Most AI giants on the internet rely on the continuous collection of personal data from their users, primarily to build and maintain machine-learning models.
The repeated delivery of ads by third-party services creates excessive bandwidth and energy usage, something consumers are noticing as ongoing data collection and analysis by background apps slows their internet connection.
As many recent cases have shown, there are now serious privacy concerns from excessive data collection and the resulting exposure from linkages of personal data across different services.
Advertisement In 2019, we will see an alternative to these practices emerging in the form of AI at the edge – machine learning that will take place “Near” the user, on their device or home hub, or at a local data-aggregation point.
While the technology is clearly ahead of the economics, the edge’s ability to correlate across a greater variety of user data that is not readily available to different cloud-based services will give it market advantage.
Training and fitting models to individuals at the edge, such as individual voice or physical-activity recognition, creates superior results, and the economic efficiency of edge solutions will slowly peel off centralised systems, much like the way outsourcing of services peeled off centralised systems in the 90s. Advertisement In 2019, we will see battles for AI solutions oscillating between centralised and decentralised models.
We will find ways of exchanging data created at the edge for new services.
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