April 4, 2026•1 min read•from Machine Learning

[D] Those of you with 10+ years in ML — what is the public completely wrong about?

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For those with over a decade of experience in machine learning and AI, the disconnect between public perception and the realities of cutting-edge research is striking. Many people overestimate the capabilities of AI, viewing it as an all-powerful solution, while underestimating the complexities and limitations inherent in these technologies. This discussion invites seasoned professionals to share insights on the misconceptions surrounding AI's current capabilities, encouraging a deeper understanding of what is genuinely achievable and what remains aspirational in the field of machine learning.

For those of you who've been in ML/AI research or applied ML for 10+ years — what's the gap between what the public thinks AI is doing vs. what's actually happening at the frontier? What are we collectively underestimating or overestimating?

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[D] Has industry effectively killed off academic machine learning research in 2026?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 due to a glut of compute and endless international talents. The only ones left in academia seems to be: niche research that delves very deeply into how some older models work (e.g., GAN, spiking NN), knowing full-well they will never see the light of day in actual applications, because those very applications are being done better by whatever industry is throwing billions at. some crazy scenario that basically would never happen in real-life (all research ever done on white-box adversarial attack for instance (or any-box, tbh), there are tens of thousands). straight-up misapplication of ML, especially for applications requiring actual domain expertise like flying a jet plane. surveys of models coming out of industry, which by the time it gets out, the models are already depreciated and basically non-existent. In other words, ML archeology. There are potential revolutionary research like using ML to decode how animals talk, but most of academia would never allow it because it is considered crazy and doesn't immediately lead to a research paper because that would require actual research (like whatever that 10 year old Japanese butterfly researcher is doing). Also notice researchers/academic faculties are overwhelmingly moving to industry or becoming dual-affiliated or even creating their own pet startups. I think ML academics are in a real tight spot at the moment. Thoughts? submitted by /u/NeighborhoodFatCat [link] [comments]

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[D] Those of you with 10+ years in ML — what is the public completely wrong about?

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