Senor Salme

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. These models are often core to the value proposition of these companies, providing recommendations, behavioural analytics and consumer insights not only to their own services, but to associated advertising networks.

This practice, however, comes at a cost to individuals. 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. And, 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.


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. This will take different forms, including local learning (where the model is trained locally); distributed or federated learning approaches (where a globally trained model is optimised and retrained locally without transferring data back to the cloud); or co-operative learning approaches (where local data contributes to a global model on an ongoing basis).

These methods aim to find the optimum balance between an individual’s privacy, the complexity and size of a model, the amount of personalisation and overfitting of a model to individual users, and the user’s resources, such as bandwidth, memory and energy. They often provide good results due to their lightweight nature and ability to optimise through local processing. And, since these models can be locally personalised to the individual user, they have been shown to present better results than their centrally-trained global machine-learning models. This is particularly useful for AI tasks such as activity recognition or mood detection.

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Over the next year we will see new businesses forming to compete for the ideal “edge” solution – whether it’s through browsers, such as Brave; Apple’s use of differential privacy; set-top boxes and home gateways, such as the Databox platform (with which one of us Hamed Haddadi is involved); or personal microservers, such as Hub-of-All-Things (of which the author Irene Ng is the creator).

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.


In 2019, we will see battles for AI solutions oscillating between centralised and decentralised models. Innovation will come from designing different type of revenue streams, through the creation of new transactions.

New payment mechanisms will emerge from the way we pay for tools, analytics, data and privacy. We will find ways of exchanging data created at the edge for new services. And those entities that acquire that data will be able to remunerate the services at the edge that created it in the first place. The internet will continue to be a place where data creates new insights and opportunities, but in 2019 it will find ways of doing so much more efficiently and fairly than at present.

Irene Ng is Professor and director of HATLAB at University of Warwick and Hamed Haddadi is an Associate Professor at Imperial College London, UK

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