HANNA WALLACH: Yeah, so I was a PhD student at the University of Cambridge, and I was working with the late David MacKay. I was focusing on machine learning for analyzing text, and at that point in time, I’d actually just begun working on Bayesian latent variable models for text analysis, and my research was really focusing on trying to combine ideas from n-gram language modeling with statistical topic modeling in order to come up with models that just did […]

Pushing the frontiers of computer-use agents with an open-weight, ultra-compact model, optimized for real-world web tasks In 2024, Microsoft introduced small language models (SLMs) to customers, starting with the release of Phi (opens in new tab) models on

Modern multimodal AI models can recognize objects, describe scenes, and answer questions about images and short video clips, but they struggle with long-form and large-scale visual data, where real-world reasoning requires moving beyond object recognition and short-clip analysis. Real-world reasoning increasingly involves analyzing long-form video content, where context spans minutes or hours, far beyond the context limits of most models. It  

Introduction Large language models (LLMs) are now widely used for automated code generation across software engineering tasks. However, this powerful capability in code generation also introduces security concerns. Code generation systems could be misused for harmful purposes, such as generating malicious code. It could also produce bias-filled code reflecting underlying logic that is discriminatory or unethical. Additionally, even when completing benign tasks, LLMs may inadvertently  

As a world leader in connected LED lighting products, systems, and services, Signify (formerly Philips Lighting) serves not only everyday consumers but also a large number of professional users who have stringent requirements for technical specifications and engineering compatibility. Faced with thousands of product models, complex component parameters, and technical documentation spanning multiple versions, delivering accurate, professional answers efficiently has become  

Autonomous AI agents are here, and they’re poised to reshape the economy. By automating discovery, negotiation, and transactions, agents can overcome inefficiencies like information asymmetries and platform lock-in, enabling faster, more transparent, and more competitive markets. We are already seeing early signs of this transformation in digital marketplaces. Customer-facing assistants like OpenAI’s Operator and Anthropic’s Computer Use can navigate websites and complete purchases.  

Introduction Code agents are AI systems that can generate high-quality code and work smoothly with code interpreters. These capabilities help streamline complex software development workflows, which has led to their widespread adoption. However, this progress also introduces critical safety and security risks. Existing static safety benchmarks and red-teaming methods—in which security researchers simulate real-world attacks to identify security vulnerabilities—often fall short when evaluating code agents. They may fail to detect emerging real-world risks, such as the  

Modern LLM Agents can debug code, analyze spreadsheets, and book complex travel. Given those capabilities, it’s reasonable to assume that they could handle something simpler: waiting. Ask an agent to monitor your email for a colleague’s response or watch for a price drop over several days, and it will fail. Not because it can’t check email or scrape prices. It can do both. It fails because it doesn’t know when to check. Agents either give up after a few  

Now, let’s rewind two years. Almost to the day, Bruce and I uncovered a vulnerability. While preparing a case study for a workshop on AI and biosecurity, we discovered that open-source AI protein design tools could be used to redesign toxic proteins in ways that could bypass biosecurity screening systems, systems set up to identify incoming orders of concern.  Now in that work, we created an AI pipeline from open-source tools that could essentially “paraphrase” the amino acid sequences—reformulating them while working to preserve their structure and potentially their function.  These […]

Advances in AI are opening extraordinary frontiers in biology. AI-assisted protein engineering holds the promise of new medicines, materials, and breakthroughs in scientific understandings. Yet these same technologies also introduce biosecurity risks and may lower barriers to designing harmful toxins or pathogens. This “dual-use” potential, where the same knowledge can be harnessed for good or misuse to cause harm, poses a critical dilemma for modern science. Great Promise—and Potential Threat I’m excited about the potential for AI-assisted protein design to […]