The Big Y #151
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TikTok is back in the congressional spotlight, and it made me think about what could be behind their successful, but hidden, algorithm. We interact with recommendation engines every day across multiple sites, but TikTok is unique in its ability to constantly update and in some ways know more about what we like than we do ourselves.
So what could be the reason TikTok’s algorithm works so well? One potential method could be applying reinforcement learning (RL) with curiosity. The RL algorithm learns from user behavior and adjusts as users interact with it and send reward signals, but it also needs to add new types of videos to the users’ feeds to keep them engaged and coming back for more. This curiosity piece of the algorithm shows videos that deviate from known interests and then takes into consideration your response to this new information i.e, whether you click the like button or not.
The idea of exploration and exploitation in reinforcement learning isn’t brand new, but it’s still an interesting art trying to balance the effort put towards one or the other to create the best outcome and avoid the echo chamber. The style of TikTok’s recommendation engine is in sharp contrast to one of the first recommendation systems many of us interacted with, Netflix’s movie suggestions. The stark difference now is that TikTok shows us what we want to see, whereas Netflix’s algorithm shows us what they want us to see (which now has made their recommendations pretty trash).
While TikTok continues to pop-up in the political arena for other reasons, the success of their algorithm keeping users forever trapped doomscrolling presents a challenge for those wanting to balance technological innovation (RL creativity) with the clear uptick in time spent with our eyes on our phones.
The deep fakes are getting good. Last week, we saw a proliferation of deep fakes as news spread of a potential arrest of Donald Trump and then a (fake) picture circulating of the Pope wearing a fashionable coat.
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