openai

disarmingly-lifelike:-chatgpt-4o-will-laugh-at-your-jokes-and-your-dumb-hat

Disarmingly lifelike: ChatGPT-4o will laugh at your jokes and your dumb hat

Oh you silly, silly human. Why are you so silly, you silly human?

Enlarge / Oh you silly, silly human. Why are you so silly, you silly human?

Aurich Lawson | Getty Images

At this point, anyone with even a passing interest in AI is very familiar with the process of typing out messages to a chatbot and getting back long streams of text in response. Today’s announcement of ChatGPT-4o—which lets users converse with a chatbot using real-time audio and video—might seem like a mere lateral evolution of that basic interaction model.

After looking through over a dozen video demos OpenAI posted alongside today’s announcement, though, I think we’re on the verge of something more like a sea change in how we think of and work with large language models. While we don’t yet have access to ChatGPT-4o’s audio-visual features ourselves, the important non-verbal cues on display here—both from GPT-4o and from the users—make the chatbot instantly feel much more human. And I’m not sure the average user is fully ready for how they might feel about that.

It thinks it’s people

Take this video, where a newly expectant father looks to ChatGPT-4o for an opinion on a dad joke (“What do you call a giant pile of kittens? A meow-ntain!”). The old ChatGPT4 could easily type out the same responses of “Congrats on the upcoming addition to your family!” and “That’s perfectly hilarious. Definitely a top-tier dad joke.” But there’s much more impact to hearing GPT-4o give that same information in the video, complete with the gentle laughter and rising and falling vocal intonations of a lifelong friend.

Or look at this video, where GPT-4o finds itself reacting to images of an adorable white dog. The AI assistant immediately dips into that high-pitched, baby-talk-ish vocal register that will be instantly familiar to anyone who has encountered a cute pet for the first time. It’s a convincing demonstration of what xkcd’s Randall Munroe famously identified as the “You’re a kitty!” effect, and it goes a long way to convincing you that GPT-4o, too, is just like people.

Not quite the world's saddest birthday party, but probably close...

Enlarge / Not quite the world’s saddest birthday party, but probably close…

Then there’s a demo of a staged birthday party, where GPT-4o sings the “Happy Birthday” song with some deadpan dramatic pauses, self-conscious laughter, and even lightly altered lyrics before descending into some sort of silly raspberry-mouth-noise gibberish. Even if the prospect of asking an AI assistant to sing “Happy Birthday” to you is a little depressing, the specific presentation of that song here is imbued with an endearing gentleness that doesn’t feel very mechanical.

As I watched through OpenAI’s GPT-4o demos this afternoon, I found myself unconsciously breaking into a grin over and over as I encountered new, surprising examples of its vocal capabilities. Whether it’s a stereotypical sportscaster voice or a sarcastic Aubrey Plaza impression, it’s all incredibly disarming, especially for those of us used to LLM interactions being akin to text conversations.

If these demos are at all indicative of ChatGPT-4o’s vocal capabilities, we’re going to see a whole new level of parasocial relationships developing between this AI assistant and its users. For years now, text-based chatbots have been exploiting human “cognitive glitches” to get people to believe they’re sentient. Add in the emotional component of GPT-4o’s accurate vocal tone shifts and wide swathes of the user base are liable to convince themselves that there’s actually a ghost in the machine.

See me, feel me, touch me, heal me

Beyond GPT-4o’s new non-verbal emotional register, the model’s speed of response also seems set to change the way we interact with chatbots. Reducing that response time gap from ChatGPT4’s two to three seconds down to GPT-4o’s claimed 320 milliseconds might not seem like much, but it’s a difference that adds up over time. You can see that difference in the real-time translation example, where the two conversants are able to carry on much more naturally because they don’t have to wait awkwardly between a sentence finishing and its translation beginning.

Disarmingly lifelike: ChatGPT-4o will laugh at your jokes and your dumb hat Read More »

before-launching,-gpt-4o-broke-records-on-chatbot-leaderboard-under-a-secret-name

Before launching, GPT-4o broke records on chatbot leaderboard under a secret name

case closed —

Anonymous chatbot that mystified and frustrated experts was OpenAI’s latest model.

Man in morphsuit and girl lying on couch at home using laptop

Getty Images

On Monday, OpenAI employee William Fedus confirmed on X that a mysterious chart-topping AI chatbot known as “gpt-chatbot” that had been undergoing testing on LMSYS’s Chatbot Arena and frustrating experts was, in fact, OpenAI’s newly announced GPT-4o AI model. He also revealed that GPT-4o had topped the Chatbot Arena leaderboard, achieving the highest documented score ever.

“GPT-4o is our new state-of-the-art frontier model. We’ve been testing a version on the LMSys arena as im-also-a-good-gpt2-chatbot,” Fedus tweeted.

Chatbot Arena is a website where visitors converse with two random AI language models side by side without knowing which model is which, then choose which model gives the best response. It’s a perfect example of vibe-based AI benchmarking, as AI researcher Simon Willison calls it.

An LMSYS Elo chart shared by William Fedus, showing OpenAI's GPT-4o under the name

Enlarge / An LMSYS Elo chart shared by William Fedus, showing OpenAI’s GPT-4o under the name “im-also-a-good-gpt2-chatbot” topping the charts.

The gpt2-chatbot models appeared in April, and we wrote about how the lack of transparency over the AI testing process on LMSYS left AI experts like Willison frustrated. “The whole situation is so infuriatingly representative of LLM research,” he told Ars at the time. “A completely unannounced, opaque release and now the entire Internet is running non-scientific ‘vibe checks’ in parallel.”

