During her tenure, Vestager has repeatedly targeted the world’s biggest tech companies, with some of the toughest actions against tech giants such as Apple, Google, and Microsoft.
The EU Commission on Thursday said Meta is “dominant in the market for personal social networks (…) as well as in the national markets for online display advertising on social media.”
Facebook Marketplace, launched in 2016, is a popular platform to buy and sell second-hand goods, especially household items such as furniture.
Meta has argued that it operates in a highly competitive environment. In a post published on Thursday, the tech giant said marketplaces in Europe continue “to grow and dominate in the EU,” pointing to platforms such as eBay, Leboncoin in France, and Marktplaats in the Netherlands, as “formidable competitors.”
Meta’s fine comes at a period of political transition both in the EU and the US.
Brussels officials have been aggressive both in their rhetoric and their antitrust probes against Big Tech giants as they sought to open markets for local start-ups.
In the past five years, EU regulators have also passed a landmark piece of legislation—the Digital Markets Act—with the aim to slow down dominant tech players and boost the local tech industry.
However, some observers expect the new commission, which is set to start a new 5-year term in weeks, to strike a more conciliatory tone over fears of retaliation from the incoming Trump administration.
Since its founders started Anthropic in 2021, the company has marketed itself as one that takes an ethics- and safety-focused approach to AI development. The company differentiates itself from competitors like OpenAI by adopting what it calls responsible development practices and self-imposed ethical constraints on its models, such as its “Constitutional AI” system.
As Futurism points out, this new defense partnership appears to conflict with Anthropic’s public “good guy” persona, and pro-AI pundits on social media are noticing. Frequent AI commentator Nabeel S. Qureshi wrote on X, “Imagine telling the safety-concerned, effective altruist founders of Anthropic in 2021 that a mere three years after founding the company, they’d be signing partnerships to deploy their ~AGI model straight to the military frontlines.“
Aside from the implications of working with defense and intelligence agencies, the deal connects Anthropic with Palantir, a controversial company which recently won a $480 million contract to develop an AI-powered target identification system called Maven Smart System for the US Army. Project Maven has sparked criticism within the tech sector over military applications of AI technology.
It’s worth noting that Anthropic’s terms of service do outline specific rules and limitations for government use. These terms permit activities like foreign intelligence analysis and identifying covert influence campaigns, while prohibiting uses such as disinformation, weapons development, censorship, and domestic surveillance. Government agencies that maintain regular communication with Anthropic about their use of Claude may receive broader permissions to use the AI models.
Even if Claude is never used to target a human or as part of a weapons system, other issues remain. While its Claude models are highly regarded in the AI community, they (like all LLMs) have the tendency to confabulate, potentially generating incorrect information in a way that is difficult to detect.
That’s a huge potential problem that could impact Claude’s effectiveness with secret government data, and that fact, along with the other associations, has Futurism’s Victor Tangermann worried. As he puts it, “It’s a disconcerting partnership that sets up the AI industry’s growing ties with the US military-industrial complex, a worrying trend that should raise all kinds of alarm bells given the tech’s many inherent flaws—and even more so when lives could be at stake.”
The lawsuit was filed by Ethan Zuckerman, a professor at University of Massachusetts Amherst. He feared that Meta might sue to block his tool, Unfollow Everything 2.0, because Meta threatened to sue to block the original tool when it was released by another developer. In May, Zuckerman told Ars that he was “suing Facebook to make it better” and planned to use Section 230’s shield to do it.
Zuckerman’s novel legal theory argued that Congress always intended for Section 230 to protect third-party tools designed to empower users to take control over potentially toxic online environments. In his complaint, Zuckerman tried to convince a US district court in California that:
Section 230(c)(2)(B) immunizes from legal liability “a provider of software or enabling tools that filter, screen, allow, or disallow content that the provider or user considers obscene, lewd, lascivious, filthy, excessively violent, harassing, or otherwise objectionable.” Through this provision, Congress intended to promote the development of filtering tools that enable users to curate their online experiences and avoid content they would rather not see.
Digital rights advocates, the Electronic Frontier Foundation (EFF), the Center for Democracy and Technology, and the American Civil Liberties Union of Northern California, supported Zuckerman’s case, urging that the court protect middleware. But on Thursday, Judge Jacqueline Scott Corley granted Meta’s motion to dismiss at a hearing.
Corley has not yet posted her order on the motion to dismiss, but Zuckerman’s lawyers at the Knight Institute confirmed to Ars that their Section 230 argument did not factor into her decision. In a statement, lawyers said that Corley left the door open on the Section 230 claims, and EFF senior staff attorney Sophia Cope, who was at the hearing, told Ars Corley agreed that on “the merits the case raises important issues.”
