ARTIFICIAL INTELLIGENCE: Evolution of Creative AI and WeChat's Payment Score

One ongoing, false refrain is that machine learning does not generate creative outcomes. Increasingly, this is proven wrong by the technologists and artists playing with the technology. What started several years ago as "neural style transfer" (i.e., transferring Picasso's visual DNA to any photo) has moved on to BigGAN, which is a machine learning algorithm to manufacture images that appear realistic but are made from machine hallucination. Notably, artists are playing not just with the realistic versions of these hallucinations, which you can see below, but with the "latent space" in between. This mathematical term for interpolation is filled with abstract, surprising, and surreal outcomes. Our takeaway from these results is both (1) that machines will be far more precise in understanding and approximating humans than we assume, and (2) that machines will be far better at creativity that we assume.

Fitting a financial product to a ranked "perception" of a human being matters -- especially when it is done at a scale of a billion people. Tencent's WeChat is running a new initiative called "WeChat Pay Score", which is analogous to the Alipay's "Sesame Credit", both of which (we expect) flow to the Chinese government to make up the national social credit score. Sesame Credit looks at 5 dimensions: safety, wealth, social, compliance, and consumption from over 3,000 specific data points collected by the app. The WeChat version is collecting data on how users chat on the messenger, what they read and buy, where they travel, and how they run their life in general. These combined attributes grant access to perks, like waiving bank account minimums.

Listen, in a massive nation where a large swath of the population doesn't have traditional financial data or bank accounts, machine-learning based estimates of credit-worthiness are a life saver. Not every economy comes with a FICO score and legacy credit agencies (though the Equifax breach wasn't particularly kind to incumbents).  But they key question comes back to the two picture sets below. Do the machines see us like those perfectly generated, accurate pictures of people? Or like the surreal goo in abstraction? The former means distributed access to well-suited financial products, while the other is a Black Mirror nightmare.

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Source: Medium (GANs), Joel Simon (GANreeder), TechCrunch (WeChat)