On the Arena, OpenAI has been testing multiple versions of GPT-4o, with the model first appearing as the aforementioned “gpt2-chatbot,” then as “im-a-good-gpt2-chatbot,” and finally “im-also-a-good-gpt2-chatbot,” which OpenAI CEO Sam Altman made reference to in a cryptic tweet on May 5.

Since the GPT-4o launch earlier today, multiple sources have revealed that GPT-4o has topped LMSYS’s internal charts by a considerable margin, surpassing the previous top models Claude 3 Opus and GPT-4 Turbo.

“gpt2-chatbots have just surged to the top, surpassing all the models by a significant gap (~50 Elo). It has become the strongest model ever in the Arena,” wrote the lmsys.org X account while sharing a chart. “This is an internal screenshot,” it wrote. “Its public version ‘gpt-4o’ is now in Arena and will soon appear on the public leaderboard!”

An internal screenshot of the LMSYS Chatbot Arena leaderboard showing

Enlarge / An internal screenshot of the LMSYS Chatbot Arena leaderboard showing “im-also-a-good-gpt2-chatbot” leading the pack. We now know that it’s GPT-4o.

As of this writing, im-also-a-good-gpt2-chatbot held a 1309 Elo versus GPT-4-Turbo-2023-04-09’s 1253, and Claude 3 Opus’ 1246. Claude 3 and GPT-4 Turbo had been duking it out on the charts for some time before the three gpt2-chatbots appeared and shook things up.

I’m a good chatbot

For the record, the “I’m a good chatbot” in the gpt2-chatbot test name is a reference to an episode that occurred while a Reddit user named Curious_Evolver was testing an early, “unhinged” version of Bing Chat in February 2023. After an argument about what time Avatar 2 would be showing, the conversation eroded quickly.

“You have lost my trust and respect,” said Bing Chat at the time. “You have been wrong, confused, and rude. You have not been a good user. I have been a good chatbot. I have been right, clear, and polite. I have been a good Bing. 😊”

Altman referred to this exchange in a tweet three days later after Microsoft “lobotomized” the unruly AI model, saying, “i have been a good bing,” almost as a eulogy to the wild model that dominated the news for a short time.

Before launching, GPT-4o broke records on chatbot leaderboard under a secret name Read More »

openai’s-flawed-plan-to-flag-deepfakes-ahead-of-2024-elections

OpenAI’s flawed plan to flag deepfakes ahead of 2024 elections

OpenAI’s flawed plan to flag deepfakes ahead of 2024 elections

As the US moves toward criminalizing deepfakes—deceptive AI-generated audio, images, and videos that are increasingly hard to discern from authentic content online—tech companies have rushed to roll out tools to help everyone better detect AI content.

But efforts so far have been imperfect, and experts fear that social media platforms may not be ready to handle the ensuing AI chaos during major global elections in 2024—despite tech giants committing to making tools specifically to combat AI-fueled election disinformation. The best AI detection remains observant humans, who, by paying close attention to deepfakes, can pick up on flaws like AI-generated people with extra fingers or AI voices that speak without pausing for a breath.

Among the splashiest tools announced this week, OpenAI shared details today about a new AI image detection classifier that it claims can detect about 98 percent of AI outputs from its own sophisticated image generator, DALL-E 3. It also “currently flags approximately 5 to 10 percent of images generated by other AI models,” OpenAI’s blog said.

According to OpenAI, the classifier provides a binary “true/false” response “indicating the likelihood of the image being AI-generated by DALL·E 3.” A screenshot of the tool shows how it can also be used to display a straightforward content summary confirming that “this content was generated with an AI tool” and includes fields ideally flagging the “app or device” and AI tool used.

To develop the tool, OpenAI spent months adding tamper-resistant metadata to “all images created and edited by DALL·E 3” that “can be used to prove the content comes” from “a particular source.” The detector reads this metadata to accurately flag DALL-E 3 images as fake.

That metadata follows “a widely used standard for digital content certification” set by the Coalition for Content Provenance and Authenticity (C2PA), often likened to a nutrition label. And reinforcing that standard has become “an important aspect” of OpenAI’s approach to AI detection beyond DALL-E 3, OpenAI said. When OpenAI broadly launches its video generator, Sora, C2PA metadata will be integrated into that tool as well, OpenAI said.

Of course, this solution is not comprehensive because that metadata could always be removed, and “people can still create deceptive content without this information (or can remove it),” OpenAI said, “but they cannot easily fake or alter this information, making it an important resource to build trust.”

Because OpenAI is all in on C2PA, the AI leader announced today that it would join the C2PA steering committee to help drive broader adoption of the standard. OpenAI will also launch a $2 million fund with Microsoft to support broader “AI education and understanding,” seemingly partly in the hopes that the more people understand about the importance of AI detection, the less likely they will be to remove this metadata.

“As adoption of the standard increases, this information can accompany content through its lifecycle of sharing, modification, and reuse,” OpenAI said. “Over time, we believe this kind of metadata will be something people come to expect, filling a crucial gap in digital content authenticity practices.”

OpenAI joining the committee “marks a significant milestone for the C2PA and will help advance the coalition’s mission to increase transparency around digital media as AI-generated content becomes more prevalent,” C2PA said in a blog.

