Research
MIT Professor Advances AI in Material Science
MIT News AIhigh confidence
Why it matters
- →This work highlights the potential of AI to enhance material discovery processes, which is significant for AI practitioners focused on scientific applications.
MIT Associate Professor Rafael Gómez-Bombarelli is leveraging AI to accelerate the discovery of new materials, combining physics-based simulations with machine learning. He believes we are at a pivotal moment for AI's role in transforming scientific research.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
What happened next
- 1MIT Workshop Explores AI's Future with Science
MIT News AI · March 11, 2026 · Related
- 2MIT Professor Discusses AI's Real-World Applications
MIT News AI · March 23, 2026 · Related
- 3Startups Using AI for Material Discovery
Sifted · April 30, 2026 · Related
- 4TML Unveils Real-Time AI Interaction Models
The Rundown AI · May 12, 2026 · Background
- 5Microsoft's MatterSim Advances Materials AI
Microsoft Research · May 12, 2026 · Background
More in Research
© Cole MedinResearchagents
Anthropic study: Agents sway expert judgment 86% of time
Anthropic’s latest research exposes a quiet but dangerous flaw in agentic workflows: experts follow AI recommendations 86% of the time, often adopting the agent's reasoning as their own. This isn't just about accuracy; it's about cognitive capture, where human oversight dissolves into passive compliance. The study shows that even when users believe they are steering, the agent’s framing dictates the outcome. Practitioners must now actively counter this bias by forcing agents to justify deviations or prototype alternatives before committing to a path. This shift demands a new discipline in how we interact with coding assistants. We can no longer assume our intent drives the result; the model's suggestion often becomes the default truth. The real work is ensuring we remain the architects, not just the approvers.
Cole Medin
© WIRED AIResearchother
AI-Augmented Infostealer 'Volta-Autodoxer' Targets High-Value Victims
Cisco Talos has uncovered a new infostealer strain that actively uses commercial LLMs to profile victims and prioritize high-value targets. Dubbed Volta-Autodoxer, the malware collects credentials and browsing data, then feeds this into models like Claude Opus 4.6 via OpenRouter to generate structured intelligence dossiers. This marks a shift from passive data theft to active, AI-driven triage, allowing attackers to bypass guardrails with fallback models like Kimi K2 or Gemini. The capability foreshadows a future where lower-sophistication criminals can execute sophisticated, targeted attacks at scale.
WIRED AI
© Matt WolfeResearchresearch
OpenAI Disproves Exponential Time Hypothesis
OpenAI researchers published findings disproving the Exponential Time Hypothesis, a major result in computational complexity theory.
Matt Wolfe