Frontier AI AI News & Updates
Adaption Launches AutoScientist: AI System for Automated Model Training and Self-Improvement
Adaption, a new AI research lab, has released AutoScientist, a tool that automates the fine-tuning process by co-optimizing data and models to help AI systems learn capabilities more efficiently. The system is designed to enable continuous model improvement and could democratize frontier AI training beyond major labs. The company claims AutoScientist has more than doubled win-rates across different models and is offering free access for the first 30 days.
Skynet Chance (+0.04%): Self-improving AI systems that can optimize themselves with minimal human oversight represent a step toward recursive self-improvement, a key concern in AI safety and loss of control scenarios. However, this system appears focused on task-specific fine-tuning rather than fundamental architectural changes, limiting immediate risk elevation.
Skynet Date (-1 days): By democratizing advanced model training capabilities beyond major labs and accelerating the fine-tuning process, this tool could accelerate the development of increasingly capable systems across more actors. The automation of what was previously human-intensive work speeds the overall pace of AI capability advancement.
AGI Progress (+0.03%): AutoScientist represents meaningful progress toward automated AI development pipelines and self-improving systems, which are important capabilities on the path to AGI. The ability to co-optimize data and models automatically addresses key bottlenecks in scaling AI capabilities and suggests movement toward more autonomous AI research.
AGI Date (-1 days): The tool significantly accelerates model training and fine-tuning processes while democratizing access to frontier-level capabilities, potentially multiplying the effective research capacity working on advanced AI. This automation of previously manual optimization processes could materially speed the timeline toward AGI by reducing iteration cycles and expanding the number of teams capable of frontier research.
Former OpenAI Leaders Launch Thinking Machines Lab to Build More Customizable AI
Former OpenAI CTO Mira Murati has launched Thinking Machines Lab, a startup focused on developing more customizable and capable AI systems that address key gaps in current AI technologies. The company, which includes OpenAI co-founder John Schulman and other high-profile AI researchers, aims to build frontier multimodal systems for applications in science and programming while emphasizing AI safety.
Skynet Chance (+0.01%): The emphasis on building highly capable frontier models increases potential risks, but the explicit focus on customizability, safety practices, and sharing alignment knowledge provides some counterbalance. Their stated commitment to understanding systems indicates awareness of control issues.
Skynet Date (-1 days): The formation of another highly-credentialed team pursuing frontier capabilities at the limits of AI, particularly with multimodal systems for science and programming, will likely accelerate development timelines toward more advanced systems with potentially unpredictable emergent behaviors.
AGI Progress (+0.03%): The assembly of key technical leaders from OpenAI, including those who helped develop ChatGPT and other breakthrough systems, focusing explicitly on frontier multimodal models represents a significant concentration of talent that will likely drive substantial technical progress toward more AGI-like capabilities.
AGI Date (-1 days): The emergence of another well-funded company founded by architects of today's most advanced AI systems, explicitly focused on frontier capabilities in domains like science and programming, will likely accelerate development timelines through additional competitive pressure and parallel research efforts.