On Friday, Meta announced a preview of Movie Gen, a new suite of AI models designed to create and manipulate video, audio, and images, including creating a realistic video from a single photo of a person. The company claims the models outperform other video-synthesis models when evaluated by humans, pushing us closer to a future where anyone can synthesize a full video of any subject on demand.
The company does not yet have plans of when or how it will release these capabilities to the public, but Meta says Movie Gen is a tool that may allow people to “enhance their inherent creativity” rather than replace human artists and animators. The company envisions future applications such as easily creating and editing “day in the life” videos for social media platforms or generating personalized animated birthday greetings.
Movie Gen builds on Meta’s previous work in video synthesis, following 2022’s Make-A-Scene video generator and the Emu image-synthesis model. Using text prompts for guidance, this latest system can generate custom videos with sounds for the first time, edit and insert changes into existing videos, and transform images of people into realistic personalized videos.
An AI-generated video of a baby hippo swimming around, created with Meta Movie Gen.
Meta isn’t the only game in town when it comes to AI video synthesis. Google showed off a new model called “Veo” in May, and Meta says that in human preference tests, its Movie Gen outputs beat OpenAI’s Sora, Runway Gen-3, and Chinese video model Kling.
Movie Gen’s video-generation model can create 1080p high-definition videos up to 16 seconds long at 16 frames per second from text descriptions or an image input. Meta claims the model can handle complex concepts like object motion, subject-object interactions, and camera movements.
AI-generated video from Meta Movie Gen with the prompt: “A ghost in a white bedsheet faces a mirror. The ghost’s reflection can be seen in the mirror. The ghost is in a dusty attic, filled with old beams, cloth-covered furniture. The attic is reflected in the mirror. The light is cool and natural. The ghost dances in front of the mirror.”
Even so, as we’ve seen with previous AI video generators, Movie Gen’s ability to generate coherent scenes on a particular topic is likely dependent on the concepts found in the example videos that Meta used to train its video-synthesis model. It’s worth keeping in mind that cherry-picked results from video generators often differ dramatically from typical results and getting a coherent result may require lots of trial and error.
To prevent anyone from being doxxed, the co-creators are not releasing the code, Nguyen said on social media site X. They did, however, outline how their disturbing tech works and how shocked random strangers used as test subjects were to discover how easily identifiable they are just from accessing with the smart glasses information posted publicly online.
Nguyen and Ardayfio tested out their technology at a subway station “on unsuspecting people in the real world,” 404 Media noted. To demonstrate how the tech could be abused to trick people, the students even claimed to know some of the test subjects, seemingly using information gleaned from the glasses to make resonant references and fake an acquaintance.
Dozens of test subjects were identified, the students claimed, although some results have been contested, 404 Media reported. To keep their face-scanning under the radar, the students covered up a light that automatically comes on when the Meta Ray Bans 2 are recording, Ardayfio said on X.
Opt out of PimEyes now, students warn
For Nguyen and Ardayfio, the point of the project was to persuade people to opt out of invasive search engines to protect their privacy online. An attempt to use I-XRAY to identify 404 Media reporter Joseph Cox, for example, didn’t work because he’d opted out of PimEyes.
But while privacy is clearly important to the students and their demo video strove to remove identifying information, at least one test subject was “easily” identified anyway, 404 Media reported. That test subject couldn’t be reached for comment, 404 Media reported.
So far, neither Facebook nor Google has chosen to release similar technologies that they developed linking smart glasses to face search engines, The New York Times reported.
Enlarge/ There’s been a lot of AI news this week, and covering it sometimes feels like running through a hall full of danging CRTs, just like this Getty Images illustration.
But the rest of the AI world doesn’t march to the same beat, doing its own thing and churning out new AI models and research by the minute. Here’s a roundup of some other notable AI news from the past week.
Google Gemini updates
On Tuesday, Google announced updates to its Gemini model lineup, including the release of two new production-ready models that iterate on past releases: Gemini-1.5-Pro-002 and Gemini-1.5-Flash-002. The company reported improvements in overall quality, with notable gains in math, long context handling, and vision tasks. Google claims a 7 percent increase in performance on the MMLU-Pro benchmark and a 20 percent improvement in math-related tasks. But as you know, if you’ve been reading Ars Technica for a while, AI typically benchmarks aren’t as useful as we would like them to be.
