freshman in ML: how do you identify actually open research problems? [D]

<!-- SC_OFF --><div class="md"><p>I work at Microsoft CoreAI as an engineer, and have offers from three equally competitive PhD programs starting Fall 2026 and the Claude Code

<!-- SC_OFF --><div class="md"><p>Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow;

<!-- SC_OFF --><div class="md"><p>I did my B.Tech in AI/ML where I learned core machine learning concepts like model training, evaluation, etc., and also completed an ML

<!-- SC_OFF --><div class="md"><p>This wasn't always the case, but now almost any research topic in machine learning that you can imagine is now being done MUCH BETTER in industry

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<!-- SC_OFF --><div class="md"><p><a href="https://youtu.be/vczBo0AvbTI?si=pglMPmTjsq-TNJa9&amp;t=375">https://youtu.be/vczBo0AvbTI?si=pglMPmTjsq-TNJa9&amp;t=375</a></p>

<!-- SC_OFF --><div class="md"><p>I’ve been thinking about the current state of machine learning PhDs, including my own work, and I’d like to hear how others see it.<br /> My

<!-- SC_OFF --><div class="md"><p>I want to make an AI Project or something, I want to know how realistic it is for me to be able to make my own like chatbot or something. I know