Estimating the frequency of searches for Britney Spears, for example, compared to other terms might seem a simple task for AI, but this has presented a significant challenge for researchers since the early days of the internet. MIT CSAIL might have come up with a solution, called LearnedSketch.
I write about AI, data, deep tech & self-management in the digital ageLearnedSketch from MIT CSAIL could solve the Britney Spears problem once and for all - and it's got nothing to do with the noughties diva.‘I think, therefore I am.’ ‘I google, therefore I think.’ Not sure that Descartes would agree with this. Search engines are not as omniscient as many people may think - as anything powered by AI is limited by the statistical methods that humans have devised.
Estimating the online popularity of Britney Spears for example, or the frequency of searches for the singer compared to other terms, might seem a simple task for AI to compute. But this scenario has presented a significant challenge for researchers since the early days of the internet, for a number of reasons.
Imagine a random stream of numbers being counted one by one to determine the size of the sequence. This counting function only needs to factor in one number at a time and add it to the total, so it only needs the memory and capacity to register that single digit. Now imagine that you want to count how many numbers have appeared in the sequence and also see which number has appeared the most.
There have been a number of ways to address this challenge, most of which involve splitting up the stream to analyze samples independently and estimating the result based on that.
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