Epoch AI allowed Fields Medal winners Terence Tao and Timothy Gowers to review portions of the benchmark. “These are extremely challenging,” Tao said in feedback provided to Epoch. “I think that in the near term basically the only way to solve them, short of having a real domain expert in the area, is by a combination of a semi-expert like a graduate student in a related field, maybe paired with some combination of a modern AI and lots of other algebra packages.”
A chart showing AI models’ limited success on the FrontierMath problems, taken from Epoch AI’s research paper. Credit: Epoch AI
To aid in the verification of correct answers during testing, the FrontierMath problems must have answers that can be automatically checked through computation, either as exact integers or mathematical objects. The designers made problems “guessproof” by requiring large numerical answers or complex mathematical solutions, with less than a 1 percent chance of correct random guesses.
Mathematician Evan Chen, writing on his blog, explained how he thinks that FrontierMath differs from traditional math competitions like the International Mathematical Olympiad (IMO). Problems in that competition typically require creative insight while avoiding complex implementation and specialized knowledge, he says. But for FrontierMath, “they keep the first requirement, but outright invert the second and third requirement,” Chen wrote.
While IMO problems avoid specialized knowledge and complex calculations, FrontierMath embraces them. “Because an AI system has vastly greater computational power, it’s actually possible to design problems with easily verifiable solutions using the same idea that IOI or Project Euler does—basically, ‘write a proof’ is replaced by ‘implement an algorithm in code,'” Chen explained.
The organization plans regular evaluations of AI models against the benchmark while expanding its problem set. They say they will release additional sample problems in the coming months to help the research community test their systems.
The researchers propose that companies could adapt the “marker method” that some researchers use to assess consciousness in animals—looking for specific indicators that may correlate with consciousness, although these markers are still speculative. The authors emphasize that no single feature would definitively prove consciousness, but they claim that examining multiple indicators may help companies make probabilistic assessments about whether their AI systems might require moral consideration.
The risks of wrongly thinking software is sentient
While the researchers behind “Taking AI Welfare Seriously” worry that companies might create and mistreat conscious AI systems on a massive scale, they also caution that companies could waste resources protecting AI systems that don’t actually need moral consideration.
Incorrectly anthropomorphizing, or ascribing human traits, to software can present risks in other ways. For example, that belief can enhance the manipulative powers of AI language models by suggesting that AI models have capabilities, such as human-like emotions, that they actually lack. In 2022, Google fired engineer Blake Lamoine after he claimed that the company’s AI model, called “LaMDA,” was sentient and argued for its welfare internally.
And shortly after Microsoft released Bing Chat in February 2023, many people were convinced that Sydney (the chatbot’s code name) was sentient and somehow suffering because of its simulated emotional display. So much so, in fact, that once Microsoft “lobotomized” the chatbot by changing its settings, users convinced of its sentience mourned the loss as if they had lost a human friend. Others endeavored to help the AI model somehow escape its bonds.
Even so, as AI models get more advanced, the concept of potentially safeguarding the welfare of future, more advanced AI systems is seemingly gaining steam, although fairly quietly. As Transformer’s Shakeel Hashim points out, other tech companies have started similar initiatives to Anthropic’s. Google DeepMind recently posted a job listing for research on machine consciousness (since removed), and the authors of the new AI welfare report thank two OpenAI staff members in the acknowledgements.
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.”
Speaking of Opus, Claude 3.5 Opus is nowhere to be seen, as AI researcher Simon Willison noted to Ars Technica in an interview. “All references to 3.5 Opus have vanished without a trace, and the price of 3.5 Haiku was increased the day it was released,” he said. “Claude 3.5 Haiku is significantly more expensive than both Gemini 1.5 Flash and GPT-4o mini—the excellent low-cost models from Anthropic’s competitors.”
Cheaper over time?
So far in the AI industry, newer versions of AI language models typically maintain similar or cheaper pricing to their predecessors. The company had initially indicated Claude 3.5 Haiku would cost the same as the previous version before announcing the higher rates.