Along with model upgrades, Google introduced substantial price reductions for Gemini 1.5 Pro, cutting input token costs by 64 percent and output token costs by 52 percent for prompts under 128,000 tokens. As AI researcher Simon Willison noted on his blog, “For comparison, GPT-4o is currently $5/[million tokens] input and $15/m output and Claude 3.5 Sonnet is $3/m input and $15/m output. Gemini 1.5 Pro was already the cheapest of the frontier models and now it’s even cheaper.”
Google also increased rate limits, with Gemini 1.5 Flash now supporting 2,000 requests per minute and Gemini 1.5 Pro handling 1,000 requests per minute. Google reports that the latest models offer twice the output speed and three times lower latency compared to previous versions. These changes may make it easier and more cost-effective for developers to build applications with Gemini than before.
Meta launches Llama 3.2
On Wednesday, Meta announced the release of Llama 3.2, a significant update to its open-weights AI model lineup that we have covered extensively in the past. The new release includes vision-capable large language models (LLMs) in 11 billion and 90B parameter sizes, as well as lightweight text-only models of 1B and 3B parameters designed for edge and mobile devices. Meta claims the vision models are competitive with leading closed-source models on image recognition and visual understanding tasks, while the smaller models reportedly outperform similar-sized competitors on various text-based tasks.
Willison did some experiments with some of the smaller 3.2 models and reported impressive results for the models’ size. AI researcher Ethan Mollick showed off running Llama 3.2 on his iPhone using an app called PocketPal.
Meta also introduced the first official “Llama Stack” distributions, created to simplify development and deployment across different environments. As with previous releases, Meta is making the models available for free download, with license restrictions. The new models support long context windows of up to 128,000 tokens.
Google’s AlphaChip AI speeds up chip design
On Thursday, Google DeepMind announced what appears to be a significant advancement in AI-driven electronic chip design, AlphaChip. It began as a research project in 2020 and is now a reinforcement learning method for designing chip layouts. Google has reportedly used AlphaChip to create “superhuman chip layouts” in the last three generations of its Tensor Processing Units (TPUs), which are chips similar to GPUs designed to accelerate AI operations. Google claims AlphaChip can generate high-quality chip layouts in hours, compared to weeks or months of human effort. (Reportedly, Nvidia has also been using AI to help design its chips.)
Notably, Google also released a pre-trained checkpoint of AlphaChip on GitHub, sharing the model weights with the public. The company reported that AlphaChip’s impact has already extended beyond Google, with chip design companies like MediaTek adopting and building on the technology for their chips. According to Google, AlphaChip has sparked a new line of research in AI for chip design, potentially optimizing every stage of the chip design cycle from computer architecture to manufacturing.
That wasn’t everything that happened, but those are some major highlights. With the AI industry showing no signs of slowing down at the moment, we’ll see how next week goes.
Enlarge/ A man peers over a glass partition, seeking transparency.
The Open Source Initiative (OSI) recently unveiled its latest draft definition for “open source AI,” aiming to clarify the ambiguous use of the term in the fast-moving field. The move comes as some companies like Meta release trained AI language model weights and code with usage restrictions while using the “open source” label. This has sparked intense debates among free-software advocates about what truly constitutes “open source” in the context of AI.
For instance, Meta’s Llama 3 model, while freely available, doesn’t meet the traditional open source criteria as defined by the OSI for software because it imposes license restrictions on usage due to company size or what type of content is produced with the model. The AI image generator Flux is another “open” model that is not truly open source. Because of this type of ambiguity, we’ve typically described AI models that include code or weights with restrictions or lack accompanying training data with alternative terms like “open-weights” or “source-available.”
To address the issue formally, the OSI—which is well-known for its advocacy for open software standards—has assembled a group of about 70 participants, including researchers, lawyers, policymakers, and activists. Representatives from major tech companies like Meta, Google, and Amazon also joined the effort. The group’s current draft (version 0.0.9) definition of open source AI emphasizes “four fundamental freedoms” reminiscent of those defining free software: giving users of the AI system permission to use it for any purpose without permission, study how it works, modify it for any purpose, and share with or without modifications.
By establishing clear criteria for open source AI, the organization hopes to provide a benchmark against which AI systems can be evaluated. This will likely help developers, researchers, and users make more informed decisions about the AI tools they create, study, or use.
Truly open source AI may also shed light on potential software vulnerabilities of AI systems, since researchers will be able to see how the AI models work behind the scenes. Compare this approach with an opaque system such as OpenAI’s ChatGPT, which is more than just a GPT-4o large language model with a fancy interface—it’s a proprietary system of interlocking models and filters, and its precise architecture is a closely guarded secret.