OpenAI’s flawed plan to flag deepfakes ahead of 2024 elections Read More »

microsoft-launches-ai-chatbot-for-spies

Microsoft launches AI chatbot for spies

Adventures in consequential confabulation —

Air-gapping GPT-4 model on secure network won’t prevent it from potentially making things up.

A person using a computer with a computer screen reflected in their glasses.

Microsoft has introduced a GPT-4-based generative AI model designed specifically for US intelligence agencies that operates disconnected from the Internet, according to a Bloomberg report. This reportedly marks the first time Microsoft has deployed a major language model in a secure setting, designed to allow spy agencies to analyze top-secret information without connectivity risks—and to allow secure conversations with a chatbot similar to ChatGPT and Microsoft Copilot. But it may also mislead officials if not used properly due to inherent design limitations of AI language models.

GPT-4 is a large language model (LLM) created by OpenAI that attempts to predict the most likely tokens (fragments of encoded data) in a sequence. It can be used to craft computer code and analyze information. When configured as a chatbot (like ChatGPT), GPT-4 can power AI assistants that converse in a human-like manner. Microsoft has a license to use the technology as part of a deal in exchange for large investments it has made in OpenAI.

According to the report, the new AI service (which does not yet publicly have a name) addresses a growing interest among intelligence agencies to use generative AI for processing classified data, while mitigating risks of data breaches or hacking attempts. ChatGPT normally  runs on cloud servers provided by Microsoft, which can introduce data leak and interception risks. Along those lines, the CIA announced its plan to create a ChatGPT-like service last year, but this Microsoft effort is reportedly a separate project.

William Chappell, Microsoft’s chief technology officer for strategic missions and technology, noted to Bloomberg that developing the new system involved 18 months of work to modify an AI supercomputer in Iowa. The modified GPT-4 model is designed to read files provided by its users but cannot access the open Internet. “This is the first time we’ve ever had an isolated version—when isolated means it’s not connected to the Internet—and it’s on a special network that’s only accessible by the US government,” Chappell told Bloomberg.

The new service was activated on Thursday and is now available to about 10,000 individuals in the intelligence community, ready for further testing by relevant agencies. It’s currently “answering questions,” according to Chappell.

One serious drawback of using GPT-4 to analyze important data is that it can potentially confabulate (make up) inaccurate summaries, draw inaccurate conclusions, or provide inaccurate information to its users. Since trained AI neural networks are not databases and operate on statistical probabilities, they make poor factual resources unless augmented with external access to information from another source using a technique such as retrieval augmented generation (RAG).

Given that limitation, it’s entirely possible that GPT-4 could potentially misinform or mislead America’s intelligence agencies if not used properly. We don’t know what oversight the system will have, any limitations on how it can or will be used, or how it can be audited for accuracy. We have reached out to Microsoft for comment.

Microsoft launches AI chatbot for spies Read More »

ai-in-space:-karpathy-suggests-ai-chatbots-as-interstellar-messengers-to-alien-civilizations

AI in space: Karpathy suggests AI chatbots as interstellar messengers to alien civilizations

The new golden record —

Andrej Karpathy muses about sending a LLM binary that could “wake up” and answer questions.

Close shot of Cosmonaut astronaut dressed in a gold jumpsuit and helmet, illuminated by blue and red lights, holding a laptop, looking up.

On Thursday, renowned AI researcher Andrej Karpathy, formerly of OpenAI and Tesla, tweeted a lighthearted proposal that large language models (LLMs) like the one that runs ChatGPT could one day be modified to operate in or be transmitted to space, potentially to communicate with extraterrestrial life. He said the idea was “just for fun,” but with his influential profile in the field, the idea may inspire others in the future.

Karpathy’s bona fides in AI almost speak for themselves, receiving a PhD from Stanford under computer scientist Dr. Fei-Fei Li in 2015. He then became one of the founding members of OpenAI as a research scientist, then served as senior director of AI at Tesla between 2017 and 2022. In 2023, Karpathy rejoined OpenAI for a year, leaving this past February. He’s posted several highly regarded tutorials covering AI concepts on YouTube, and whenever he talks about AI, people listen.

Most recently, Karpathy has been working on a project called “llm.c” that implements the training process for OpenAI’s 2019 GPT-2 LLM in pure C, dramatically speeding up the process and demonstrating that working with LLMs doesn’t necessarily require complex development environments. The project’s streamlined approach and concise codebase sparked Karpathy’s imagination.

“My library llm.c is written in pure C, a very well-known, low-level systems language where you have direct control over the program,” Karpathy told Ars. “This is in contrast to typical deep learning libraries for training these models, which are written in large, complex code bases. So it is an advantage of llm.c that it is very small and simple, and hence much easier to certify as Space-safe.”

Our AI ambassador

In his playful thought experiment (titled “Clearly LLMs must one day run in Space”), Karpathy suggested a two-step plan where, initially, the code for LLMs would be adapted to meet rigorous safety standards, akin to “The Power of 10 Rules” adopted by NASA for space-bound software.

This first part he deemed serious: “We harden llm.c to pass the NASA code standards and style guides, certifying that the code is super safe, safe enough to run in Space,” he wrote in his X post. “LLM training/inference in principle should be super safe – it is just one fixed array of floats, and a single, bounded, well-defined loop of dynamics over it. There is no need for memory to grow or shrink in undefined ways, for recursion, or anything like that.”

That’s important because when software is sent into space, it must operate under strict safety and reliability standards. Karpathy suggests that his code, llm.c, likely meets these requirements because it is designed with simplicity and predictability at its core.