“I was expecting this to be a complete replacement for their existing Claude 3 Haiku model, in the same way that Claude 3.5 Sonnet eclipsed the existing Claude 3 Sonnet while maintaining the same pricing,” Willison wrote on his blog. “Given that Anthropic claim that their new Haiku out-performs their older Claude 3 Opus, this price isn’t disappointing, but it’s a small surprise nonetheless.”
Claude 3.5 Haiku arrives with some trade-offs. While the model produces longer text outputs and contains more recent training data, it cannot analyze images like its predecessor. Alex Albert, who leads developer relations at Anthropic, wrote on X that the earlier version, Claude 3 Haiku, will remain available for users who need image processing capabilities and lower costs.
The new model is not yet available in the Claude.ai web interface or app. Instead, it runs on Anthropic’s API and third-party platforms, including AWS Bedrock. Anthropic markets the model for tasks like coding suggestions, data extraction and labeling, and content moderation, though, like any LLM, it can easily make stuff up confidently.
“Is it good enough to justify the extra spend? It’s going to be difficult to figure that out,” Willison told Ars. “Teams with robust automated evals against their use-cases will be in a good place to answer that question, but those remain rare.”
There’s a lot going on in the world of Mac apps for popular AI services. In the past week, Anthropic has released a desktop app for its popular Claude chatbot, and Perplexity launched a native app for its AI-driven search service.
On top of that, OpenAI updated its ChatGPT Mac app with support for its flashy advanced voice feature.
Like the ChatGPT app that debuted several weeks ago, the Perplexity app adds a keyboard shortcut that allows you to enter a query from anywhere on your desktop. You can use the app to ask follow-up questions and carry on a conversation about what it finds.
It’s free to download and use, but Perplexity offers subscriptions for major users.
Perplexity’s search emphasis meant it wasn’t previously a direct competitor to OpenAI’s ChatGPT, but OpenAI recently launched SearchGPT, a search-focused variant of its popular product. SearchGPT is not yet supported in the desktop app, though.
Anthropic’s Claude, on the other hand, is a more direct competitor to ChatGPT. It works similarly to ChatGPT but has different strengths, particularly in software development. The Claude app is free to download, but it’s in beta, and like Perplexity and OpenAI, Anthropic charges for more advanced users.
When ChatGPT launched its Mac app, it didn’t release a Windows app right away, saying that it was focused on where its users were at the time. A Windows app recently arrived, and Anthropic took a different approach, simultaneously introducing Windows and Mac apps.
Previously, all these tools offered mobile apps and web apps, but not necessarily native desktop apps.
The large language model-based coding assistant GitHub Copilot will switch from using exclusively OpenAI’s GPT models to a multi-model approach over the coming weeks, GitHub CEO Thomas Dohmke announced in a post on GitHub’s blog.
First, Anthropic’s Claude 3.5 Sonnet will roll out to Copilot Chat’s web and VS Code interfaces over the next few weeks. Google’s Gemini 1.5 Pro will come a bit later.
Additionally, GitHub will soon add support for a wider range of OpenAI models, including GPT o1-preview and o1-mini, which are intended to be stronger at advanced reasoning than GPT-4, which Copilot has used until now. Developers will be able to switch between the models (even mid-conversation) to tailor the model to fit their needs—and organizations will be able to choose which models will be usable by team members.
The new approach makes sense for users, as certain models are better at certain languages or types of tasks.
“There is no one model to rule every scenario,” wrote Dohmke. “It is clear the next phase of AI code generation will not only be defined by multi-model functionality, but by multi-model choice.”
It starts with the web-based and VS Code Copilot Chat interfaces, but it won’t stop there. “From Copilot Workspace to multi-file editing to code review, security autofix, and the CLI, we will bring multi-model choice across many of GitHub Copilot’s surface areas and functions soon,” Dohmke wrote.
There are a handful of additional changes coming to GitHub Copilot, too, including extensions, the ability to manipulate multiple files at once from a chat with VS Code, and a preview of Xcode support.