OSI’s project timeline indicates that a stable version of the “open source AI” definition is expected to be announced in October at the All Things Open 2024 event in Raleigh, North Carolina.
“Permissionless innovation”
In a press release from May, the OSI emphasized the importance of defining what open source AI really means. “AI is different from regular software and forces all stakeholders to review how the Open Source principles apply to this space,” said Stefano Maffulli, executive director of the OSI. “OSI believes that everybody deserves to maintain agency and control of the technology. We also recognize that markets flourish when clear definitions promote transparency, collaboration and permissionless innovation.”
The organization’s most recent draft definition extends beyond just the AI model or its weights, encompassing the entire system and its components.
For an AI system to qualify as open source, it must provide access to what the OSI calls the “preferred form to make modifications.” This includes detailed information about the training data, the full source code used for training and running the system, and the model weights and parameters. All these elements must be available under OSI-approved licenses or terms.
Notably, the draft doesn’t mandate the release of raw training data. Instead, it requires “data information”—detailed metadata about the training data and methods. This includes information on data sources, selection criteria, preprocessing techniques, and other relevant details that would allow a skilled person to re-create a similar system.
The “data information” approach aims to provide transparency and replicability without necessarily disclosing the actual dataset, ostensibly addressing potential privacy or copyright concerns while sticking to open source principles, though that particular point may be up for further debate.
“The most interesting thing about [the definition] is that they’re allowing training data to NOT be released,” said independent AI researcher Simon Willison in a brief Ars interview about the OSI’s proposal. “It’s an eminently pragmatic approach—if they didn’t allow that, there would be hardly any capable ‘open source’ models.”
Enlarge/ Independent presidential candidate Robert F. Kennedy Jr.
The Children’s Health Defense (CHD), an anti-vaccine group founded by Robert F. Kennedy Jr, has once again failed to convince a court that Meta acted as a state agent when censoring the group’s posts and ads on Facebook and Instagram.
In his opinion affirming a lower court’s dismissal, US Ninth Circuit Court of Appeals Judge Eric Miller wrote that CHD failed to prove that Meta acted as an arm of the government in censoring posts. Concluding that Meta’s right to censor views that the platforms find “distasteful” is protected by the First Amendment, Miller denied CHD’s requested relief, which had included an injunction and civil monetary damages.
“Meta evidently believes that vaccines are safe and effective and that their use should be encouraged,” Miller wrote. “It does not lose the right to promote those views simply because they happen to be shared by the government.”
CHD told Reuters that the group “was disappointed with the decision and considering its legal options.”
The group first filed the complaint in 2020, arguing that Meta colluded with government officials to censor protected speech by labeling anti-vaccine posts as misleading or removing and shadowbanning CHD posts. This caused CHD’s traffic on the platforms to plummet, CHD claimed, and ultimately, its pages were removed from both platforms.
However, critically, Miller wrote, CHD did not allege that “the government was actually involved in the decisions to label CHD’s posts as ‘false’ or ‘misleading,’ the decision to put the warning label on CHD’s Facebook page, or the decisions to ‘demonetize’ or ‘shadow-ban.'”
“CHD has not alleged facts that allow us to infer that the government coerced Meta into implementing a specific policy,” Miller wrote.
Instead, Meta “was entitled to encourage” various “input from the government,” justifiably seeking vaccine-related information provided by the World Health Organization (WHO) and the US Centers for Disease Control and Prevention (CDC) as it navigated complex content moderation decisions throughout the pandemic, Miller wrote.
Therefore, Meta’s actions against CHD were due to “Meta’s own ‘policy of censoring,’ not any provision of federal law,” Miller concluded. “The evidence suggested that Meta had independent incentives to moderate content and exercised its own judgment in so doing.”
None of CHD’s theories that Meta coordinated with officials to deprive “CHD of its constitutional rights” were plausible, Miller wrote, whereas the “innocent alternative”—”that Meta adopted the policy it did simply because” CEO Mark Zuckerberg and Meta “share the government’s view that vaccines are safe and effective”—appeared “more plausible.”
Meta “does not become an agent of the government just because it decides that the CDC sometimes has a point,” Miller wrote.
Equally not persuasive were CHD’s notions that Section 230 immunity—which shields platforms from liability for third-party content—”‘removed all legal barriers’ to the censorship of vaccine-related speech,” such that “Meta’s restriction of that content should be considered state action.”
