Models & LabsUnited States

Google debuts SynthID Detector tool for flagging AI-generated content, but it’s far from perfect

Credited to SiliconANGLE · siliconangle.com9h ago

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Google LLC is making its SynthID identification tools for artificial intelligence-created and AI-edited videos and images more widely available with the launch of a new service today that everyone can use.

The web-based SynthID Detector can identify images, video and audio clips that have been created using AI models from Google, OpenAI Group PBC, Nvidia Corp. and Kakao Corp. But in order to use it, people will have to sign in first, using a Google, OpenAI or Apple account. In a few weeks, the tool’s capabilities will be expanded to identify images created using Apple Inc.’s AI tools as well, such as Image Playground.

Google first developed SynthID in 2023. It’s an imperceptible watermark that’s added to AI-generated content created using tools from the above companies, but the only problem is that special tools have always been required to spot it.

Though the company has made different versions of its detection tool available to journalists and other media professionals, availability was severely restricted. Google also offers a few other ways to detect AI-generated content. For instance, there’s its Circle to Search smartphone feature, and users can also upload content to Gemini to ask if it has been created with AI or not. But with the launch of a dedicated detector tool, it’s making these capabilities more accessible,

The downside to SynthID Detector is that it has, well, downsides. For instance, because it’s designed to identify the hidden SynthID watermarks in AI content, it’s not very good at detecting AI-generated content that doesn’t have a SynthID watermark. And there’s a lot of that about.

Anthropic PBC’s Claude doesn’t use SynthID, and neither does xAI Corp.’s Grok chatbot. None of the many open-weights Chinese AI models use SynthID either. So it’s going to miss a lot of AI-generated content regardless.

Another issue with SynthID Detector is that it cannot distinguish between images, video and audio that has been entirely created from scratch or just edited using AI. So in the case of content that has been edited, it won’t be able to reveal which part is fake and which part is real.

SynthID Detector is still a step in the right direction, but it’s not nearly enough. The reality is that there’s still no foolproof way of verifying AI-generated content, partly because there are so many different standards for watermarking model’s creations.

Meta Platforms Inc. has been experimenting with its own watermarking tools and has developed a system for identifying images created with Muse Image, but it’s useless when confronted with images created with third-party models. Unless all of the major AI model makers, including the Chinese ones, decide to get on the same page and create some kind of universal standard for watermarking AI content, it’s likely that there will never be a reliable way to detect such material.

Given its influence and the fact it already has several technology heavyweights on board already, it’s likely that Google is in the best position to make some kind of universal standard happen. The company does at least seem to recognize the problem, and said on the SynthID Detector’s website that it’s “continuously working on extending our partnerships” to allow it to detect more types of AI-generated media.

“Building industry-wide standards takes time, and different content types (image, audio, video) have different technical requirements,” Google said. “The goal is a layered ecosystem where watermarking, metadata standards, and verification tools work together across the industry.”

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Models & Labs · United States

OpenAI publishes 722 AI-generated math discoveries in major scientific milestone

Credited to SiliconANGLE · siliconangle.com · 16h ago

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OpenAI Group PBC has published 722 math papers that it generated using an unreleased artificial intelligence model.

Some of the papers, which were posted to GitHub late Tuesday, prove long-running hypotheses. Others disapprove proposed explanations of mathematical phenomena or narrow down the possible answers to complex, yet-unsolved puzzles. Overall, OpenAI’s papers span about 20 mathematical subfields.

One of the most notable discoveries relates to the Riemann hypothesis. It’s a more than 150-year-old paper about prime numbers, numbers that can only be divided by themselves or one. The Riemann hypothesis argues that prime numbers follow a pattern described by a mathematical object called the Riemann zeta function.

Proving the conjecture is considered to be one of the most important challenges in mathematics. Its significance partly stems from the fact that many later math papers are based on the assumption the Riemann hypothesis is correct. In other words, proving the Riemann hypothesis would verify those papers.

OpenAI’s unreleased AI model didn’t manage to come up with a complete answer. However, it did prove an important piece of the puzzle called the quasi-Riemann hypothesis.

Theoretical computer science was another major focus of OpenAI’s research exercise. The company’s unreleased AI model produced more than 80 papers about the topic.

Three of the papers focus on matrix multiplications, the mathematical operations that AI models use to process data. For the past few decades, researchers have been working to develop faster, more hardware-efficient ways to perform such calculations. It’s believed that matrix multiplications can only be sped up so much before a limit is reached.

OpenAI’s model developed a clearer definition of that limit. In a separate paper, it developed a new algorithm for multiplying integers. An integer is a data structure that contains a whole number. Like matrix multiplications, integer multiplications are foundational building blocks of many programs.

The research breakthrough is also relevant for physicists. The company published more than a dozen proofs that relate to partial differential equations, or PDEs. Those are physics equations that are essential to chip design, architecture, quantum mechanics and a range of other fields.

The model proved a version of De Giorgi’s conjecture, a hypothesis that relates to an equation used to study metal alloys. It also clarified a number of questions related to the Navier–Stokes equations, which engineers use to study the flow of liquids.

In September, the AI model that produced the papers solved a different problem related to the Navier–Stokes equations. That puzzle ranked as one of the most difficult open questions in mathematics.

Many of OpenAI’s new papers contain Lean files. Those are code snippets that make it possible to quickly verify a newly published proof using a computer. Going forward, OpenAI plans to release Lean proofs for more of the papers. The company also intends to finance of research events and programs that will focus on reviewing AI-generated math discoveries.