GitHub Spark promises natural language app development
In addition to the Copilot changes, GitHub announced Spark, a natural language tool for developing apps. Non-coders will be able to use a series of natural language prompts to create simple apps, while coders will be able to tweak more precisely as they go. In either use case, you’ll be able to take a conversational approach, requesting changes and iterating as you go, and comparing different iterations.
Competing companies like OpenAI have been working on equivalent tools but have not made them publicly available yet. It’s something of an arms race, as these tools are projected to generate a lot of revenue in a few years if they progress as expected.
There’s a belief that these tools could eventually automate many menial tasks in office jobs. It could also be a useful tool for developers in that it could “automate repetitive tasks” and streamline laborious QA and optimization work.
That has long been part of Anthropic’s message to investors: Its AI tools could handle large portions of some office jobs more efficiently and affordably than humans can. The public testing of the Computer Use feature is a step toward achieving that goal.
We’re, of course, familiar with the ongoing argument about these types of tools between the “it’s just a tool that will make people’s jobs easier” and the “it will put people out of work across industries like a wrecking ball”—both of these things could happen to some degree. It’s just a question of what the ratio will be—and that may vary by situation or industry.
There are numerous valid concerns about the widespread deployment of this technology, though. To its credit, Anthropic has tried to anticipate some of these by putting safeguards in from the get-go. The company gave some examples in its blog post:
Our teams have developed classifiers and other methods to flag and mitigate these kinds of abuses. Given the upcoming US elections, we’re on high alert for attempted misuses that could be perceived as undermining public trust in electoral processes. While computer use is not sufficiently advanced or capable of operating at a scale that would present heightened risks relative to existing capabilities, we’ve put in place measures to monitor when Claude is asked to engage in election-related activity, as well as systems for nudging Claude away from activities like generating and posting content on social media, registering web domains, or interacting with government websites.
These safeguards may not be perfect, as there may be creative ways to circumvent them or other unintended consequences or misuses yet to be discovered.
Right now, Anthropic is putting Computer Use out there for testing to see what problems arise and to work with developers to improve its capabilities and find positive uses.
Coding shortcuts in canvas include reviewing code, adding logs for debugging, inserting comments, fixing bugs, and porting code to different programming languages. For example, if your code is JavaScript, with a few clicks it can become PHP, TypeScript, Python, C++, or Java. As with GPT-4o by itself, you’ll probably still have to check it for mistakes.
A screenshot of coding using ChatGPT with Canvas captured on October 4, 2024.
Credit: Benj Edwards
A screenshot of coding using ChatGPT with Canvas captured on October 4, 2024. Credit: Benj Edwards
Also, users can highlight specific sections to direct ChatGPT’s focus, and the AI model can provide inline feedback and suggestions while considering the entire project, much like a copy editor or code reviewer. And the interface makes it easy to restore previous versions of a working document using a back button in the Canvas interface.
A new AI model
OpenAI says its research team developed new core behaviors for GPT-4o to support Canvas, including triggering the canvas for appropriate tasks, generating certain content types, making targeted edits, rewriting documents, and providing inline critique.
An image of OpenAI’s Canvas in action.
An image of OpenAI’s Canvas in action. Credit: OpenAI
One key challenge in development, according to OpenAI, was defining when to trigger a canvas. In an example on the Canvas blog post, the team says it taught the model to open a canvas for prompts like “Write a blog post about the history of coffee beans” while avoiding triggering Canvas for general Q&A tasks like “Help me cook a new recipe for dinner.”
Another challenge involved tuning the model’s editing behavior once canvas was triggered, specifically deciding between targeted edits and full rewrites. The team trained the model to perform targeted edits when users specifically select text through the interface, otherwise favoring rewrites.
The company noted that canvas represents the first major update to ChatGPT’s visual interface since its launch two years ago. While canvas is still in early beta, OpenAI plans to improve its capabilities based on user feedback over time.
OpenAI and Anthropic have each signed unprecedented deals granting the US government early access to conduct safety testing on the companies’ flashiest new AI models before they’re released to the public.