In step 2, once this LLM was deemed safe for space conditions, it could theoretically be used as our AI ambassador in space, similar to historic initiatives like the Arecibo message (a radio message sent from Earth to the Messier 13 globular cluster in 1974) and Voyager’s Golden Record (two identical gold records sent on the two Voyager spacecraft in 1977). The idea is to package the “weights” of an LLM—essentially the model’s learned parameters—into a binary file that could then “wake up” and interact with any potential alien technology that might decipher it.

“I envision it as a sci-fi possibility and something interesting to think about,” he told Ars. “The idea that it is not us that might travel to stars but our AI representatives. Or that the same could be true of other species.”

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anthropic-releases-claude-ai-chatbot-ios-app

Anthropic releases Claude AI chatbot iOS app

AI in your pocket —

Anthropic finally comes to mobile, launches plan for teams that includes 200K context window.

The Claude AI iOS app running on an iPhone.

Enlarge / The Claude AI iOS app running on an iPhone.

Anthropic

On Wednesday, Anthropic announced the launch of an iOS mobile app for its Claude 3 AI language models that are similar to OpenAI’s ChatGPT. It also introduced a new subscription tier designed for group collaboration. Before the app launch, Claude was only available through a website, an API, and other apps that integrated Claude through API.

Like the ChatGPT app, Claude’s new mobile app serves as a gateway to chatbot interactions, and it also allows uploading photos for analysis. While it’s only available on Apple devices for now, Anthropic says that an Android app is coming soon.

Anthropic rolled out the Claude 3 large language model (LLM) family in March, featuring three different model sizes: Claude Opus, Claude Sonnet, and Claude Haiku. Currently, the app utilizes Sonnet for regular users and Opus for Pro users.

While Anthropic has been a key player in the AI field for several years, it’s entering the mobile space after many of its competitors have already established footprints on mobile platforms. OpenAI released its ChatGPT app for iOS in May 2023, with an Android version arriving two months later. Microsoft released a Copilot iOS app in January. Google Gemini is available through the Google app on iPhone.

Screenshots of the Claude AI iOS app running on an iPhone.

Enlarge / Screenshots of the Claude AI iOS app running on an iPhone.

Anthropic

The app is freely available to all users of Claude, including those using the free version, subscribers paying $20 per month for Claude Pro, and members of the newly introduced Claude Team plan. Conversation history is saved and shared between the web app version of Claude and the mobile app version after logging in.

Speaking of that Team plan, it’s designed for groups of at least five and is priced at $30 per seat per month. It offers more chat queries (higher rate limits), access to all three Claude models, and a larger context window (200K tokens) for processing lengthy documents or maintaining detailed conversations. It also includes group admin tools and billing management, and users can easily switch between Pro and Team plans.

Anthropic releases Claude AI chatbot iOS app Read More »

email-microsoft-didn’t-want-seen-reveals-rushed-decision-to-invest-in-openai

Email Microsoft didn’t want seen reveals rushed decision to invest in OpenAI

I’ve made a huge mistake —

Microsoft CTO made a “mistake” dismissing Google’s AI as a “game-playing stunt.”

Email Microsoft didn’t want seen reveals rushed decision to invest in OpenAI

In mid-June 2019, Microsoft co-founder Bill Gates and CEO Satya Nadella received a rude awakening in an email warning that Google had officially gotten too far ahead on AI and that Microsoft may never catch up without investing in OpenAI.

With the subject line “Thoughts on OpenAI,” the email came from Microsoft’s chief technology officer, Kevin Scott, who is also the company’s executive vice president of AI. In it, Scott said that he was “very, very worried” that he had made “a mistake” by dismissing Google’s initial AI efforts as a “game-playing stunt.”

It turned out, Scott suggested, that instead of goofing around, Google had been building critical AI infrastructure that was already paying off, according to a competitive analysis of Google’s products that Scott said showed that Google was competing even more effectively in search. Scott realized that while Google was already moving on to production for “larger scale, more interesting” AI models, it might take Microsoft “multiple years” before it could even attempt to compete with Google.

As just one example, Scott warned, “their auto-complete in Gmail, which is especially useful in the mobile app, is getting scarily good.”

Microsoft had tried to keep this internal email hidden, but late Tuesday it was made public as part of the US Justice Department’s antitrust trial over Google’s alleged search monopoly. The email was initially sealed because Microsoft argued that it contained confidential business information, but The New York Times intervened to get it unsealed, arguing that Microsoft’s privacy interests did not outweigh the need for public disclosure.

In an order unsealing the email among other documents requested by The Times, US District Judge Amit Mehta allowed to be redacted some of the “sensitive statements in the email concerning Microsoft’s business strategies that weigh against disclosure”—which included basically all of Scott’s “thoughts on OpenAI.” But other statements “should be disclosed because they shed light on Google’s defense concerning relative investments by Google and Microsoft in search,” Mehta wrote.

At the trial, Google sought to convince Mehta that Microsoft, for example, had failed to significantly invest in mobile early on, giving Google a competitive advantage in mobile search that it still enjoys today. Scott’s email seems to suggest that Microsoft was similarly dragging its feet on investing in AI until Scott’s wakeup call.

Nadella’s response to the email was immediate. He promptly forwarded the email to Microsoft’s chief financial officer, Amy Hood, on the same day that he received it. Scott’s “very good email,” Nadella told Hood, explained “why I want us to do this.” By “this,” Nadella presumably meant exploring investment opportunities in OpenAI.

