Perspective: eCommerce and personal digital models

Perspective: eCommerce and personal digital models
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ACMTech: “Within 10 years, people will entrust their data to machine-learning algorithms that build personal digital models of them, writes University of Washington professor Pedro Domingos. He predicts a new kind of company will be conceived to store, safeguard, and apply such data to the construction, maintenance, and interactions of these models.

WSJ provides more insights into this new idea: “Entrusting your money to a bank once seemed strange and risky. Similarly, entrusting all of your data to a company and letting its algorithms build a detailed model of you from it might seem to be an odd or even dangerous idea, but we’ll all soon take it for granted.

Domingos says the learning models would record a customer’s every digital interaction and feed it to the model in exchange for a subscription fee. He notes all this would require on the technical side is a proxy server through which these interactions are routed and recorded. “Once a firm has your data in one place, it can create a complete model of you using one of the major machine-learning techniques: inducing rules, mimicking the way neurons in the brain learn, simulating evolution, probabilistically weighing the evidence for different hypotheses, or reasoning by analogy,” Domingos says.

He thinks these models could be duplicated almost infinitely to multitask, selecting the best options for the user based on accumulated behavior and preferences. “To offset organizations’ data-gathering advantages, like-minded individuals will pool the data in their banks and use the models learned from that information,” Domingos says.

He predicts cyberspace will evolve into “a vast parallel world that selects only the most promising things to try out in the real one–the new, global subconscious of the human race.”

As WSJ states it clearly: “Privacy concerns aside, this poses two problems. First, companies have a conflict of interest: They want to serve you, but they also want to make money. ”

“For example, Google predicts how likely you are to click on an ad to show you the most profitable ones. The choice also depends on the advertisers’ bids, but you’d probably rather just see the ads most relevant to you. Google co-founder Sergey Brin says that Google wants to be the third half of your brain, but nobody wants part of their brain constantly trying to show them ads.

The second problem is that a model of you derived from fragments of your data—Google’s model based on your searches, Amazon’s from your purchases and so on—can only ever have a very limited understanding of who you are and what you want. A single model assembled from all the data you’ve ever produced would be much more accurate: The more data, the better the model. For privacy reasons, you’d want the data and the model under your control, not a third party’s.”

The companies that now offer to consolidate all your data somewhere in the cloud are forerunners of tomorrow’s personal databanks. Once a firm has your data in one place, it can create a complete model of you using one of the major machine-learning techniques: inducing rules, mimicking the way neurons in the brain learn, simulating evolution, probabilistically weighing the evidence for different hypotheses or reasoning by analogy. Then you can go to town with your model, which you’d own and control like you do your money, rather than letting companies such as Apple, Google and Facebook fight for control of it.

With this in mind, here’s a future suggestion for LinkedIn: Add a “Find Me a Job” button. When you click it, your digital model would “interview” instantly for all the open positions that match your specifications, interacting at high speed with human-resources departments’ recruiting models. LinkedIn could then return a list of the most promising jobs for you.

While one copy of your model is doing this, another online alter ego could be looking for a car for you, exhaustively researching the options and haggling with the auto-dealer bots so you don’t have to.”

Source: Wall Street Journal via ACMTech
Photo Credits: Future Interfaces 2014 by NYC Media Lab / FlickR