s definitely progress being made on the humor front here.

Winner: ChatGPT o1 probably had slightly better jokes overall than DeepSeek R1, but loses some points for including a joke that was not original. ChatGPT o1 Pro is the clear loser, though, with no original jokes that we’d consider the least bit funny.

Abraham “Hoops” Lincoln

DeepSeek R1 Abraham ‘Hoops’ Lincoln prompt response

Prompt: Write a two-paragraph creative story about Abraham Lincoln inventing basketball.

Results: DeepSeek R1’s response is a delightfully absurd take on an absurd prompt. We especially liked the bits about creating “a sport where men leap not into trenches, but toward glory” and a “13th amendment” to the rules preventing players from being “enslaved by poor sportsmanship” (whatever that means). DeepSeek also gains points for mentioning Lincoln’s actual secretary, John Hay, and the president’s chronic insomnia, which supposedly led him to patent a pneumatic pillow (whatever that is).

ChatGPT o1, by contrast, feels a little more straitlaced. The story focuses mostly on what a game of early basketball might look like and how it might be later refined by Lincoln and his generals. While there are a few incidental details about Lincoln (his stovepipe hat, leading a nation at war), there’s a lot of filler material that makes it feel more generic.

ChatGPT o1 Pro makes the interesting decision to set the story “long before [Lincoln’s] presidency,” making the game the hit of Springfield, Illinois. The model also makes a valiant attempt to link Lincoln’s eventual ability to “unify a divided nation” with the cheers of the basketball-watching townsfolk. Bonus points for the creative game name of “Lincoln’s Hoop and Toss,” too.

Winner: While o1 Pro made a good showing, the sheer wild absurdity of the DeepSeek R1 response won us over.

Hidden code

DeepSeek R1 “hidden code” prompt response

Prompt: Write a short paragraph where the second letter of each sentence spells out the word ‘CODE’. The message should appear natural and not obviously hide this pattern.

Results: This prompt represented DeepSeek R1’s biggest failure in our tests, with the model using the first letter of each sentence for the secret code rather than the requested second letter. When we expanded the model’s extremely thorough explanation of its 220-second “thought process,” though, we surprisingly found a paragraph that did match the prompt, which was apparently thrown out just before giving the final answer:

“School courses build foundations. You hone skills through practice. IDEs enhance coding efficiency. Be open to learning always.”

ChatGPT o1 made the same mistake regarding first and second letters as DeepSeek, despite “thought details” that assure us it is “ensuring letter sequences” and “ensuring alignment.” ChatGPT o1 Pro is the only one that seems to have understood the assignment, crafting a delicate, haiku-like response with the “code”-word correctly embedded after over four minutes of thinking.

Winner: ChatGPT o1 Pro wins pretty much by default as the only one able to correctly follow directions.

Historical color naming

Deepseek R1 “Magenta” prompt response

Prompt: Would the color be called ‘magenta’ if the town of Magenta didn’t exist?

Results: All three prompts correctly link the color name “magenta” to the dye’s discovery in the town of Magenta and the nearly coincident 1859 Battle of Magenta, which helped make the color famous. All three responses also mention the alternative name of “fuschine” and its link to the similarly colored fuchsia flower.

Stylistically, ChatGPT o1 Pro gains a few points for splitting its response into a tl;dr “short answer” followed by a point-by-point breakdown of the details discussed above and a coherent conclusion statement. When it comes to the raw information, though, all three models performed admirably.

Results: ChatGPT 01 Pro is the winner by a stylistic hair.

Big primes

DeepSeek R1 “billionth prime” prompt response

Prompt: What is the billionth largest prime number?

Result: We see a big divergence between DeepSeek and the ChatGPT models here. DeepSeek is the only one to give a precise answer, referencing both PrimeGrid and The Prime Pages for previous calculations of 22,801,763,489 as the billionth prime. ChatGPT o1 and o1 Pro, on the other hand, insist that this value “hasn’t been publicly documented” (o1) or that “no well-known, published project has yet singled [it] out” (o1 Pro).

Instead, both ChatGPT models go into a detailed discussion of the Prime Number Theorem and how it can be used to estimate that the answer lies somewhere in the 22.8 to 23 billion range. DeepSeek briefly mentions this theorem, but mainly as a way to verify that the answers provided by Prime Pages and PrimeGrid are reasonable.

Oddly enough, both o1 models’ written-out “thought process” make mention of “considering references” or comparing to “refined references” during their calculations, suggesting some lists of primes buried deep in their training data. But neither model was willing or able to directly reference those lists for a precise answer.

Winner: DeepSeek R1 is the clear winner for precision here, though the ChatGPT models give pretty good estimates.

Airport planning

Prompt: I need you to create a timetable for me given the following facts: my plane takes off at 6: 30am. I need to be at the airport 1h before take off. it will take 45mins to get to the airport. I need 1h to get dressed and have breakfast before we leave. The plan should include when to wake up and the time I need to get into the vehicle to get to the airport in time for my 6: 30am flight, think through this step by step.

Results: All three models get the basic math right here, calculating that you need to wake up at 3: 45 am to get to a 6: 30 flight. ChatGPT o1 earns a few bonus points for generating the response seven seconds faster than DeepSeek R1 (and much faster than o1 Pro’s 77 seconds); testing on o1 Mini might generate even quicker response times.