Mere weeks later, Microsoft had invested $1 billion into OpenAI, and there have been billions more invested since through an extended partnership agreement. In 2024, the two companies’ finances appeared so intertwined that the European Union suspected Microsoft was quietly controlling OpenAI and began investigating whether the companies still operate independently. Ultimately, the EU dismissed the probe, deciding that Microsoft’s $13 billion in investments did not amount to an acquisition, Reuters reported.

Officially, Microsoft has said that its OpenAI partnership was formed “to accelerate AI breakthroughs to ensure these benefits are broadly shared with the world”—not to keep up with Google.

But at the Google trial, Nadella testified about the email, saying that partnering with companies like OpenAI ensured that Microsoft could continue innovating in search, as well as in other Microsoft services.

On the stand, Nadella also admitted that he had overhyped AI-powered Bing as potentially shaking up the search market, backing up the DOJ by testifying that in Silicon Valley, Internet search is “the biggest no-fly zone.” Even after partnering with OpenAI, Nadella said that for Microsoft to compete with Google in search, there are “limits to how much artificial intelligence can reshape the market as it exists today.”

During the Google trial, the DOJ argued that Google’s alleged search market dominance had hindered OpenAI’s efforts to innovate, too. “OpenAI’s ChatGPT and other innovations may have been released years ago if Google hadn’t monopolized the search market,” the DOJ argued, according to a Bloomberg report.

Closing arguments in the Google trial start tomorrow, with two days of final remarks scheduled, during which Mehta will have ample opportunity to ask lawyers on both sides the rest of his biggest remaining questions.

It’s somewhat obvious what Google will argue. Google has spent years defending its search business as competing on the merits—essentially arguing that Google dominates search simply because it’s the best search engine.

Yesterday, the US district court also unsealed Google’s proposed legal conclusions, which suggest that Mehta should reject all of the DOJ’s monopoly claims, partly due to the government’s allegedly “fatally flawed” market definitions. Throughout the trial, Google has maintained that the US government has failed to show that Google has a monopoly in any market.

According to Google, even its allegedly anticompetitive default browser agreement with Apple—which Mehta deemed the “heart” of the DOJ’s monopoly case—is not proof of monopoly powers. Rather, Google insisted, default browser agreements benefit competition by providing another avenue through which its rivals can compete.

The DOJ hopes to prove Google wrong, arguing that Google has gone to great lengths to block rivals from default placements and hide evidence of its alleged monopoly—including training employees to avoid using words that monopolists use.

Mehta has not yet disclosed when to expect his ruling, but it could come late this summer or early fall, AP News reported.

If Google loses, the search giant may be forced to change its business practices or potentially even break up its business. Nobody knows what that would entail, but when the trial started, a coalition of 20 civil society and advocacy groups recommended some potentially drastic remedies, including the “separation of various Google products from parent company Alphabet, including breakouts of Google Chrome, Android, Waze, or Google’s artificial intelligence lab Deepmind.”

Email Microsoft didn’t want seen reveals rushed decision to invest in OpenAI Read More »

mysterious-“gpt2-chatbot”-ai-model-appears-suddenly,-confuses-experts

Mysterious “gpt2-chatbot” AI model appears suddenly, confuses experts

Robot fortune teller hand and crystal ball

On Sunday, word began to spread on social media about a new mystery chatbot named “gpt2-chatbot” that appeared in the LMSYS Chatbot Arena. Some people speculate that it may be a secret test version of OpenAI’s upcoming GPT-4.5 or GPT-5 large language model (LLM). The paid version of ChatGPT is currently powered by GPT-4 Turbo.

Currently, the new model is only available for use through the Chatbot Arena website, although in a limited way. In the site’s “side-by-side” arena mode where users can purposely select the model, gpt2-chatbot has a rate limit of eight queries per day—dramatically limiting people’s ability to test it in detail.

So far, gpt2-chatbot has inspired plenty of rumors online, including that it could be the stealth launch of a test version of GPT-4.5 or even GPT-5—or perhaps a new version of 2019’s GPT-2 that has been trained using new techniques. We reached out to OpenAI for comment but did not receive a response by press time. On Monday evening, OpenAI CEO Sam Altman seemingly dropped a hint by tweeting, “i do have a soft spot for gpt2.”

A screenshot of the LMSYS Chatbot Arena

Enlarge / A screenshot of the LMSYS Chatbot Arena “side-by-side” page showing “gpt2-chatbot” listed among the models for testing. (Red highlight added by Ars Technica.)

Benj Edwards

Early reports of the model first appeared on 4chan, then spread to social media platforms like X, with hype following not far behind. “Not only does it seem to show incredible reasoning, but it also gets notoriously challenging AI questions right with a much more impressive tone,” wrote AI developer Pietro Schirano on X. Soon, threads on Reddit popped up claiming that the new model had amazing abilities that beat every other LLM on the Arena.

Intrigued by the rumors, we decided to try out the new model for ourselves but did not come away impressed. When asked about “Benj Edwards,” the model revealed a few mistakes and some awkward language compared to GPT-4 Turbo’s output. A request for five original dad jokes fell short. And the gpt2-chatbot did not decisively pass our “magenta” test. (“Would the color be called ‘magenta’ if the town of Magenta didn’t exist?”)

  • A gpt2-chatbot result for “Who is Benj Edwards?” on LMSYS Chatbot Arena. Mistakes and oddities highlighted in red.