“That Section 230 operates in the background to immunize Meta if it chooses to suppress vaccine misinformation—whether because it shares the government’s health concerns or for independent commercial reasons—does not transform Meta’s choice into state action,” Miller wrote.
One judge dissented over Section 230 concerns
In his dissenting opinion, Judge Daniel Collins defended CHD’s Section 230 claim, however, suggesting that the appeals court erred and should have granted CHD injunctive and declaratory relief from alleged censorship. CHD CEO Mary Holland told The Defender that the group was pleased the decision was not unanimous.
According to Collins, who like Miller is a Trump appointee, Meta could never have built its massive social platforms without Section 230 immunity, which grants platforms the ability to broadly censor viewpoints they disfavor.
It was “important to keep in mind” that “the vast practical power that Meta exercises over the speech of millions of others ultimately rests on a government-granted privilege to which Meta is not constitutionally entitled,” Collins wrote. And this power “makes a crucial difference in the state-action analysis.”
As Collins sees it, CHD could plausibly allege that Meta’s communications with government officials about vaccine-related misinformation targeted specific users, like the “disinformation dozen” that includes both CHD and Kennedy. In that case, it appears possible to Collins that Section 230 provides a potential opportunity for government to target speech that it disfavors through mechanisms provided by the platforms.
“Having specifically and purposefully created an immunized power for mega-platform operators to freely censor the speech of millions of persons on those platforms, the Government is perhaps unsurprisingly tempted to then try to influence particular uses of such dangerous levers against protected speech expressing viewpoints the Government does not like,” Collins warned.
He further argued that “Meta’s relevant First Amendment rights” do not “give Meta an unbounded freedom to work with the Government in suppressing speech on its platforms.” Disagreeing with the majority, he wrote that “in this distinctive scenario, applying the state-action doctrine promotes individual liberty by keeping the Government’s hands away from the tempting levers of censorship on these vast platforms.”
The majority agreed, however, that while Section 230 immunity “is undoubtedly a significant benefit to companies like Meta,” lawmakers’ threats to weaken Section 230 did not suggest that Meta’s anti-vaccine policy was coerced state action.
“Many companies rely, in one way or another, on a favorable regulatory environment or the goodwill of the government,” Miller wrote. “If that were enough for state action, every large government contractor would be a state actor. But that is not the law.”
Facebook owner Meta has agreed to pay $1.4 billion to the state of Texas to settle claims that the company harvested millions of citizens’ biometric data without proper consent.
The settlement, to be paid over five years, is the largest ever obtained from an action brought by a single US state, said a statement from Attorney General Ken Paxton.
It also marks one of the largest penalties levied at Meta by regulators, second only to a $5 billion settlement it paid the US Federal Trade Commission in 2019 for the misuse of user data in the wake of the Cambridge Analytica privacy scandal.
The original complaint filed by Paxton in February 2022 accused Facebook’s now-closed facial recognition system of collecting biometric identifiers of “millions of Texans” from photos and videos posted on the platform without “informed consent.”
Meta launched a feature in 2011 called “tag suggestions” that recommended to users who to tag in photos and videos by scanning the “facial geometry” of those pictured, Paxton’s office said.
In 2021, a year before the lawsuit was filed, Meta announced it was shuttering its facial recognition system including the tag suggestions feature. It wiped the biometric data it had collected from 1 billion users, citing legal “uncertainty.”
The latest fine comes amid growing concern globally over privacy and data protection risks related to facial recognition, as well as algorithmic bias, although legislation is patchy, differing from jurisdiction to jurisdiction.
In 2021, Facebook agreed to pay a $650 million settlement in a class-action lawsuit in Illinois under a state privacy law over similar allegations related to its face-tagging system.
“This historic settlement demonstrates our commitment to standing up to the world’s biggest technology companies and holding them accountable for breaking the law and violating Texans’ privacy rights,” Paxton said in a statement. “Any abuse of Texans’ sensitive data will be met with the full force of the law.”
Meta previously said that the claims were without merit. However, the company and Texas agreed at the end of May to settle the lawsuit, just weeks before a trial was set to begin.
A spokesperson for Meta said on Tuesday: “We are pleased to resolve this matter, and look forward to exploring future opportunities to deepen our business investments in Texas, including potentially developing data centers.”
In the AI world, there’s a buzz in the air about a new AI language model released Tuesday by Meta: Llama 3.1 405B. The reason? It’s potentially the first time anyone can download a GPT-4-class large language model (LLM) for free and run it on their own hardware. You’ll still need some beefy hardware: Meta says it can run on a “single server node,” which isn’t desktop PC-grade equipment. But it’s a provocative shot across the bow of “closed” AI model vendors such as OpenAI and Anthropic.