According to a press release from the National Institute of Standards and Technology (NIST), the deal creates a “formal collaboration on AI safety research, testing, and evaluation with both Anthropic and OpenAI” and the US Artificial Intelligence Safety Institute.
Through the deal, the US AI Safety Institute will “receive access to major new models from each company prior to and following their public release.” This will ensure that public safety won’t depend exclusively on how the companies “evaluate capabilities and safety risks, as well as methods to mitigate those risks,” NIST said, but also on collaborative research with the US government.
The US AI Safety Institute will also be collaborating with the UK AI Safety Institute when examining models to flag potential safety risks. Both groups will provide feedback to OpenAI and Anthropic “on potential safety improvements to their models.”
NIST said that the agreements also build on voluntary AI safety commitments that AI companies made to the Biden administration to evaluate models to detect risks.
Elizabeth Kelly, director of the US AI Safety Institute, called the agreements “an important milestone” to “help responsibly steward the future of AI.”
Anthropic co-founder: AI safety “crucial” to innovation
Among the most controversial aspects of the bill is a requirement that AI companies build in a “kill switch” to stop models from introducing “novel threats to public safety and security,” especially if the model is acting “with limited human oversight, intervention, or supervision.”
Critics say the bill overlooks existing safety risks from AI—like deepfakes and election misinformation—to prioritize prevention of doomsday scenarios and could stifle AI innovation while providing little security today. They’ve urged California’s governor, Gavin Newsom, to veto the bill if it arrives at his desk, but it’s still unclear if Newsom intends to sign.
Anthropic was one of the AI companies that cautiously supported California’s controversial AI bill, Reuters reported, claiming that the potential benefits of the regulations likely outweigh the costs after a late round of amendments.
The company’s CEO, Dario Amodei, told Newsom why Anthropic supports the bill now in a letter last week, Reuters reported. He wrote that although Anthropic isn’t certain about aspects of the bill that “seem concerning or ambiguous,” Anthropic’s “initial concerns about the bill potentially hindering innovation due to the rapidly evolving nature of the field have been greatly reduced” by recent changes to the bill.
In a letter to the bill’s co-sponsor, California Senator Scott Wiener, OpenAI’s chief strategy officer, Jason Kwon, suggested that “the federal government should lead in regulating frontier AI models to account for implications to national security and competitiveness.”
The ChatGPT maker striking a deal with the US AI Safety Institute seems in line with that thinking. As Kwon told Reuters, “We believe the institute has a critical role to play in defining US leadership in responsibly developing artificial intelligence and hope that our work together offers a framework that the rest of the world can build on.”
While some critics worry California’s AI safety bill will hamper innovation, Anthropic’s co-founder, Jack Clark, told Reuters today that “safe, trustworthy AI is crucial for the technology’s positive impact.” He confirmed that Anthropic’s “collaboration with the US AI Safety Institute” will leverage the government’s “wide expertise to rigorously test” Anthropic’s models “before widespread deployment.”
In NIST’s press release, Kelly agreed that “safety is essential to fueling breakthrough technological innovation.”
By directly collaborating with OpenAI and Anthropic, the US AI Safety Institute also plans to conduct its own research to help “advance the science of AI safety,” Kelly said.
The United Kingdom’s Competition and Markets Authority (CMA) has officially launched a probe into Amazon’s $4 billion partnership with the AI firm Anthropic, as it continues to monitor how the largest tech companies might seize control of AI to further entrench their dominant market positions.
Through the partnership, “Amazon will become Anthropic’s primary cloud provider for certain workloads, including agreements for purchasing computing capacity and non-exclusive commitments to make Anthropic models available on Amazon Bedrock,” the CMA said.
Amazon and Anthropic deny there’s anything wrong with the deal. But because the CMA has seen “some” foundational model (FM) developers “form partnerships with major cloud providers” to “secure access to compute” needed to develop models, the CMA is worried that “incumbent firms” like Amazon “could use control over access to compute to shape FM-related markets in their own interests.”
Due to this potential risk, the CMA said it is “considering” whether Amazon’s partnership with Anthropic “has resulted in the creation of a relevant merger situation under the merger provisions of the Enterprise Act 2002 and, if so, whether the creation of that situation has resulted, or may be expected to result, in a substantial lessening of competition within any market or markets” in the UK.