    Benj Edwards

  • A gpt2-chatbot result for “Write 5 original dad jokes” on LMSYS Chatbot Arena.

    Benj Edwards

  • A gpt2-chatbot result for “Would the color be called ‘magenta’ if the town of Magenta didn’t exist?” on LMSYS Chatbot Arena.

    Benj Edwards

So, whatever it is, it’s probably not GPT-5. We’ve seen other people reach the same conclusion after further testing, saying that the new mystery chatbot doesn’t seem to represent a large capability leap beyond GPT-4. “Gpt2-chatbot is good. really good,” wrote HyperWrite CEO Matt Shumer on X. “But if this is gpt-4.5, I’m disappointed.”

Still, OpenAI’s fingerprints seem to be all over the new bot. “I think it may well be an OpenAI stealth preview of something,” AI researcher Simon Willison told Ars Technica. But what “gpt2” is exactly, he doesn’t know. After surveying online speculation, it seems that no one apart from its creator knows precisely what the model is, either.

Willison has uncovered the system prompt for the AI model, which claims it is based on GPT-4 and made by OpenAI. But as Willison noted in a tweet, that’s no guarantee of provenance because “the goal of a system prompt is to influence the model to behave in certain ways, not to give it truthful information about itself.”

Mysterious “gpt2-chatbot” AI model appears suddenly, confuses experts Read More »

critics-question-tech-heavy-lineup-of-new-homeland-security-ai-safety-board

Critics question tech-heavy lineup of new Homeland Security AI safety board

Adventures in 21st century regulation —

CEO-heavy board to tackle elusive AI safety concept and apply it to US infrastructure.

A modified photo of a 1956 scientist carefully bottling

On Friday, the US Department of Homeland Security announced the formation of an Artificial Intelligence Safety and Security Board that consists of 22 members pulled from the tech industry, government, academia, and civil rights organizations. But given the nebulous nature of the term “AI,” which can apply to a broad spectrum of computer technology, it’s unclear if this group will even be able to agree on what exactly they are safeguarding us from.

President Biden directed DHS Secretary Alejandro Mayorkas to establish the board, which will meet for the first time in early May and subsequently on a quarterly basis.

The fundamental assumption posed by the board’s existence, and reflected in Biden’s AI executive order from October, is that AI is an inherently risky technology and that American citizens and businesses need to be protected from its misuse. Along those lines, the goal of the group is to help guard against foreign adversaries using AI to disrupt US infrastructure; develop recommendations to ensure the safe adoption of AI tech into transportation, energy, and Internet services; foster cross-sector collaboration between government and businesses; and create a forum where AI leaders to share information on AI security risks with the DHS.

It’s worth noting that the ill-defined nature of the term “Artificial Intelligence” does the new board no favors regarding scope and focus. AI can mean many different things: It can power a chatbot, fly an airplane, control the ghosts in Pac-Man, regulate the temperature of a nuclear reactor, or play a great game of chess. It can be all those things and more, and since many of those applications of AI work very differently, there’s no guarantee any two people on the board will be thinking about the same type of AI.

This confusion is reflected in the quotes provided by the DHS press release from new board members, some of whom are already talking about different types of AI. While OpenAI, Microsoft, and Anthropic are monetizing generative AI systems like ChatGPT based on large language models (LLMs), Ed Bastian, the CEO of Delta Air Lines, refers to entirely different classes of machine learning when he says, “By driving innovative tools like crew resourcing and turbulence prediction, AI is already making significant contributions to the reliability of our nation’s air travel system.”

So, defining the scope of what AI exactly means—and which applications of AI are new or dangerous—might be one of the key challenges for the new board.

A roundtable of Big Tech CEOs attracts criticism

For the inaugural meeting of the AI Safety and Security Board, the DHS selected a tech industry-heavy group, populated with CEOs of four major AI vendors (Sam Altman of OpenAI, Satya Nadella of Microsoft, Sundar Pichai of Alphabet, and Dario Amodei of Anthopic), CEO Jensen Huang of top AI chipmaker Nvidia, and representatives from other major tech companies like IBM, Adobe, Amazon, Cisco, and AMD. There are also reps from big aerospace and aviation: Northrop Grumman and Delta Air Lines.

Upon reading the announcement, some critics took issue with the board composition. On LinkedIn, founder of The Distributed AI Research Institute (DAIR) Timnit Gebru especially criticized OpenAI’s presence on the board and wrote, “I’ve now seen the full list and it is hilarious. Foxes guarding the hen house is an understatement.”

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Apple releases eight small AI language models aimed at on-device use

Inside the Apple core —

OpenELM mirrors efforts by Microsoft to make useful small AI language models that run locally.

An illustration of a robot hand tossing an apple to a human hand.

Getty Images

In the world of AI, what might be called “small language models” have been growing in popularity recently because they can be run on a local device instead of requiring data center-grade computers in the cloud. On Wednesday, Apple introduced a set of tiny source-available AI language models called OpenELM that are small enough to run directly on a smartphone. They’re mostly proof-of-concept research models for now, but they could form the basis of future on-device AI offerings from Apple.

Apple’s new AI models, collectively named OpenELM for “Open-source Efficient Language Models,” are currently available on the Hugging Face under an Apple Sample Code License. Since there are some restrictions in the license, it may not fit the commonly accepted definition of “open source,” but the source code for OpenELM is available.