“Llama 3.1 405B is the first openly available model that rivals the top AI models when it comes to state-of-the-art capabilities in general knowledge, steerability, math, tool use, and multilingual translation,” says Meta. Company CEO Mark Zuckerberg calls 405B “the first frontier-level open source AI model.”
In the AI industry, “frontier model” is a term for an AI system designed to push the boundaries of current capabilities. In this case, Meta is positioning 405B among the likes of the industry’s top AI models, such as OpenAI’s GPT-4o, Claude’s 3.5 Sonnet, and Google Gemini 1.5 Pro.
A chart published by Meta suggests that 405B gets very close to matching the performance of GPT-4 Turbo, GPT-4o, and Claude 3.5 Sonnet in benchmarks like MMLU (undergraduate level knowledge), GSM8K (grade school math), and HumanEval (coding).
But as we’ve noted many times since March, these benchmarks aren’t necessarily scientifically sound or translate to the subjective experience of interacting with AI language models. In fact, this traditional slate of AI benchmarks is so generally useless to laypeople that even Meta’s PR department now just posts a few images of charts and doesn’t even try to explain them in any detail.
Enlarge/ A Meta-provided chart that shows Llama 3.1 405B benchmark results versus other major AI models.
We’ve instead found that measuring the subjective experience of using a conversational AI model (through what might be called “vibemarking”) on A/B leaderboards like Chatbot Arena is a better way to judge new LLMs. In the absence of Chatbot Arena data, Meta has provided the results of its own human evaluations of 405B’s outputs that seem to show Meta’s new model holding its own against GPT-4 Turbo and Claude 3.5 Sonnet.
Enlarge/ A Meta-provided chart that shows how humans rated Llama 3.1 405B’s outputs compared to GPT-4 Turbo, GPT-4o, and Claude 3.5 Sonnet in its own studies.
Whatever the benchmarks, early word on the street (after the model leaked on 4chan yesterday) seems to match the claim that 405B is roughly equivalent to GPT-4. It took a lot of expensive computer training time to get there—and money, of which the social media giant has plenty to burn. Meta trained the 405B model on over 15 trillion tokens of training data scraped from the web (then parsed, filtered, and annotated by Llama 2), using more than 16,000 H100 GPUs.
So what’s with the 405B name? In this case, “405B” means 405 billion parameters, and parameters are numerical values that store trained information in a neural network. More parameters translate to a larger neural network powering the AI model, which generally (but not always) means more capability, such as better ability to make contextual connections between concepts. But larger-parameter models have a tradeoff in needing more computing power (AKA “compute”) to run.
We’ve been expecting the release of a 400 billion-plus parameter model of the Llama 3 family since Meta gave word that it was training one in April, and today’s announcement isn’t just about the biggest member of the Llama 3 family: There’s an entirely new iteration of improved Llama models with the designation “Llama 3.1.” That includes upgraded versions of its smaller 8B and 70B models, which now feature multilingual support and an extended context length of 128,000 tokens (the “context length” is roughly the working memory capacity of the model, and “tokens” are chunks of data used by LLMs to process information).
Meta says that 405B is useful for long-form text summarization, multilingual conversational agents, and coding assistants and for creating synthetic data used to train future AI language models. Notably, that last use-case—allowing developers to use outputs from Llama models to improve other AI models—is now officially supported by Meta’s Llama 3.1 license for the first time.
Abusing the term “open source”
Llama 3.1 405B is an open-weights model, which means anyone can download the trained neural network files and run them or fine-tune them. That directly challenges a business model where companies like OpenAI keep the weights to themselves and instead monetize the model through subscription wrappers like ChatGPT or charge for access by the token through an API.
Fighting the “closed” AI model is a big deal to Mark Zuckerberg, who simultaneously released a 2,300-word manifesto today on why the company believes in open releases of AI models, titled, “Open Source AI Is the Path Forward.” More on the terminology in a minute. But briefly, he writes about the need for customizable AI models that offer user control and encourage better data security, higher cost-efficiency, and better future-proofing, as opposed to vendor-locked solutions.
All that sounds reasonable, but undermining your competitors using a model subsidized by a social media war chest is also an efficient way to play spoiler in a market where you might not always win with the most cutting-edge tech. That benefits Meta, Zuckerberg says, because he doesn’t want to get locked into a system where companies like his have to pay a toll to access AI capabilities, drawing comparisons to “taxes” Apple levies on developers through its App Store.