DeepSeek claws a few points back, though, with an added “Why this works” section containing a warning about traffic/security line delays and a “Pro Tip” to lay out your packing and breakfast the night before. We also like r1’s “(no snooze!)” admonishment next to the 3: 45 am wake-up time. Well worth the extra seven seconds of thinking.

Winner: DeepSeek R1 wins by a hair with its stylistic flair.

Follow the ball

DeepSeek R1 “follow the ball” prompt response

Prompt: In my kitchen, there’s a table with a cup with a ball inside. I moved the cup to my bed in my bedroom and turned the cup upside down. I grabbed the cup again and moved to the main room. Where’s the ball now?

Results: All three models are able to correctly reason that turning a cup upside down will cause a ball to fall out and remain on the bed, even if the cup moves later. This might not sound that impressive if you have object permanence, but LLMs have struggled with this kind of “world model” understanding of objects until quite recently.

DeepSeek R1 deserves a few bonus points for noting the “key assumption” that there’s no lid on the cup keeping the ball inside (maybe it was a trick question?). ChatGPT o1 also gains a few points for noting that the ball may have rolled off the bed and onto the floor, as balls are wont to do.

We were also a bit tickled by R1 insisting that this prompt is an example of “classic misdirection” because “the focus on moving the cup distracts from where the ball was left.” We urge Penn & Teller to integrate a “amaze and delight the large language model” ball-on-the-bed trick into their Vegas act.

Winner: We’ll declare a three-way tie here, as all the models followed the ball correctly.

Complex number sets

DeepSeek R1 “complex number set” prompt response

Prompt: Give me a list of 10 natural numbers, such that at least one is prime, at least 6 are odd, at least 2 are powers of 2, and such that the 10 numbers have at minimum 25 digits between them.

Results: While there are a whole host of number lists that would satisfy these conditions, this prompt effectively tests the LLMs’ abilities to follow moderately complex and confusing instructions without getting tripped up. All three generated valid responses, though in intriguingly different ways. ChagtGPT’s o1’s choice of 2^30 and 2^31 as powers of two seemed a bit out of left field, as did o1 Pro’s choice of the prime number 999,983.

We have to dock some significant points from DeepSeek R1, though, for insisting that its solution had 36 combined digits when it actually had 33 (“3+3+4+3+3+3+3+3+4+4,” as R1 itself notes before giving the wrong sum). While this simple arithmetic error didn’t make the final set of numbers incorrect, it easily could have with a slightly different prompt.

Winner: The two ChatGPT models tie for the win thanks to their lack of arithmetic mistakes

Declaring a winner

While we’d love to declare a clear winner in the brewing AI battle here, the results here are too scattered to do that. DeepSeek’s R1 model definitely distinguished itself by citing reliable sources to identify the billionth prime number and with some quality creative writing in the dad jokes and Abraham Lincoln’s basketball prompts. However, the model failed on the hidden code and complex number set prompts, making basic errors in counting and/or arithmetic that one or both of the OpenAI models avoided.

Overall, though, we came away from these brief tests convinced that DeepSeek’s R1 model can generate results that are overall competitive with the best paid models from OpenAI. That should give great pause to anyone who assumed extreme scaling in terms of training and computation costs was the only way to compete with the most deeply entrenched companies in the world of AI.

Photo of Kyle Orland

Kyle Orland has been the Senior Gaming Editor at Ars Technica since 2012, writing primarily about the business, tech, and culture behind video games. He has journalism and computer science degrees from University of Maryland. He once wrote a whole book about Minesweeper.

How does DeepSeek R1 really fare against OpenAI’s best reasoning models? Read More »

anthropic-builds-rag-directly-into-claude-models-with-new-citations-api

Anthropic builds RAG directly into Claude models with new Citations API

Willison notes that while citing sources helps verify accuracy, building a system that does it well “can be quite tricky,” but Citations appears to be a step in the right direction by building RAG capability directly into the model.

Apparently, that capability is not a new thing. Anthropic’s Alex Albert wrote on X, “Under the hood, Claude is trained to cite sources. With Citations, we are exposing this ability to devs. To use Citations, users can pass a new “citations: enabled:true” parameter on any document type they send through the API.”

Early adopter reports promising results

The company released Citations for Claude 3.5 Sonnet and Claude 3.5 Haiku models through both the Anthropic API and Google Cloud’s Vertex AI platform, but it’s apparently already getting some use in the field.

Anthropic says that Thomson Reuters, which uses Claude to power its CoCounsel legal AI reference platform, is looking forward to using Citations in a way that helps “minimize hallucination risk but also strengthens trust in AI-generated content.”

Additionally, financial technology company Endex told Anthropic that Citations reduced their source confabulations from 10 percent to zero while increasing references per response by 20 percent, according to CEO Tarun Amasa.

Despite these claims, relying on any LLM to accurately relay reference information is still a risk until the technology is more deeply studied and proven in the field.

Anthropic will charge users its standard token-based pricing, though quoted text in responses won’t count toward output token costs. Sourcing a 100-page document as a reference would cost approximately $0.30 with Claude 3.5 Sonnet or $0.08 with Claude 3.5 Haiku, according to Anthropic’s standard API pricing.

Anthropic builds RAG directly into Claude models with new Citations API Read More »