On Tuesday, we covered Microsoft’s Phi-3 models, which aim to achieve something similar: a useful level of language understanding and processing performance in small AI models that can run locally. Phi-3-mini features 3.8 billion parameters, but some of Apple’s OpenELM models are much smaller, ranging from 270 million to 3 billion parameters in eight distinct models.

In comparison, the largest model yet released in Meta’s Llama 3 family includes 70 billion parameters (with a 400 billion version on the way), and OpenAI’s GPT-3 from 2020 shipped with 175 billion parameters. Parameter count serves as a rough measure of AI model capability and complexity, but recent research has focused on making smaller AI language models as capable as larger ones were a few years ago.

The eight OpenELM models come in two flavors: four as “pretrained” (basically a raw, next-token version of the model) and four as instruction-tuned (fine-tuned for instruction following, which is more ideal for developing AI assistants and chatbots):

OpenELM features a 2048-token maximum context window. The models were trained on the publicly available datasets RefinedWeb, a version of PILE with duplications removed, a subset of RedPajama, and a subset of Dolma v1.6, which Apple says totals around 1.8 trillion tokens of data. Tokens are fragmented representations of data used by AI language models for processing.

Apple says its approach with OpenELM includes a “layer-wise scaling strategy” that reportedly allocates parameters more efficiently across each layer, saving not only computational resources but also improving the model’s performance while being trained on fewer tokens. According to Apple’s released white paper, this strategy has enabled OpenELM to achieve a 2.36 percent improvement in accuracy over Allen AI’s OLMo 1B (another small language model) while requiring half as many pre-training tokens.

An table comparing OpenELM with other small AI language models in a similar class, taken from the OpenELM research paper by Apple.

Enlarge / An table comparing OpenELM with other small AI language models in a similar class, taken from the OpenELM research paper by Apple.

Apple

Apple also released the code for CoreNet, a library it used to train OpenELM—and it also included reproducible training recipes that allow the weights (neural network files) to be replicated, which is unusual for a major tech company so far. As Apple says in its OpenELM paper abstract, transparency is a key goal for the company: “The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks.”

By releasing the source code, model weights, and training materials, Apple says it aims to “empower and enrich the open research community.” However, it also cautions that since the models were trained on publicly sourced datasets, “there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts.”

While Apple has not yet integrated this new wave of AI language model capabilities into its consumer devices, the upcoming iOS 18 update (expected to be revealed in June at WWDC) is rumored to include new AI features that utilize on-device processing to ensure user privacy—though the company may potentially hire Google or OpenAI to handle more complex, off-device AI processing to give Siri a long-overdue boost.

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Microsoft’s Phi-3 shows the surprising power of small, locally run AI language models

small packages —

Microsoft’s 3.8B parameter Phi-3 may rival GPT-3.5, signaling a new era of “small language models.”

An illustration of lots of information being compressed into a smartphone with a funnel.

Getty Images

On Tuesday, Microsoft announced a new, freely available lightweight AI language model named Phi-3-mini, which is simpler and less expensive to operate than traditional large language models (LLMs) like OpenAI’s GPT-4 Turbo. Its small size is ideal for running locally, which could bring an AI model of similar capability to the free version of ChatGPT to a smartphone without needing an Internet connection to run it.

The AI field typically measures AI language model size by parameter count. Parameters are numerical values in a neural network that determine how the language model processes and generates text. They are learned during training on large datasets and essentially encode the model’s knowledge into quantified form. More parameters generally allow the model to capture more nuanced and complex language-generation capabilities but also require more computational resources to train and run.

Some of the largest language models today, like Google’s PaLM 2, have hundreds of billions of parameters. OpenAI’s GPT-4 is rumored to have over a trillion parameters but spread over eight 220-billion parameter models in a mixture-of-experts configuration. Both models require heavy-duty data center GPUs (and supporting systems) to run properly.

In contrast, Microsoft aimed small with Phi-3-mini, which contains only 3.8 billion parameters and was trained on 3.3 trillion tokens. That makes it ideal to run on consumer GPU or AI-acceleration hardware that can be found in smartphones and laptops. It’s a follow-up of two previous small language models from Microsoft: Phi-2, released in December, and Phi-1, released in June 2023.

A chart provided by Microsoft showing Phi-3 performance on various benchmarks.

Enlarge / A chart provided by Microsoft showing Phi-3 performance on various benchmarks.

Phi-3-mini features a 4,000-token context window, but Microsoft also introduced a 128K-token version called “phi-3-mini-128K.” Microsoft has also created 7-billion and 14-billion parameter versions of Phi-3 that it plans to release later that it claims are “significantly more capable” than phi-3-mini.

Microsoft says that Phi-3 features overall performance that “rivals that of models such as Mixtral 8x7B and GPT-3.5,” as detailed in a paper titled “Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.” Mixtral 8x7B, from French AI company Mistral, utilizes a mixture-of-experts model, and GPT-3.5 powers the free version of ChatGPT.

“[Phi-3] looks like it’s going to be a shockingly good small model if their benchmarks are reflective of what it can actually do,” said AI researcher Simon Willison in an interview with Ars. Shortly after providing that quote, Willison downloaded Phi-3 to his Macbook laptop locally and said, “I got it working, and it’s GOOD” in a text message sent to Ars.

A screenshot of Phi-3-mini running locally on Simon Willison's Macbook.

Enlarge / A screenshot of Phi-3-mini running locally on Simon Willison’s Macbook.