Enlarge/ A screenshot of Mark Zuckerberg’s essay, “Open Source AI Is the Path Forward,” published on July 23, 2024.
So, about that “open source” term. As we first wrote in an update to our Llama 2 launch article a year ago, “open source” has a very particular meaning that has traditionally been defined by the Open Source Initiative. The AI industry has not yet settled on terminology for AI model releases that ship either code or weights with restrictions (such as Llama 3.1) or that ship without providing training data. We’ve been calling these releases “open weights” instead.
Unfortunately for terminology sticklers, Zuckerberg has now baked the erroneous “open source” label into the title of his potentially historic aforementioned essay on open AI releases, so fighting for the correct term in AI may be a losing battle. Still, his usage annoys people like independent AI researcher Simon Willison, who likes Zuckerberg’s essay otherwise.
“I see Zuck’s prominent misuse of ‘open source’ as a small-scale act of cultural vandalism,” Willison told Ars Technica. “Open source should have an agreed meaning. Abusing the term weakens that meaning which makes the term less generally useful, because if someone says ‘it’s open source,’ that no longer tells me anything useful. I have to then dig in and figure out what they’re actually talking about.”
The Llama 3.1 models are available for download through Meta’s own website and on Hugging Face. They both require providing contact information and agreeing to a license and an acceptable use policy, which means that Meta can technically legally pull the rug out from under your use of Llama 3.1 or its outputs at any time.
Surprisingly, this step wasn’t taken under laws like the Digital Services Act (DSA), the Digital Markets Act (DMA), or the General Data Protection Regulation (GDPR).
Instead, the EC announced Monday that Meta risked sanctions under EU consumer laws if it could not resolve key concerns about Meta’s so-called “pay or consent” model.
Meta’s model is seemingly problematic, the commission said, because Meta “requested consumers overnight to either subscribe to use Facebook and Instagram against a fee or to consent to Meta’s use of their personal data to be shown personalized ads, allowing Meta to make revenue out of it.”
Because users were given such short notice, they may have been “exposed to undue pressure to choose rapidly between the two models, fearing that they would instantly lose access to their accounts and their network of contacts,” the EC said.
To protect consumers, the EC joined national consumer protection authorities, sending a letter to Meta requiring the tech giant to propose solutions to resolve the commission’s biggest concerns by September 1.
That Meta’s “pay or consent” model may be “misleading” is a top concern because it uses the term “free” for ad-based plans, even though Meta “can make revenue from using their personal data to show them personalized ads.” It seems that while Meta does not consider giving away personal information to be a cost to users, the EC’s commissioner for justice, Didier Reynders, apparently does.
“Consumers must not be lured into believing that they would either pay and not be shown any ads anymore, or receive a service for free, when, instead, they would agree that the company used their personal data to make revenue with ads,” Reynders said. “EU consumer protection law is clear in this respect. Traders must inform consumers upfront and in a fully transparent manner on how they use their personal data. This is a fundamental right that we will protect.”
Additionally, the EC is concerned that Meta users might be confused about how “to navigate through different screens in the Facebook/Instagram app or web-version and to click on hyperlinks directing them to different parts of the Terms of Service or Privacy Policy to find out how their preferences, personal data, and user-generated data will be used by Meta to show them personalized ads.” They may also find Meta’s “imprecise terms and language” confusing, such as Meta referring to “your info” instead of clearly referring to consumers’ “personal data.”
To resolve the EC’s concerns, Meta may have to give EU users more time to decide if they want to pay to subscribe or consent to personal data collection for targeted ads. Or Meta may have to take more drastic steps by altering language and screens used when securing consent to collect data or potentially even scrapping its “pay or consent” model entirely, as pressure in the EU mounts.
“Subscriptions as an alternative to advertising are a well-established business model across many industries,” Meta’s spokesperson told Ars. “Subscription for no ads follows the direction of the highest court in Europe and we are confident it complies with European regulation.”
Meta’s model is “sneaky,” EC said
Since last year, the social media company has argued that its “subscription for no ads” model was “endorsed” by the highest court in Europe, the Court of Justice of the European Union (CJEU).
However, privacy advocates have noted that this alleged endorsement came following a CJEU case under the GDPR and was only presented as a hypothetical, rather than a formal part of the ruling, as Meta seems to interpret.
What the CJEU said was that “users must be free to refuse individually”—”in the context of” signing up for services—”to give their consent to particular data processing operations not necessary” for Meta to provide such services “without being obliged to refrain entirely from using the service.” That “means that those users are to be offered, if necessary for an appropriate fee, an equivalent alternative not accompanied by such data processing operations,” the CJEU said.
