Using Technology to Foster Innovation in Research

Today, we release a preprint describing a new AI system built with Gemini, designed to help scientists write empirical software. Unlike conventional software, empirical software is optimized to maximize a predefined quality score. Our system can hypothesize new methods, implement them as code, and validate performance by iterating through thousands of code variants. AI-powered empirical software has the potential of accelerating scientific discovery. Here is how it works (also on the visual graphic): ➡️ The system takes a "scorable task" as input, which includes a problem description, a scoring metric, and data for training and evaluation. ➡️ It generates research ideas, and an LLM implements these ideas as executable code in a sandbox. ➡️ Using a tree search algorithm, it creates a tree of software candidates to iteratively improve the quality score. ➡️ This process allows for exhaustive solution searches at an unprecedented scale, identifying high-quality solutions quickly. We rigorously tested our system on six challenging and diverse benchmarks and demonstrated its effectiveness. The outputs of our system are verifiable, interpretable, and reproducible. The top solutions to each benchmark problem are openly available. We look forward to taking this research through full peer-review. This new ability for AI systems to devise and implement novel solutions highlights AI’s capacity to help accelerate scientific innovation and discovery. The role of AI is evolving from a lab assistant to a collaborator that can transform the speed and scale of research. Read  the blog: https://lnkd.in/dPCZCCHS the preprint: https://lnkd.in/dQqfq8yg

We're still at the starting line of AI-powered scientific discovery—but every indication points toward a transformative shift. Artificial intelligence not only might accelerate research but redefine the process of discovery. Human-AI collaboration in labs, driven by predictive models, could significantly shorten the path from initial findings to real-world industry applications. AI tools are beginning to handle repetitive research tasks (e.g. literature reviews), allowing scientists to focus more fully on innovative hypotheses and groundbreaking insights. The result will increasingly be greater productivity, deeper insights, and improved returns per research dollar. Imagine accomplishing discoveries that once required decades within just a single funding cycle—this future isn't merely speculative; it's rapidly approaching reality in discrete domains. Given these changes, tomorrow’s laboratories must evolve into “teaching hospitals,” preparing an AI-fluent generation of researchers who amplify human expertise and ingenuity. This is not about replacing researchers, but empowering them—enabling more experiments, more robust datasets, and higher-quality science per NSF or NIH grant. Yet, without robust investment, the full promise of AI-powered science will remain unfulfilled. Countries like China, Singapore, and South Korea recognize this potential and are doubling down on foundational research that employs AI. America faces a new "Endless Frontier" moment. We must commit ambitiously to basic research funding now to secure our technological leadership—or risk falling behind at precisely the moment when innovation is accelerating. AI has yet to fully transform scientific research, but it's already multiplying the value of every research dollar spent—making this the decisive moment to invest boldly.

Researchers at #MIT have developed #SPARKS, an AI system built not just to analyze data, but to mimic the entire scientific process. It works as a team of AI agents where one proposes a hypothesis, and another immediately critiques it. 👉 https://lnkd.in/eD5KtuJY This continuous loop of generation and reflection pushes the system to explore ideas beyond its initial training. When turned loose on the complex world of protein science, SPARKS operated autonomously. It generated its own hypothesis about protein stability, designed the computational experiments to test it, refined the process as the data came in, and wrote a report on its findings. In doing so, it uncovered a previously unknown "frustration zone" in certain protein structures. This work demonstrates a new potential for AI in science, where the system acts less like an assistant and more like an independent investigator. It makes you wonder: what happens to the pace of discovery when our tools can not only help find answers but also formulate the questions? Read the full research review at https://lnkd.in/eD5KtuJY Research from Markus J. Buehler and Alireza Ghafarollahi at Massachusetts Institute of Technology #ScientificDiscovery #ArtificialIntelligence #ComputationalBiology #AIinScience #Research

"Australian scientists have successfully developed a research system that uses 'biological artificial intelligence' to design and evolve molecules with new or improved functions directly in mammal cells. The researchers said this system provides a powerful new tool that will help scientists develop more specific and effective research tools or gene therapies. Named PROTEUS (PROTein Evolution Using Selection) the system harnesses 'directed evolution', a lab technique that mimics the natural power of evolution. However, rather than taking years or decades, this method accelerates cycles of evolution and natural selection, allowing them to create molecules with new functions in weeks. This could have a direct impact on finding new, more effective medicines. For example, this system can be applied to improve gene editing technology like CRISPR to improve its effectiveness." #bioai

🔬 Exciting Progress in AI for Science this week as Google Unveils AI Co-Scientist - A New Era of Accelerated Scientific Discovery! Key takeaways from this new paper published yesterday: 🤖 Introduction of AI Co-Scientist: Google has developed an AI system named "AI Co-Scientist," built on Gemini 2.0, designed to function as a virtual collaborator for scientists. This system aims to assist in generating novel hypotheses and accelerating scientific and biomedical discoveries. 👨👩👦👦 Multi-Agent Architecture: The AI Co-Scientist employs a multi-agent framework that mirrors the scientific method. It utilizes a "generate, debate, and evolve" approach, allowing for flexible scaling of computational resources and iterative improvement of hypothesis quality. 🧬 Biomedical Applications: In its initial applications, the AI Co-Scientist has demonstrated potential in several areas: 1. Drug Repurposing: Identified candidates for acute myeloid leukemia that exhibited tumor inhibition in vitro at clinically relevant concentrations. 2. Novel Target Discovery: Proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids. 3. Understanding Bacterial Evolution: Recapitulated unpublished experimental results by discovering a novel gene transfer mechanism in bacterial evolution through in silico methods. 🤝 Collaborative Enhancement: The system is designed to augment, not replace, human researchers. By handling extensive literature synthesis and proposing innovative research directions, it allows scientists to focus more on experimental validation and creative problem-solving. 💡 Implications for Future Research: The AI Co-Scientist represents a significant advancement in AI-assisted research, potentially accelerating the pace of scientific breakthroughs and fostering deeper interdisciplinary collaboration. This development underscores the transformative role AI can play in scientific inquiry, offering tools that enhance human ingenuity and expedite the journey from hypothesis to discovery.