Ask HN: How to get back into AI?
I am an ML researcher working in the industry: <i>by far</i> the most effective way to maintain&#x2F;advance my understanding of  ML methods is implement the core of an interesting paper and reproduce (some) of their results. Completing a working implementation really forces your understanding to be on another level than if you just read the paper and think &quot;I get it&quot;. It can be easy to read (for example) a diffusion&#x2F;neural ode paper and come away thinking that you &quot;get it&quot; while still having a wildly inadequate understanding of how to actually get it to work yourself.<p>You can view this approach in the same way that a beginner learns to program. The best way to learn is by attempting to implement (as much on your own as possible) something that solves a problem you&#x27;re interested in. This has been my approach from the start (for both programming and ML), and is also what I would recommend for a beginner. I&#x27;ve found that continuing this practice, even while working on AI systems professionally, has been critical to maintaining a robust understanding of the evolving field of ML.<p>The key is finding a good method&#x2F;paper that meets all of the following<p>0) is inherently very interesting to you<p>1) you don&#x27;t already have a robust understanding of the method<p>2) isn&#x27;t so far above your head that you can&#x27;t begin to grasp it<p>3) doesn&#x27;t require access to datasets&#x2F;compute resources you don&#x27;t have<p>of course, finding such a method isn&#x27;t always easy and often takes some searching.<p>I want to contrast this with other types of approaches to learning AI with include<p>- downloading and running other people&#x27;s ML code (in a jupyter notebook or otherwise)<p>- watching lecture series &#x2F; talks giving overviews of AI methods<p>- reading (without putting into action) the latest ML papers<p>all of which I have found to be significantly less impactful on my learning.