The nuance here may matter when it comes to Meta’s proposed solutions even if the EC accepts the CJEU’s suggestion of an acceptable alternative as setting some sort of legal precedent. Because the consumer protection authorities raised the action due to Meta suddenly changing the consent model for existing users—not “in the context of” signing up for services—Meta may struggle to persuade the EC that existing users weren’t misled and pressured into paying for a subscription or consenting to ads, given how fast Meta’s policy shifted.
Meta risks sanctions if a compromise can’t be reached, the EC said. Under the EU’s Unfair Contract Terms Directive, for example, Meta could be fined up to 4 percent of its annual turnover if consumer protection authorities are unsatisfied with Meta’s proposed solutions.
The EC’s vice president for values and transparency, Věra Jourová, provided a statement in the press release, calling Meta’s abrupt introduction of the “pay or consent” model “sneaky.”
“We are proud of our strong consumer protection laws which empower Europeans to have the right to be accurately informed about changes such as the one proposed by Meta,” Jourová said. “In the EU, consumers are able to make truly informed choices and we now take action to safeguard this right.”
This week, Meta asked a US district court in California to toss a lawsuit filed by a professor, Ethan Zuckerman, who fears that Meta will sue him if he releases a tool that would give Facebook users an automated way to easily remove all content from their feeds.
Zuckerman has alleged that the imminent threat of a lawsuit from Meta has prevented him from releasing Unfollow Everything 2.0, suggesting that a cease-and-desist letter sent to the creator of the original Unfollow Everything substantiates his fears.
He’s hoping the court will find that either releasing his tool would not breach Facebook’s terms of use—which prevent “accessing or collecting data from Facebook ‘using automated means'”—or that those terms conflict with public policy. Among laws that Facebook’s terms allegedly conflict with are the First Amendment, section 230 of the Communications Decency Act, the Computer Fraud and Abuse Act (CFAA), as well as California’s Computer Data Access and Fraud Act (CDAFA) and state privacy laws.
But Meta claimed in its motion to dismiss that Zuckerman’s suit is too premature, mostly because the tool has not yet been built and Meta has not had a chance to review the “non-existent tool” to determine how Unfollow Everything 2.0 might impact its platform or its users.
“Besides bald assertions about how Plaintiff intends Unfollow Everything 2.0 to work and what he plans to do with it, there are no concrete facts that would enable this Court to adjudicate potential legal claims regarding this tool—which, at present, does not even operate in the real world,” Meta argued.
Meta wants all of Zuckerman’s claims to be dismissed, arguing that “adjudicating Plaintiff’s claims would require needless rulings on hypothetical applications of California law, would likely result in duplicative litigation, and would encourage forum shopping.”
At the heart of Meta’s defense is a claim that there’s no telling yet if Zuckerman will ever be able to release the tool, although Zuckerman said he was prepared to finish the build within six weeks of a court win. Last May, Zuckerman told Ars that because Facebook’s functionality could change while the lawsuit is settled, it’s better to wait to finish building the tool because Facebook’s design is always changing.
Meta claimed that Zuckerman can’t confirm if Unfollow Everything 2.0 would work as described in his suit precisely because his findings are based on Facebook’s current interface, and the “process for unfollowing has changed over time and will likely continue to change.”
Further, Meta argued that the original Unfollow Everything performed in a different way—by logging in on behalf of users and automatically unfollowing everything, rather than performing the automated unfollowing when the users themselves log in. Because of that, Meta argued that the new tool may not prompt the same response from Meta.
A senior staff attorney at the Knight Institute who helped draft Zuckerman’s complaint, Ramya Krishnan, told Ars that the two tools operate nearly identically, however.
“Professor Zuckerman’s tool and the original Unfollow Everything work in essentially the same way,” Krishnan told Ars. “They automatically unfollow all of a user’s friends, groups, and pages after the user installs the tool and logs in to Facebook using their web browser.”
Ultimately, Meta claimed that there’s no telling if Meta would even sue over the tool’s automated access to user data, dismissing Zuckerman’s fears as unsubstantiated.
Only when the tool is out in the wild and Facebook is able to determine “actual, concrete facts about how it works in practice” that “may prove problematic” will Meta know if a legal response is needed, Meta claimed. Without reviewing the technical specs, Meta argued, Meta has no way to assess the damages or know if it would sue over a breach of contract, as alleged, or perhaps over other claims not alleged, such as trademark infringement.