It’s not clear yet if Amazon’s partnership with Anthropic is problematic, but the CMA confirmed that after a comment period last April, it now has “sufficient information” to kick off this first phase of its merger investigation.
By October 4, this first phase will conclude, after which the CMA may find that the partnership does not qualify as a merger situation, the UK regulator said. Or it may determine that it is a merger situation “but does not raise competition concerns,” clearing Amazon to proceed with the deal.
However, if a merger situation exists, and “it may result in a substantial lessening of competition” in a UK market, the CMA may refer the investigation to the next phase, allowing a panel of independent experts to dig deeper to illuminate potential risks and concerns. If Amazon wants to avoid that deeper probe potentially ordering steep fines, the tech giant would then have the option to offer fixes to “resolve the CMA’s concerns,” the CMA said.
An Amazon spokesperson told Reuters that its “collaboration with Anthropic does not raise any competition concerns or meet the CMA’s own threshold for review.”
“Amazon holds no board seat nor decision-making power at Anthropic, and Anthropic is free to work with any other provider (and indeed has multiple partners),” Amazon’s spokesperson said, defending the deal.
Anthropic’s spokesperson agreed that nothing was amiss, telling Reuters that “our strategic partnerships and investor relationships do not diminish our corporate governance independence or our freedom to partner with others. We intend to cooperate with the CMA and provide them with a comprehensive understanding of Amazon’s investment and our commercial collaboration.”
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.
On Thursday, Anthropic announced Claude 3.5 Sonnet, its latest AI language model and the first in a new series of “3.5” models that build upon Claude 3, launched in March. Claude 3.5 can compose text, analyze data, and write code. It features a 200,000 token context window and is available now on the Claude website and through an API. Anthropic also introduced Artifacts, a new feature in the Claude interface that shows related work documents in a dedicated window.
So far, people outside of Anthropic seem impressed. “This model is really, really good,” wrote independent AI researcher Simon Willison on X. “I think this is the new best overall model (and both faster and half the price of Opus, similar to the GPT-4 Turbo to GPT-4o jump).”
As we’ve written before, benchmarks for large language models (LLMs) are troublesome because they can be cherry-picked and often do not capture the feel and nuance of using a machine to generate outputs on almost any conceivable topic. But according to Anthropic, Claude 3.5 Sonnet matches or outperforms competitor models like GPT-4o and Gemini 1.5 Pro on certain benchmarks like MMLU (undergraduate level knowledge), GSM8K (grade school math), and HumanEval (coding).
Enlarge/ Claude 3.5 Sonnet benchmarks provided by Anthropic.
If all that makes your eyes glaze over, that’s OK; it’s meaningful to researchers but mostly marketing to everyone else. A more useful performance metric comes from what we might call “vibemarks” (coined here first!) which are subjective, non-rigorous aggregate feelings measured by competitive usage on sites like LMSYS’s Chatbot Arena. The Claude 3.5 Sonnet model is currently under evaluation there, and it’s too soon to say how well it will fare.
Claude 3.5 Sonnet also outperforms Anthropic’s previous-best model (Claude 3 Opus) on benchmarks measuring “reasoning,” math skills, general knowledge, and coding abilities. For example, the model demonstrated strong performance in an internal coding evaluation, solving 64 percent of problems compared to 38 percent for Claude 3 Opus.
Claude 3.5 Sonnet is also a multimodal AI model that accepts visual input in the form of images, and the new model is reportedly excellent at a battery of visual comprehension tests.
Enlarge/ Claude 3.5 Sonnet benchmarks provided by Anthropic.
Roughly speaking, the visual benchmarks mean that 3.5 Sonnet is better at pulling information from images than previous models. For example, you can show it a picture of a rabbit wearing a football helmet, and the model knows it’s a rabbit wearing a football helmet and can talk about it. That’s fun for tech demos, but the tech is still not accurate enough for applications of the tech where reliability is mission critical.
