"Our research is greatly sped up by AI but AI still needs us" (twitter.com)

Researchers report that AI is dramatically accelerating stages of the research lifecycle—from rapid literature reviews, hypothesis generation, and experiment design to code scaffolding, data cleaning, and automated hyperparameter searches—allowing teams to iterate far faster than before. Practical uses cited include large language models drafting experiment protocols, AutoML reducing model selection time, and lab automation scripts or notebooks generated by AI that cut mundane engineering hours. The headline: AI is a force-multiplier for productivity, shortening time-to-insight and enabling smaller teams to tackle more ambitious projects. Yet the community still needs human expertise. AI systems introduce risks (hallucinations, dataset bias, hidden confounders, and poor calibration) and can’t replace domain knowledge, causal reasoning, or ethical judgement. Critical tasks—defining sound experimental priors, curating high-quality training data, setting evaluation metrics, validating results, and ensuring reproducibility and provenance—remain human responsibilities. Technically, this means continued investment in human-in-the-loop workflows, robust benchmarks, interpretability tools, provenance tracking, and rigorous validation pipelines (including adversarial testing and statistical controls). The takeaway for AI/ML practitioners: leverage AI to accelerate iteration, but design processes that keep humans in control to maintain scientific rigor, safety, and trustworthiness.