"Among the top 100 words mentioned, those related to knowledge-based systems—like “logic,” “constraint,” and “rule”—saw the greatest decline. Those related to machine learning—like “data,” “network,” and “performance”—saw the highest growth."
If you couldn't guess by the fact they "analyzed" thousands of papers, all they did was word counting. This doesn't tell you where AI is going, it only tells you where it already is. "Neural Network" of course has had a large increase over the last few years too.
If you want to know where AI is heading, you already know where to look just by reading a few news articles on recent AI breakthroughs (e.g. from DeepMind).
At the end, they mention "reinforcement learning" as well as having gained momentum, but end the article with roughly the sentiment that the author has no clue what the next thing in AI is after the current trend.
If they had applied ML or AI techniques in trying to guess, instead of word counting, that would at least be an interesting article.
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[ 0.23 ms ] story [ 13.5 ms ] threadwhat a turn off, I closed the tab.
If you couldn't guess by the fact they "analyzed" thousands of papers, all they did was word counting. This doesn't tell you where AI is going, it only tells you where it already is. "Neural Network" of course has had a large increase over the last few years too.
If you want to know where AI is heading, you already know where to look just by reading a few news articles on recent AI breakthroughs (e.g. from DeepMind).
At the end, they mention "reinforcement learning" as well as having gained momentum, but end the article with roughly the sentiment that the author has no clue what the next thing in AI is after the current trend.
If they had applied ML or AI techniques in trying to guess, instead of word counting, that would at least be an interesting article.