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Computational models for brain science
The No. 1 Country Song in America Is AI-Generated
According to Billboard’s “Country Digital Song Sales” chart, the No. 1 song in the U.S. is “Walk My Walk” by Breaking Rust—an artist that was created by artificial intelligence (AI).
This is a new development in the music industry as it is the first time an AI-created song has reached the top of the charts.
There have long been concerns about the use of generative AI in creative sectors. Discourse about this came into the fold a few years ago following protests in Hollywood from the writer and actor guilds, which took place shortly after the public release of ChatGPT, and concerns that came in tandem with the new technology and its implications.
… As the AI revolution continues to impact creative industries, it could be that more AI-generated artists continue to pop up in the charts, with pushback likely to be inevitable. — Read More
AI Red-Teaming Design: Threat Models and Tools
Red-teaming is a popular evaluation methodology for AI systems, but it is still severely lacking in theoretical grounding and technical best practices. This blog introduces the concept of threat modeling for AI red-teaming and explores the ways that software tools can support or hinder red teams. To do effective evaluations, red-team designers should ensure their tools fit with their threat model and their testers.
AI red-teaming is an evaluation methodology to discover flaws and vulnerabilities in AI systems. Although this type of evaluation has been adopted across the AI industry (as seen in Anthropic’s Responsible Scaling Policy, Google Deepmind’s Frontier Safety Framework, and OpenAI’s Safety & Responsibility documents), red-teaming practices vary widely, and there are few established standards or best practices. This is due in part to the versatility and flexibility of the methodology, such that red-team designers and testers have to make many decisions in the red-teaming process. While this blog post is primarily aimed at AI red-teamers, it may also be useful for policymakers and other readers interested in the design of AI evaluation.
This post will discuss two key factors in designing an AI red-teaming exercise: the red team’s threat model, and the selection of the software tools that testers use to engage with the target system. The threat model is the key concept around which the red-teaming exercise is constructed, while the design features of various tools shape which testers can use them and which threat models they can address. Appropriate tools can empower testers, but inappropriate ones can obscure evaluation results and lead to false conclusions. — Read More
Spiky Superintelligence vs. Generality
The Piss Average Problem
The Age of AI is a Crisis of Faith
The fundamental question facing online spaces in 2025 is no longer can AI pass as human? but rather can humans prove they’re not AI?
This represents a profound shift from technical doubt to existential uncertainty. It’s a crisis of faith where the bedrock assumption that we interact with other humans online has collapsed. And I’m not being hyperbolic. In 2024, bot traffic exceeded human traffic for the first time in a decade, hitting 51%. We’ve crossed the threshold. The internet is now majority non-human.
When I personally veer onto the Internet, particularly places like LinkedIn or Substack or any social media’s comment section, Dead Internet Theory truly shines as a valid hypothesis. This once-fringe conspiracy theory which speculates that the Internet is now mostly bots talking to bots is now many people’s lived experience — Read More
Android Dreams
“The danger is never that robots disobey, but that they obey perfectly.”
At the convergence of frontier research breakthroughs, billions in capital, and rising geopolitical tensions lies a dream for a new physical world. After the LLM wave, robotics is seen as the next exponential growth domain.0Chinese manufacturing is viewed as an existential threat to the US, adding to incentives. And, though robotics is the hardest domain of AI1, multiple new AI strategies now offer clear paths to Embodied General Intelligence (EGI).2
Informed by conversations with frontier researchers, intuitions gained at Optimus and Dyna2.5, and my own syntheses, I predict inference-controlled robots will comprise half the world’s GDP by 2045. This scenario illustrates how. — Read More
Google DeepMind is using Gemini to train agents inside Goat Simulator 3
Google DeepMind has built a new video-game-playing agent called SIMA 2 that can navigate and solve problems in a wide range of 3D virtual worlds. The company claims it’s a big step toward more general-purpose agents and better real-world robots.
Google DeepMind first demoed SIMA (which stands for “scalable instructable multiworld agent”) last year. But SIMA 2 has been built on top of Gemini, the firm’s flagship large language model, which gives the agent a huge boost in capability.
The researchers claim that SIMA 2 can carry out a range of more complex tasks inside virtual worlds, figure out how to solve certain challenges by itself, and chat with its users. It can also improve itself by tackling harder tasks multiple times and learning through trial and error. — Read More
Disrupting the first reported AI-orchestrated cyber espionage campaign
We recently argued that an inflection point had been reached in cybersecurity: a point at which AI models had become genuinely useful for cybersecurity operations, both for good and for ill. This was based on systematic evaluations showing cyber capabilities doubling in six months; we’d also been tracking real-world cyberattacks, observing how malicious actors were using AI capabilities. While we predicted these capabilities would continue to evolve, what has stood out to us is how quickly they have done so at scale.
In mid-September 2025, we detected suspicious activity that later investigation determined to be a highly sophisticated espionage campaign. The attackers used AI’s “agentic” capabilities to an unprecedented degree—using AI not just as an advisor, but to execute the cyberattacks themselves. — Read More
Read the Report
Russia’s first AI-powered robot walked on stage to triumphant music, took a few steps, and then immediately faceplanted
Russia’s first domestically produced artificial intelligence-powered humanoid robot faceplanted during its first public demonstration this week, underscoring the challenges Russia faces in competing with more established leaders in AI and robotics like the U.S. and China.
The robot, named AIdol, was unveiled during a tech showcase at the Yarovit Hall Congress Center in Moscow on Monday. As the machine walked onto the stage accompanied by two handlers to “Gonna Fly Now,” the theme from the 1976 film Rocky, it waved to the audience before taking a few steps, losing its balance, and toppling over. — Read More
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