👇 Honestly, this thread is 80% wrong. This is treating science as like front-end frameworks. Yes, if you’re a front-end developer who only knows 3-years-old JavaScript frameworks, then you’ll have trouble getting a gig. But that’s not what we’re teaching students 1/7 https://twitter.com/tunguz/status/1348323493876801536
To use the currently trendy terminology, what we’re teaching students is ✨meta-learning✨—a strong foundation of approaches, ideas, understanding, and tools so that they will be able to quickly learn and evolve over the following decades, as science and engineering changes 2/7
Yes, we at @Stanford do try to ensure we also teach some up-to-date approaches—and we have a great up-to-date course on neural meta-learning too https://cs330.stanford.edu  😇—but when AI faculty talk, they mainly wring their hands about how little students learn from before 2010 3/7
I think your sense of a “paradigm” is off—scientists immediately think of Kuhn’s paradigms: https://en.wikipedia.org/wiki/Paradigm_shift. At most they’d count modern deep learning as a paradigm shift. It’s now 15 years from the initial deep learning breakthrough. So seeing some old ideas helps. 4/7
And many might argue that it’s just a development of the machine learning approach to AI that replaced knowledge-based approaches beginning in the late 1980s. We want students to be able to absorb and be productive beyond paradigm shifts, but they don’t happen that often. 5/7
Finally, universities do not only educate you, they also provide you with an extensive network of people, who will be of enormous value to you in your future life in many ways, one of which is that they make it easier for you to hear about and learn new things as time passes 6/7
What parts are right? Yes, all technical professionals today need to engage in continuous education to keep their skills up-to-date. The meta-learning will come in useful. 🙂 And, yes, experience on @kaggle is a really useful way to build and refresh your skills—recommended! 7/7
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