AI Decodes DNA Sequence Critical for Gene Activation

Billionaire Uses AI to Predict Evolutionary Changes

Ben Lamm, co-founder of Colossal, is leveraging AI to predict evolutionary changes in entire biological systems through his new startup, Astromech. Backed by a $20 million investment led by Arch Ventures, the company aims to use data from Colossal's de-extinction research to model gene evolution. This could help improve crop breeding, drug discovery, and disease prediction. The technology focuses on longevity genes and plans to expand with new hires. Astromech's unique dataset offers insights into genetic changes over time, giving it an edge in this field.

Forrester Report Maps AI Security Controls To Tech Stack

Forrester has released a new report to help organizations secure agentic AI systems. The report, "Navigate AEGIS Technologies To Secure Agentic AI," maps specific security controls to available technologies and vendors. It categorizes tools into must-have-now, should-have-next, and specialized use cases, addressing gaps in areas like AI runtime security and governance. The research covers 23 technology domains, including AI identity management and data loss prevention for AI. It helps teams identify control gaps first, then find the right technologies to fill them. By reversing the usual product-first approach, this guide provides a clearer path to securing AI systems without overhauling existing tools. This report offers actionable insights for security leaders looking to enhance their AI security strategies. Future updates will likely expand on these categories as AI technology evolves.

Nvidia's AI Research Highlights Harness Over Model for Long-Horizon Tasks

Nvidia revealed that the "harness"-the software tools and memory management around an AI model-plays a bigger role than the model itself in handling long-term tasks. By fine-tuning Opus 5's harness to improve memory and add a "supervisor" feature, researchers achieved a perfect score on ARC-AGI-3, a benchmark where models must solve 2D games without instructions. Without the optimized harness, Opus 5 scored just 30%, topping other models. This shows that while the model is important, the harness is what truly turns it into an effective agent for complex, multi-step tasks. Nvidia's findings emphasize the need to focus on the systems and tools that guide AI behavior, not just the underlying algorithms. As AI becomes more autonomous, understanding how these "harnesses" work will be key to creating reliable and capable agents.

How Much of the Internet Is Written With AI?

A recent study reveals that around 10% of English-language webpages show signs of being written or edited by AI. This figure is based on an analysis of nearly half a million web pages from the past five years, using an AI detection tool called Open Pangram. The study highlights a noticeable upward trend since the release of ChatGPT in late 2022. More recent content, especially those published after ChatGPT became available, shows even higher rates-over one-third of such pages exhibit AI influence. The presence of AI-written content varies across different types of websites. While .com domains have about 10% AI-generated content, .org sites show less at around 4.6%. Government and educational sites (.gov and .edu) have the lowest rates, each under 1%. This suggests that AI adoption in content creation is not uniform across all sectors of the internet. As AI technology continues to evolve, the share of webpages with AI-generated content is expected to rise further. This shift could reshape how information is created and consumed online.

AI Agents Evolve Through Dynamic Graphs

A new study reveals that AI agents, powered by large language models, are becoming self-evolving systems capable of learning and adapting over time. These agents maintain memories, use tools, and even coordinate with other agents to improve their tasks. The key innovation is modeling agent evolution as dynamic graph transformations-where the relationships between nodes (memories, skills) and edges (connections between them) change as the agent learns. The researchers organized existing methods into four main categories: how nodes and features evolve, how edges and topologies change, which parts of the graph are active at any time, and how different components influence each other. This framework helps design better AI systems that can learn from their experiences and adapt to new situations. The study also highlights five types of evaluation protocols needed for ensuring these agents work as intended. Looking ahead, understanding dynamic graphs will be crucial for creating more sophisticated AI systems. Researchers are now focusing on how these evolving graphs can be used to evaluate and govern AI behavior effectively, setting the stage for future advancements in self-evolving agents.