If an algorithm plays sufficiently well with zero training, do we still need to have a training stage? If so, why is deep learning so hot right now?
All kinds of machine learning have been greatly celebrated in recent memory. There is no AI winter for learning, however I have yet to see decent coverage of advances in zero-knowledge solvers. Though there has been a glut of breakthroughs recently in these topics, it seems that people are less likely to engage and identify with the story.
People don’t care about Artificial Intelligence anymore, everything needs to be Machine Learning. This seems to fit the narrative of machines ready to evolve and surpass human intelligence before we notice. The intrigue still captivates like nothing else, despite many years of setbacks.
Though public approval for ML is high right now, it would be prudent to lend at least some attention to the smaller improvements to traditional AI. Doing so may prolong the effect of our current AI Spring and perhaps eliminate the possibility of another AI Winter. For all our sakes, please take the basic Math seriously, if you truly care about the field.
Though breakthroughs are celebrated when relevant, the untold stories of Academia are the more common long slow marches to achievement. Discovery is more often a discipline of perseverance, than one of eureka insights. So don’t disparage researchers who don’t have the most accepted papers to prestigious journals. Sometimes all eyes can be blind to progress, particularly the quiet kind.
Here is to all the silent striders, those men and women that represent the common progress, and to them I will give my support. Fame is for actors and politicians, not research.
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