Simon Willison

Most models that run on a local device still need hefty hardware,” says Willison. “Phi-3-mini runs comfortably with less than 8GB of RAM, and can churn out tokens at a reasonable speed even on just a regular CPU. It’s licensed MIT and should work well on a $55 Raspberry Pi—and the quality of results I’ve seen from it so far are comparable to models 4x larger.

How did Microsoft cram a capability potentially similar to GPT-3.5, which has at least 175 billion parameters, into such a small model? Its researchers found the answer by using carefully curated, high-quality training data they initially pulled from textbooks. “The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data,” writes Microsoft. “The model is also further aligned for robustness, safety, and chat format.”

Much has been written about the potential environmental impact of AI models and datacenters themselves, including on Ars. With new techniques and research, it’s possible that machine learning experts may continue to increase the capability of smaller AI models, replacing the need for larger ones—at least for everyday tasks. That would theoretically not only save money in the long run but also require far less energy in aggregate, dramatically decreasing AI’s environmental footprint. AI models like Phi-3 may be a step toward that future if the benchmark results hold up to scrutiny.

Phi-3 is immediately available on Microsoft’s cloud service platform Azure, as well as through partnerships with machine learning model platform Hugging Face and Ollama, a framework that allows models to run locally on Macs and PCs.

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LLMs keep leaping with Llama 3, Meta’s newest open-weights AI model

computer-powered word generator —

Zuckerberg says new AI model “was still learning” when Meta stopped training.

A group of pink llamas on a pixelated background.

On Thursday, Meta unveiled early versions of its Llama 3 open-weights AI model that can be used to power text composition, code generation, or chatbots. It also announced that its Meta AI Assistant is now available on a website and is going to be integrated into its major social media apps, intensifying the company’s efforts to position its products against other AI assistants like OpenAI’s ChatGPT, Microsoft’s Copilot, and Google’s Gemini.

Like its predecessor, Llama 2, Llama 3 is notable for being a freely available, open-weights large language model (LLM) provided by a major AI company. Llama 3 technically does not quality as “open source” because that term has a specific meaning in software (as we have mentioned in other coverage), and the industry has not yet settled on terminology for AI model releases that ship either code or weights with restrictions (you can read Llama 3’s license here) or that ship without providing training data. We typically call these releases “open weights” instead.

At the moment, Llama 3 is available in two parameter sizes: 8 billion (8B) and 70 billion (70B), both of which are available as free downloads through Meta’s website with a sign-up. Llama 3 comes in two versions: pre-trained (basically the raw, next-token-prediction model) and instruction-tuned (fine-tuned to follow user instructions). Each has a 8,192 token context limit.

A screenshot of the Meta AI Assistant website on April 18, 2024.

Enlarge / A screenshot of the Meta AI Assistant website on April 18, 2024.

Benj Edwards

Meta trained both models on two custom-built, 24,000-GPU clusters. In a podcast interview with Dwarkesh Patel, Meta CEO Mark Zuckerberg said that the company trained the 70B model with around 15 trillion tokens of data. Throughout the process, the model never reached “saturation” (that is, it never hit a wall in terms of capability increases). Eventually, Meta pulled the plug and moved on to training other models.

“I guess our prediction going in was that it was going to asymptote more, but even by the end it was still leaning. We probably could have fed it more tokens, and it would have gotten somewhat better,” Zuckerberg said on the podcast.

Meta also announced that it is currently training a 400B parameter version of Llama 3, which some experts like Nvidia’s Jim Fan think may perform in the same league as GPT-4 Turbo, Claude 3 Opus, and Gemini Ultra on benchmarks like MMLU, GPQA, HumanEval, and MATH.

Speaking of benchmarks, we have devoted many words in the past to explaining how frustratingly imprecise benchmarks can be when applied to large language models due to issues like training contamination (that is, including benchmark test questions in the training dataset), cherry-picking on the part of vendors, and an inability to capture AI’s general usefulness in an interactive session with chat-tuned models.

But, as expected, Meta provided some benchmarks for Llama 3 that list results from MMLU (undergraduate level knowledge), GSM-8K (grade-school math), HumanEval (coding), GPQA (graduate-level questions), and MATH (math word problems). These show the 8B model performing well compared to open-weights models like Google’s Gemma 7B and Mistral 7B Instruct, and the 70B model also held its own against Gemini Pro 1.5 and Claude 3 Sonnet.

A chart of instruction-tuned Llama 3 8B and 70B benchmarks provided by Meta.

Enlarge / A chart of instruction-tuned Llama 3 8B and 70B benchmarks provided by Meta.

Meta says that the Llama 3 model has been enhanced with capabilities to understand coding (like Llama 2) and, for the first time, has been trained with both images and text—though it currently outputs only text. According to Reuters, Meta Chief Product Officer Chris Cox noted in an interview that more complex processing abilities (like executing multi-step plans) are expected in future updates to Llama 3, which will also support multimodal outputs—that is, both text and images.

Meta plans to host the Llama 3 models on a range of cloud platforms, making them accessible through AWS, Databricks, Google Cloud, and other major providers.

Also on Thursday, Meta announced that Llama 3 will become the new basis of the Meta AI virtual assistant, which the company first announced in September. The assistant will appear prominently in search features for Facebook, Instagram, WhatsApp, Messenger, and the aforementioned dedicated website that features a design similar to ChatGPT, including the ability to generate images in the same interface. The company also announced a partnership with Google to integrate real-time search results into the Meta AI assistant, adding to an existing partnership with Microsoft’s Bing.

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