Excited to share our perspective in @CellCellPress, where we discuss “AI scientists” as collaborative AI agents designed to empower biomedical research While the concept of an “AI scientist” is aspirational, advances in agent-based AI are paving the way for AI agents as conversable systems with reflective and reasoning abilities that coordinate LLMs, ML tools, experimental platforms, or combinations thereof. We outline initial autonomy levels for agents based on proficiency in hypothesis generation, experimental design, execution, and reasoning: Level 0: No AI agents Level 1: AI agents as research assistants Level 2: AI agents as collaborators Level 3: AI agents as scientists These “AI scientists” could enhance discovery workflows by introducing skepticism and reasoning. Our vision is to amplify human creativity, enabling AI to handle large datasets, navigate complex hypotheses, and perform repetitive tasks faster. Many ethical considerations arise with biomedical AI agents. We discuss issues of governance, robustness, evaluation protocols, dataset generation, and associated risks. Imagine AI agents that help: 🔬 Design discovery workflows 🌱 Simulate virtual cells 🎛️ Control phenotypes programmatically ⚙️ Design cellular circuits 💊 Optimize therapeutic discovery and development Many thanks to fantastic group of co-authors @GaoShanghua, @AdaFang_ @YepHuang, @valegiunca, @ayushnoori, @schwarzjn_, @YEktefaie, @kondic_jovana @HarvardDBMI @harvardmed @KempnerInst @harvard_data @broadinstitute

Excited about this @NeurIPSConf 2024 paper led by @GaoShanghua. UniTS is a multitasking pro that can predict, generate, and handle many time series tasks in one unified model. Time is definitely on its side⏳⏳⏳ With task tokenization and a modified transformer block for universal time series representations, UniTS transfers knowledge across domains and tasks. It’s been tested on 38 datasets (healthcare, engineering, physiological signals) and outperforms specialized models, reprogrammed LLMs, and strong linear models. It even dabbles in few-shot learning. Paper: Code: @HarvardDBMI @harvard_data @KempnerInst @MIT Fantastic team @GaoShanghua, T. Koker, O. Queen, T. Hartvigsen, T. Tsiligkaridis